Volume 12 Issue S1 | Journal of Medical Imaging

Journal of Medical Imaging

VOL. 12 · NO. S1 | January 2025

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Special Issue Celebrating Digital Tomosynthesis: Past, Present, and Future (6)

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Special Issue Celebrating Digital Tomosynthesis: Past, Present, and Future

In-silico study of the impact of system design parameters on microcalcification detection in wide-angle digital breast tomosynthesis

Xiaoyu Duan, Hailiang Huang, Wei Zhao

Journal of Medical Imaging, Vol. 12, Issue S1, S13002, (July 2024) https://doi.org/10.1117/1.JMI.12.S1.S13002

TOPICS: Digital breast tomosynthesis, Signal to noise ratio, Imaging systems, Design, Sensors, Tunable filters, Breast, Quantum noise, Object detection, CMOS sensors

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Purpose

Accurate detection of microcalcifications (μCalcs) is crucial for the early detection of breast cancer. Some clinical studies have indicated that digital breast tomosynthesis (DBT) systems with a wide angular range have inferior μCalc detectability compared with those with a narrow angular range. This study aims to (1) provide guidance for optimizing wide-angle (WA) DBT for improving μCalcs detectability and (2) prioritize key optimization factors.

Approach

An in-silico DBT pipeline was constructed to evaluate μCalc detectability of a WA DBT system under various imaging conditions: focal spot motion (FSM), angular dose distribution (ADS), detector pixel pitch, and detector electronic noise (EN). Images were simulated using a digital anthropomorphic breast phantom inserted with 120 μm μCalc clusters. Evaluation metrics included the signal-to-noise ratio (SNR) of the filtered channel observer and the area under the receiver operator curve (AUC) of multiple-reader multiple-case analysis.

Results

Results showed that FSM degraded μCalcs sharpness and decreased the SNR and AUC by 5.2% and 1.8%, respectively. Non-uniform ADS increased the SNR by 62.8% and the AUC by 10.2% for filtered backprojection reconstruction with a typical clinical filter setting. When EN decreased from 2000 to 200 electrons, the SNR and AUC increased by 21.6% and 5.0%, respectively. Decreasing the detector pixel pitch from 85 to 50 μm improved the SNR and AUC by 55.6% and 7.5%, respectively. The combined improvement of a 50 μm pixel pitch and EN200 was 89.2% in the SNR and 12.8% in the AUC.

Conclusions

Based on the magnitude of impact, the priority for enhancing μCalc detectability in WA DBT is as follows: (1) utilizing detectors with a small pixel pitch and low EN level, (2) allocating a higher dose to central projections, and (3) reducing FSM. The results from this study can potentially provide guidance for DBT system optimization in the future.

Estimation of the absorbed dose in simultaneous digital breast tomosynthesis and mechanical imaging

Anna Bjerkén, Hanna Tomic, Sophia Zackrisson, Magnus Dustler, Predrag Bakic, Anders Tingberg

Journal of Medical Imaging, Vol. 12, Issue S1, S13003, (July 2024) https://doi.org/10.1117/1.JMI.12.S1.S13003

TOPICS: Digital breast tomosynthesis, Breast, Sensors, Polymethylmethacrylate, Mammography, Radiography, Breast cancer, Imaging systems, Medicine, Image sensors

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Breathing motion compensation in chest tomosynthesis: evaluation of the effect on image quality and presence of artifacts

Maral Mirzai, Jenny Nilsson, Patrik Sund, Rauni Rossi Norrlund, Micael Oliveira Diniz, Bengt Gottfridsson, Ida Häggström, Åse Johnsson, Magnus Båth, Angelica Svalkvist

Journal of Medical Imaging, Vol. 12, Issue S1, S13004, (September 2024) https://doi.org/10.1117/1.JMI.12.S1.S13004

TOPICS: Image quality, Chest, Tomosynthesis, Anatomy, Image restoration, X-ray computed tomography, Computed tomography, Statistical analysis, Motion analysis, Chest imaging

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Purpose

Chest tomosynthesis (CTS) has a relatively longer acquisition time compared with chest X-ray, which may increase the risk of motion artifacts in the reconstructed images. Motion artifacts induced by breathing motion adversely impact the image quality. This study aims to reduce these artifacts by excluding projection images identified with breathing motion prior to the reconstruction of section images and to assess if motion compensation improves overall image quality.

Approach

In this study, 2969 CTS examinations were analyzed to identify examinations where breathing motion has occurred using a method based on localizing the diaphragm border in each of the projection images. A trajectory over diaphragm positions was estimated from a second-order polynomial curve fit, and projection images where the diaphragm border deviated from the trajectory were removed before reconstruction. The image quality between motion-compensated and uncompensated examinations was evaluated using the image quality criteria for anatomical structures and image artifacts in a visual grading characteristic (VGC) study. The resulting rating data were statistically analyzed using the software VGC analyzer.

Results

A total of 58 examinations were included in this study with breathing motion occurring either at the beginning or end (n=17) or throughout the entire acquisition (n=41). In general, no significant difference in image quality or presence of motion artifacts was shown between the motion-compensated and uncompensated examinations. However, motion compensation significantly improved the image quality and reduced the motion artifacts in cases where motion occurred at the beginning or end. In examinations where motion occurred throughout the acquisition, motion compensation led to a significant increase in ripple artifacts and noise.

Conclusions

Compensation for respiratory motion in CTS by excluding projection images may improve the image quality if the motion occurs mainly at the beginning or end of the examination. However, the disadvantages of excluding projections may outweigh the benefits of motion compensation.

Automated assessment of task-based performance of digital mammography and tomosynthesis systems using an anthropomorphic breast phantom and deep learning-based scoring

Andrey Makeev, Kaiyan Li, Mark Anastasio, Arthur Emig, Paul Jahnke, Stephen Glick

Journal of Medical Imaging, Vol. 12, Issue S1, S13005, (October 2024) https://doi.org/10.1117/1.JMI.12.S1.S13005

TOPICS: Education and training, Breast, Digital breast tomosynthesis, Mammography, Imaging systems, Image quality, Data modeling, Signal detection, X-rays, Tomosynthesis

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Purpose

Conventional metrics used for assessing digital mammography (DM) and digital breast tomosynthesis (DBT) image quality, including noise, spatial resolution, and detective quantum efficiency, do not necessarily predict how well the system will perform in a clinical task. A number of existing phantom-based methods have their own limitations, such as unrealistic uniform backgrounds, subjective scoring using humans, and regular signal patterns unrepresentative of common clinical findings. We attempted to address this problem with a realistic breast phantom with random hydroxyapatite microcalcifications and semi-automated deep learning-based image scoring. Our goal was to develop a methodology for objective task-based assessment of image quality for tomosynthesis and DM systems, which includes an anthropomorphic phantom, a detection task (microcalcification clusters), and automated performance evaluation using a convolutional neural network.

Approach

Experimental 2D and pseudo-3D mammograms of an anthropomorphic inkjet-printed breast phantom with inserted microcalcification clusters were collected on clinical mammography systems to train a signal-present/signal-absent image classifier based on Resnet-18 architecture. In a separate validation study using simulations, this Resnet-18 classifier was shown to approach the performance of an ideal observer. Microcalcification detection performance was evaluated as a function of four dose levels using receiver operating characteristic (ROC) analysis [i.e., area under the ROC curve (AUC)]. To demonstrate the use of this evaluation approach for assessing different technologies, the method was applied to two different mammography systems, as well as to mammograms with re-binned pixels emulating a lower-resolution X-ray detector.

Results

Microcalcification detectability, as assessed by the deep learning classifier, was observed to vary with the exposure incident on the breast phantom for both DM and tomosynthesis. At full dose, experimental AUC was 0.96 (for DM) and 0.95 (for DBT), whereas at half dose, it dropped to 0.85 and 0.71, respectively. AUC performance on DM was significantly decreased with an effective larger pixel size obtained with re-binning. The task-based assessment approach also showed the superiority of a newer mammography system compared with an older system.

Conclusions

An objective task-based methodology for assessing the image quality of mammography and tomosynthesis systems is proposed. Possible uses for this tool could be quality control, acceptance, and constancy testing, assessing the safety and effectiveness of new technology for regulatory submissions, and system optimization. The results from this study showed that the proposed evaluation method using a deep learning model observer can track differences in microcalcification signal detectability with varied exposure conditions.

Our journey toward implementation of digital breast tomosynthesis in breast cancer screening: the Malmö Breast Tomosynthesis Screening Project

Anders Tingberg, Victor Dahlblom, Magnus Dustler, Daniel Förnvik, Kristin Johnson, Pontus Timberg, Sophia Zackrisson

Journal of Medical Imaging, Vol. 12, Issue S1, S13006, (October 2024) https://doi.org/10.1117/1.JMI.12.S1.S13006

TOPICS: Digital breast tomosynthesis, Breast cancer, Breast, Cancer, Cancer detection, Mammography, Tomosynthesis, Tumors, Image compression, Artificial intelligence

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Lung nodule localization and size estimation on chest tomosynthesis

Micael Oliveira Diniz, Mohammad Khalil, Erika Fagman, Jenny Vikgren, Faiz Haj, Angelica Svalkvist, Magnus Båth, Åse Allansdotter Johnsson

Journal of Medical Imaging, Vol. 12, Issue S1, S13007, (October 2024) https://doi.org/10.1117/1.JMI.12.S1.S13007

TOPICS: Lung, Computed tomography, Tomosynthesis, Chest, Computer aided detection, Visualization, Visibility, Statistical analysis, X-ray computed tomography, Radiology

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