Faculty Bibliography

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.

MR neurography offers a specialized approach for detailed nerve imaging when clinical symptoms indicate involvement. Imaging peripheral nerves in the foot and ankle is demanding due to their small size and challenging anatomy. This article provides an overview of foot and ankle nerve assessment, highlighting the importance of technical expertise and anatomic knowledge for accurate diagnosis.

OBJECTIVE:This study aims to determine the diagnostic performance of sub-5-min combined sixfold parallel imaging (PIx3)-simultaneous multislice (SMSx2)-accelerated deep learning (DL) super-resolution 3T knee MRI in children and adolescents. MATERIALS AND METHODS/METHODS:Children with painful knee conditions who underwent PIx3-SMSx2-accelerated DL super-resolution 3T knee MRI and arthroscopy between October 2022 and December 2023 were retrospectively included. Nine fellowship-trained musculoskeletal radiologists independently scored the MRI studies for image quality and the presence of artifacts (Likert scales, range: 1 = very bad/severe, 5 = very good/absent), as well as structural abnormalities. Interreader agreements and diagnostic performance testing was performed. RESULTS:Forty-four children (mean age: 15 ± 2 years; range: 9-17 years; 24 boys) who underwent knee MRI and arthroscopic surgery within 22 days (range, 2-133) were evaluated. Overall image quality was very good (median rating: 5 [IQR: 4-5]). Motion artifacts (5 [5-5]) and image noise (5 [4-5]) were absent. Arthroscopy-verified abnormalities were detected with good or better interreader agreement (κ ≥ 0.74). Sensitivity, specificity, accuracy, and AUC values were 100%, 84%, 93%, and 0.92, respectively, for anterior cruciate ligament tears; 71%, 97%, 93%, and 0.84 for medial meniscus tears; 65%, 100%, 86%, and 0.82 for lateral meniscus tears; 100%, 100%, 100%, and 1.00 for discoid lateral menisci; 100%, 95%, 96%, and 0.98 for medial patellofemoral ligament tears; and 55%, 100%, 98%, and 0.77 for articular cartilage defects. CONCLUSION/CONCLUSIONS:Clinical sub-5-min PIx3-SMSx2-accelerated DL super-resolution 3T knee MRI provides excellent image quality and high diagnostic performance for diagnosing internal derangement in children and adolescents.

OBJECTIVES/OBJECTIVE:To perform a Delphi-based consensus on published evidence on image-guided injections for facet joint pain (FJP) and provide clinical indications. METHODS:We report the results of an evidence-based Delphi consensus of 38 experts from the European Society of Musculoskeletal Radiology and the European Society of Neuroradiology, who reviewed the published literature for evidence on image-guided injections for FJP. Experts drafted a list of statements and graded them according to the Oxford Centre for evidence-based medicine levels of evidence. Consensus was considered strong when ≥ 95% of experts agreed with the statement or broad when > 80% but < 95% agreed. The results of the consensus were used to write the paper. RESULTS:Twenty statements on image-guided FJP treatment have been drafted. Eighteen statements received strong consensus, while two received broad consensus. Three statements reached the highest level of evidence, all of them regarding the lumbar spine. All radiological methods are used for image-guided injections for FJP, and regardless of the radiological method used, all show good safety and efficacy. Facet joint injections and medial branch blocks are used in all spinal regions to treat FJP, and both show similar clinical outcomes. Advanced technological solutions have been studied in the field of lumbar FJP; however, the level of evidence for these is low. CONCLUSION/CONCLUSIONS:Despite promising results reported by published papers on image-guided injections for FJP, there is still a lack of evidence on injection efficacy, appropriateness of imaging methods, and optimal medication. KEY POINTS/CONCLUSIONS:Question Image-guided injections to treat facet joint pain (FJP) are performed throughout the spine; however, the highest level of evidence exists for the lumbar spine. Findings Regardless of the imaging method used, image-guided injections for facet joint pain treatment are safe, with only minor adverse events in rare cases. Clinical relevance All imaging methods are used for injection guidance to treat FJP, each with advantages and disadvantages. These statements on image-guided injections for FJP provide a concise and up to date overview on the topic, serving as a list of clinical indications.

Dual-energy computed tomography (DECT) has emerged as a transformative tool in the past decade. Initially employed in gout within the field of rheumatology to distinguish and quantify monosodium urate crystals through its dual-material discrimination capability, DECT has since broadened its clinical applications. It now encompasses various rheumatic diseases, employing advanced techniques such as bone marrow edema assessment, iodine mapping, and collagen-specific imaging. This review article aims to examine the unique characteristics of DECT, discuss its strengths and limitations, illustrate its applications for accurately evaluating various rheumatic diseases in clinical practice, and propose future directions for DECT in rheumatology.