Faculty Bibliography

This article provides a focused overview of emerging technology in musculoskeletal MRI and CT. These technological advances have primarily focused on decreasing examination times, obtaining higher quality images, providing more convenient and economical imaging alternatives, and improving patient safety through lower radiation doses. New MRI acceleration methods using deep learning and novel reconstruction algorithms can reduce scanning times while maintaining high image quality. New synthetic techniques are now available that provide multiple tissue contrasts from a limited amount of MRI and CT data. Modern low-field-strength MRI scanners can provide a more convenient and economical imaging alternative in clinical practice, while clinical 7.0-T scanners have the potential to maximize image quality. Three-dimensional MRI curved planar reformation and cinematic rendering can provide improved methods for image representation. Photon-counting detector CT can provide lower radiation doses, higher spatial resolution, greater tissue contrast, and reduced noise in comparison with currently used energy-integrating detector CT scanners. Technological advances have also been made in challenging areas of musculoskeletal imaging, including MR neurography, imaging around metal, and dual-energy CT. While the preliminary results of these emerging technologies have been encouraging, whether they result in higher diagnostic performance requires further investigation.

OBJECTIVES/OBJECTIVE:To test whether a 4-fold accelerated 3D T2-weighted (T2) CAIPIRINHA SPACE TSE sequence with isotropic voxel size is equivalent to conventional 2DT2 TSE for the evaluation of intrinsic and perilesional soft tissue tumors (STT) characteristics. METHODS:For 108 patients with histologically-proven STTs, MRI, including 3DT2 (CAIPIRINHA SPACE TSE) and 2DT2 (TSE) sequences, was performed. Two radiologists evaluated each sequence for quality (diagnostic, non-diagnostic), tumor characteristics (heterogeneity, signal intensity, margin), and the presence or absence of cortical involvement, marrow edema, and perilesional edema (PLE); tumor size and PLE extent were measured. Signal-to-noise (SNR) and contrast-to-noise (CNR) ratios and acquisition times for 2DT2 in two planes and 3DT2 sequences were reported. Descriptive statistics and inter-method agreement were reported. RESULTS:Image quality was diagnostic for all sequences (100% [108/108]). No difference was observed between 3DT2 and 2DT2 tumor characteristics (p < 0.05). There was no difference in mean tumor size (3DT2: 2.9 ± 2.5 cm, 2DT2: 2.8 ± 2.6 cm, p = 0.4) or PLE extent (3DT2:0.5 ± 1.2 cm, 2DT2:0.5 ± 1.0 cm, p = 0.9) between the sequences. There was no difference in the SNR of tumors, marrow, and fat between the sequences, whereas the SNR of muscle was higher (p < 0.05) on 3DT2 than 2DT2. CNR measures on 3DT2 were similar to 2DT2 (p > 0.1). The average acquisition time was shorter for 3DT2 compared with 2DT2 (343 ± 127 s vs 475 ± 162 s, respectively). CONCLUSION/CONCLUSIONS:Isotropic 3DT2 MRI offers higher spatial resolution, faster acquisition times, and equivalent assessments of STT characteristics compared to conventional 2DT2 MRI in two planes. 3DT2 is interchangeable with a 2DT2 sequence in tumor protocols. KEY POINTS/CONCLUSIONS:• Isotropic 3DT2 CAIPIRINHA SPACE TSE offers higher spatial resolution than 2DT2 TSE and is equivalent to 2DT2 TSE for assessments of soft tissue tumor intrinsic and perilesional characteristics. • Multiplanar reformats of 3DT2 CAIPIRINHA SPACE TSE can substitute for 2DT2 TSE acquired in multiple planes, thereby reducing the acquisition time of MRI tumor protocols. • 3DT2 CAIPIRINHA SPACE TSE and 2DT2 TSE had similar CNR of tissues.

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.

OBJECTIVE:Deep learning has the potential to automatically triage orthopedic emergencies, such as joint dislocations. However, due to the rarity of these injuries, collecting large numbers of images to train algorithms may be infeasible for many centers. We evaluated if the Internet could be used as a source of images to train convolutional neural networks (CNNs) for joint dislocations that would generalize well to real-world clinical cases. METHODS:We collected datasets from online radiology repositories of 100 radiographs each (50 dislocated, 50 located) for four joints: native shoulder, elbow, hip, and total hip arthroplasty (THA). We trained a variety of CNN binary classifiers using both on-the-fly and static data augmentation to identify the various joint dislocations. The best-performing classifier for each joint was evaluated on an external test set of 100 corresponding radiographs (50 dislocations) from three hospitals. CNN performance was evaluated using area under the ROC curve (AUROC). To determine areas emphasized by the CNN for decision-making, class activation map (CAM) heatmaps were generated for test images. RESULTS:The best-performing CNNs for elbow, hip, shoulder, and THA dislocation achieved high AUROCs on both internal and external test sets (internal/external AUC): elbow (1.0/0.998), hip (0.993/0.880), shoulder (1.0/0.993), THA (1.0/0.950). Heatmaps demonstrated appropriate emphasis of joints for both located and dislocated joints. CONCLUSION/CONCLUSIONS:With modest numbers of images, radiographs from the Internet can be used to train clinically-generalizable CNNs for joint dislocations. Given the rarity of joint dislocations at many centers, online repositories may be a viable source for CNN-training data.

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.

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.

A standardized guideline and scoring system should be used for the MR imaging diagnosis of peripheral neuropathy. The MR imaging-based Neuropathy Score Reporting and Data System (NS-RADS) is a newly devised classification system (in press in AJR) that can be used to communicate both type and severity of peripheral neuropathy in the light of clinical history and examination findings. The spectrum of neuropathic conditions and peripheral nerve disorders covered in this system includes nerve injury, entrapment, neoplasm, diffuse neuropathy, and post-interventional states. This classification system also describes the temporal MR imaging appearances of regional muscle denervation changes. This review article is based on the multicenter validation study pre-published in American journal of Roentgenology and discusses technical considerations of optimal MR imaging for peripheral nerve evaluation and discusses the NS-RADS classification and its severity scales with illustration of conditions that fall under each classification. The readers can gain knowledge of the NS-RADS classification system and learn to apply it in their practices for improved inter-disciplinary communications and timely patient management.

OBJECTIVE:Periprosthetic dislocations of total hip arthroplasty (THA) are time-sensitive injuries, as the longer diagnosis and treatment are delayed, the more difficult they are to reduce. Automated triage of radiographs with dislocations could help reduce these delays. We trained convolutional neural networks (CNNs) for the detection of THA dislocations, and evaluated their generalizability by evaluating them on external datasets. METHODS:We used 357 THA radiographs from a single hospital (185 with dislocation [51.8%]) to develop and internally test a variety of CNNs to identify THA dislocation. We performed external testing of these CNNs on two datasets to evaluate generalizability. CNN performance was evaluated using area under the receiving operating characteristic curve (AUROC). Class activation mapping (CAM) was used to create heatmaps of test images for visualization of regions emphasized by the CNNs. RESULTS:Multiple CNNs achieved AUCs of 1 for both internal and external test sets, indicating good generalizability. Heatmaps showed that CNNs consistently emphasized the THA for both dislocated and located THAs. CONCLUSION/CONCLUSIONS:CNNs can be trained to recognize THA dislocation with high diagnostic performance, which supports their potential use for triage in the emergency department. Importantly, our CNNs generalized well to external data from two sources, further supporting their potential clinical utility.

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.

Flexible radiofrequency coils for magnetic resonance imaging (MRI) have garnered attention in research and industrial communities because they provide improved accessibility and performance and can accommodate a range of anatomic postures. Most recent flexible coil developments involve customized conductors or substrate materials and/or target applications at 3 T or above. In contrast, we set out to design a flexible coil based on an off-the-shelf conductor that is suitable for operation at 0.55 T (23.55 MHz). Signal-to-noise ratio (SNR) degradation can occur in such an environment because the resistance of the coil conductor can be significant with respect to the sample. We found that resonating a commercially available RG-223 coaxial cable shield with a lumped capacitor while the inner conductor remained electrically floating gave rise to a highly effective "cable coil." A 10-cm diameter cable coil was flexible enough to wrap around the knee, an application that can benefit from flexible coils, and had similar conductor loss and SNR as a standard-of-reference rigid copper coil. A two-channel cable coil array also provided good SNR robustness against geometric variability, outperforming a two-channel coaxial coil array by 26 and 16% when the elements were overlapped by 20-40% or gapped by 30-50%, respectively. A 6-channel cable coil array was constructed for 0.55 T knee imaging. Incidental cartilage and bone pathologies were clearly delineated in T1- and T2-weighted turbo spin echo images acquired in 3-4 min with the proposed coil, suggesting that clinical quality knee imaging is feasible in an acceptable examination timeframe. Correcting for T1, the SNR measured with the cable coil was approximately threefold lower than that measured with a 1.5 T state-of-the-art 18-channel coil, which is expected given the threefold difference in main magnetic field strength. This result suggests that the 0.55 T cable coil conductor loss does not deleteriously impact SNR, which might be anticipated at low field.