Faculty Bibliography

OBJECTIVE:To evaluate the clinical utility of selective magnetic resonance neurography-(MRN)-guided anterior femoral cutaneous nerve (AFCN) blocks for diagnosing anterior thigh neuralgia. MATERIALS AND METHODS/METHODS:Following institutional review board approval and informed consent, participants with intractable anterior thigh pain and clinically suspected AFCN neuralgia were included. AFCN blocks were performed under MRN guidance using an anterior groin approach along the medial sartorius muscle margin. Outcome variables included AFCN identification on MRN, technical success of perineural drug delivery, rate of AFCN anesthesia, complications, total procedure time, patient-reported procedural experiences, rate of positive diagnostic AFCN blocks, and positive subsequent treatment rate. RESULTS:Eighteen MRN-guided AFCN blocks (six unilateral and six bilateral blocks) were performed in 12 participants (6 women; age, 49 (30-65) years). Successful MRN identified the AFCN, successful perineural drug delivery, and AFCN anesthesia was achieved in all thighs. No complications occurred. The total procedure time was 19 (10-28) min. Patient satisfaction and experience were high without adverse MRI effects. AFCN blocks identified the AFCN as the symptom generator in 16/18 (89%) cases, followed by 14/16 (88%) successful treatments. CONCLUSION/CONCLUSIONS:Our results suggest that selective MR neurography-guided AFCN blocks effectively diagnose anterior femoral cutaneous neuralgia and are well-tolerated.

OBJECTIVES/OBJECTIVE:Despite significant progress, artifact-free visualization of the bone and soft tissues around hip arthroplasty implants remains an unmet clinical need. New-generation low-field magnetic resonance imaging (MRI) systems now include slice encoding for metal artifact correction (SEMAC), which may result in smaller metallic artifacts and better image quality than standard-of-care 1.5 T MRI. This study aims to assess the feasibility of SEMAC on a new-generation 0.55 T system, optimize the pulse protocol parameters, and compare the results with those of a standard-of-care 1.5 T MRI. MATERIALS AND METHODS/METHODS:Titanium (Ti) and cobalt-chromium total hip arthroplasty implants embedded in a tissue-mimicking American Society for Testing and Materials gel phantom were evaluated using turbo spin echo, view angle tilting (VAT), and combined VAT and SEMAC (VAT + SEMAC) pulse sequences. To refine an MRI protocol at 0.55 T, the type of metal artifact reduction techniques and the effect of various pulse sequence parameters on metal artifacts were assessed through qualitative ranking of the images by 3 expert readers while taking measured spatial resolution, signal-to-noise ratios, and acquisition times into consideration. Signal-to-noise ratio efficiency and artifact size of the optimized 0.55 T protocols were compared with the 1.5 T standard and compressed-sensing SEMAC sequences. RESULTS:Overall, the VAT + SEMAC sequence with at least 6 SEMAC encoding steps for Ti and 9 for cobalt-chromium implants was ranked higher than other sequences for metal reduction (P < 0.05). Additional SEMAC encoding partitions did not result in further metal artifact reductions. Permitting minimal residual artifacts, low magnetic susceptibility Ti constructs may be sufficiently imaged with optimized turbo spin echo sequences obviating the need for SEMAC. In cross-platform comparison, 0.55 T acquisitions using the optimized protocols are associated with 45% to 64% smaller artifacts than 1.5 T VAT + SEMAC and VAT + compressed-sensing/SEMAC protocols at the expense of a 17% to 28% reduction in signal-to-noise ratio efficiency. B1-related artifacts are invariably smaller at 0.55 T than 1.5 T; however, artifacts related to B0 distortion, although frequently smaller, may appear as signal pileups at 0.55 T. CONCLUSIONS:Our results suggest that new-generation low-field SEMAC MRI reduces metal artifacts around hip arthroplasty implants to better advantage than current 1.5 T MRI standard of care. While the appearance of B0-related artifacts changes, reduction in B1-related artifacts plays a major role in the overall benefit of 0.55 T.

OBJECTIVE:To introduce cooled radiofrequency nerve ablation (C-RFA) as an alternative to managing symptomatically moderate to severe glenohumeral osteoarthritis (OA) in patients who have failed other conservative treatments and who are not surgical candidates or refuse surgery. MATERIAL AND METHODS/METHODS:This prospective pilot study includes a total of 12 patients experiencing chronic shoulder pain from moderate to severe glenohumeral OA. Patients underwent anesthetic blocks of the axillary, lateral pectoral, and suprascapular nerves to determine candidacy for C-RFA treatment. Adequate response after anesthetic block was over 50% immediate pain relief. Once patients were deemed candidates, they underwent C-RFA of the three nerves 2-3 weeks later. Treatment response was evaluated using the clinically validated American Shoulder and Elbow Surgeons (ASES) score and visual analog scale (VAS) to assess pain, stiffness, and functional activities of daily living. Follow-up outcome scores were collected up to 6 months after C-RFA procedure. RESULTS:Twelve patients underwent C-RFA procedure for shoulder OA. VAS scores significantly improved from 8.8 ± 0.6 to 2.2 ± 0.4 6 months after the C-RFA treatment (p < 0.001). Patient's ASES score results significantly improved in total ASES from 17.2 ± 6.6 to 65.7 ± 5.9 (p < 0.0005). No major complications arose. No patients received re-treatment or underwent shoulder arthroplasty. CONCLUSION/CONCLUSIONS:Image-guided axillary, lateral pectoral, and suprascapular nerve C-RFA has minimal complications and is a promising alternative to treat chronic shoulder pain and stiffness from glenohumeral arthritis.

OBJECTIVE:To compare computer-based 3D-analysis for quantification of the femorotibial joint space width (JSW) using weight-bearing cone beam CT (WB-CT), non-weight-bearing multi-detector CT (NWB-CT), and weight-bearing conventional radiographs (WB-XR). DESIGN/METHODS:Twenty-six participants prospectively underwent NWB-CT, WB-CT, and WB-XR of the knee. For WB-CT and NWB-CT, the average and minimal JSW was quantified by 3D-analysis of the minimal distance of any point of the subchondral tibial bone surface and the femur. Associations with mechanical leg axes and osteoarthritis were evaluated. Minimal JSW of WB-CT was further compared to WB-XR. Two-tailed p-values of <0.05 were considered significant. RESULTS:Significant differences existed of the average medial and lateral JSW between WB-CT and NWB-CT (medial: 4.7 vs 5.1 mm [P = 0.028], lateral: 6.3 vs 6.8 mm [P = 0.008]). The minimal JSW on WB-XR (medial:3.1 mm, lateral:5.8 mm) were significantly wider compared to WB-CT and NWB-CT (both medial:1.8 mm, lateral:2.9 mm, all p < 0.001), but not significantly different between WB-CT and NWB-CT (all p ≥ 0.869). Significant differences between WB-CT and NWB-CT existed in participants with varus knee alignment for the average and the minimal medial JSW (p = 0.004 and p = 0.011) and for participants with valgus alignment for the average lateral JSW (p = 0.013). On WB-CT, 25% of the femorotibial compartments showed bone-on-bone apposition, which was significantly higher when compared to NWB-CT (10%,P = 0.008) and WB-XR (8%,P = 0.012). CONCLUSION/CONCLUSIONS:Combining WB-CT with 3D-based assessment allows detailed quantification of the femorotibial joint space and the effect of knee alignment on JSW. WB-CT demonstrates significantly more bone-on-bone appositions, which are underestimated or even undetectable on NWB-CT and WB-XR.

OBJECTIVE:To establish the effectiveness of cooled radiofrequency ablation in managing hip pain from osteoarthritis at 6 months after receiving treatment in patients who failed conservative treatments and are not surgical candidates due to comorbidities or unwillingness to undergo arthroplasty surgery by targeting the femoral and obturator branches and assessing the degree of hip pain relief and change of function. MATERIALS AND METHODS/METHODS:This prospective pilot study includes a total of 11 consecutive patients experiencing persistent chronic hip pain in the setting of advanced osteoarthritis. Patients initially underwent anesthetic blocks of the obturator and femoral nerve branches to determine cooled radiofrequency ablation candidacy. After adequate response to the anesthetic blocks (> 50% immediate pain relief), patients were subjected to the procedures 2-3 weeks later. Treatment response was evaluated utilizing clinically validated questionnaires and visual analog score in order to assess impact on pain severity, stiffness, and functional activities of daily living. Follow-up outcome scores were collected up to 6 months after cooled radiofrequency ablation procedure. RESULTS:A total of 11 hips were treated consecutively between August 2019 and March 2020 (mean patient age 61.4 years; 8 M:3F). The mean total HOOS score improved significantly from baseline at 17.0 ± 6.0 to 52.9 ± 5.4 at a mean of 6.2 months after treatment (p < 0.0001), with significant improvement in mean pain score from 16.1 ± 6.6 to 53.4 ± 7.4 (p < 0.0001) and mean stiffness score from 15.0 ± 8.1 to 53.6 ± 11.0 (p < 0.0001). No major complications were encountered. No patients went on to re-treatment, surgery, or other intervention. CONCLUSION/CONCLUSIONS:Image-guided obturator and femoral nerve cooled radiofrequency ablation is effective and safe in treating chronic hip pain/stiffness in the setting of advanced osteoarthritis.

Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of artificial intelligence, which offers many exciting possibilities for musculoskeletal radiology. A variety of investigational deep learning algorithms have been developed to detect anterior cruciate ligament tears, meniscus tears, and rotator cuff disorders. Additional deep learning-based MRI algorithms have been investigated to detect Achilles tendon tears, recurrence prediction of musculoskeletal neoplasms, and complex segmentation of nerves, bones, and muscles. Proof-of-concept studies suggest that deep learning algorithms may achieve similar diagnostic performances when compared to human readers in meta-analyses; however, musculoskeletal radiologists outperformed most deep learning algorithms in studies including a direct comparison. Earlier investigations and developments of deep learning algorithms focused on the binary classification of the presence or absence of an abnormality, whereas more advanced deep learning algorithms start to include features for characterization and severity grading. While many studies have focused on comparing deep learning algorithms against human readers, there is a paucity of data on the performance differences of radiologists interpreting musculoskeletal MRI studies without and with artificial intelligence support. Similarly, studies demonstrating the generalizability and clinical applicability of deep learning algorithms using realistic clinical settings with workflow-integrated deep learning algorithms are sparse. Contingent upon future studies showing the clinical utility of deep learning algorithms, artificial intelligence may eventually translate into clinical practice to assist detection and characterization of various conditions on musculoskeletal MRI exams.

Artificial intelligence (AI) and deep learning have multiple potential uses in aiding the musculoskeletal radiologist in the radiological evaluation of orthopedic implants. These include identification of implants, characterization of implants according to anatomic type, identification of specific implant models, and evaluation of implants for positioning and complications. In addition, natural language processing (NLP) can aid in the acquisition of clinical information from the medical record that can help with tasks like prepopulating radiology reports. Several proof-of-concept works have been published in the literature describing the application of deep learning toward these various tasks, with performance comparable to that of expert musculoskeletal radiologists. Although much work remains to bring these proof-of-concept algorithms into clinical deployment, AI has tremendous potential toward automating these tasks, thereby augmenting the musculoskeletal radiologist.