| 1. |
- Wang, Adam, et al.
(författare)
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Science and practice of imaging physics through 50 years of SPIE Medical Imaging conferences
- 2022
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Ingår i: Journal of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 2329-4302 .- 2329-4310. ; 9:S1
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: For 50 years now, SPIE Medical Imaging (MI) conferences have been the premier forum for disseminating and sharing new ideas, technologies, and concepts on the physics of MI. Approach: Our overarching objective is to demonstrate and highlight the major trajectories of imaging physics and how they are informed by the community and science present and presented at SPIE MI conferences from its inception to now. Results: These contributions range from the development of image science, image quality metrology, and image reconstruction to digital x-ray detectors that have revolutionized MI modalities including radiography, mammography, fluoroscopy, tomosynthesis, and computed tomography (CT). Recent advances in detector technology such as photon-counting detectors continue to enable new capabilities in MI. Conclusion: As we celebrate the past 50 years, we are also excited about what the next 50 years of SPIE MI will bring to the physics of MI.
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| 2. |
- Bjerkén, Anna, et al.
(författare)
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Dose evaluation of simultaneous breast radiography and mechanical imaging
- 2023
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Ingår i: Medical Imaging 2023 : Physics of Medical Imaging - Physics of Medical Imaging. ; 12463
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Konferensbidrag (refereegranskat)abstract
- This study investigates the impact in terms of radiation dose when performing simultaneous digital breast tomosynthesis(DBT) and mechanical imaging (MI) – DBTMI. DBTMI has demonstrated the potential to increase specificity of cancerdetection, and reduce unnecessary biopsies, as compared to digital mammography (DM) screening. The presence of theMI sensor during simultaneous image acquisition may increase the radiation dose when automatic exposure control is used.In this project, a radiation dose study was conducted on clinically available breast imaging systems with and without theMI sensor. We have investigated three approaches to analyse the dose increase in DBTMI, using (i) the estimates of averageglandular dose (AGD) reported in DICOM headers of radiography images; (ii) AGD measured by a conventionaldosemeter; and (iii) AGD measured by optically stimulated luminescence using NaCl pellets. The relative increase in AGDestimated from DICOM headers when using the MI sensor was on average 10.7% and 12.4%, for DM and DBTmeasurements, respectively. The relative increase in AGD using the conventional dosemeter was 11.2% in DM mode and12.2% in DBT mode. The relative increase in AGD using NaCl pellets was 14.6% in DM mode. Our measurements suggestthat the use of simultaneous breast radiography and MI increases the AGD by 13% on average. The increase in dose is stillbelow the acceptable values in mammography screening recommended by the European Guidelines.
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| 3. |
- Costa, Arthur C., et al.
(författare)
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Assessment of projection interpolation to compensate for the increased radiation dose in DBTMI
- 2023
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Ingår i: Medical Imaging 2023 : Physics of Medical Imaging - Physics of Medical Imaging. - 1605-7422. - 9781510660311 ; 12463
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Konferensbidrag (refereegranskat)abstract
- The combination of digital breast tomosynthesis (DBT) with other imaging modalities has been investigated in order to improve the detection and diagnosis of breast cancer. Mechanical Imaging (MI) measures the stress over the surface of the compressed breast, using a pressure sensor, during radiographic examination and its response has shown a correlation with the presence of malignant lesions. Thus, the combination of DBT and MI (DBTMI) has shown potential to reduce false positive results in breast cancer screening. However, compared to the conventional DBT exam, the presence of the MI sensor during mammographic image acquisition may cause a slight increase in the radiation dose. This work presents a proposal to reduce the radiation dose in DBTMI exams by removing some projections from the original set and replacing them with synthetic projections generated by a video frame interpolation (VFI) neural network. We compared several DBTMI acquisition arrangements, considering the removal of 16% of the original projections, using a deformable physical breast phantom, and evaluated the quality of the reconstructed images based on the Normalized Root Mean Squared Error (NRMSE). The results showed that, for some arrangements, the slices reconstructed with the addition of synthetic DBTMI projections presented better quality than when they were reconstructed with the reduced set of projections. Further studies must be carried out to optimize the interpolation approach.
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| 4. |
- Dobbins, James T, et al.
(författare)
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Multi-Institutional Evaluation of Digital Tomosynthesis, Dual-Energy Radiography, and Conventional Chest Radiography for the Detection and Management of Pulmonary Nodules.
- 2017
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Ingår i: Radiology. - : Radiological Society of North America (RSNA). - 1527-1315 .- 0033-8419. ; 282:1
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Tidskriftsartikel (refereegranskat)abstract
- Purpose To conduct a multi-institutional, multireader study to compare the performance of digital tomosynthesis, dual-energy (DE) imaging, and conventional chest radiography for pulmonary nodule detection and management. Materials and Methods In this binational, institutional review board-approved, HIPAA-compliant prospective study, 158 subjects (43 subjects with normal findings) were enrolled at four institutions. Informed consent was obtained prior to enrollment. Subjects underwent chest computed tomography (CT) and imaging with conventional chest radiography (posteroanterior and lateral), DE imaging, and tomosynthesis with a flat-panel imaging device. Three experienced thoracic radiologists identified true locations of nodules (n = 516, 3-20-mm diameters) with CT and recommended case management by using Fleischner Society guidelines. Five other radiologists marked nodules and indicated case management by using images from conventional chest radiography, conventional chest radiography plus DE imaging, tomosynthesis, and tomosynthesis plus DE imaging. Sensitivity, specificity, and overall accuracy were measured by using the free-response receiver operating characteristic method and the receiver operating characteristic method for nodule detection and case management, respectively. Results were further analyzed according to nodule diameter categories (3-4 mm, >4 mm to 6 mm, >6 mm to 8 mm, and >8 mm to 20 mm). Results Maximum lesion localization fraction was higher for tomosynthesis than for conventional chest radiography in all nodule size categories (3.55-fold for all nodules, P < .001; 95% confidence interval [CI]: 2.96, 4.15). Case-level sensitivity was higher with tomosynthesis than with conventional chest radiography for all nodules (1.49-fold, P < .001; 95% CI: 1.25, 1.73). Case management decisions showed better overall accuracy with tomosynthesis than with conventional chest radiography, as given by the area under the receiver operating characteristic curve (1.23-fold, P < .001; 95% CI: 1.15, 1.32). There were no differences in any specificity measures. DE imaging did not significantly affect nodule detection when paired with either conventional chest radiography or tomosynthesis. Conclusion Tomosynthesis outperformed conventional chest radiography for lung nodule detection and determination of case management; DE imaging did not show significant differences over conventional chest radiography or tomosynthesis alone. These findings indicate performance likely achievable with a range of reader expertise. (©) RSNA, 2016 Online supplemental material is available for this article.
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| 5. |
- Fransson, V., et al.
(författare)
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Deep learning volumetric brain segmentation based on spectral CT
- 2023
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Ingår i: Medical Imaging 2023 : Physics of Medical Imaging - Physics of Medical Imaging. - 1605-7422. - 9781510660311 ; 12463
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Konferensbidrag (refereegranskat)abstract
- The purpose of this pilot study was to evaluate if a deep learning network can be used for brain segmentation of grey and white matter using spectral computed tomography (CT) images. Spectral CT has the advantage of a lower noise level and an increased soft tissue contrast, compared to conventional CT, which should make it better suited for segmentation tasks. Being able to do volumetric assessments on CT, not only magnetic resonance imaging (MRI) would be of great clinical benefit. The training set consisted of two patients and the validation data set of one patient. Included patients had a brain CT from a spectral CT as well as a T1-weighted MRI. MRI was used for an MR-based segmentation using FreeSurfer. A convolutional neural network was trained to identify grey and white matter in virtual monoenergetic images (70 keV) from spectral CT, using the MR-based segmentation as reference, and tested to assess its' performance. The network was able to identify both grey and white matter in roughly the correct areas. In general, there was an overestimation of grey matter. These results motivate further studies, as we predict that the network will be more accurate when trained on a larger data set.
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| 6. |
- Sechopoulos, Ioannis, et al.
(författare)
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Radiation dosimetry in digital breast tomosynthesis : report of AAPM Tomosynthesis Subcommittee Task Group 223.
- 2014
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Ingår i: Medical physics. - : Wiley. - 2473-4209 .- 0094-2405. ; 41:9
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Tidskriftsartikel (refereegranskat)abstract
- The radiation dose involved in any medical imaging modality that uses ionizing radiation needs to be well understood by the medical physics and clinical community. This is especially true of screening modalities. Digital breast tomosynthesis (DBT) has recently been introduced into the clinic and is being used for screening for breast cancer in the general population. Therefore, it is important that the medical physics community have the required information to be able to understand, estimate, and communicate the radiation dose levels involved in breast tomosynthesis imaging. For this purpose, the American Association of Physicists in Medicine Task Group 223 on Dosimetry in Tomosynthesis Imaging has prepared this report that discusses dosimetry in breast imaging in general, and describes a methodology and provides the data necessary to estimate mean breast glandular dose from a tomosynthesis acquisition. In an effort to maximize familiarity with the procedures and data provided in this Report, the methodology to perform the dose estimation in DBT is based as much as possible on that used in mammography dose estimation.
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| 7. |
- Tomic, Hanna, et al.
(författare)
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Using simulated breast lesions based on Perlin noise for evaluation of lesion segmentation
- 2024
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Ingår i: Medical Imaging 2024 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 1605-7422. - 9781510671546 ; 12925
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Konferensbidrag (refereegranskat)abstract
- Segmentation of diagnostic radiography images using deep learning is progressively expanding, which sets demands on the accessibility, availability, and accuracy of the software tools used. This study aimed at evaluating the performance of a segmentation model for digital breast tomosynthesis (DBT), with the use of computer-simulated breast anatomy. We have simulated breast anatomy and soft tissue breast lesions, by utilizing a model approach based on the Perlin noise algorithm. The obtained breast phantoms were projected and reconstructed into DBT slices using a publicly available open-source reconstruction method. Each lesion was then segmented using two approaches: 1. the Segment Anything Model (SAM), a publicly available AI-based method for image segmentation and 2. manually by three human observers. The lesion area in each slice was compared to the ground truth area, derived from the binary mask of the lesion model. We found similar performance between SAM and manual segmentation. Both SAM and the observers performed comparably in the central slice (mean absolute relative error compared to the ground truth and standard deviation SAM: 4 ± 3 %, observers: 3 ± 3 %). Similarly, both SAM and the observers overestimated the lesion area in the peripheral reconstructed slices (mean absolute relative error and standard deviation SAM: 277 ± 190 %, observers: 295 ± 182 %). We showed that 3D voxel phantoms can be used for evaluating different segmentation methods. In preliminary comparison, tumor segmentation in simulated DBT images using SAM open-source method showed a similar performance as manual tumor segmentation.
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