| 1. |
- Berglund, Johan, et al.
(författare)
-
Energy weighting improves dose efficiency in clinical practice : implementation on a spectral photon-counting mammography system.
- 2014
-
Ingår i: Journal of Medical Imaging. - 2329-4302 .- 2329-4310. ; 1:3
-
Tidskriftsartikel (refereegranskat)abstract
- In x-ray imaging, contrast information content varies with photon energy. It is, therefore, possible to improve image quality by weighting photons according to energy. We have implemented and evaluated so-called energy weighting on a commercially available spectral photon-counting mammography system. The technique was evaluated using computer simulations, phantom experiments, and analysis of screening mammograms. The CNR benefit of energy weighting for a number of relevant target-background combinations measured by the three methods fell in the range of 2.2 to 5.2% when using optimal weight factors. This translates to a potential dose reduction at constant CNR in the range of 4.5 to 11%. We expect the choice of weight factor in practical implementations to be straightforward because (1) the CNR improvement was not very sensitive to weight, (2) the optimal weight was similar for all investigated target-background combinations, (3) aluminum/PMMA phantoms were found to represent clinically relevant tasks well, and (4) the optimal weight could be calculated directly from pixel values in phantom images. Reasonable agreement was found between the simulations and phantom measurements. Manual measurements on microcalcifications and automatic image analysis confirmed that the CNR improvement was detectable in energy-weighted screening mammograms.
|
|
| 2. |
- Breznik, Eva, et al.
(författare)
-
Multiple comparison correction methods for whole-body magnetic resonance imaging
- 2020
-
Ingår i: Journal of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 2329-4302 .- 2329-4310. ; 7:1
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: Voxel-level hypothesis testing on images suffers from test multiplicity. Numerous correction methods exist, mainly applied and evaluated on neuroimaging and synthetic datasets. However, newly developed approaches like Imiomics, using different data and less common analysis types, also require multiplicity correction for more reliable inference. To handle the multiple comparisons in Imiomics, we aim to evaluate correction methods on whole-body MRI and correlation analyses, and to develop techniques specifically suited for the given analyses. Approach: We evaluate the most common familywise error rate (FWER) limiting procedures on whole-body correlation analyses via standard (synthetic no-activation) nominal error rate estimation as well as smaller prior-knowledge based stringency analysis. Their performance is compared to our anatomy-based method extensions. Results: Results show that nonparametric methods behave better for the given analyses. The proposed prior-knowledge based evaluation shows that the devised extensions including anatomical priors can achieve the same power while keeping the FWER closer to the desired rate. Conclusions: Permutation-based approaches perform adequately and can be used within Imiomics. They can be improved by including information on image structure. We expect such method extensions to become even more relevant with new applications and larger datasets.
|
|
| 3. |
- Brunskog, Rickard, et al.
(författare)
-
First experimental evaluation of a high-resolution deep silicon photon-counting sensor
- 2024
-
Ingår i: Journal of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 2329-4302 .- 2329-4310. ; 11:1
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: Current photon-counting computed tomography detectors are limited to a pixel size of around 0.3 to 0.5 mm due to excessive charge sharing degrading the dose efficiency and energy resolution as the pixels become smaller. In this work, we present measurements of a prototype photon-counting detector that leverages the charge sharing to reach a theoretical sub-pixel resolution in the order of 1 μm. The goal of the study is to validate our Monte-Carlo simulation using measurements, enabling further development. Approach: We measure the channel response at the MAX IV Lab, in the DanMAX beamline, with a 35 keV photon beam, and compare the measurements with a 2D Monte Carlo simulation combined with a charge transport model. Only a few channels on the prototype are connected to keep the number of wire bonds low. Results: The measurements agree generally well with the simulations with the beam close to the electrodes but diverge as the beam is moved further away. The induced charge cloud signals also seem to increase linearly as the beam is moved away from the electrodes. Conclusions: The agreement between measurements and simulations indicates that the Monte-Carlo simulation can accurately model the channel response of the detector with the photon interactions close to the electrodes, which indicates that the unconnected electrodes introduce unwanted effects that need to be further explored. With the same Monte-Carlo simulation previously indicating a resolution of around 1 μm with similar geometry, the results are promising that an ultra-high resolution detector is not far in the future.
|
|
| 4. |
- Chen, Han, 1986-, et al.
(författare)
-
Optimization Of Beam Quality For Photon-Counting Spectral Computed Tomography In Head Imaging: Simulation Study
- 2015
-
Ingår i: Journal of Medical Imaging. - : SPIE. - 2329-4302 .- 2329-4310. ; 2:4, s. 043504-1-043504-16
-
Tidskriftsartikel (refereegranskat)abstract
- Head computed tomography (CT) plays an important role in the comprehensive evaluation of acutestroke. Photon-counting spectral detectors, as promising candidates for use in the next generation of x-ray CTsystems, allow for assigning more weight to low-energy x-rays that generally contain more contrast information.Most importantly, the spectral information can be utilized to decompose the original set of energy-selectiveimages into several basis function images that are inherently free of beam-hardening artifacts, a potential ad-vantage for further improving the diagnosis accuracy. We are developing a photon-counting spectral detector forCT applications. The purpose of this work is to determine the optimal beam quality for material decomposition intwo head imaging cases: nonenhanced imaging and K-edge imaging. A cylindrical brain tissue of 16-cm diam-eter, coated by a 6-mm-thick bone layer and 2-mm-thick skin layer, was used as a head phantom. The imagingtarget was a 5-mm-thick blood vessel centered in the head phantom. In K-edge imaging, two contrast agents,iodine and gadolinium, with the same concentration (5mg∕mL) were studied. Three parameters that affect beamquality were evaluated: kVp settings (50 to 130 kVp), filter materials (Z¼13to 83), and filter thicknesses [0 to 2half-value layer (HVL)]. The image qualities resulting from the varying x-ray beams were compared in terms oftwo figures of merit (FOMs): squared signal-difference-to-noise ratio normalized by brain dose (SDNR2∕BD) andthat normalized by skin dose (SDNR2∕SD). For nonenhanced imaging, the results show that the use of the 120-kVp spectrum filtered by 2 HVL copper (Z¼29) provides the best performance in both FOMs. When iodine isused in K-edge imaging, the optimal filter is 2 HVL iodine (Z¼53) and the optimal kVps are 60 kVp in terms ofSDNR2∕BD and 75 kVp in terms of SDNR2∕SD. A tradeoff of 65 kVp was proposed to lower the potential riskof skin injuries if a relatively long exposure time is necessarily performed in the iodinated imaging. In the case ofgadolinium imaging, both SD and BD can be minimized at 120 kVp filtered with 2 HVL thulium (Z¼69). Theresults also indicate that with the same concentration and their respective optimal spectrum, the values ofSDNR2∕BD and SDNR2∕SD in gadolinium imaging are, respectively, around 3 and 10 times larger thanthose in iodine imaging. However, since gadolinium is used in much lower concentrations than iodine in theclinic, iodine may be a preferable candidate for K-edge imaging.
|
|
| 5. |
- Clarke, Emily L., et al.
(författare)
-
Display evaluation for primary diagnosis using digital pathology
- 2020
-
Ingår i: Journal of Medical Imaging. - : SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS. - 2329-4302 .- 2329-4310. ; 7:2
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: As pathology departments around the world contemplate digital microscopy for primary diagnosis, making an informed choice regarding display procurement is very challenging in the absence of defined minimum standards. In order to help inform the decision, we aimed to conduct an evaluation of displays with a range of technical specifications and sizes. Approach: We invited histopathologists within our institution to take part in a survey evaluation of eight short-listed displays. Pathologists reviewed a single haematoxylin and eosin whole slide image of a benign nevus on each display and gave a single score to indicate their preference in terms of image quality and size of the display. Results: Thirty-four pathologists took part in the display evaluation experiment. The preferred display was the largest and had the highest technical specifications (11.8-MP resolution, 2100 cd/m(2) maximum luminance). The least preferred display had the lowest technical specifications (2.3-MP resolution, 300 cd/m(2) maximum luminance). A trend was observed toward an increased preference for displays with increased luminance and resolution. Conclusions: This experiment demonstrates a preference for large medical-grade displays with the high luminance and high resolution. As cost becomes implicated in procurement, significantly less expensive medical-grade displays with slightly lower technical specifications may be the most cost-effective option. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)
|
|
| 6. |
- Cossío, Fernando, et al.
(författare)
-
VAI-B: a multicenter platform for the external validation of artificial intelligence algorithms in breast imaging
- 2023
-
Ingår i: Journal of Medical Imaging. - : SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS. - 2329-4302 .- 2329-4310. ; 10:06
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: Multiple vendors are currently offering artificial intelligence (AI) computer-aided systems for triage detection, diagnosis, and risk prediction of breast cancer based on screening mammography. There is an imminent need to establish validation platforms that enable fair and transparent testing of these systems against external data.Approach: We developed validation of artificial intelligence for breast imaging (VAI-B), a platform for independent validation of AI algorithms in breast imaging. The platform is a hybrid solution, with one part implemented in the cloud and another in an on-premises environment at Karolinska Institute. Cloud services provide the flexibility of scaling the computing power during inference time, while secure on-premises clinical data storage preserves their privacy. A MongoDB database and a python package were developed to store and manage the data on-premises. VAI-B requires four data components: radiological images, AI inferences, radiologist assessments, and cancer outcomes.Results: To pilot test VAI-B, we defined a case-control population based on 8080 patients diagnosed with breast cancer and 36,339 healthy women based on the Swedish national quality registry for breast cancer. Images and radiological assessments from more than 100,000 mammography examinations were extracted from hospitals in three regions of Sweden. The images were processed by AI systems from three vendors in a virtual private cloud to produce abnormality scores related to signs of cancer in the images. A total of 105,706 examinations have been processed and stored in the database.Conclusions: We have created a platform that will allow downstream evaluation of AI systems for breast cancer detection, which enables faster development cycles for participating vendors and safer AI adoption for participating hospitals. The platform was designed to be scalable and ready to be expanded should a new vendor want to evaluate their system or should a new hospital wish to obtain an evaluation of different AI systems on their images.
|
|
| 7. |
- Cossío, Fernando, et al.
(författare)
-
VAI-B: A multicenter platform for the external validation of artificial intelligence algorithms in breast imaging
- 2023
-
Ingår i: Journal of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 2329-4302 .- 2329-4310. ; 10:6
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: Multiple vendors are currently offering artificial intelligence (AI) computer-aided systems for triage detection, diagnosis, and risk prediction of breast cancer based on screening mammography. There is an imminent need to establish validation platforms that enable fair and transparent testing of these systems against external data. Approach: We developed validation of artificial intelligence for breast imaging (VAI-B), a platform for independent validation of AI algorithms in breast imaging. The platform is a hybrid solution, with one part implemented in the cloud and another in an on-premises environment at Karolinska Institute. Cloud services provide the flexibility of scaling the computing power during inference time, while secure on-premises clinical data storage preserves their privacy. A MongoDB database and a python package were developed to store and manage the data onpremises. VAI-B requires four data components: radiological images, AI inferences, radiologist assessments, and cancer outcomes. Results: To pilot test VAI-B, we defined a case-control population based on 8080 patients diagnosed with breast cancer and 36,339 healthy women based on the Swedish national quality registry for breast cancer. Images and radiological assessments from more than 100,000 mammography examinations were extracted from hospitals in three regions of Sweden. The images were processed by AI systems from three vendors in a virtual private cloud to produce abnormality scores related to signs of cancer in the images. A total of 105,706 examinations have been processed and stored in the database. Conclusions: We have created a platform that will allow downstream evaluation of AI systems for breast cancer detection, which enables faster development cycles for participating vendors and safer AI adoption for participating hospitals. The platform was designed to be scalable and ready to be expanded should a new vendor want to evaluate their system or should a new hospital wish to obtain an evaluation of different AI systems on their images.
|
|
| 8. |
- da Silva, Joakim, et al.
(författare)
-
Resolution characterization of a silicon-based, photon-counting computed tomography prototype capable of patient scanning
- 2019
-
Ingår i: Journal of Medical Imaging. - USA : SPIE - International Society for Optical Engineering. - 2329-4302 .- 2329-4310. ; 6:4
-
Tidskriftsartikel (refereegranskat)abstract
- Photon-counting detectors are expected to bring a range of improvements to patient imaging with x-ray computed tomography (CT). One is higher spatial resolution. We demonstrate the resolution obtained using a commercial CT scanner where the original energy-integrating detector has been replaced by a single-slice, silicon-based, photon-counting detector. This prototype constitutes the first full-field-of-view silicon-based CT scanner capable of patient scanning. First, the pixel response function and focal spot profile are measured and, combining the two, the system modulation transfer function is calculated. Second, the prototype is used to scan a resolution phantom and a skull phantom. The resolution images are compared to images from a state-of-the-art CT scanner. The comparison shows that for the prototype 19 lp∕cm are detectable with the same clarity as 14 lp∕cm on the reference scanner at equal dose and reconstruction grid, with more line pairs visible with increasing dose and decreasing image pixel size. The high spatial resolution remains evident in the anatomy of the skull phantom and is comparable to that of other photon-counting CT prototypes present in the literature. We conclude that the deep silicon-based detector used in our study could provide improved spatial resolution in patient imaging without increasing the x-ray dose.
|
|
| 9. |
- Ekström, Simon, 1991-, et al.
(författare)
-
Faster dense deformable image registration by utilizing both CPU and GPU
- 2021
-
Ingår i: Journal of Medical Imaging. - 2329-4302 .- 2329-4310. ; 8:1
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: Image registration is an important aspect of medical image analysis and a key component in many analysis concepts. Applications include fusion of multimodal images, multi-atlas segmentation, and whole-body analysis. Deformable image registration is often computationally expensive, and the need for efficient registration methods is highlighted by the emergence of large-scale image databases, e.g., the UK Biobank, providing imaging from 100,000 participants. Approach: We present a heterogeneous computing approach, utilizing both the CPU and the graphics processing unit (GPU), to accelerate a previously proposed image registration method. The parallelizable task of computing the matching criterion is offloaded to the GPU, where it can be computed efficiently, while the more complex optimization task is performed on the CPU. To lessen the impact of data synchronization between the CPU and GPU, we propose a pipeline model, effectively overlapping computational tasks with data synchronization. The performance is evaluated on a brain labeling task and compared with a CPU implementation of the same method and the popular advanced normalization tools (ANTs) software. Results: The proposed method presents a speed-up by factors of 4 and 8 against the CPU implementation and the ANTs software, respectively. A significant improvement in labeling quality was also observed, with measured mean Dice overlaps of 0.712 and 0.701 for our method and ANTs, respectively. Conclusions: We showed that the proposed method compares favorably to the ANTs software yielding both a significant speed-up and an improvement in labeling quality. The registration method together with the proposed parallelization strategy is implemented as an open-source software package, deform.
|
|
| 10. |
- Jansen, Marielle J. A., et al.
(författare)
-
Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification
- 2020
-
Ingår i: Journal of Medical Imaging. - : SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS. - 2329-4302 .- 2329-4310. ; 7:6
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: Convolutional neural network (CNN) methods have been proposed to quantify lesions in medical imaging. Commonly, more than one imaging examination is available for a patient, but the serial information in these images often remains unused. CNN-based methods have the potential to extract valuable information from previously acquired imaging to better quantify lesions on current imaging of the same patient.Approach: A pretrained CNN can be updated with a patient's previously acquired imaging: patient-specific fine-tuning (FT). In this work, we studied the improvement in performance of lesion quantification methods on magnetic resonance images after FT compared to a pretrained base CNN. We applied the method to two different approaches: the detection of liver metastases and the segmentation of brain white matter hyperintensities (WMH).Results: The patient-specific fine-tuned CNN has a better performance than the base CNN. For the liver metastases, the median true positive rate increases from 0.67 to 0.85. For the WMH segmentation, the mean Dice similarity coefficient increases from 0.82 to 0.87.Conclusions: We showed that patient-specific FT has the potential to improve the lesion quantification performance of general CNNs by exploiting a patient's previously acquired imaging.
|
|
