Faculty Bibliography

Magnetic resonance imaging (MRI) is the keystone of modern musculoskeletal imaging; however, long pulse sequence acquisition times may restrict patient tolerability and access. Advances in MRI scanners, coil technology, and innovative pulse sequence acceleration methods enable 4-fold turbo spin echo pulse sequence acceleration in clinical practice; however, at this speed, conventional image reconstruction approaches the signal-to-noise limits of temporal, spatial, and contrast resolution. Novel deep learning image reconstruction methods can minimize signal-to-noise interdependencies to better advantage than conventional image reconstruction, leading to unparalleled gains in image speed and quality when combined with parallel imaging and simultaneous multislice acquisition. The enormous potential of deep learning-based image reconstruction promises to facilitate the 10-fold acceleration of the turbo spin echo pulse sequence, equating to a total acquisition time of 2-3 minutes for entire MRI examinations of joints without sacrificing spatial resolution or image quality. Current investigations aim for a better understanding of stability and failure modes of image reconstruction networks, validation of network reconstruction performance with external data sets, determination of diagnostic performances with independent reference standards, establishing generalizability to other centers, scanners, field strengths, coils, and anatomy, and building publicly available benchmark data sets to compare methods and foster innovation and collaboration between the clinical and image processing community. In this article, we review basic concepts of deep learning-based acquisition and image reconstruction techniques for accelerating and improving the quality of musculoskeletal MRI, commercially available and developing deep learning-based MRI solutions, superresolution, denoising, generative adversarial networks, and combined strategies for deep learning-driven ultra-fast superresolution musculoskeletal MRI. This article aims to equip radiologists and imaging scientists with the necessary practical knowledge and enthusiasm to meet this exciting new era of musculoskeletal MRI.

ABSTRACT/UNASSIGNED:Magnetic resonance imaging (MRI) provides essential information for diagnosing and treating musculoskeletal disorders. Although most musculoskeletal MRI examinations are performed at 1.5 and 3.0 T, modern low-field MRI systems offer new opportunities for affordable MRI worldwide. In 2021, a 0.55 T modern low-field, whole-body MRI system with an 80-cm-wide bore was introduced for clinical use in the United States and Europe. Compared with current higher-field-strength MRI systems, the 0.55 T MRI system has a lower total ownership cost, including purchase price, installation, and maintenance. Although signal-to-noise ratios scale with field strength, modern signal transmission and receiver chains improve signal yield compared with older low-field magnetic resonance scanner generations. Advanced radiofrequency coils permit short echo spacing and overall compacter echo trains than previously possible. Deep learning-based advanced image reconstruction algorithms provide substantial improvements in perceived signal-to-noise ratios, contrast, and spatial resolution. Musculoskeletal tissue contrast evolutions behave differently at 0.55 T, which requires careful consideration when designing pulse sequences. Similar to other field strengths, parallel imaging and simultaneous multislice acquisition techniques are vital for efficient musculoskeletal MRI acquisitions. Pliable receiver coils with a more cost-effective design offer a path to more affordable surface coils and improve image quality. Whereas fat suppression is inherently more challenging at lower field strengths, chemical shift selective fat suppression is reliable and homogeneous with modern low-field MRI technology. Dixon-based gradient echo pulse sequences provide efficient and reliable multicontrast options, including postcontrast MRI. Metal artifact reduction MRI benefits substantially from the lower field strength, including slice encoding for metal artifact correction for effective metal artifact reduction of high-susceptibility metallic implants. Wide-bore scanner designs offer exciting opportunities for interventional MRI. This review provides an overview of the economical aspects, signal and image quality considerations, technological components and coils, musculoskeletal tissue relaxation times, and image contrast of modern low-field MRI and discusses the mainstream and new applications, challenges, and opportunities of musculoskeletal MRI.

ABSTRACT/UNASSIGNED:Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radiology for various tasks, including acute injuries, chronic conditions, spinal abnormalities, and neoplasms. While early radiomics-based methods were often limited to a smaller number of higher-order image feature extractions, applying machine learning-based analytic models, multifactorial correlations, and classifiers now permits big data processing and testing thousands of features to identify relevant markers. A growing number of novel deep learning-based methods describe magnetic resonance imaging- and computed tomography-based algorithms for diagnosing anterior cruciate ligament tears, meniscus tears, articular cartilage defects, rotator cuff tears, fractures, metastatic skeletal disease, and soft tissue tumors. Initial radiomics and deep learning techniques have focused on binary detection tasks, such as determining the presence or absence of a single abnormality and differentiation of benign versus malignant. Newer-generation algorithms aim to include practically relevant multiclass characterization of detected abnormalities, such as typing and malignancy grading of neoplasms. So-called delta-radiomics assess tumor features before and after treatment, with temporal changes of radiomics features serving as surrogate markers for tumor responses to treatment. New approaches also predict treatment success rates, surgical resection completeness, and recurrence risk. Practice-relevant goals for the next generation of algorithms include diagnostic whole-organ and advanced classification capabilities. Important research objectives to fill current knowledge gaps include well-designed research studies to understand how diagnostic performances and suggested efficiency gains of isolated research settings translate into routine daily clinical practice. This article summarizes current radiomics- and machine learning-based magnetic resonance imaging and computed tomography approaches for musculoskeletal disease detection and offers a perspective on future goals and objectives.

Common etiologies of low back pain include degenerative arthrosis and inflammatory arthropathy of the sacroiliac joints. The diagnostic workup revolves around identifying and confirming the sacroiliac joints as a pain generator. Diagnostic sacroiliac joint injections often serve as functional additions to the diagnostic workup through eliciting a pain response that tests the hypothesis that the sacroiliac joints do or do not contribute to the patient's pain syndrome. Therapeutic sacroiliac joint injections aim to provide medium- to long-term relief of symptoms and reduce inflammatory activity and, ultimately, irreversible structural damage. Ultrasonography, fluoroscopy, computed tomography, and magnetic resonance imaging (MRI) may be used to guide sacroiliac joint injections. The populations that may benefit most from MRI-guided sacroiliac joint procedures include children, adolescents, adults of childbearing age, and patients receiving serial injections due to the ability of interventional MRI to avoid radiation exposure. Most clinical wide-bore MRI systems can be used for MRI-guided sacroiliac joint injections. Turbo spin echo pulse sequences optimized for interventional needle display visualize the needle tip with an error margin of < 1 mm or less. Published success rates of intra-articular sacroiliac joint drug delivery with MRI guidance range between 87 and 100%. The time required for MR-guided sacroiliac joint injections in adults range between 23-35 min and 40 min in children. In this article, we describe techniques for MRI-guided sacroiliac joint injections, share our practice of incorporating interventional MRI in the care of patients with sacroiliac joint mediated pain, discuss the rationales, benefits, and limitations of interventional MRI, and conclude with future developments.

RATIONAL AND OBJECTIVES/OBJECTIVE:Comparison of radiation dose and image quality in routine abdominal and pelvic contrast-enhanced computed tomography (CECT) between a photon-counting detector CT (PCD-CT) and a dual energy dual source CT (DSCT). MATERIALS AND METHODS/METHODS:), dose length product (DLP) and size-specific dose estimation (SSDE), objective and subjective measurements of image quality were scored by two emergency radiologists including lesion conspicuity. RESULTS:of T3D reconstructions from PCD-CT were significantly higher than those of DSCT (all, p < 0.05). Qualitative image noise analysis from PCD-CT and DSCT yielded a mean of 4 each. Lesion conspicuity was rated significantly higher in PCD-CT (Q3 strength) compared to DSCT images. CTDI, DLP and SSDE mean values for PCD-CT and DSCT were 7.98 ± 2.56 mGy vs. 14.11 ± 2.92 mGy, 393.13 ± 153.55 mGy*cm vs. 693.61 ± 185.76 mGy*cm and 9.98 ± 2.41 vs. 14.63 ± 1.63, respectively, translating to a dose reduction of around 32% (SSDE). CONCLUSION/CONCLUSIONS:-generation DSCT.

Postoperative MR imaging of joints is now commonly requested, yet artifacts caused by metallic orthopedic implants remain a significant challenge during image interpretation. Effective artifact reduction is essential to identify postsurgical complications, such as prosthesis loosening, infection, adverse local tissue reaction, and periarticular soft tissue injuries. This article reviews basic and advanced metal artifact reduction MR imaging techniques applied to various clinical protocols for successful postoperative MR imaging of small and large joints.

A 54-year-old woman presented with progressive right hip pain after hip arthroplasty 9 years earlier. The emerging role of metal artifact reduction MRI in the noninvasive diagnosis of infectious synovitis as the surrogate marker for periprosthetic hip joint infection and differentiation from other synovitis types is discussed.

Many surgical procedures and operations are used to treat ankle and foot disorders. Radiography is the first-line imaging for postoperative surveillance and evaluation of pain and dysfunction. Computed tomography scans and MR imaging are used for further evaluation. MR imaging is the most accurate test for soft tissues assessments. MR imaging protocol adjustments include basic and advanced metal artifact reduction. We chose a surgical approach to select the common types of procedures and discuss the normal and abnormal postoperative MR imaging appearances, highlighting potential complications. This article reviews commonly used surgical techniques and their normal and abnormal MR imaging appearances.

OBJECTIVE:Joint dislocations are orthopedic emergencies that require prompt intervention. Automatic identification of these injuries could help improve timely patient care because diagnostic delays increase the difficulty of reduction. In this study, we developed convolutional neural networks (CNNs) to detect elbow and shoulder dislocations, and tested their generalizability on external datasets. METHODS:We collected 106 elbow radiographs (53 with dislocation [50 %]) and 140 shoulder radiographs (70 with dislocation [50 %]) from a level-1 trauma center. After performing 24× data augmentation on training/validation data, we trained multiple CNNs to detect elbow and shoulder dislocations, and also evaluated the best-performing models using external datasets from an external hospital and online radiology repositories. To examine CNN decision-making, we generated class activation maps (CAMs) to visualize areas of images that contributed the most to model decisions. RESULTS:On all internal test sets, CNNs achieved AUCs >0.99, and on all external test sets, CNNs achieved AUCs >0.97. CAMs demonstrated that the CNNs were focused on relevant joints in decision-making regardless of whether or not dislocations were present. CONCLUSION/CONCLUSIONS:Joint dislocations in both shoulders and elbows were readily identified with high accuracy by CNNs with excellent generalizability to external test sets. These findings suggest that CNNs could expedite access to intervention by assisting in diagnosing dislocations.