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Members of the International Skeletal Society compiled a glossary of terms for musculoskeletal radiology. The authors also represent national radiology or pathology societies in Asia, Australia, Europe, and the USA. We provide brief descriptions of musculoskeletal structures, disease processes, and syndromes and address their imaging features. Given the abundance of musculoskeletal disorders and derangements, we chose to omit most terms relating to neoplasm, spine, intervention, and pediatrics. Consensus agreement was obtained from 19 musculoskeletal radiology societies worldwide.

Objective: To assess the interobserver reliability of the main periarticular and intra-articular ultrasonographic pathologies and to establish the principal disagreements on scanning technique and diagnostic criteria between a group of experts in musculoskeletal ultrasonography. Methods: The shoulder, wrist/hand, ankle/foot, or knee of 24 patients with rheumatic diseases were evaluated by 23 musculoskeletal ultrasound experts from different European countries randomly assigned to six groups. The participants did not reach consensus on scanning method or diagnostic criteria before the investigation. They were unaware of the patients’ clinical and imaging data. The experts from each group undertook a blinded ultrasound examination of the four anatomical regions. The ultrasound investigation included the presence/absence of joint effusion/synovitis, bony cortex abnormalities, tenosynovitis, tendon lesions, bursitis, and power Doppler signal. Afterwards they compared the ultrasound findings and re-examined the patients together while discussing their results. Results: Overall agreements were 91% for joint effusion/synovitis and tendon lesions, 87% for cortical abnormalities, 84% for tenosynovitis, 83.5% for bursitis, and 83% for power Doppler signal; κ values were good for the wrist/hand and knee (0.61 and 0.60) and fair for the shoulder and ankle/foot (0.50 and 0.54). The principal differences in scanning method and diagnostic criteria between experts were related to dynamic examination, definition of tendon lesions, and pathological v physiological fluid within joints, tendon sheaths, and bursae. Conclusions: Musculoskeletal ultrasound has a moderate to good interobserver reliability. Further consensus on standardisation of scanning technique and diagnostic criteria is necessary to improve musculoskeletal ultrasonography reproducibility.

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.

In musculoskeletal imaging, CT is used in a wide range of indications, either alone or in a synergistic approach with MRI. While MRI is the preferred modality for the assessment of soft tissues and bone marrow, CT excels in the imaging of high-contrast structures, such as mineralized tissue. Additionally, the introduction of dual-energy CT in clinical practice two decades ago opened the door for spectral imaging applications. Recently, the advent of photon-counting detectors (PCDs) has further advanced the potential of CT, at least in theory. Compared to conventional energy-integrating detectors (EIDs), PCDs provide superior spatial resolution, reduced noise, and intrinsic spectral imaging capabilities. This review briefly describes the technical advantages of PCDs. For each technical feature, the corresponding applications in musculoskeletal imaging will be discussed, including high-spatial resolution imaging for the assessment of bone and crystal deposits, low-dose applications such as whole-body CT, as well as spectral imaging applications including the characterization of crystal deposits and imaging of metal hardware. Finally, we will highlight the potential of PCD-CT in emerging applications, underscoring the need for further preclinical and clinical validation to unleash its full clinical potential.

Mice are one of the most commonly used laboratory animals, with an extensive array of disease models in existence, including for many neuromuscular diseases. The hindlimb is of particular interest due to several close muscle analogues/homologues to humans and other species. A detailed anatomical study describing the adult morphology is lacking, however. This study describes in detail the musculoskeletal geometry and skeletal muscle architecture of the mouse hindlimb and pelvis, determining the extent to which the muscles are adapted for their function, as inferred from their architecture. Using I2KI enhanced microCT scanning and digital segmentation, it was possible to identify 39 distinct muscles of the hindlimb and pelvis belonging to nine functional groups. The architecture of each of these muscles was determined through microdissections, revealing strong architectural specialisations between the functional groups. The hip extensors and hip adductors showed significantly stronger adaptations towards high contraction velocities and joint control relative to the distal functional groups, which exhibited larger physiological cross sectional areas and longer tendons, adaptations for high force output and elastic energy savings. These results suggest that a proximo-distal gradient in muscle architecture exists in the mouse hindlimb. Such a gradient has been purported to function in aiding locomotor stability and efficiency. The data presented here will be especially valuable to any research with a focus on the architecture or gross anatomy of the mouse hindlimb and pelvis musculature, but also of use to anyone interested in the functional significance of muscle design in relation to quadrupedal locomotion.

Artificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult world than in pediatrics), to the extent that there are unfounded fears it will completely take over the role of the radiologist. In relation to musculoskeletal applications of AI in pediatric radiology, we are far from the time when AI will replace radiologists; even for the commonest application (bone age assessment), AI is more often employed in an AI-assist mode rather than an AI-replace or AI-extend mode. AI for bone age assessment has been in clinical use for more than a decade and is the area in which most research has been conducted. Most other potential indications in children (such as appendicular and vertebral fracture detection) remain largely in the research domain. This article reviews the areas in which AI is most prominent in relation to the pediatric musculoskeletal system, briefly summarizing the current literature and highlighting areas for future research. Pediatric radiologists are encouraged to participate as members of the research teams conducting pediatric radiology artificial intelligence research.

Musculoskeletal malignancies are a rare type of cancer. Consequently, sufficient imaging data for machine learning (ML) applications is difficult to obtain. The main purpose of this review was to investigate whether ML is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies and what the respective reasons for this might be. A scoping review was conducted by a radiologist, an orthopaedic surgeon and a data scientist to identify suitable articles based on the PRISMA statement. Studies meeting the following criteria were included: primary malignant musculoskeletal tumours, machine/deep learning application, imaging data or data retrieved from images, human/preclinical, English language and original research. Initially, 480 articles were found and 38 met the eligibility criteria. Several continuous and discrete parameters related to publication, patient distribution, tumour specificities, ML methods, data and metrics were extracted from the final articles. For the synthesis, diagnosis-oriented studies were further examined by retrieving the number of patients and labels and metric scores. No significant correlations between metrics and mean number of samples were found. Several studies presented that ML could support imaging-driven diagnosis of musculoskeletal malignancies in distinct cases. However, data quality and quantity must be increased to achieve clinically relevant results. Compared to the experience of an expert radiologist, the studies used small datasets and mostly included only one type of data. Key to critical advancement of ML models for rare diseases such as musculoskeletal malignancies is a systematic, structured data collection and the establishment of (inter)national networks to obtain substantial datasets in the future. Key Points • Machine learning does not yet significantly impact imaging-driven diagnosis for musculoskeletal malignancies compared to other disciplines such as lung, breast or CNS cancer. • Research in the area of musculoskeletal tumour imaging and machine learning is still very limited. • Machine learning in musculoskeletal tumour imaging is impeded by insufficient availability of data and rarity of the disease.

Large Language Models (LLMs) hold potential as clinical decision support tools, particularly when integrated with domain-specific knowledge. In radiology, there is limited research on LLMs for assessing imaging appropriateness. This study evaluates a contextualized GPT-4-based LLM’s performance in assessing the appropriateness of musculoskeletal MRI scan requests with standard models and different versions of optimization. The LLMs’ performances was also compared against human clinicians with varying experience (two radiology residents, two subspecialist attendings, an orthopaedic surgeon). Using a retrieval-augmented generation framework, the LLM was provided with a domain-specific knowledge base from 33 American College of Radiology Appropriateness Criteria guidelines. A test dataset of 70 fictional case scenarios was created, including cases with insufficient clinical information. Quantitative analysis using the McNemar mid-P test revealed that the optimized LLM achieved 92.86% accuracy, significantly outperforming the baseline model (61.43%, P  < .001) and the standard GPT-4 model (51.29%, P  < .001). The optimized model also excelled in identifying cases with insufficient clinical information. In comparison to human clinicians, the optimized LLM performed better than all but one radiologist. This study demonstrates that with contextualization and optimization, GPT-4-based LLMs can improve performance in assessing imaging appropriateness and show promise as clinical decision support tools in radiology.

During the past four decades, ultrasound has become popular as an imaging modality applied to the musculoskeletal (MSK) system, particularly outside the USA, due to its low cost, accessibility, and lack of ionizing radiation. A basic requirement in performing these examinations is to have a core group of radiologists and ultrasound technologists with expertise in MSK ultrasound. The extent to which ultrasound will be part of the imaging offered by a particular radiology practice or in an academic institution will vary according to expertise, availability, and reimbursements. A brief discussion of the technical capabilities of the current generation of ultrasound scanners will be followed by a description of some of the more prevalent MSK ultrasound imaging applications. The extent to which training to perform these exams within and outside of Radiology plays a role is discussed. Applications that are unique to ultrasound, such as dynamic evaluation of musculoskeletal anatomy and some, US-guided interventions are an important part of MSK imaging. Ultrasound is increasingly important in the assessment of superficial structures, such as tendons, small joints, and peripheral nerves. These applications help to establish the place of ultrasound as an important part of the Radiologists approach to MSK imaging. Outside of radiology, for a variety of clinical subspecialties, ultrasound already plays an integral role in MSK imaging.

Objective To assess visualization tasks using cone-beam CT (CBCT) compared to multi-detector CT (MDCT) for musculoskeletal extremity imaging. Methods Ten cadaveric hands and ten knees were examined using a dedicated CBCT prototype and a clinical multi-detector CT using nominal protocols (80kVp-108mAs for CBCT; 120kVp- 300mAs for MDCT). Soft tissue and bone visualization tasks were assessed by four radiologists using five-point satisfaction (for CBCT and MDCT individually) and five-point preference (side-by-side CBCT versus MDCT image quality comparison) rating tests. Ratings were analyzed using Kruskal–Wallis and Wilcoxon signed-rank tests, and observer agreement was assessed using the Kappa-statistic. Results Knee CBCT images were rated “excellent” or “good” (median scores 5 and 4) for “bone” and “soft tissue” visualization tasks. Hand CBCT images were rated “excellent” or “adequate” (median scores 5 and 3) for “bone” and “soft tissue” visualization tasks. Preference tests rated CBCT equivalent or superior to MDCT for bone visualization and favoured the MDCT for soft tissue visualization tasks. Intraobserver agreement for CBCT satisfaction tests was fair to almost perfect (κ ~ 0.26–0.92), and interobserver agreement was fair to moderate (κ ~ 0.27–0.54). Conclusion CBCT provided excellent image quality for bone visualization and adequate image quality for soft tissue visualization tasks. Key Points • CBCT provided adequate image quality for diagnostic tasks in extremity imaging. • CBCT images were “excellent” for “bone” and “good/adequate” for “soft tissue” visualization tasks. • CBCT image quality was equivalent/superior to MDCT for bone visualization tasks.