Faculty Bibliography

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.

The past decade has witnessed remarkable advancements in musculoskeletal radiology, driven by increasing demand for medical imaging and rapid technological innovations. Contrary to early concerns about artificial intelligence (AI) replacing radiologists, AI has instead enhanced imaging capabilities, aiding in automated abnormality detection and workflow efficiency. MRI has benefited from acceleration techniques that significantly reduce scan times while maintaining high-quality imaging. In addition, novel MRI methodologies now support precise anatomic and quantitative imaging across a broad spectrum of field strengths. In CT, dual-energy and photon-counting technologies have expanded diagnostic possibilities for musculoskeletal applications. This review explores these key developments, examining their impact on clinical practice and the future trajectory of musculoskeletal radiology.

OBJECTIVES/OBJECTIVE:Metal artifact reduction MRI can exceed specific absorption rate (SAR) limits due to high-bandwidth radiofrequency pulses, causing scan interruptions and prolonged acquisition times. The aim of the current study is to reduce SAR and potentially scan time in metal artifact reduction MRI using an optimized variable refocusing flip angle (VRFA) scheme compared with the standard constant refocusing flip angle (CRFA). MATERIALS AND METHODS/METHODS:Three VRFA variants (VRFA1 to VRFA3) were developed to maximize tissue signal and contrast while minimizing SAR and image blur. The optimal variant was selected through quantification of metal artifacts and image blur in phantoms and tissue signal in a volunteer. Patients with hip arthroplasty underwent CRFA and optimal VRFA imaging using high-bandwidth turbo-spin-echo (HBW-TSE) and compressed-sensing slice-encoding-for-metal-artifact-correction (CS-SEMAC) sequences. Three readers ranked paired CRFA and VRFA scans for quality. Analyses included repeated measures ANOVA, noninferiority testing, and paired t/Wilcoxon signed-rank tests. RESULTS:CRFA and VRFA1 to VRFA3 showed no significant differences in image blur (full-width-at-half-maximum, mean ± SD, 1.9 ± 0.2 vs 1.9 ± 0.2 vs 1.9 ± 0.3 vs 1.9 ± 0.3 pixels, P = 0.06) or metal artifacts (8.2 ± 2.8 vs 8.4 ± 2.7 vs 8.4 ± 2.6 vs 8.4 ± 2.7 pixels, P = 0.57). The optimal VRFA variant (VRFA3) preserved 81% of CRFA fat-muscle contrast at 77% SAR and 70% scan time on proton-density (PD), and 94% of fluid-muscle contrast at 80% SAR and 67% scan time on short-tau-inversion-recovery (STIR). In 23 patients [mean age, 67.3 y ± 12.2 (SD); 14 females], the optimal VRFA was noninferior to CRFA in all quality metrics (all 95% CI < noninferiority margin = 0.1) and significantly reduced SAR (mean, PD-HBW-TSE/STIR-HBW-TSE/PD-CS-SEMAC/STIR-CS-SEMAC: 1.11/1.35/1.17/1.18 vs 1.85/1.83/1.49/1.46 W/kg, all P ≤ 0.001). In HBW-TSE, reduced SAR allowed longer echo trains and 15% to 32% shorter scan times. CONCLUSION/CONCLUSIONS:Metal artifact reduction MRI with VRFA reduces SAR without compromising image quality. It allows shorter acquisitions in HBW-TSE.

Musculoskeletal (MSK) imaging was among the first radiology subspecialties to adopt artificial intelligence (AI), with applications now spanning the entire MSK workflow, from image acquisition to reporting. Deep learning-based reconstruction protocols can accelerate MRI by reducing scan times and artefacts, improving accessibility in high-volume and resource-limited settings. Furthermore, AI interpretation tools have demonstrated strong performance in fracture detection, assessment of meniscal and ligament tears, bone tumour characterization and automated quantification of measurements, supporting greater diagnostic consistency across radiologists with varying experience levels. Large language models (LLMs) extend AI's impact beyond image analysis by simplifying reports for patients, automating classification systems, and streamlining clinical communication. Despite these advances, important challenges remain. Integration of AI into already established clinical workflows can be complex, and requires robust technical solutions, regulatory compliance, and strategies to maintain radiologist oversight. Questions of liability, cost-effectiveness, and the role of AI in medical education further underscore the need for careful implementation. AI is poised to fundamentally reshape MSK radiology by enhancing efficiency, improving diagnostic accuracy, and enabling more patient-centred communication. To fully realize this potential, adoption must balance innovation with safety, equity, and sustainability, ensuring AI remains a trusted assistive tool that strengthens rather than replaces radiologist expertise.

BACKGROUND:Deep Learning (DL) techniques have enabled up to 6-fold acceleration in musculoskeletal magnetic resonance imaging (MRI) while preserving diagnostic image quality. Further, improvements in acceleration and generalization require novel approaches. We propose a DL framework that integrates Simultaneous Multislice (SMS) imaging with Parallel Imaging (PI) to enhance current DL-based reconstruction. PURPOSE/OBJECTIVE:To advance musculoskeletal Magnetic Resonance Imaging (MRI), by developing a DL reconstruction framework that combines SMS and PI, enabling acceleration of up to 8-fold and beyond, while maintaining image quality suitable for clinical interpretation. METHODS:End-to-End (E2E) DL framework for reconstructing Turbo Spin Echo (TSE) MRI data acquired with SMS and PI acceleration. The method unrolls a proximal gradient algorithm with Nesterov momentum and integrates a novel DL network for joint regularization across simultaneously acquired slices. Slice separation and k-space-to-image reconstruction are unified by embedding the full SMS forward model into the DL architecture. Data Consistency (DC) is modulated to enhance denoising, and a super-resolution module improves image sharpness. The robust DL model was trained on over 200 000 slices from 1.5T to 3T scans with diverse acquisition settings. RESULTS:The proposed E2E DL model outperforms prior methods at 8-fold and 12-fold acceleration, as measured by PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) metrics. Evaluation on prospectively acquired clinical scans by two radiologists confirms, that image quality and abnormality detection are comparable to standard acquisitions at lower acceleration. CONCLUSIONS:We extend state-of-the-art DL reconstruction frameworks by integrating slice separation directly into the model for SMS acquisitions. Our E2E DL approach achieves clinical-grade image quality at 8-fold acceleration across 20 subjects, reducing acquisition time by 27%. Preliminary results suggest potential for further acceleration up to 12-fold, demonstrating significant advancement beyond existing DL techniques.