Correlation of Automated in Vivo Image Quality With Radiologist's Performance in Abdomen Computed Tomography Across Conventional and Deep Learning Reconstructions.
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2026-01
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Image quality evaluation in radiology is most relevant when reflects radiologists' performance. This study assessed how image quality measurement in terms of in vivo-characterized detectability index () for low-contrast liver lesion assessment in CT is correlated with radiologists' performance across 2 different CT reconstructions.Methods
Fifty-one contrast-enhanced abdominal studies for investigating colorectal liver metastases were prospectively performed using 2 radiation dose exposures and reconstructed with Filtered back projection (FBP) and deep learning image reconstruction (DL) algorithms for a total of 161 noncalcified hypoattenuating lesions for 3 lesion size (D) subsets (<6 mm, 6 to 10 mm, and >10 mm). Images were assessed by expert radiologists for hepatic lesion detection task and likelihood of malignancy across the 2 imaging conditions. All cases were also evaluated automatically in terms of in vivo as a metric of task-based performance, both using a conventional technique and a new formalism of an added frequency term in the internal noise component of to accommodate the nonlinearity of the DL reconstruction (adj).Results
The study found conventionally defined d' well-reflective of radiologists' evaluation of FBP images but not well-aligned with that of DL images. The new formalism provided more consistent reflection of performance across reconstruction techniques. In particular, in the lesion group D <=6 mm, the difference between radiologists' accuracy in images acquired with DL and images acquired with FBP was -26%, and the related adj difference was -9%, whereas the was 34%. Analogously, for the lesion group 6 mm < D <=10 mm, the differences were -15%, -13%, and 29%, respectively. Lastly, for the lesion group D>10 mm, radiologists showed the same accuracy in both FPB and DL images, difference in adj was -11%, and difference in was 31%.Conclusion
The new formalism can robustly reflect CT systems clinical performance irrespective of reconstruction algorithm. The methodology can be more readily applied to assess the real-world performance of CT systems.Type
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Zarei, Mojtaba, Francesco Ria, Corey T Jensen, Xinming Liu, Craig K Abbey and Ehsan Samei (2026). Correlation of Automated in Vivo Image Quality With Radiologist's Performance in Abdomen Computed Tomography Across Conventional and Deep Learning Reconstructions. Journal of computer assisted tomography. 10.1097/rct.0000000000001845 Retrieved from https://hdl.handle.net/10161/33948.
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Francesco Ria
Dr. Francesco Ria is a medical physicist and he serves as an Assistant Professor in the Department of Radiology. Francesco has an extensive expertise in the assessment of procedure performances in radiology. In particular, his research activities focus on the simultaneous evaluation of radiation dose and image quality in vivo in computed tomography providing a comprehensive evaluation of radiological exams. Moreover, Francesco is developing and investigating novel mathematical models that, uniquely in the radiology field, can incorporate a comprehensive and quantitative risk-to-benefit assessment of the procedures; he is continuing to apply his expertise towards the definition of new patient specific risk metrics, and in the assessment of image quality in vivo also using state-of-the-art imaging technology, such as photon counting computed tomography scanners, and machine learning reconstruction algorithms.
Dr. Ria is a member of the American Association of Physicists in Medicine (AAPM) task group 392 (Investigation and Quality Control of Automatic Exposure Control System in CT), of the AAPM task group 430 (Comprehensive quantification and dissemination of patient-model-based organ and effective dose estimations and their associated uncertainties for CT examinations), of the AAPM Medicine Public Education working group (WGATE), and of the Italian Association of Medical Physics task group Dose Monitoring in Diagnostic Imaging.
Ehsan Samei
Dr. Ehsan Samei, PhD, DABR, FAAPM, FSPIE, FAIMBE, FIOMP, FACR is a Persian-American medical physicist. He is the Reed and Martha Rice Distinguished Professor of Radiology, and Professor of Medical Physics, Biomedical Engineering, Physics, and Electrical and Computer Engineering at Duke University. He serves as the Chief Imaging Physicist for Duke University Health System, the Director of the Carl E Ravin Advanced Imaging Laboratories and the Center for Virtual Imaging Trials (CVIT), and Co-Director of the Triangle Centers of Excellence in Regulatory Science and Innovation (Triangle CERSI). Certified by the American Board of Radiology, he is recognized as a Distinguished Investigator by the Academy of Radiology Research, awarded Fellow by five professional organizations, and has been the recipient of the Jimmy O. Fenn Lifetime Achievement Award of SEAAPM and the Marie Sklodowska-Curie Award by IOMP. He founded/co-founded the Duke Medical Physics Program, the Duke Imaging Physics Residency Program, the Duke Clinical Imaging Physics Group, the Center for Virtual Imaging Trials, and the Society of Directors of Academic Medical Physics Programs (SDAMPP). He has held senior leadership positions in the AAPM, SPIE, SDAMPP, and RSNA, including serving as president of the SEAAPM (2010-2011), SDAMPP (2011), and AAPM (2023). He is ranked 8th among over 56,000 medical physicists worldwide for his lifetime contribution to medical physics.
Dr. Samei has mentored over 160 trainees (graduate and postgraduate). He has over 1500 scientific publications, including 4 books, over 430 refereed journal papers, over 260 full-length conference articles, over 350 conference presentations, and over 340 invited talks. Citations to his work are reflected in an h-index of 82 and a Weighted Relative Citation Ratio of 759. His laboratory has been supported continuously for over two decades by 50 extramural grants totaling $47 million. Those include a Program Project grant from the NIH in 2021 to establish the National Center for Virtual Imaging Trials (CVIT), and a multi-institutional grant in 2023 from the FDA to establish the Triangle Center of Excellence in Regulatory Science and Innovation (Triangle CERSI), both joining highly selective biomedical research and regulatory science centers nationwide.
On the personal side, Ehsan Samei is a semi-professional musician, leads a chamber ensemble, and has an active interest in common traits and interactions between science, art, and faith. He has led workshops on leadership and leadership development. He has run five marathons (3:17 PR), including the 2013 Boston Marathon, is married to Dr. Maija Samei, a scholar of medieval Chinese literature, and is blessed with four gifted children.
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