How AI May Transform Musculoskeletal Imaging.

While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist's workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities.

MRI-based Neuropathy Score Reporting And Data System (NS-RADS): multi-institutional wider-experience usability study of peripheral neuropathy conditions among 32 radiology readers.

OBJECTIVE: To determine the inter-reader reliability and diagnostic performance of classification and severity scales of Neuropathy Score Reporting And Data System (NS-RADS) among readers of differing experience levels after limited teaching of the scoring system. METHODS: This is a multi-institutional, cross-sectional, retrospective study of MRI cases of proven peripheral neuropathy (PN) conditions. Thirty-two radiology readers with varying experience levels were recruited from different institutions. Each reader attended and received a structured presentation that described the NS-RADS classification system containing examples and reviewed published articles on this subject. The readers were then asked to perform NS-RADS scoring with recording of category, subcategory, and most likely diagnosis. Inter-reader agreements were evaluated by Conger's kappa and diagnostic accuracy was calculated for each reader as percent correct diagnosis. A linear mixed model was used to estimate and compare accuracy between trainees and attendings. RESULTS: Across all readers, agreement was good for NS-RADS category and moderate for subcategory. Inter-reader agreement of trainees was comparable to attendings (0.65 vs 0.65). Reader accuracy for attendings was 75% (95% CI 73%, 77%), slightly higher than for trainees (71% (69%, 72%), p = 0.0006) for nerves and comparable for muscles (attendings, 87.5% (95% CI 86.1-88.8%) and trainees, 86.6% (95% CI 85.2-87.9%), p = 0.4). NS-RADS accuracy was also higher than average accuracy for the most plausible diagnosis for attending radiologists at 67% (95% CI 63%, 71%) and for trainees at 65% (95% CI 60%, 69%) (p = 0.036). CONCLUSION: Non-expert radiologists interpreted PN conditions with good accuracy and moderate-to-good inter-reader reliability using the NS-RADS scoring system. CLINICAL RELEVANCE STATEMENT: The Neuropathy Score Reporting And Data System (NS-RADS) is an accurate and reliable MRI-based image scoring system for practical use for the diagnosis and grading of severity of peripheral neuromuscular disorders by both experienced and general radiologists. KEY POINTS: • The Neuropathy Score Reporting And Data System (NS-RADS) can be used effectively by non-expert radiologists to categorize peripheral neuropathy. • Across 32 different experience-level readers, the agreement was good for NS-RADS category and moderate for NS-RADS subcategory. • NS-RADS accuracy was higher than the average accuracy for the most plausible diagnosis for both attending radiologists and trainees (at 75%, 71% and 65%, 65%, respectively).

Soft tissue tumor imaging in adults: whole-body staging in sarcoma, non-malignant entities requiring special algorithms, pitfalls and special imaging aspects. Guidelines 2024 from the European Society of Musculoskeletal Radiology (ESSR).

OBJECTIVES: The revised European Society of Musculoskeletal Radiology (ESSR) consensus guidelines on soft tissue tumor imaging represent an update of 2015 after technical advancements, further insights into specific entities, and revised World Health Organization (2020) and AJCC (2017) classifications. This second of three papers covers algorithms once histology is confirmed: (1) standardized whole-body staging, (2) special algorithms for non-malignant entities, and (3) multiplicity, genetic tumor syndromes, and pitfalls. MATERIALS AND METHODS: A validated Delphi method based on peer-reviewed literature was used to derive consensus among a panel of 46 specialized musculoskeletal radiologists from 12 European countries. Statements that had undergone interdisciplinary revision were scored online by the level of agreement (0 to 10) during two iterative rounds, that could result in 'group consensus', 'group agreement', or 'lack of agreement'. RESULTS: The three sections contain 24 statements with comments. Group consensus was reached in 95.8% and group agreement in 4.2%. For whole-body staging, pulmonary MDCT should be performed in all high-grade sarcomas. Whole-body MRI is preferred for staging bone metastasis, with [18F]FDG-PET/CT as an alternative modality in PET-avid tumors. Patients with alveolar soft part sarcoma, clear cell sarcoma, and angiosarcoma should be screened for brain metastases. Special algorithms are recommended for entities such as rhabdomyosarcoma, extraskeletal Ewing sarcoma, myxoid liposarcoma, and neurofibromatosis type 1 associated malignant peripheral nerve sheath tumors. Satisfaction of search should be avoided in potential multiplicity. CONCLUSION: Standardized whole-body staging includes pulmonary MDCT in all high-grade sarcomas; entity-dependent modifications and specific algorithms are recommended for sarcomas and non-malignant soft tissue tumors. CLINICAL RELEVANCE STATEMENT: These updated ESSR soft tissue tumor imaging guidelines aim to provide support in decision-making, helping to avoid common pitfalls, by providing general and entity-specific algorithms, techniques, and reporting recommendations for whole-body staging in sarcoma and non-malignant soft tissue tumors. KEY POINTS: An early, accurate, diagnosis is crucial for the prognosis of patients with soft tissue tumors. These updated guidelines provide best practice expert consensus for standardized imaging algorithms, techniques, and reporting. Standardization can improve the comparability examinations and provide databases for large data analysis.

Modern acceleration in musculoskeletal MRI: applications, implications, and challenges.

Magnetic resonance imaging (MRI) is crucial for accurately diagnosing a wide spectrum of musculoskeletal conditions due to its superior soft tissue contrast resolution. However, the long acquisition times of traditional two-dimensional (2D) and three-dimensional (3D) fast and turbo spin-echo (TSE) pulse sequences can limit patient access and comfort. Recent technical advancements have introduced acceleration techniques that significantly reduce MRI times for musculoskeletal examinations. Key acceleration methods include parallel imaging (PI), simultaneous multi-slice acquisition (SMS), and compressed sensing (CS), enabling up to eightfold faster scans while maintaining image quality, resolution, and safety standards. These innovations now allow for 3- to 6-fold accelerated clinical musculoskeletal MRI exams, reducing scan times to 4 to 6 min for joints and spine imaging. Evolving deep learning-based image reconstruction promises even faster scans without compromising quality. Current research indicates that combining acceleration techniques, deep learning image reconstruction, and superresolution algorithms will eventually facilitate tenfold accelerated musculoskeletal MRI in routine clinical practice. Such rapid MRI protocols can drastically reduce scan times by 80-90% compared to conventional methods. Implementing these rapid imaging protocols does impact workflow, indirect costs, and workload for MRI technologists and radiologists, which requires careful management. However, the shift from conventional to accelerated, deep learning-based MRI enhances the value of musculoskeletal MRI by improving patient access and comfort and promoting sustainable imaging practices. This article offers a comprehensive overview of the technical aspects, benefits, and challenges of modern accelerated musculoskeletal MRI, guiding radiologists and researchers in this evolving field.

Imaging approach to prosthetic joint infection.

The diagnosis of prosthetic joint infection (PJI) remains challenging, despite multiple available laboratory tests for both serum and synovial fluid analysis. The clinical symptoms of PJI are not always characteristic, particularly in the chronic phase, and there is often significant overlap in symptoms with non-infectious forms of arthroplasty failure. Further exacerbating this challenge is lack of a universally accepted definition for PJI, with publications from multiple professional societies citing different diagnostic criteria. While not included in many of the major societies' guidelines for diagnosis of PJI, diagnostic imaging can play an important role in the workup of suspected PJI. In this article, we will review an approach to diagnostic imaging modalities (radiography, ultrasound, CT, MRI) in the workup of suspected PJI, with special attention to the limitations and benefits of each modality. We will also discuss the role that image-guided interventions play in the workup of these patients, through ultrasound and fluoroscopically guided joint aspirations. While there is no standard imaging algorithm that can universally applied to all patients with suspected PJI, we will discuss a general approach to diagnostic imaging and image-guided intervention in this clinical scenario.

[Accelerated musculoskeletal magnetic resonance imaging with deep learning-based image reconstruction at 0.55 T-3 T]. / Beschleunigte muskuloskeletale Magnetresonanztomographie mit Deep-Learning-gestützter Bildrekonstruktion bei 0,55 T­3 T.

CLINICAL/METHODICAL ISSUE: Magnetic resonance imaging (MRI) is a central component of musculoskeletal imaging. However, long image acquisition times can pose practical barriers in clinical practice. STANDARD RADIOLOGICAL METHODS: MRI is the established modality of choice in the diagnostic workup of injuries and diseases of the musculoskeletal system due to its high spatial resolution, excellent signal-to-noise ratio (SNR), and unparalleled soft tissue contrast. METHODOLOGICAL INNOVATIONS: Continuous advances in hardware and software technology over the last few decades have enabled four-fold acceleration of 2D turbo-spin-echo (TSE) without compromising image quality or diagnostic performance. The recent clinical introduction of deep learning (DL)-based image reconstruction algorithms helps to minimize further the interdependency between SNR, spatial resolution and image acquisition time and allows the use of higher acceleration factors. PERFORMANCE: The combined use of advanced acceleration techniques and DL-based image reconstruction holds enormous potential to maximize efficiency, patient comfort, access, and value of musculoskeletal MRI while maintaining excellent diagnostic accuracy. ACHIEVEMENTS: Accelerated MRI with DL-based image reconstruction has rapidly found its way into clinical practice and proven to be of added value. Furthermore, recent investigations suggest that the potential of this technology does not yet appear to be fully harvested. PRACTICAL RECOMMENDATIONS: Deep learning-reconstructed fast musculoskeletal MRI examinations can be reliably used for diagnostic work-up and follow-up of musculoskeletal pathologies in clinical practice.

Dual-Energy Computed Tomography Applications in Rheumatology.

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.

Simulation-based Flexible Needle Control with Single-core FBG Feedback for Spinal Injections.

Objective: We present a general framework of simultaneous needle shape reconstruction and control input generation for robot-assisted spinal injection procedures, without continuous imaging feedback. Methods: System input-output mapping is generated with a real-time needle-tissue interaction simulation, and single-core FBG sensor readings are used as local needle shape feedback within the same simulation framework. FBG wavelength shifts due to temperature variation is removed by exploiting redundancy in fiber arrangement. Results: Targeting experiments performed on both plastisol lumbar phantoms as well as an ex vivo porcine lumbar section achieved in-plane tip errors of 0.6 ± 0.3 mm and 1.6 ± 0.9 mm , and total tip errors of 0.9 ± 0.7 mm and 2.1 ± 0.8 mm for the two testing environments. Significance: Our clinically inspired control strategy and workflow is self-contained and not dependent on the modality of imaging guidance. The generalizability of the proposed approach can be applied to other needle-based interventions where medical imaging cannot be reliably utilized as part of a closed-loop control system for needle guidance.