Faculty Bibliography

Accurately detecting and characterizing articular cartilage defects is critical in assessing patients with osteoarthritis. While radiography is the first-line imaging modality, magnetic resonance imaging (MRI) is the most accurate for the noninvasive assessment of articular cartilage. Multiple semiquantitative grading systems for cartilage lesions in MRI were developed. The Outerbridge and modified Noyes grading systems are commonly used in clinical practice and for research. Other useful grading systems were developed for research, many of which are joint-specific. Both two-dimensional (2D) and three-dimensional (3D) pulse sequences are used to assess cartilage morphology and biochemical composition. MRI techniques for morphological assessment of articular cartilage can be categorized into 2D and 3D FSE/TSE spin-echo and gradient-recalled echo sequences. T2 mapping is most commonly used to qualitatively assess articular cartilage microstructural composition and integrity, extracellular matrix components, and water content. Quantitative techniques may be able to label articular cartilage alterations before morphological defects are visible. Accurate detection and characterization of shallow low-grade partial and small articular cartilage defects are the most challenging for any technique, but where high spatial resolution 3D MRI techniques perform best. This review article provides a practical overview of commonly used 2D and 3D MRI techniques for articular cartilage assessments in osteoarthritis.

Artificial intelligence (AI) has made tremendous advances in recent years and will presumably have a major impact in health care. These advancements are expected to affect different aspects of clinical medicine and lead to improvement of delivered care but also optimization of available resources. As a modern specialty that extensively relies on imaging, interventional radiology (IR) is primed to be on the forefront of this development. This is especially relevant since IR is a highly advanced specialty that heavily relies on technology and thus is naturally susceptible to disruption by new technological developments. Disruption always means opportunity and interventionalists must therefore understand AI and be a central part of decision-making when such systems are developed, trained, and implemented. Furthermore, interventional radiologist must not only embrace but lead the change that AI technology will allow. The CIRSE position paper discusses the status quo as well as current developments and challenges.

Image-guided minimally invasive radiofrequency ablation (RFA) of sensory nerves has emerged as a treatment option for pain and swelling associated with advanced symptomatic joint and spine degeneration to bridge the gap between optimal medical therapy and surgical treatments. RFA of articular sensory nerves and the basivertebral nerve use image-guided percutaneous approaches resulting in faster recovery time and minimal risks. The current published evidence indicates clinical effectiveness; however, further research must be performed comparing other conservative treatments with RFA to understand further its role in different clinical settings, such as osteonecrosis. This review article discusses and illustrates the applications of RFA for treating symptomatic joint and spine degeneration.

Deep learning (DL) is one of the most exciting new areas in medical imaging. This article will provide a review of current applications of DL in osteoarthritis (OA) imaging, including methods used for cartilage lesion detection, OA diagnosis, cartilage segmentation, and OA risk assessment. DL techniques have been shown to have similar diagnostic performance as human readers for detecting and grading cartilage lesions within the knee on MRI. A variety of DL methods have been developed for detecting and grading the severity of knee OA and various features of knee OA on X-rays using standardized classification systems with diagnostic performance similar to human readers. Multiple DL approaches have been described for fully automated segmentation of cartilage and other knee tissues and have achieved higher segmentation accuracy than currently used methods with substantial reductions in segmentation times. Various DL models analyzing baseline X-rays and MRI have been developed for OA risk assessment. These models have shown high diagnostic performance for predicting a wide variety of OA outcomes, including the incidence and progression of radiographic knee OA, the presence and progression of knee pain, and future total knee replacement. The preliminary results of DL applications in OA imaging have been encouraging. However, many DL techniques require further technical refinement to maximize diagnostic performance. Furthermore, the generalizability of DL approaches needs to be further investigated in prospective studies using large image datasets acquired at different institutions with different imaging hardware before they can be implemented in clinical practice and research studies.

Osteoarthritis is a complex whole-organ disorder that involves molecular, anatomic, and physiologic derangement. Advances in imaging techniques have expanded the role of imaging in evaluating osteoarthritis and functional changes. Radiography, magnetic resonance imaging, computed tomography (CT), and ultrasonography are commonly used imaging modalities, each with advantages and limitations in evaluating osteoarthritis. Radiography comprehensively analyses alignment and osseous features, while MRI provides detailed information about cartilage damage, bone marrow edema, synovitis, and soft tissue abnormalities. Compositional imaging derives quantitative data for detecting cartilage and tendon degeneration before structural damage occurs. Ultrasonography permits real-time scanning and dynamic joint evaluation, whereas CT is useful for assessing final osseous detail. Imaging plays an essential role in the diagnosis, management, and research of osteoarthritis. The use of imaging can help differentiate osteoarthritis from other diseases with similar symptoms, and recent advances in deep learning have made the acquisition, management, and interpretation of imaging data more efficient and accurate. Imaging is useful in monitoring and predicting the prognosis of osteoarthritis, expanding our understanding of its pathophysiology. Ultimately, this enables early detection and personalized medicine for patients with osteoarthritis. This article reviews the current state of imaging in osteoarthritis, focusing on the strengths and limitations of various imaging modalities, and introduces advanced techniques, including deep learning, applied in clinical practice.

Over the past decades, MRI has become increasingly important for diagnosing and longitudinally monitoring musculoskeletal disorders, with ongoing hardware and software improvements aiming to optimize image quality and speed. However, surging demand for musculoskeletal MRI and increased interest to provide more personalized care will necessitate a stronger emphasis on efficiency and specificity. Ongoing hardware developments include more powerful gradients, improvements in wide-bore magnet designs to maintain field homogeneity, and high-channel phased-array coils. There is also interest in low-field-strength magnets with inherently lower magnetic footprints and operational costs to accommodate global demand in middle- and low-income countries. Previous approaches to decrease acquisition times by means of conventional acceleration techniques (eg, parallel imaging or compressed sensing) are now largely overshadowed by deep learning reconstruction algorithms. It is expected that greater emphasis will be placed on improving synthetic MRI and MR fingerprinting approaches to shorten overall acquisition times while also addressing the demand of personalized care by simultaneously capturing microstructural information to provide greater detail of disease severity. Authors also anticipate increased research emphasis on metal artifact reduction techniques, bone imaging, and MR neurography to meet clinical needs.

MRI is a valuable tool for diagnosing a broad spectrum of acute and chronic ankle disorders, including ligament tears, tendinopathy, and osteochondral lesions. Traditional two-dimensional (2D) MRI provides a high image signal and contrast of anatomic structures for accurately characterizing articular cartilage, bone marrow, synovium, ligaments, tendons, and nerves. However, 2D MRI limitations are thick slices and fixed slice orientations. In clinical practice, 2D MRI is limited to 2 to 3 mm slice thickness, which can cause blurred contours of oblique structures due to volume averaging effects within the image slice. In addition, image plane orientations are fixated and cannot be changed after the scan, resulting in 2D MRI lacking multiplanar and multiaxial reformation abilities for individualized image plane orientations along oblique and curved anatomic structures, such as ankle ligaments and tendons. In contrast, three-dimensional (3D) MRI is a newer, clinically available MRI technique capable of acquiring high-resolution ankle MRI data sets with isotropic voxel size. The inherently high spatial resolution of 3D MRI permits up to five times thinner (0.5 mm) image slices. In addition, 3D MRI can be acquired image voxel with the same edge length in all three space dimensions (isotropism), permitting unrestricted multiplanar and multiaxial image reformation and postprocessing after the MRI scan. Clinical 3D MRI of the ankle with 0.5 to 0.7 mm isotropic voxel size resolves the smallest anatomic ankle structures and abnormalities of ligament and tendon fibers, osteochondral lesions, and nerves. After acquiring the images, operators can align image planes individually along any anatomic structure of interest, such as ligaments and tendons segments. In addition, curved multiplanar image reformations can unfold the entire course of multiaxially curved structures, such as perimalleolar tendons, into one image plane. We recommend adding 3D MRI pulse sequences to traditional 2D MRI protocols to visualize small and curved ankle structures to better advantage. This article provides an overview of the clinical application of 3D MRI of the ankle, compares diagnostic performances of 2D and 3D MRI for diagnosing ankle abnormalities, and illustrates clinical 3D ankle MRI applications.

The T12 to S4 spinal nerves form the lumbosacral plexus in the retroperitoneum, providing sensory and motor innervation to the pelvis and lower extremities. The lumbosacral plexus has a wide range of anatomic variations and interchange of fibers between nerve anastomoses. Neuropathies of the lumbosacral plexus cause a broad spectrum of complex pelvic and lower extremity pain syndromes, which can be challenging to diagnose and treat successfully. In their workup, selective nerve blocks are employed to test the hypothesis that a lumbosacral plexus nerve contributes to a suspected pelvic and extremity pain syndrome, whereas therapeutic perineural injections aim to alleviate pain and paresthesia symptoms. While the sciatic and femoral nerves are large in caliber, the iliohypogastric and ilioinguinal, genitofemoral, lateral femoral cutaneous, anterior femoral cutaneous, posterior femoral cutaneous, obturator, and pudendal nerves are small, measuring a few millimeters in diameter and have a wide range of anatomic variants. Due to their minuteness, direct visualization of the smaller lumbosacral plexus branches can be difficult during selective nerve blocks, particularly in deeper pelvic locations or larger patients. In this setting, the high spatial and contrast resolution of interventional MR neurography guidance benefits nerve visualization and targeting, needle placement, and visualization of perineural injectant distribution, providing a highly accurate alternative to more commonly used ultrasonography, fluoroscopy, and computed tomography guidance for perineural injections. This article offers a practical guide for MR neurography-guided lumbosacral plexus perineural injections, including interventional setup, pulse sequence protocols, lumbosacral plexus MR neurography anatomy, anatomic variations, and injection targets.