Faculty Bibliography

OBJECTIVE:To quantify the operational burden of motion-related repeat MRI sequences across a high-volume health system and evaluate whether repeat acquisitions improve image quality and confidence in brain MRI and magnetic resonance cholangiopancreatography (MRCP). METHODS:This retrospective study analyzed six months of MRI examinations on scanners with sequence-level analytics. Examinations with technologist-labeled repeated sequences were identified. Brain MRI and MRCP baseline-repeat pairs were evaluated by four and two readers, respectively. Readers scored image quality and diagnostic confidence and indicated whether another repeat would be requested. Scores were compared using linear mixed-effects models with patient-level random intercepts. RESULTS:Among 85,349 MRI examinations, 4,072 (4.8%) included at least one repeated sequence. Repeat frequency was highest in pediatrics (9.9%) and lowest in breast MRI (3.5%). In the brain MRI study, 87 baseline-repeat pairs from 77 patients were evaluated. Repeat acquisition significantly improved image quality and diagnostic confidence across all metrics (all p<0.001). 19.5% of repeat sequences did not improve. Improvement declined with greater elapsed examination time. In the MRCP study, 30 baseline-repeat pairs from 27 patients were evaluated. Repeat acquisition did not significantly improve image quality or diagnostic confidence, and both readers would have requested another repeat for half of repeated acquisitions. DISCUSSION/CONCLUSIONS:Motion-driven repeat MRI sequences impose measurable operational burden, but diagnostic yield varies substantially by anatomic region and sequence type. Brain MRI repeats generally improved image quality and diagnostic confidence, whereas MRCP repeats showed limited benefit. These findings support sequence-level tracking to guide targeted motion-mitigation strategies and reduce low-value repeat imaging.

Opportunistic CT screening refers to the extraction of clinically relevant imaging biomarkers from CT examinations routinely performed for unrelated primary indications. This screening does not require additional radiation exposure, imaging time, or patient burden. Advances in automated image analysis and artificial intelligence (AI) have substantially expanded the feasibility of opportunistic CT screening across a range of conditions, including low bone mineral density, hepatic steatosis, sarcopenia, adiposity, and vascular calcifications. These biomarkers are increasingly being recognized as important contributors to fracture risk, cardiometabolic disease, and frailty; yet they often remain undiagnosed in routine clinical practice. While numerous studies have demonstrated technical feasibility and diagnostic performance, widespread clinical adoption of opportunistic CT screening remains limited. Key challenges include those related to workflow integration, standardization of measurement techniques, quality assurance, interpretability of results, and uncertainty regarding downstream clinical management and value. In particular, not all opportunistic imaging biomarkers are equally actionable, and inappropriate or indiscriminate reporting may contribute to overdiagnosis or unnecessary follow-up. The authors summarize common applications of opportunistic CT screening, highlighting typical imaging findings and biomarkers. The authors discuss the evolution of automated and AI-based approaches, with emphasis on practical considerations for deployment, including pre- and postdeployment quality assurance, algorithm monitoring, and practical considerations for clinical integration. Emerging applications also are reviewed, with attention to current limitations. With a focus on clinical integration, this review is intended to inform radiologists, informaticians, and others working in health care systems who seek to translate opportunistic CT screening from proof-of-concept studies into scalable, clinically meaningful practice.

RATIONALE AND OBJECTIVES/OBJECTIVE:Radiology residency often fails to account for individual differences between residents or provide sufficient exposure to diverse pathologies. We sought to evaluate whether artificial intelligence (AI)-enabled "Precision Education" can accurately identify and address individual radiology resident pathology exposure gaps through supplemental personalized teaching cases. MATERIALS AND METHODS/METHODS:A curriculum outlined types and frequencies of important pathologies (IPs) residents should encounter during postgraduate years 2 through 4 (PGY-2 through PGY-4). Daily resident "live" clinical reports were analyzed by ChatGPT-4o prompts to detect IPs encountered. Each resident's live cases were then supplemented with curated anonymized teaching cases, with priority given to IPs encountered below curriculum-defined target thresholds to date. Volumes and IP exposure were compared between pre- (2022-2023) and postintervention (2024-2025) academic years. RESULTS:ChatGPT-4o demonstrated over 91% precision and recall in accurately identifying IPs. Unique IPs encountered by residents significantly increased postintervention from median 75-107 to 93.5-144 in abdominal, 43.5-70 to 73-99 in musculoskeletal, 32.5-38 to 64.5-79 in neuro-, 39.5-49 to 82-96.5 in pediatric, and 42.5-56 to 49.3-85.3 in thoracic imaging (all p < 0.05). Residents met significantly more curriculum-defined targets postintervention, increasing from a median 54-64 to 78.5-131.5 in abdominal, 21-49 to 51.5-75.5 in musculoskeletal, 3.5-9 to 21-38 in neuro-, 13.5-21 to 51.5-72 in pediatric, and 12.5-33 to 23-58 in thoracic imaging (all p < 0.05). Median live case interpretations were not significantly reduced by the intervention, aside from PGY-3 abdominal imaging cases (p = 0.0489). CONCLUSION/CONCLUSIONS:Personalized AI-enabled Precision Education accurately identified resident pathology exposure gaps, enhanced exposure to IPs, and maintained clinical training opportunities.

Opportunistic screening leverages existing imaging examinations performed for unrelated routine clinical indications to systematically extract quantitative biomarkers. Artificial intelligence tools have made deployment at scale increasingly feasible. However, the pathway from a validated algorithm to a functioning clinical program remains poorly defined, and prospective implementation at scale is uncommon. Successful deployment requires coordinated engagement from radiologists, information technology and operational teams, and clinical care teams, each facing distinct decisions that determine whether a program functions reliably and delivers patient benefit. This article presents a practical framework for opportunistic screening implementation organized around these three stakeholder groups. We apply this framework to opportunistic CT osteoporosis screening, drawing on our experience developing such a program at a large academic medical center. The framework presented is intended to be broadly applicable across opportunistic screening applications as the field moves from algorithmic validation toward clinical translation.

OBJECTIVE:To assess whether accelerated knee MRI protocols using simultaneous multi-slice (SMS) and deep learning reconstruction (DLR) are non-inferior to a conventional parallel imaging protocol for detecting internal derangement injuries. METHODS:This retrospective cohort study included 1055 patients who underwent knee MRI followed by arthroscopy within 180 days. Patients were scanned using either a conventional protocol (n = 226), an accelerated SMS protocol (n = 406), or a SMS with DLR protocol (n = 423). Each group included consecutive exams. Imaging was performed on 3 T MRI using five standardized two-dimensional turbo spin echo sequences. Radiology interpretations were compared with arthroscopy (reference standard) for anterior cruciate ligament (ACL), medial meniscus (MM), and lateral meniscus (LM) tears. Sensitivity and specificity were calculated with 95% confidence intervals using non-parametric bootstrapping. Non-inferiority was concluded if the upper bound of the 95% confidence interval for the difference in sensitivity and specificity was ≤ 0.05. RESULTS:Among all patients, 666 had MM tears, 417 had LM tears, and 220 had ACL tears. Sensitivity for ACL tears was higher with accelerated protocols (0.96 and 0.98) than the conventional (0.85), with non-inferiority confirmed. Specificity was ≥ 0.98 across all protocols. MM sensitivity (0.94-0.95) met non-inferiority criteria. MM specificity (0.88-0.91) and LM sensitivity (0.63-0.68) were not statistically different across protocols but did not meet the non-inferiority margin. LM specificity (0.94) met non-inferiority criteria. CONCLUSION/CONCLUSIONS:Accelerated MRI protocols using SMS and DLR demonstrated comparable diagnostic performance to the reference protocol. Although not all metrics met the strict non-inferiority margin, none showed statistically significant reductions in sensitivity or specificity. These findings support the clinical adoption of accelerated protocols for faster, high-throughput knee imaging.

Background Osteoporosis is underdiagnosed and undertreated, prompting the exploration of opportunistic screening using CT and artificial intelligence. Purpose To develop a reproducible convolutional neural network to automatically identify a three-dimensional (3D) region of interest (ROI) in trabecular bone, develop a correction method to normalize attenuation values across different CT protocols and scanner models, and establish thresholds for diagnosing osteoporosis in a large diverse population. Materials and Methods In this retrospective study, a deep learning-based method was developed to automatically quantify trabecular attenuation of the thoracic and lumbar spine on CT images with use of a 3D ROI. A statistical method was developed to adjust for different tube voltages and scanner models. Normative values and diagnostic thresholds for trabecular attenuation of the spine for osteoporosis were established based on the reported prevalence of osteoporosis by the World Health Organization. Differences between groups were assessed using the Student t test. Results A total of 538 946 CT examinations from 283 499 patients (mean age, 65 years ± 15 [SD]; 145 021 [51.2%] female; 157 457 [55.5%] White patients) were analyzed, representing 43 scanner models and six different tube voltages. The attenuation values at 80 kVp and 120 kVp differed by 23%, and different scanner models resulted in differences in values of less than 10%. The automated ROI placement of 1496 vertebrae was validated by manual radiologist review and demonstrated greater than 99% agreement. Trabecular attenuation was greater in young women (age <50 years) than in young men (P < .001) and decreased with age, with a steeper decline in postmenopausal women. In patients older than 50 years, trabecular attenuation was greater in male than in female patients (P < .001). Trabecular attenuation was highest in Black patients, followed by Asian patients, and lowest in White patients (P < .001). Conclusion Deep learning-based automated opportunistic osteoporosis screening can identify patients with low bone mineral density using CT scans obtained for clinical purposes with use of different scanners and protocols. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Feuerriegel and Sutter in this issue.