1. |
- Fredenberg, Erik, 1979-
(author)
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Spectral Mammography with X-Ray Optics and a Photon-Counting Detector
- 2009
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Doctoral thesis (other academic/artistic)abstract
- Early detection is vital to successfully treating breast cancer, and mammography screening is the most efficient and wide-spread method to reach this goal. Imaging low-contrast targets, while minimizing the radiation exposure to a large population is, however, a major challenge. Optimizing the image quality per unit radiation dose is therefore essential. In this thesis, two optimization schemes with respect to x-ray photon energy have been investigated: filtering the incident spectrum with refractive x-ray optics (spectral shaping), and utilizing the transmitted spectrum with energy-resolved photon-counting detectors (spectral imaging). Two types of x-ray lenses were experimentally characterized, and modeled using ray tracing, field propagation, and geometrical optics. Spectral shaping reduced dose approximately 20% compared to an absorption-filtered reference system with the same signal-to-noise ratio, scan time, and spatial resolution. In addition, a focusing pre-object collimator based on the same type of optics reduced divergence of the radiation and improved photon economy by about 50%. A photon-counting silicon detector was investigated in terms of energy resolution and its feasibility for spectral imaging. Contrast-enhanced tumor imaging with a system based on the detector was characterized and optimized with a model that took anatomical noise into account. Improvement in an ideal-observer detectability index by a factor of 2 to 8 over that obtained by conventional absorption imaging was found for different levels of anatomical noise and breast density. Increased conspicuity was confirmed by experiment. Further, the model was extended to include imaging of unenhanced lesions. Detectability of microcalcifications increased no more than a few percent, whereas the ability to detect large tumors might improve on the order of 50% despite the low attenuation difference between glandular and cancerous tissue. It is clear that inclusion of anatomical noise and imaging task in spectral optimization may yield completely different results than an analysis based solely on quantum noise.
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2. |
- Reza, Salim, 1985-, et al.
(author)
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Smart dosimetry by pattern recognition using a single photon counting detector system in time over threshold mode
- 2012
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In: Journal of Instrumentation. - 1748-0221. ; 7:1
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Journal article (peer-reviewed)abstract
- The function of a dosimeter is to determine the absorbed dose of radiation, for those cases in which, generally, the particular type of radiation is already known. Lately, a number of applications have emerged in which all kinds of radiation are absorbed and are sorted by pattern recognition, such as the Medipix2 application in [1]. This form of smart dosimetry enables measurements where not only the total dosage is measured, but also the contributions of different types of radiation impacting upon the detector surface. Furthermore, the use of a photon counting system, where the energy deposition can be measured in each individual pixel, ensures measurements with a high degree of accuracy in relation to the pattern recognition. In this article a Timepix [2] detector system has been used in the creation of a smart dosimeter for Alpha, Beta and Gamma radiation. When a radioactive particle hits the detector surface it generates charge clusters and those impacting upon the detector surface are read out and image processing algorithms are then used to classify each charge cluster. The individual clusters are calculated and as a result, the dosage for each type of radiation is given. In some cases, several particles can impact in roughly the same place, forming overlapping clusters. In order to handle this problem, a cluster separation method has been added to the pattern recognition algorithm. When the clusters have been separated, they are classified by shape and sorted into the correct type of radiation. The algorithms and methods used in this dosimeter have been developed so as to be simple and computationally effective, in order to enable implementation on a portable device. © 2012 IOP Publishing Ltd and SISSA.
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3. |
- Y Banaem, Hossein, et al.
(author)
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Brain tumor modeling : glioma growth and interaction with chemotherapy
- 2011
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In: International Conference on Graphic and Image Processing (ICGIP 2011). - : SPIE. ; 8285
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Conference paper (peer-reviewed)abstract
- In last decade increasingly mathematical models of tumor growths have been studied, particularly on solid tumors which growth mainly caused by cellular proliferation. In this paper we propose a modified model to simulate the growth of gliomas in different stages. Glioma growth is modeled by a reaction-advection-diffusion. We begin with a model of untreated gliomas and continue with models of polyclonal glioma following chemotherapy. From relatively simple assumptions involving homogeneous brain tissue bounded by a few gross anatomical landmarks (ventricles and skull) the models have been expanded to include heterogeneous brain tissue with different motilities of glioma cells in grey and white matter. Tumor growth is characterized by a dangerous change in the control mechanisms, which normally maintain a balance between the rate of proliferation and the rate of apoptosis (controlled cell death). Result shows that this model closes to clinical finding and can simulate brain tumor behavior properly.
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4. |
- Bondesson, Johan, 1991, et al.
(author)
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Definition of Tubular Anatomic Structures from Arbitrary Stereo Lithographic Surface
- 2017
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In: Initiative Seminar Engineering Health, 8-9 November 2017, Chalmers.
-
Conference paper (other academic/artistic)abstract
- An accurate description of anatomies and dynamics of vessels is crucial to understand their characteristics and improve surgical techniques, thus it is the basis, in addition to surgeon experience, on which stent design and operation procedures rely. The process of producing this description is user intensive, and recent improvement in image processing of medical3D imaging allows for a more automated workflow. However, there is a need to bridge the gap from a processed geometry to a robust mathematical computational grid. By sequentially segmenting a tubular anatomic structure, here defined by a stereo lithographic (STL) surface, an initial centerline is formed by connecting centroids of orthogonal cross-sectional contours along the length of the structure. Relying on the initial centerline, a set of non-overlapping 2D cross sectional contours are defined along the centerline, a centerline which is updated after the 2D contours are produced. After a second iteration of producing 2D contours and updating the centerline, a full description of the structure is created. Our method for describing vessel geometry shows good coherence to existing method. The main advantages of our method include the possibility of having arbitrary triangulated STL surface input, automated centerline definition, safety against intersecting cross-sectional contours and automatic clean-up of local kinks and wrinkles.
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5. |
- Ge, Chenjie, 1991, et al.
(author)
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Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification
- 2020
-
In: IEEE Access. - 2169-3536 .- 2169-3536. ; 8:1, s. 22560-22570
-
Journal article (peer-reviewed)abstract
- This paper addresses issues of brain tumor subtype classification using Magnetic Resonance Images (MRIs) from different scanner modalities like T1 weighted, T1 weighted with contrast-enhanced, T2 weighted and FLAIR images. Currently most available glioma datasets are relatively moderate in size, and often accompanied with incomplete MRIs in different modalities. To tackle the commonly encountered problems of insufficiently large brain tumor datasets and incomplete modality of image for deep learning, we propose to add augmented brain MR images to enlarge the training dataset by employing a pairwise Generative Adversarial Network (GAN) model. The pairwise GAN is able to generate synthetic MRIs across different modalities. To achieve the patient-level diagnostic result, we propose a post-processing strategy to combine the slice-level glioma subtype classification results by majority voting. A two-stage course-to-fine training strategy is proposed to learn the glioma feature using GAN-augmented MRIs followed by real MRIs. To evaluate the effectiveness of the proposed scheme, experiments have been conducted on a brain tumor dataset for classifying glioma molecular subtypes: isocitrate dehydrogenase 1 (IDH1) mutation and IDH1 wild-type. Our results on the dataset have shown good performance (with test accuracy 88.82%). Comparisons with several state-of-the-art methods are also included.
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6. |
- Fredenberg, Erik, PhD, 1979-, et al.
(author)
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A low-absorption x-ray energy filter for small-scale applications
- 2009
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In: Optics Express. - : The Optical Society. - 1094-4087. ; 17:14, s. 11388-11398
-
Journal article (peer-reviewed)abstract
- We present an experimental and theoretical evaluation of an x-ray energy filter based on the chromatic properties of a prism-array lens (PAL). It is intended for small-scale applications such as medical imaging. The PAL approximates a Fresnel lens and allows for high efficiency compared to filters based on ordinary refractive lenses, however at the cost of a lower energy resolution. Geometrical optics was found to provide a good approximation for the performance of a flawless lens, but a field-propagation model was used for quantitative predictions. The model predicted a 0.29 ΔE/E energy resolution and an intensity gain of 6.5 for a silicon PAL at 23.5 keV. Measurements with an x-ray tube showed good agreement with the model in energy resolution and peak energy, but a blurred focal line contributed to a 29% gain reduction. We believe the blurring to be caused mainly by lens imperfections, in particular at the periphery of the lens.
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7. |
- Fredenberg, Erik, PhD, 1979-, et al.
(author)
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A Tunable Energy Filter for Medical X-Ray Imaging
- 2008
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In: X-Ray Optics and Instrumentation. - : Hindawi. - 1687-7632 .- 1687-7640. ; 2008
-
Journal article (peer-reviewed)abstract
- A multiprism lens (MPL) is a refractive X-ray lens, and its chromatic properties can be employed in an energy filtering setup to obtain a narrow tunable X-ray spectrum. We present the first evaluation of such a filter for medical X-ray imaging. The experimental setup yields a 6.6 gain of flux at 20 keV, and we demonstrate tunability by altering the energy spectrum to center also around 17 and 23 keV. All measurements are found to agree well with ray-tracing and a proposed geometrical model. Compared to a model mammography system with absorption filtering, the experimental MPL filter reduces dose 13–25% for 3–7 cm breasts if the spectrum is centered around the optimal energy. Additionally, the resolution is improved 2.5 times for a 5 cm breast. The scan time is increased 3 times but can be reduced with a slightly decreased energy filtering and resolution.
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8. |
- Fredenberg, Erik, PhD, 1979-, et al.
(author)
-
Contrast-enhanced spectral mammography with a photon-counting detector
- 2010
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In: Medical physics (Lancaster). - : Wiley. - 0094-2405. ; 37:5, s. 2017-2029
-
Journal article (peer-reviewed)abstract
- Purpose: Spectral imaging is a method in medical x-ray imaging to extract information about the object constituents by the material-specific energy dependence of x-ray attenuation. In particular, the detectability of a contrast agent can be improved over a lumpy background. We have investigated a photon-counting spectral imaging system with two energy bins for contrast-enhanced mammography. System optimization and the potential benefit compared to conventional non-energy-resolved imaging was studied.Methods: A framework for system characterization was set up that included quantum and anatomical noise, and a theoretical model of the system was benchmarked to phantom measurements.Results: It was found that optimal combination of the energy-resolved images corresponded approximately to minimization of the anatomical noise, and an ideal-observer detectability index could be improved more than a factor of two compared to absorption imaging in the phantom study. In the clinical case, an improvement close to 80% was predicted for an average glandularity breast, and a factor of eight for dense breast tissue. Another 70% was found to be within reach for an optimized system.Conclusions: Contrast-enhanced spectral mammography is feasible and beneficial with the current system, and there is room for additional improvements.
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9. |
- Fredenberg, Erik, PhD, 1979-, et al.
(author)
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Energy filtering with x-ray lenses: Optimization for photon-counting mammography
- 2010
-
In: Radiation Protection Dosimetry. - : Oxford University Press (OUP). - 0144-8420 .- 1742-3406. ; 139, s. 339-342
-
Journal article (peer-reviewed)abstract
- Chromatic properties of the multi-prism and prism-array x-ray lenses (MPL and PAL) can potentially be utilized for efficient energy filtering and dose reduction in mammography. The line-shaped foci of the lenses are optimal for coupling to photon-counting silicon strip detectors in a scanning system. A theoretical model was developed and used to investigate the benefit of two lenses compared to an absorption-filtered reference system. The dose reduction of the MPL filter was 15% compared to the reference system at matching scan time, and the spatial resolution was higher. The dose of the PAL-filtered system was found to be 20% lower than for the reference system at equal scan time and resolution, and only 20% higher than for a monochromatic beam. An investigation of some practical issues remains, including the feasibility of brilliant-enough x-ray sources and manufacturing of a polymer PAL.
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10. |
- Ge, Chenjie, 1991, et al.
(author)
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3D Multi-Scale Convolutional Networks for Glioma Grading Using MR Images
- 2018
-
In: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. - 9781479970612 ; , s. 141-145
-
Conference paper (peer-reviewed)abstract
- This paper addresses issues of grading brain tumor, glioma, from Magnetic Resonance Images (MRIs). Although feature pyramid is shown to be useful to extract multi-scale features for object recognition, it is rarely explored in MRI images for glioma classification/grading. For glioma grading, existing deep learning methods often use convolutional neural networks (CNNs) to extract single-scale features without considering that the scales of brain tumor features vary depending on structure/shape, size, tissue smoothness, and locations. In this paper, we propose to incorporate the multi-scale feature learning into a deep convolutional network architecture, which extracts multi-scale semantic as well as fine features for glioma tumor grading. The main contributions of the paper are: (a) propose a novel 3D multi-scale convolutional network architecture for the dedicated task of glioma grading; (b) propose a novel feature fusion scheme that further refines multi-scale features generated from multi-scale convolutional layers; (c) propose a saliency-aware strategy to enhance tumor regions of MRIs. Experiments were conducted on an open dataset for classifying high/low grade gliomas. Performance on the test set using the proposed scheme has shown good results (with accuracy of 89.47%).
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11. |
- Abbaspour, S., et al.
(author)
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Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements
- 2021
-
In: Sensors. - : MDPI AG. - 1424-8220. ; 21:16
-
Journal article (peer-reviewed)abstract
- Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.
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12. |
- Ge, Chenjie, 1991, et al.
(author)
-
Multiscale Deep Convolutional Networks for Characterization and Detection of Alzheimer's Disease using MR Images
- 2019
-
In: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. ; 2019-September, s. 789-793
-
Conference paper (peer-reviewed)abstract
- This paper addresses the issues of Alzheimer's disease (AD) characterization and detection from Magnetic Resonance Images (MRIs). Many existing AD detection methods use single-scale feature learning from brain scans. In this paper, we propose a multiscale deep learning architecture for learning AD features. The main contributions of the paper include: (a) propose a novel 3D multiscale CNN architecture for the dedicated task of AD detection; (b) propose a feature fusion and enhancement strategy for multiscale features; (c) empirical study on the impact of several settings, including two dataset partitioning approaches, and the use of multiscale and feature enhancement. Experiments were conducted on an open ADNI dataset (1198 brain scans from 337 subjects), test results have shown the effectiveness of the proposed method with test accuracy of 93.53%, 87.24% (best, average) on subject separated dataset, and 99.44%, 98.80% (best, average) on random brain scan-partitioned dataset. Comparison with eight existing methods has provided further support to the proposed method.
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13. |
- Hagberg, Eva, et al.
(author)
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Semi-supervised learning with natural language processing for right ventricle classification in echocardiography—a scalable approach
- 2022
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In: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 143
-
Journal article (peer-reviewed)abstract
- We created a deep learning model, trained on text classified by natural language processing (NLP), to assess right ventricular (RV) size and function from echocardiographic images. We included 12,684 examinations with corresponding written reports for text classification. After manual annotation of 1489 reports, we trained an NLP model to classify the remaining 10,651 reports. A view classifier was developed to select the 4-chamber or RV-focused view from an echocardiographic examination (n = 539). The final models were two image classification models trained on the predicted labels from the combined manual annotation and NLP models and the corresponding echocardiographic view to assess RV function (training set n = 11,008) and size (training set n = 9951. The text classifier identified impaired RV function with 99% sensitivity and 98% specificity and RV enlargement with 98% sensitivity and 98% specificity. The view classification model identified the 4-chamber view with 92% accuracy and the RV-focused view with 73% accuracy. The image classification models identified impaired RV function with 93% sensitivity and 72% specificity and an enlarged RV with 80% sensitivity and 85% specificity; agreement with the written reports was substantial (both κ = 0.65). Our findings show that models for automatic image assessment can be trained to classify RV size and function by using model-annotated data from written echocardiography reports. This pipeline for auto-annotation of the echocardiographic images, using a NLP model with medical reports as input, can be used to train an image-assessment model without manual annotation of images and enables fast and inexpensive expansion of the training dataset when needed. © 2022
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14. |
- Xu, Cheng
(author)
-
A Segmented Silicon Strip Detector for Photon-Counting Spectral Computed Tomography
- 2012
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Artistic work (other academic/artistic)abstract
- Spectral computed tomography with energy-resolving detectors has a potential to improve the detectability of images and correspondingly reduce the radiation dose to patients by extracting and properly using the energy information in the broad x-ray spectrum. A silicon photon-counting detector has been developed for spectral CT and it has successfully solved the problem of high photon flux in clinical CT applications by adopting the segmented detector structure and operating the detector in edge-on geometry. The detector was evaluated by both the simulation and measurements.The effects of energy loss and charge sharing on the energy response of this segmented silicon strip detector with different pixel sizes were investigated by Monte Carlo simulation and a comparison to pixelated CdTe detectors is presented. The validity of spherical approximations of initial charge cloud shape in silicon detectors was evaluated and a more accurate statistical model has been proposed.A photon-counting energy-resolving application specific integrated circuit (ASIC) developed for spectral CT was characterized extensively by electrical pulses, pulsed laser and real x-ray photons from both the synchrotron and an x-ray tube. It has been demonstrated that the ASIC performs as designed. A noise level of 1.09 keV RMS has been measured and a threshold dispersion of 0.89 keV RMS has been determined. The count rate performance of the ASIC in terms of count loss and energy resolution was evaluated by real x-rays and promising results have been obtained.The segmented silicon strip detector was evaluated using synchrotron radiation. An energy resolution of 16.1% has been determined with 22 keV photons in the lowest flux limit, which deteriorates to 21.5% at an input count rate of 100 Mcps mm−2. The fraction of charge shared events has been estimated and found to be 11.1% for 22 keV and 15.3% for 30 keV. A lower fraction of charge shared events and an improved energy resolution can be expected by applying a higher bias voltage to the detector.
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15. |
- Ali, Muhaddisa Barat, 1986, et al.
(author)
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Multi-stream Convolutional Autoencoder and 2D Generative Adversarial Network for Glioma Classification
- 2019
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11678 LNCS, s. 234-245
-
Conference paper (peer-reviewed)abstract
- Diagnosis and timely treatment play an important role in preventing brain tumor growth. Deep learning methods have gained much attention lately. Obtaining a large amount of annotated medical data remains a challenging issue. Furthermore, high dimensional features of brain images could lead to over-fitting. In this paper, we address the above issues. Firstly, we propose an architecture for Generative Adversarial Networks to generate good quality synthetic 2D MRIs from multi-modality MRIs (T1 contrast-enhanced, T2, FLAIR). Secondly, we propose a deep learning scheme based on 3-streams of Convolutional Autoencoders (CAEs) followed by sensor information fusion. The rational behind using CAEs is that it may improve glioma classification performance (as comparing with conventional CNNs), since CAEs offer noise robustness and also efficient feature reduction hence possibly reduce the over-fitting. A two-round training strategy is also applied by pre-training on GAN augmented synthetic MRIs followed by refined-training on original MRIs. Experiments on BraTS 2017 dataset have demonstrated the effectiveness of the proposed scheme (test accuracy 92.04%). Comparison with several exiting schemes has provided further support to the proposed scheme.
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16. |
- Ali, Muhaddisa Barat, 1986, et al.
(author)
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A novel federated deep learning scheme for glioma and its subtype classification
- 2023
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In: Frontiers in Neuroscience. - 1662-4548 .- 1662-453X. ; 17
-
Journal article (peer-reviewed)abstract
- Background: Deep learning (DL) has shown promising results in molecular-based classification of glioma subtypes from MR images. DL requires a large number of training data for achieving good generalization performance. Since brain tumor datasets are usually small in size, combination of such datasets from different hospitals are needed. Data privacy issue from hospitals often poses a constraint on such a practice. Federated learning (FL) has gained much attention lately as it trains a central DL model without requiring data sharing from different hospitals. Method: We propose a novel 3D FL scheme for glioma and its molecular subtype classification. In the scheme, a slice-based DL classifier, EtFedDyn, is exploited which is an extension of FedDyn, with the key differences on using focal loss cost function to tackle severe class imbalances in the datasets, and on multi-stream network to exploit MRIs in different modalities. By combining EtFedDyn with domain mapping as the pre-processing and 3D scan-based post-processing, the proposed scheme makes 3D brain scan-based classification on datasets from different dataset owners. To examine whether the FL scheme could replace the central learning (CL) one, we then compare the classification performance between the proposed FL and the corresponding CL schemes. Furthermore, detailed empirical-based analysis were also conducted to exam the effect of using domain mapping, 3D scan-based post-processing, different cost functions and different FL schemes. Results: Experiments were done on two case studies: classification of glioma subtypes (IDH mutation and wild-type on TCGA and US datasets in case A) and glioma grades (high/low grade glioma HGG and LGG on MICCAI dataset in case B). The proposed FL scheme has obtained good performance on the test sets (85.46%, 75.56%) for IDH subtypes and (89.28%, 90.72%) for glioma LGG/HGG all averaged on five runs. Comparing with the corresponding CL scheme, the drop in test accuracy from the proposed FL scheme is small (−1.17%, −0.83%), indicating its good potential to replace the CL scheme. Furthermore, the empirically tests have shown that an increased classification test accuracy by applying: domain mapping (0.4%, 1.85%) in case A; focal loss function (1.66%, 3.25%) in case A and (1.19%, 1.85%) in case B; 3D post-processing (2.11%, 2.23%) in case A and (1.81%, 2.39%) in case B and EtFedDyn over FedAvg classifier (1.05%, 1.55%) in case A and (1.23%, 1.81%) in case B with fast convergence, which all contributed to the improvement of overall performance in the proposed FL scheme. Conclusion: The proposed FL scheme is shown to be effective in predicting glioma and its subtypes by using MR images from test sets, with great potential of replacing the conventional CL approaches for training deep networks. This could help hospitals to maintain their data privacy, while using a federated trained classifier with nearly similar performance as that from a centrally trained one. Further detailed experiments have shown that different parts in the proposed 3D FL scheme, such as domain mapping (make datasets more uniform) and post-processing (scan-based classification), are essential.
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17. |
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18. |
- Ge, Chenjie, 1991, et al.
(author)
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Multi-Stream Multi-Scale Deep Convolutional Networks for Alzheimer's Disease Detection using MR Images
- 2019
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In: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 350, s. 60-69
-
Journal article (peer-reviewed)abstract
- This paper addresses the issue of Alzheimer's disease (AD) detection from Magnetic Resonance Images (MRIs). Existing AD detection methods rely on global feature learning from the whole brain scans, while depending on the tissue types, AD related features in dierent tissue regions, e.g. grey matter (GM), white matter (WM), and cerebrospinal uid (CSF), show different characteristics. In this paper, we propose a deep learning method for multi-scale feature learning based on segmented tissue areas. A novel deep 3D multi-scale convolutional network scheme is proposed to generate multi-resolution features for AD detection. The proposed scheme employs several parallel 3D multi-scale convolutional networks, each applying to individual tissue regions (GM, WM and CSF) followed by feature fusions. The proposed fusion is applied in two separate levels: the rst level fusion is applied on different scales within the same tissue region, and the second level is on dierent tissue regions. To further reduce the dimensions of features and mitigate overtting, a feature boosting and dimension reduction method, XGBoost, is utilized before the classication. The proposed deep learning scheme has been tested on a moderate open dataset of ADNI (1198 scans from 337 subjects), with excellent test performance on randomly partitioned datasets (best 99.67%, average 98.29%), and good test performance on subject-separated partitioned datasets (best 94.74%, average 89.51%). Comparisons with state-of-the-art methods are also included.
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19. |
- Khodadad, Davood, 1985-, et al.
(author)
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Optimized breath detection algorithm in electrical impedance tomography
- 2018
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In: Physiological Measurement. - : IOP Publishing. - 0967-3334 .- 1361-6579. ; 39:9
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Journal article (peer-reviewed)abstract
- Objective: This paper defines a method for optimizing the breath delineation algorithms used in electrical impedance tomography (EIT). In lung EIT the identification of the breath phases is central for generating tidal impedance variation images, subsequent data analysis and clinical evaluation. The optimisation of these algorithms is particularly important in neonatal care since the existing breath detectors developed for adults may give insufficient reliability in neonates due to their very irregular breathing pattern.Approach: Our approach is generic in the sense that it relies on the definition of a gold standard and the associated definition of detector sensitivity and specificity, an optimisation criterion and a set of detector parameters to be investigated. The gold standard has been defined by 11 clinicians with previous experience with EIT and the performance of our approach is described and validated using a neonatal EIT dataset acquired within the EU-funded CRADL project.Main results: Three different algorithms are proposed that improve the breath detector performance by adding conditions on (1) maximum tidal breath rate obtained from zero-crossings of the EIT breathing signal, (2) minimum tidal impedance amplitude and (3) minimum tidal breath rate obtained from time-frequency analysis. As a baseline a zero-crossing algorithm has been used with some default parameters based on the Swisstom EIT device.Significance: Based on the gold standard, the most crucial parameters of the proposed algorithms are optimised by using a simple exhaustive search and a weighted metric defined in connection with the receiver operating characterics. This provides a practical way to achieve any desirable trade-off between the sensitivity and the specificity of the detectors.
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20. |
- Lagerstrand, Kerstin M, et al.
(author)
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Reliable phase-contrast flow volume magnetic resonance measurements are feasible without adjustment of the velocity encoding parameter
- 2020
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In: Journal of Medical Imaging. - 2329-4310 .- 2329-4302. ; 7:6
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Journal article (peer-reviewed)abstract
- Purpose: To show that adjustment of velocity encoding (VENC) for phase-contrast (PC) flow volume measurements is not necessary in modern MR scanners with effective background velocity offset corrections. Approach: The independence on VENC was demonstrated theoretically, but also experimentally on dedicated phantoms and on patients with chronic aortic regurgitation (n = 17) and one healthy volunteer. All PC measurements were performed using a modern MR scanner, where the pre-emphasis circuit but also a subsequent post-processing filter were used for effective correction of background velocity offset errors. Results: The VENC level strongly affected the velocity noise level in the PC images and, hence, the estimated peak flow velocity. However, neither the regurgitant blood flow volume nor the mean flow velocity displayed any clinically relevant dependency on the VENC level. Also, the background velocity offset was shown to be close to zero (
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21. |
- Albinsson, John, et al.
(author)
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Improved tracking performance of lagrangian block-matching methodologies using block expansion in the time domain : In silico, phantom and invivo evaluations
- 2014
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In: Ultrasound in Medicine and Biology. - : Elsevier. - 0301-5629 .- 1879-291X. ; 40:10, s. 2508-2520
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Journal article (peer-reviewed)abstract
- The aim of this study was to evaluate tracking performance when an extra reference block is added to a basic block-matching method, where the two reference blocks originate from two consecutive ultrasound frames. The use of an extra reference block was evaluated for two putative benefits: (i) an increase in tracking performance while maintaining the size of the reference blocks, evaluated using in silico and phantom cine loops; (ii) a reduction in the size of the reference blocks while maintaining the tracking performance, evaluated using in vivo cine loops of the common carotid artery where the longitudinal movement of the wall was estimated. The results indicated that tracking accuracy improved (mean - 48%, p<0.005 [in silico]; mean - 43%, p<0.01 [phantom]), and there was a reduction in size of the reference blocks while maintaining tracking performance (mean - 19%, p<0.01 [in vivo]). This novel method will facilitate further exploration of the longitudinal movement of the arterial wall. (C) 2014 World Federation for Ultrasound in Medicine & Biology.
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22. |
- Bäcklin, Emelie, et al.
(author)
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Pulmonary volumes and signs of chronic airflow limitation in quantitative computed tomography
- 2024
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In: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 44:4, s. 340-348
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Journal article (peer-reviewed)abstract
- BackgroundComputed tomography (CT) offers pulmonary volumetric quantification but is not commonly used in healthy individuals due to radiation concerns. Chronic airflow limitation (CAL) is one of the diagnostic criteria for chronic obstructive pulmonary disease (COPD), where early diagnosis is important. Our aim was to present reference values for chest CT volumetric and radiodensity measurements and explore their potential in detecting early signs of CAL.MethodsFrom the population-based Swedish CArdioPulmonarybioImage Study (SCAPIS), 294 participants aged 50–64, were categorized into non-CAL (n = 258) and CAL (n = 36) groups based on spirometry. From inspiratory and expiratory CT images we compared lung volumes, mean lung density (MLD), percentage of low attenuation volume (LAV%) and LAV cluster volume between groups, and against reference values from static pulmonary function test (PFT).ResultsThe CAL group exhibited larger lung volumes, higher LAV%, increased LAV cluster volume and lower MLD compared to the non-CAL group. Lung volumes significantly deviated from PFT values. Expiratory measurements yielded more reliable results for identifying CAL compared to inspiratory. Using a cut-off value of 0.6 for expiratory LAV%, we achieved sensitivity, specificity and positive/negative predictive values of 72%, 85% and 40%/96%, respectively.ConclusionWe present volumetric reference values from inspiratory and expiratory chest CT images for a middle-aged healthy cohort. These results are not directly comparable to those from PFTs. Measures of MLD and LAV can be valuable in the evaluation of suspected CAL. Further validation and refinement are necessary to demonstrate its potential as a decision support tool for early detection of COPD.
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23. |
- Kylberg, Gustaf, et al.
(author)
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Segmentation of virus particle candidates in transmission electron microscopy images
- 2012
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In: Journal of Microscopy. - : Blackwell Publishing. - 0022-2720 .- 1365-2818. ; 245:2, s. 140-147
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Journal article (peer-reviewed)abstract
- In this paper, we present an automatic segmentation method that detects virus particles of various shapes in transmission electron microscopy images. The method is based on a statistical analysis of local neighbourhoods of all the pixels in the image followed by an object width discrimination and finally, for elongated objects, a border refinement step. It requires only one input parameter, the approximate width of the virus particles searched for. The proposed method is evaluated on a large number of viruses. It successfully segments viruses regardless of shape, from polyhedral to highly pleomorphic.
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24. |
- Norlén, Alexander, 1988, et al.
(author)
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Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography
- 2016
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In: Journal of Mecial Imaging. - 2329-4302 .- 2329-4310. ; 3:3
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Journal article (peer-reviewed)abstract
- Recent findings indicate a strong correlation between the risk of future heart disease and the volume ofadipose tissue inside of the pericardium. So far, large-scale studies have been hindered by the fact that manual delin-eation of the pericardium is extremely time-consuming and that existing methods for automatic delineation strugglewith accuracy. In this paper, an efficient and fully automatic approach to pericardium segmentation and epicardial fatvolume estimation is presented, based on a variant of multi-atlas segmentation for spatial initialization and a randomforest classifier for accurate pericardium detection. Experimental validation on a set of 30 manually delineated Com-puter Tomography Angiography (CTA) volumes shows a significant improvement on state-of-the-art in terms of EFVestimation (mean absolute epicardial fat volume difference: 3.8 ml (4.7%), Pearson correlation: 0.99) with run-timessuitable for large-scale studies (52 s). Further, the results compare favorably to inter-observer variability measured on10 volumes.
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25. |
- Fredenberg, Erik, 1979-, et al.
(author)
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An efficient pre-object collimator based on an x-ray lens
- 2009
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In: Medical physics (Lancaster). - : Wiley. - 0094-2405. ; 36:2, s. 626-633
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Journal article (peer-reviewed)abstract
- A multiprism lens (MPL) is a refractive x-ray lens with one-dimensional focusing properties. If used as a pre-object collimator in a scanning system for medical x-ray imaging, it reduces the divergence of the radiation and improves on photon economy compared to a slit collimator. Potential advantages include shorter acquisition times, a reduced tube loading, or improved resolution. We present the first images acquired with a MPL in a prototype for a scanning mammography system. The lens showed a gain of flux of 1.32 compared to a slit collimator at equal resolution, or a gain in resolution of 1.31–1.44 at equal flux. We expect the gain of flux in a clinical setup with an optimized MPL and a custom-made absorption filter to reach 1.67, or 1.45–1.54 gain in resolution.
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26. |
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27. |
- Romu, Thobias, et al.
(author)
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MANA - Multi scale adaptive normalized averaging
- 2011
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In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. - : IEEE conference proceedings. - 9781424441280 ; , s. 361-364
-
Conference paper (peer-reviewed)abstract
- It is possible to correct intensity inhomogeneity in fat–water Magnetic Resonance Imaging (MRI) by estimating a bias field based on the observed intensities of voxels classified as the pure adipose tissue. The same procedure can also be used to quantify fat volume and its distribution which opens up for new medical applications. The bias field estimation method has to be robust since pure fat voxels are irregularly located and the density varies greatly within and between image volumes. This paper introduces Multi scale Adaptive Normalized Average (MANA) that solves this problem bybasing the estimate on a scale space of weighted averages. By usingthe local certainty of the data MANA preserves details where the local data certainty is high and provides realistic values in sparse areas.
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28. |
- Fredriksson, Alexandru Grigorescu, et al.
(author)
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Turbulent kinetic energy in the right ventricle : Potential MR marker for risk stratification of adults with repaired Tetralogy of Fallot
- 2018
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In: Journal of Magnetic Resonance Imaging. - Hoboken : John Wiley & Sons. - 1053-1807 .- 1522-2586. ; 47:4, s. 1043-1053
-
Journal article (peer-reviewed)abstract
- Purpose: To assess right ventricular (RV) turbulent kinetic energy (TKE) in patients with repaired Tetralogy of Fallot (rToF) and a spectrum of pulmonary regurgitation (PR), as well as to investigate the relationship between these 4D flow markers and RV remodeling.Materials and Methods: Seventeen patients with rToF and 10 healthy controls were included in the study. Patients were divided into two groups based on PR fraction: one lower PR fraction group (11%) and one higher PR fraction group (>11%). Field strength/sequences: 3D cine phase contrast (4D flow), 2D cine phase contrast (2D flow), and balanced steady-state free precession (bSSFP) at 1.5T. Assessment: The RV volume was segmented in the morphologic short-axis images and TKE parameters were computed inside the segmented RV volume throughout diastole. Statistical tests: One-way analysis of variance with Bonferroni post-hoc test; unpaired t-test; Pearson correlation coefficients; simple and stepwise multiple regression models; intraclass correlation coefficient (ICC).Results: The higher PR fraction group had more remodeled RVs (140 6 25 vs. 107 6 22 [lower PR fraction, P < 0.01] and 93 6 15 ml/m2[healthy, P < 0.001] for RV end-diastolic volume index [RVEDVI]) and higher TKE values (5.95 6 3.15 vs. 2.23 6 0.81 [lower PR fraction, P < 0.01] and 1.91 6 0.78 mJ [healthy, P < 0.001] for Peak Total RV TKE). Multiple regression analysis between RVEDVI and 4D/2D flow parameters showed that Peak Total RV TKE was the strongest predictor of RVEDVI (R25 0.47, P 5 0.002).Conclusion: The 4D flow-specific TKE markers showed a slightly stronger association with RV remodeling than conventional 2D flow PR parameters. These results suggest novel hemodynamic aspects of PR in the development of late complications after ToF repair.
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29. |
- Piri, R., et al.
(author)
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Common carotid segmentation in F-18-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based and manual method
- 2023
-
In: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 43:2, s. 71-77
-
Journal article (peer-reviewed)abstract
- Background Carotid atherosclerosis is a major cause of stroke, traditionally diagnosed late. Positron emission tomography/computed tomography (PET/CT) with F-18-sodium fluoride (NaF) detects arterial wall micro-calcification long before macro-calcification becomes detectable by ultrasound, CT or magnetic resonance imaging. However, manual PET/CT processing is time-consuming and requires experience. We compared a convolutional neural network (CNN) approach with manual segmentation of the common carotids. Methods Segmentation in NaF-PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients were compared for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, and SUVtotal). SUVmean was the average of SUVmeans within the VOI, SUVmax the highest SUV in all voxels in the VOI, and SUVtotal the SUVmean multiplied by the Vol of the VOI. Intra and Interobserver variability with manual segmentation was examined in 25 randomly selected scans. Results Bias for Vol, SUVmean, SUVmax, and SUVtotal were 1.33 +/- 2.06, -0.01 +/- 0.05, 0.09 +/- 0.48, and 1.18 +/- 1.99 in the left and 1.89 +/- 1.5, -0.07 +/- 0.12, 0.05 +/- 0.47, and 1.61 +/- 1.47, respectively, in the right common carotid artery. Manual segmentation lasted typically 20 min versus 1 min with the CNN-based approach. Mean Vol deviation at repeat manual segmentation was 14% and 27% in left and right common carotids. Conclusions CNN-based segmentation was much faster and provided SUVmean values virtually identical to manually obtained ones, suggesting CNN-based analysis as a promising substitute of slow and cumbersome manual processing.
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30. |
- Polymeri, Erini, et al.
(author)
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Deep learning-based quantification of PET/CT prostate gland uptake : association with overall survival
- 2020
-
In: Clinical Physiology and Functional Imaging. - Chichester : Blackwell Publishing. - 1475-0961 .- 1475-097X. ; 40:2, s. 106-113
-
Journal article (peer-reviewed)abstract
- Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-naïve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18F-choline PET images above a standardized uptake value (SUV) of 2·65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results: The SDI between the automated and the manual volume segmentations was 0·78 and 0·79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0·02), whereas age, PSA, and Gleason score were not. Conclusion: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival. © 2019 The Authors. Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and Nuclear Medicine
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31. |
- Kothapalli, Satya V.V.N. 1985-
(author)
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Nano-Engineered Contrast Agents : Toward Multimodal Imaging and Acoustophoresis
- 2015
-
Doctoral thesis (other academic/artistic)abstract
- Diagnostic ultrasound (US) is safer, quicker and cheaper than other diagnostic imaging modalities. Over the past two decades, the applications of US imaging has been widened due to the development of injectable, compressible and encapsulated microbubbles (MBs) that provide an opportunity to improve conventional echocardiographic imaging, blood flow assessment and molecular imaging. The encapsulating material is manufactured by different biocompatible materials such as proteins, lipids or polymers. In current research, researchers modify the encapsulated shell with the help of advanced molecular chemistry techniques to load them with dyes (for fluorescent imaging), nanoparticles and radioisotopes (for multimodal imaging) or functional ligands or therapeutic gases (for local drug delivery). The echogenicity and the radial oscillation of MBs is the result of their compressibility, which undoubtedly varies with the encapsulated shell characteristics such as rigidity or elasticity.In this thesis, we present acoustic properties of novel type of polyvinyl alcohol (PVA)-shelled microbubble (PVA-MB) that was further modified with superparamagnetic iron oxide nanoparticles (SPIONs) to work as a dual-modal contrast agent for magnetic resonance (MR) imaging along with US imaging. Apparently, the shell modification changes their mechanical characteristics, which affects their acoustic properties. The overall objective of the thesis is to investigate the acoustic properties of modified and unmodified PVA-MBs at different ultrasound parameters.The acoustic and mechanical characterization of SPIONs modified PVA-MBs revealed that the acoustical response depends on the SPION inclusion strategy. However they retain the same structural characteristics after the modification. The modified MBs with SPIONs included on the surface of the PVA shell exhibit a soft-shelled behavior and produce a higher echogenicity than the MBs with the SPIONs inside the PVA shell. The fracturing mechanism of the unmodified PVA-MBs was identified to be different from the other fracturing mechanisms of conventional MBs. With the interaction of high-pressure bursts, the air gas core is squeezed out through small punctures in the PVA shell. During the fracturing, the PVA-MBs exhibit asymmetric (other modes) oscillations, resulting in sub- and ultra-harmonic generation. Exploiting the US imaging at the other modes of the oscillation of the PVA-MBs would provide an opportunity to visualize very low concentrations of (down to single) PVA-MBs. We further introduced the PVA-MBs along with particles mimicking red blood cells in an acoustic standing-wave field to observe the acoustic radiation force effect. We observed that the compressible PVA-MBs drawn toward pressure antinode while the solid blood phantoms moved toward the pressure node. This acoustic separation method (acoustophoresis) could be an efficient tool for studying the bioclearance of the PVA-MBs in the body, either by collecting blood samples (in-vitro) or by using the extracorporeal medical procedure (ex-vivo) at different organs.Overall, this work contributes significant feedback for chemists (to optimize the nanoparticle inclusion) and imaging groups (to develop new imaging sequences), and the positive findings pave new paths and provide triggers to engage in further research.
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32. |
- Khodadad, Davood, 1985-, et al.
(author)
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The Value of Phase Angle in Electrical Impedance Tomography Breath Detection
- 2018
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In: 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama). - : Electromagnetics Academy. - 9784885523168 - 9781538654552 ; , s. 1040-1043
-
Conference paper (peer-reviewed)abstract
- The objective of this paper is to report our investigation demonstrating that the phase angle information of complex impedance could be a simple indicator of a breath cycle in chest Electrical Impedance Tomography (EIT). The study used clinical neonatal EIT data. The results show that measurement of the phase angle from complex EIT data can be used as a complementary information for improving the conventional breath detection algorithms.
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33. |
- Pfeiffer, Christoph, 1989, et al.
(author)
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On-scalp MEG sensor localization using magnetic dipole-like coils: A method for highly accurate co-registration
- 2020
-
In: Neuroimage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 212
-
Journal article (peer-reviewed)abstract
- Source modelling in magnetoencephalography (MEG) requires precise co-registration of the sensor array and the anatomical structure of the measured individual's head. In conventional MEG, the positions and orientations of the sensors relative to each other are fixed and known beforehand, requiring only localization of the head relative to the sensor array. Since the sensors in on-scalp MEG are positioned on the scalp, locations of the individual sensors depend on the subject's head shape and size. The positions and orientations of on-scalp sensors must therefore be measured a every recording. This can be achieved by inverting conventional head localization, localizing the sensors relative to the head - rather than the other way around. In this study we present a practical method for localizing sensors using magnetic dipole-like coils attached to the subject's head. We implement and evaluate the method in a set of on-scalp MEG recordings using a 7-channel on-scalp MEG system based on high critical temperature superconducting quantum interference devices (high-T-c SQUIDs). The method allows individually localizing the sensor positions, orientations, and responsivities with high accuracy using only a short averaging time (<= 2 mm, < 3 degrees and < 3%, respectively, with 1-s averaging), enabling continuous sensor localization. Calibrating and jointly localizing the sensor array can further improve the accuracy of position and orientation (< 1 mm and < 1 degrees, respectively, with 1-s coil recordings). We demonstrate source localization of on-scalp recorded somatosensory evoked activity based on coregistration with our method. Equivalent current dipole fits of the evoked responses corresponded well (within 4.2 mm) with those based on a commercial, whole-head MEG system.
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34. |
- Liu, Ruishan, et al.
(author)
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Modeling spatial correlation of transcripts with application to developing pancreas
- 2019
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In: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9
-
Journal article (peer-reviewed)abstract
- Recently high-throughput image-based transcriptomic methods were developed and enabled researchers to spatially resolve gene expression variation at the molecular level for the first time. In this work, we develop a general analysis tool to quantitatively study the spatial correlations of gene expression in fixed tissue sections. As an illustration, we analyze the spatial distribution of single mRNA molecules measured by in situ sequencing on human fetal pancreas at three developmental time points–80, 87 and 117 days post-fertilization. We develop a density profile-based method to capture the spatial relationship between gene expression and other morphological features of the tissue sample such as position of nuclei and endocrine cells of the pancreas. In addition, we build a statistical model to characterize correlations in the spatial distribution of the expression level among different genes. This model enables us to infer the inhibitory and clustering effects throughout different time points. Our analysis framework is applicable to a wide variety of spatially-resolved transcriptomic data to derive biological insights.
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35. |
- Borrelli, P., et al.
(author)
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Artificial intelligence-aided CT segmentation for body composition analysis: a validation study
- 2021
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In: European Radiology Experimental. - : Springer Science and Business Media LLC. - 2509-9280. ; 5:1
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Journal article (peer-reviewed)abstract
- BackgroundBody composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images.MethodsEthical approvals from Gothenburg and Lund Universities were obtained. Convolutional neural networks were trained to segment SAT and muscle using manual segmentations on CT images from a training group of 50 patients. The method was applied to a separate test group of 74 cancer patients, who had two CT studies each with a median interval between the studies of 3days. Manual segmentations in a single CT slice were used for comparison. The accuracy was measured as overlap between the automated and manual segmentations.ResultsThe accuracy of the AI method was 0.96 for SAT and 0.94 for muscle. The average differences in volumes were significantly lower than the corresponding differences in areas in a single CT slice: 1.8% versus 5.0% (p <0.001) for SAT and 1.9% versus 3.9% (p < 0.001) for muscle. The 95% confidence intervals for predicted volumes in an individual subject from the corresponding single CT slice areas were in the order of 20%.Conclusions The AI-based tool for quantification of SAT and muscle volumes showed high accuracy and reproducibility and provided a body composition analysis that is more relevant than manual analysis of a single CT slice.
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36. |
- Isaksson-Daun, Johan
(author)
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A Sound Approach Toward a Mobility Aid for Blind and Low-Vision Individuals
- 2023
-
Doctoral thesis (other academic/artistic)abstract
- Reduced independent mobility of blind and low-vision individuals (BLVIs) cause considerable societal cost, burden on relatives, and reduced quality of life for the individuals, including increased anxiety, depression symptoms, need of assistance, risk of falls, and mortality. Despite the numerous electronic travel aids proposed since at least the 1940’s, along with ever-advancing technology, the mobility issues persist. A substantial reason for this is likely several and severe shortcomings of the field, both in regards to aid design and evaluation.In this work, these shortcomings are addressed with a generic design model called Desire of Use (DoU), which describes the desire of a given user to use an aid for a given activity. It is then applied on mobility of BLVIs (DoU-MoB), to systematically illuminate and structure possibly all related aspects that such an aid needs to aptly deal with, in order for it to become an adequate aid for the objective. These aspects can then both guide user-centered design as well as choice of test methods and measures.One such measure is then demonstrated in the Desire of Use Questionnaire for Mobility of Blind and Low-Vision Individuals (DoUQ-MoB), an aid-agnostic and comprehensive patient-reported outcome measure. The question construction originates from the DoU-MoB to ensure an encompassing focus on mobility of BLVIs, something that has been missing in the field. Since it is aid-agnostic it facilitates aid comparison, which it also actively promotes. To support the reliability of the DoUQ-MoB, it utilizes the best known practices of questionnaire design and has been validated once with eight orientation and mobility professionals, and six BLVIs. Based on this, the questionnaire has also been revised once.To allow for relevant and reproducible methodology, another tool presented herein is a portable virtual reality (VR) system called the Parrot-VR. It uses a hybrid control scheme of absolute rotation by tracking the user’s head in reality, affording intuitive turning; and relative movement where simple button presses on a controller moves the virtual avatar forward and backward, allowing for large-scale traversal while not walking physically. VR provides excellent reproducibility, making various aggregate movement analysis feasible, while it is also inherently safe. Meanwhile, the portability of the system facilitates testing near the participants, substantially increasing the number of potential blind and low-vision recruits for user tests.The thesis also gives a short account on the state of long-term testing in the field; it being short is mainly due to that there is not much to report. It then provides an initial investigation into possible outcome measures for such tests by taking instruments in use by Swedish orientation and mobility professionals as a starting point. Two of these are also piloted in an initial single-session trial with 19 BLVIs, and could plausibly be used for long-term tests after further evaluation.Finally, a discussion is presented regarding the Audomni project — the development of a primary mobility aid for BLVIs. Audomni is a visuo-auditory sensory supplementation device, which aims to take visual information and translate it to sound. A wide field-of-view, 3D-depth camera records the environment, which is then transformed to audio through the sonification algorithms of Audomni, and finally presented in a pair of open-ear headphones that do not block out environmental sounds. The design of Audomni leverages the DoU-MoB to ensure user-centric development and evaluation, in the aim of reaching an aid with such form and function that it grants the users better mobility, while the users still want to use it.Audomni has been evaluated with user tests twice, once in pilot tests with two BLVIs, and once in VR with a heterogenous set of 19 BLVIs, utilizing the Parrot-VR and the DoUQ-MoB. 76 % of responders (13 / 17) answered that it was very or extremely likely that they would want use Audomni along with their current aid. This might be the first result in the field demonstrating a majority of blind and low-vision participants reporting that they actually want to use a new electronic travel aid. This shows promise that eventual long-term tests will demonstrate an increased mobility of blind and low-vision users — the overarching project aim. Such results would ultimately mean that Audomni can become an aid that alleviates societal cost, reduces burden on relatives, and improves users’ quality of life and independence.
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37. |
- Koriakina, Nadezhda, 1991-, et al.
(author)
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Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection
- 2024
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 19:4 April
-
Journal article (peer-reviewed)abstract
- The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Using a microscope, skilled cytotechnologists are able to detect changes due to malignancy; however, introducing this approach into clinical routine is associated with challenges such as a lack of resources and experts. To design a trustworthy OC detection system that can assist cytotechnologists, we are interested in deep learning based methods that can reliably detect cancer, given only per-patient labels (thereby minimizing annotation bias), and also provide information regarding which cells are most relevant for the diagnosis (thereby enabling supervision and understanding). In this study, we perform a comparison of two approaches suitable for OC detection and interpretation: (i) conventional single instance learning (SIL) approach and (ii) a modern multiple instance learning (MIL) method. To facilitate systematic evaluation of the considered approaches, we, in addition to a real OC dataset with patient-level ground truth annotations, also introduce a synthetic dataset—PAP-QMNIST. This dataset shares several properties of OC data, such as image size and large and varied number of instances per bag, and may therefore act as a proxy model of a real OC dataset, while, in contrast to OC data, it offers reliable per-instance ground truth, as defined by design. PAP-QMNIST has the additional advantage of being visually interpretable for non-experts, which simplifies analysis of the behavior of methods. For both OC and PAP-QMNIST data, we evaluate performance of the methods utilizing three different neural network architectures. Our study indicates, somewhat surprisingly, that on both synthetic and real data, the performance of the SIL approach is better or equal to the performance of the MIL approach. Visual examination by cytotechnologist indicates that the methods manage to identify cells which deviate from normality, including malignant cells as well as those suspicious for dysplasia. We share the code as open source.
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38. |
- Lidell, Martin, 1970, et al.
(author)
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Evidence for two types of brown adipose tissue in humans
- 2013
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In: Nature Medicine. - : Springer Science and Business Media LLC. - 1078-8956 .- 1546-170X. ; 19:5, s. 631-634
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Journal article (peer-reviewed)abstract
- The previously observed supraclavicular depot of brown adipose tissue (BAT) in adult humans was
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39. |
- Björnsdotter, Malin, et al.
(author)
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Clustered sampling improves random subspace brain mapping
- 2012
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In: Pattern recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 45:6, s. 2035-2040
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Journal article (peer-reviewed)abstract
- Intuitive and efficient, the random subspace ensemble approach provides an appealing solution to the problem of the vast dimensionality of functional magnetic resonance imaging (fMRI) data for maximal-accuracy brain state decoding. Recently, efforts to generate biologically plausible and interpretable maps of brain regions which contribute information to the ensemble decoding task have been made and two approaches have been introduced: globally multivariate random subsampling and locally multivariate Monte Carlo mapping. Both types of maps reflect voxel-wise decoding accuracies averaged across repeatedly randomly sampled voxel subsets, highlighting voxels which consistently participate in high-classification subsets. We compare the mapping sensitivities of the approaches on realistic simulated data containing both locally and globally multivariate information and demonstrate that utilizing the inherent volumetric nature of fMRI through clustered Monte Carlo mapping yields dramatically improved performances in terms of voxel detection sensitivity and efficiency. These results suggest that, unless a priori information specifically dictates a global search, variants of clustered sampling should be the priority for random subspace brain mapping.
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40. |
- Khodadad, Davood, 1985-, et al.
(author)
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B-spline based free form deformation thoracic non-rigid registration of CT and PET images
- 2011
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In: International Conference on Graphic and Image Processing (ICGIP 2011). - : SPIE - International Society for Optical Engineering. ; 8285
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Conference paper (peer-reviewed)abstract
- Accurate attenuation correction of emission data is mandatory for quantitative analysis of PET images. One of the main concerns in CT-based attenuation correction(CTAC) of PET data in multimodality PET/CT imaging is misalignment between PET and CT images. The aim of this study, is to proposed a hybrid method which is simple, fast and accurate, for registration of PET and CT data which affected from respiratory motion in order to improve the quality of CTAC. The algorithm is composed of three methods: First, using B-spline Free Form Deformation to describe both images and deformation field. Then applying a pre-filtering on both PET and CT images before segmentation of structures in order to reduce the respiratory related attenuation correction artifacts of PET emission data. In this approach, B-spline using FFD provide more accurate adaptive transformation to align the images, and structure constraints obtained from prefiltering applied to guide the algorithm to be more fast and accurate. Also it helps to reduce the radiation dose in PET/CT by avoiding repetition of CT imaging. These advances increase the potential of the method for routine clinical application.
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41. |
- Mosin, Vasilii, 1993, et al.
(author)
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Comparing autoencoder-based approaches for anomaly detection in highway driving scenario images
- 2022
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In: SN Applied Sciences. - : Springer Science and Business Media LLC. - 2523-3971 .- 2523-3963. ; 4:12
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Journal article (peer-reviewed)abstract
- Autoencoder-based anomaly detection approaches can be used for precluding scope compliance failures of the automotive perception. However, the applicability of these approaches for the automotive domain should be thoroughly investigated. We study the capability of two autoencoder-based approaches using reconstruction errors and bottleneck-values for detecting semantic anomalies in automotive images. As a use-case, we consider a specific highway driving scenario identifying if there are any vehicles in the field of view of a front-looking camera. We conduct a series of experiments with two simulated driving scenario datasets and measure anomaly detection performance for different cases. We systematically test different autoencoders and training parameters, as well as the influence of image colors. We show that the autoencoder-based approaches demonstrate promising results for detecting semantic anomalies in highway driving scenario images in some cases. However, we also observe the variability of anomaly detection performance between different experiments. The autoencoder-based approaches are capable of detecting semantic anomalies in highway driving scenario images to some extent. However, further research with other use-cases and real datasets is needed before they can be safely applied in the automotive domain.
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42. |
- Simistira Liwicki, Foteini, et al.
(author)
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Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition
- 2023
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In: Scientific Data. - : Springer Nature. - 2052-4463. ; 10
-
Journal article (peer-reviewed)abstract
- The recognition of inner speech, which could give a ‘voice’ to patients that have no ability to speak or move, is a challenge for brain-computer interfaces (BCIs). A shortcoming of the available datasets is that they do not combine modalities to increase the performance of inner speech recognition. Multimodal datasets of brain data enable the fusion of neuroimaging modalities with complimentary properties, such as the high spatial resolution of functional magnetic resonance imaging (fMRI) and the temporal resolution of electroencephalography (EEG), and therefore are promising for decoding inner speech. This paper presents the first publicly available bimodal dataset containing EEG and fMRI data acquired nonsimultaneously during inner-speech production. Data were obtained from four healthy, right-handed participants during an inner-speech task with words in either a social or numerical category. Each of the 8-word stimuli were assessed with 40 trials, resulting in 320 trials in each modality for each participant. The aim of this work is to provide a publicly available bimodal dataset on inner speech, contributing towards speech prostheses.
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43. |
- Ahlander, Britt-Marie, 1954-, et al.
(author)
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An echo-planar imaging sequence is superior to a steady-state free precession sequence for visual as well as quantitative assessment of cardiac magnetic resonance stress perfusion
- 2017
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In: Clinical Physiology and Functional Imaging. - : Blackwell Publishing. - 1475-0961 .- 1475-097X. ; 37:1, s. 52-61
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Journal article (peer-reviewed)abstract
- BACKGROUND: To assess myocardial perfusion, steady-state free precession cardiac magnetic resonance (SSFP, CMR) was compared with gradient-echo-echo-planar imaging (GRE-EPI) using myocardial perfusion scintigraphy (MPS) as reference.METHODS: Cardiac magnetic resonance perfusion was recorded in 30 patients with SSFP and in another 30 patients with GRE-EPI. Timing and extent of inflow delay to the myocardium was visually assessed. Signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were calculated. Myocardial scar was visualized with a phase-sensitive inversion recovery sequence (PSIR). All scar positive segments were considered pathologic. In MPS, stress and rest images were used as in clinical reporting. The CMR contrast wash-in slope was calculated and compared with the stress score from the MPS examination. CMR scar, CMR perfusion and MPS were assessed separately by one expert for each method who was blinded to other aspects of the study.RESULTS: Visual assessment of CMR had a sensitivity for the detection of an abnormal MPS at 78% (SSFP) versus 91% (GRE-EPI) and a specificity of 58% (SSFP) versus 84% (GRE-EPI). Kappa statistics for SSFP and MPS was 0·29, for GRE-EPI and MPS 0·72. The ANOVA of CMR perfusion slopes for all segments versus MPS score (four levels based on MPS) had correlation r = 0·64 (SSFP) and r = 0·96 (GRE-EPI). SNR was for normal segments 35·63 ± 11·80 (SSFP) and 17·98 ± 8·31 (GRE-EPI), while CNR was 28·79 ± 10·43 (SSFP) and 13·06 ± 7·61 (GRE-EPI).CONCLUSION: GRE-EPI displayed higher agreement with the MPS results than SSFP despite significantly lower signal intensity, SNR and CNR.
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44. |
- Ahlander, Britt-Marie, 1954-
(author)
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Magnetic Resonance Imaging of the Heart : Image quality, measurement accuracy and patient experience
- 2016
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Doctoral thesis (other academic/artistic)abstract
- Background: Non-invasive diagnostic imaging of atherosclerotic coronary artery disease (CAD) is frequently carried out with cardiovascular magnetic resonance imaging (CMR) or myocardial perfusion single photon emission computed tomography (MPS). CMR is the gold standard for the evaluation of scar after myocardial infarction and MPS the clinical gold standard for ischemia. Magnetic Resonance Imaging (MRI) is at times difficult for patients and may induce anxiety while patient experience of MPS is largely unknown.Aims: To evaluate image quality in CMR with respect to the sequences employed, the influence of atrial fibrillation, myocardial perfusion and the impact of patient information. Further, to study patient experience in relation to MRI with the goal of improving the care of these patients.Method: Four study designs have been used. In paper I, experimental cross-over, paper (II) experimental controlled clinical trial, paper (III) psychometric crosssectional study and paper (IV) prospective intervention study. A total of 475 patients ≥ 18 years with primarily cardiac problems (I-IV) except for those referred for MRI of the spine (III) were included in the four studies.Result: In patients (n=20) with atrial fibrillation, a single shot steady state free precession (SS-SSFP) sequence showed significantly better image quality than the standard segmented inversion recovery fast gradient echo (IR-FGRE) sequence (I). In first-pass perfusion imaging the gradient echo-echo planar imaging sequence (GREEPI) (n=30) had lower signal-to-noise and contrast–to-noise ratios than the steady state free precession sequence (SSFP) (n=30) but displayed a higher correlation with the MPS results, evaluated both qualitatively and quantitatively (II). The MRIAnxiety Questionnaire (MRI-AQ) was validated on patients, referred for MRI of either the spine (n=193) or the heart (n=54). The final instrument had 15 items divided in two factors regarding Anxiety and Relaxation. The instrument was found to have satisfactory psychometric properties (III). Patients who prior CMR viewed an information video scored significantly (lower) better in the factor Relaxation, than those who received standard information. Patients who underwent MPS scored lower on both factors, Anxiety and Relaxation. The extra video information had no effect on CMR image quality (IV).Conclusion: Single shot imaging in atrial fibrillation produced images with less artefact than a segmented sequence. In first-pass perfusion imaging, the sequence GRE-EPI was superior to SSFP. A questionnaire depicting anxiety during MRI showed that video information prior to imaging helped patients relax but did not result in an improvement in image quality.
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45. |
- Bidhult, Sebastian, et al.
(author)
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Independent validation of metric optimized gating for fetal cardiovascular phase-contrast flow imaging
- 2019
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In: Magnetic Resonance in Medicine. - : Wiley. - 1522-2594 .- 0740-3194. ; 81:1, s. 495-503
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Journal article (peer-reviewed)abstract
- PURPOSE: To validate metric optimized gating phase-contrast MR (MOG PC-MR) flow measurements for a range of fetal flow velocities in phantom experiments. 2) To investigate intra- and interobserver variability for fetal flow measurements at an imaging center other than the original site.METHODS: MOG PC-MR was compared to timer/beaker measurements in a pulsatile flow phantom using a heart rate (∼145 bpm), nozzle diameter (∼6 mm), and flow range (∼130-700 mL/min) similar to fetal imaging. Fifteen healthy fetuses were included for intra- and interobserver variability in the fetal descending aorta and umbilical vein.RESULTS: Phantom MOG PC-MR flow bias and variability was 2% ± 23%. Accuracy of MOG PC-MR was degraded for flow profiles with low velocity-to-noise ratio. Intra- and interobserver coefficients of variation were 6% and 19%, respectively, for fetal descending aorta; and 10% and 17%, respectively, for the umbilical vein.CONCLUSION: Phantom validation showed good agreement between MOG and conventionally gated PC-MR, except for cases with low velocity-to-noise ratio, which resulted in MOG misgating and underestimated peak velocities and warranted optimization of sequence parameters to individual fetal vessels. Inter- and intraobserver variability for fetal MOG PC-MR imaging were comparable to previously reported values.
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46. |
- Hedström, Erik, et al.
(author)
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Factors affecting performance of fetal blood T2 measurements for noninvasive estimation of oxygen saturation
- 2023
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In: Magnetic Resonance in Medicine. - 1522-2594. ; 90:6, s. 2472-2485
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Journal article (peer-reviewed)abstract
- PurposeTo ultimately make accurate and precise fetal noninvasive oxygen saturation (sO2) measurements by T2-prepared bSSFP more widely available by systematically assessing error sources in order to potentially reduce perinatal mortality in cardiovascular malformations and fetal growth restriction.MethodsT2-prepared bSSFP data were acquired in phantoms; in flowing blood in adults in the superior sagittal sinus, ascending and descending aorta, and main pulmonary artery; and in the fetal descending aorta and umbilical vein. T2 was assessed in relation to T2 two- or three-parameter curve-fitting techniques, SSFP readout, refocusing time delay (τ), constant and pulsatile blood flow, and impact of T1 recovery. Further, fetal T2 and sO2 variability were quantified in the descending aorta and umbilical vein in healthy fetuses and fetuses with cardiovascular malformation (gestational weeks 32–38).ResultsIn phantoms, three-parameter fitting was accurate irrespective of phase FOV (ConclusionsErrors due to T2-fitting techniques, off-resonance, flow velocity, and insufficient T1 recovery between image acquisitions could be mitigated by using three-parameter fitting with included saturation-prepared images approximating infinite T2-preparation time, adequate shimming covering the fetus and placenta, and by modifying acquisition parameters. Variability in fetal blood T2 and sO2, however, indicate that it is currently not feasible to use these methods for prediction of disease.
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47. |
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48. |
- Penen, Florent, 1987, et al.
(author)
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Time of Flight Secondary Ion Mass Spectrometry imaging for precise localization of zirconium-labelled trastuzumab in xenograft cancer tumour tissues
- 2022
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In: Microchemical Journal. - : Elsevier BV. - 0026-265X. ; 181
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Journal article (peer-reviewed)abstract
- The human epidermal growth factor receptor 2 (HER2) specific radiotracer zirconium-Desferrioxamine(DFO)-trastuzumab was visualized ex-vivo by Time of Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging in ovarian and breast cancer xenograft tumor sections. Heterogeneous spatial distribution of [90Zr+] ions re-flected the heterogeneous localization of trastuzumab, observed in parallel by immunohistochemistry staining in HER2+ tumors. Our results show that ToF-SIMS imaging is a quick and sensitive technique to image zirconium labelled biologics at microscale in tissues.
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49. |
- Piri, Reza, et al.
(author)
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Aortic wall segmentation in F-18-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based versus manual segmentation
- 2022
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In: Journal of Nuclear Cardiology. - : Springer Science and Business Media LLC. - 1532-6551 .- 1071-3581. ; 29:4, s. 2001-2010
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Journal article (peer-reviewed)abstract
- Background We aimed to establish and test an automated AI-based method for rapid segmentation of the aortic wall in positron emission tomography/computed tomography (PET/CT) scans. Methods For segmentation of the wall in three sections: the arch, thoracic, and abdominal aorta, we developed a tool based on a convolutional neural network (CNN), available on the Research Consortium for Medical Image Analysis (RECOMIA) platform, capable of segmenting 100 different labels in CT images. It was tested on F-18-sodium fluoride PET/CT scans of 49 subjects (29 healthy controls and 20 angina pectoris patients) and compared to data obtained by manual segmentation. The following derived parameters were compared using Bland-Altman Limits of Agreement: segmented volume, and maximal, mean, and total standardized uptake values (SUVmax, SUVmean, SUVtotal). The repeatability of the manual method was examined in 25 randomly selected scans. Results CNN-derived values for volume, SUVmax, and SUVtotal were all slightly, i.e., 13-17%, lower than the corresponding manually obtained ones, whereas SUVmean values for the three aortic sections were virtually identical for the two methods. Manual segmentation lasted typically 1-2 hours per scan compared to about one minute with the CNN-based approach. The maximal deviation at repeat manual segmentation was 6%. Conclusions The automated CNN-based approach was much faster and provided parameters that were about 15% lower than the manually obtained values, except for SUVmean values, which were comparable. AI-based segmentation of the aorta already now appears as a trustworthy and fast alternative to slow and cumbersome manual segmentation.
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50. |
- Piri, R., et al.
(author)
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"Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in F-18-sodium fluoride PET/CT scans: Head-to-head comparison
- 2022
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In: Journal of Nuclear Cardiology. - : Springer Science and Business Media LLC. - 1071-3581 .- 1532-6551. ; 29:5, s. 2531-2539
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Journal article (peer-reviewed)abstract
- Background Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac segmentation in positron emission tomography/computed tomography (PET/CT) scans with manual segmentation to assess global cardiac atherosclerosis burden. Methods A trained convolutional neural network (CNN) was used for cardiac segmentation in F-18-sodium fluoride PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients and compared with manual segmentation. Parameters for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, SUVtotal) were analyzed by Bland-Altman Limits of Agreement. Repeatability with AI-based assessment of the same scans is 100%. Repeatability (same conditions, same operator) and reproducibility (same conditions, two different operators) of manual segmentation was examined by re-segmentation in 25 randomly selected scans. Results Mean (+/- SD) values with manual vs. CNN-based segmentation were Vol 617.65 +/- 154.99 mL vs 625.26 +/- 153.55 mL (P = .21), SUVmean 0.69 +/- 0.15 vs 0.69 +/- 0.15 (P = .26), SUVmax 2.68 +/- 0.86 vs 2.77 +/- 1.05 (P = .34), and SUVtotal 425.51 +/- 138.93 vs 427.91 +/- 132.68 (P = .62). Limits of agreement were - 89.42 to 74.2, - 0.02 to 0.02, - 1.52 to 1.32, and - 68.02 to 63.21, respectively. Manual segmentation lasted typically 30 minutes vs about one minute with the CNN-based approach. The maximal deviation at manual re-segmentation was for the four parameters 0% to 0.5% with the same and 0% to 1% with different operators. Conclusion The CNN-based method was faster and provided values for Vol, SUVmean, SUVmax, and SUVtotal comparable to the manually obtained ones. This AI-based segmentation approach appears to offer a more reproducible and much faster substitute for slow and cumbersome manual segmentation of the heart.
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