| 1. |
- Fredenberg, Erik, 1979-
(författare)
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Spectral Mammography with X-Ray Optics and a Photon-Counting Detector
- 2009
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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.
(författare)
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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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Ingår i: Journal of Instrumentation. - 1748-0221. ; 7:1
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Brain tumor modeling : glioma growth and interaction with chemotherapy
- 2011
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Ingår i: International Conference on Graphic and Image Processing (ICGIP 2011). - : SPIE. ; 8285
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Konferensbidrag (refereegranskat)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.
(författare)
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Definition of Tubular Anatomic Structures from Arbitrary Stereo Lithographic Surface
- 2017
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Ingår i: Initiative Seminar Engineering Health, 8-9 November 2017, Chalmers.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)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.
(författare)
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Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification
- 2020
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Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 8:1, s. 22560-22570
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Tidskriftsartikel (refereegranskat)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.
(författare)
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A low-absorption x-ray energy filter for small-scale applications
- 2009
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Ingår i: Optics Express. - : The Optical Society. - 1094-4087. ; 17:14, s. 11388-11398
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Tidskriftsartikel (refereegranskat)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.
(författare)
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A Tunable Energy Filter for Medical X-Ray Imaging
- 2008
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Ingår i: X-Ray Optics and Instrumentation. - : Hindawi. - 1687-7632 .- 1687-7640. ; 2008
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Contrast-enhanced spectral mammography with a photon-counting detector
- 2010
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Ingår i: Medical physics (Lancaster). - : Wiley. - 0094-2405. ; 37:5, s. 2017-2029
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Energy filtering with x-ray lenses: Optimization for photon-counting mammography
- 2010
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Ingår i: Radiation Protection Dosimetry. - : Oxford University Press (OUP). - 0144-8420 .- 1742-3406. ; 139, s. 339-342
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Tidskriftsartikel (refereegranskat)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.
(författare)
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3D Multi-Scale Convolutional Networks for Glioma Grading Using MR Images
- 2018
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Ingår i: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. - 9781479970612 ; , s. 141-145
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Konferensbidrag (refereegranskat)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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