Maggie Fung

Case: A 17-year-old girl presented with chronic right arm numbness, cramping, burning pain, and weakness. Initial nerve studies showed slowing in the median and ulnar nerves, but an outside magnetic resonance imaging was negative. Repeat magnetic resonance neurography (MRN) revealed right C8 nerve root and lower trunk enlargement, compressed by a fibrous band from a small cervical rib. The lower trunk appeared indented in a “sickle” shape—termed the wedge-sickle sign. Surgery confirmed the MRN findings. Conclusion: MRN can depict the wedge-sickle sign previously seen only on ultrasound, potentially improving sensitivity of MRN for detecting thin fibromuscular bands in neurogenic thoracic outlet syndrome.

Objective. MRI of the hand and wrist region, containing small structures such as ligaments, peripheral nerves, and thin cartilage, is challenging, requiring both high spatial resolution and coverage, along with adequate signal-to-noise ratio (SNR) for image quality. This work aimed to evaluate whether a high-density, prototype flexible coil array could better visualize small anatomic structures without compromising SNR or field-of-view (FOV), compared to conventional hand and wrist coils. Approach. A flexible, 32-channel phased-array prototype coil was developed for 3-Tesla hand and wrist MRI using novel, small dual-loop elements. A silicon oil phantom was used to analyze SNR and noise amplification arising from acceleration factors ( R ) of 2–5. Confirmation of the prototype’s phantom performance was done in wrist MRI comparisons in two healthy volunteers and against two 16-channel commercially available coils for superior-inferior (SI) coverage and at different acceleration factors ( R = 1.5–4). Additional imaging in 6 patients was performed in the hand, wrist, elbow, brachial plexus, and foot for anecdotal, qualitative assessment of the coil’s performance. Main Results. Phantom testing demonstrated ∼26% higher SNR and 19.2%–192.4% lower g -factors in the prototype compared to a 16-channel conventional coil. In comparisons against other coils, the prototype coil demonstrated larger SI coverage of 23.9 cm compared to 20.5 cm and 20.6 cm in conventional coils. Image quality was maintained at higher acceleration factors. Patient imaging successfully demonstrated visualization of small structures. Significance. The 32-channel prototype coil’s extended FOV, improved SNR, and flexible design enabled accelerated scans with high spatial resolution and broader anatomical coverage, offering technical improvements over conventional coil designs.

Fused rendering of three-dimensional (3D) MR neurography and zero echo time MRI for peripheral nerve and bone structures, respectively, provides the potential for improved surgical planning, interphysician communication, patient education, and creation of 3D-printed anatomic models.

Evaluation of the 3D bone morphology of the glenohumeral joint is necessary for pre-surgical planning. Zero echo time (ZTE) magnetic resonance imaging (MRI) provides excellent bone contrast and can potentially be used in the place of computed tomography. Segmentation of the shoulder anatomy, particularly the humeral head and the acetabulum, is needed for the detailed assessment of each anatomy and for pre-surgical preparation. In this study, we compared the performance of two popular deep learning models based on Google’s DeepLab and U-Net to perform automated segmentation on ZTE MRI of human shoulders. Axial ZTE images of normal shoulders (n = 31) acquired at 3-Tesla were annotated for training with DeepLab and 2D U-Net, and the trained model was validated with testing data (n = 13). While both models showed visually satisfactory results for segmenting the humeral bone, U-Net slightly over-estimated while DeepLab under-estimated the segmented area compared to the ground truth. Testing accuracy quantified by Dice score was significantly higher (p < 0.05) for U-Net (88%) than DeepLab (81%) for the humeral segmentation. We have also implemented the U-Net model onto an MRI console for push-button DL segmentation processing. Although this is an early work with limitations, our approach has the potential to improve shoulder MR evaluation hindered by manual post-processing and may provide clinical benefit for quickly visualizing bones of the glenohumeral joint.

To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions. This IRB approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. We used a General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) to acquire two DTI sequences of the left knee on each child at 3T: an in-plane 2.0 x 2.0 mm2 with section thickness of 3.0 mm and a 2 mm3 isovolumetric voxel, neither had an inter-section gap. We used a multi-band DTI acquisi-tion with fat-suppressed single-shot spin-echo echo-planar sequence (20 non-collinear directions; b-values of 0 and 600 sec/mm2). The MR vendor-provided a commercially available DL model applied with 75% noise reduction settings to same subject DTI sequences at different spatial res-olutions. We compared DTI tract metrics from both DL-reconstructed scans and non-denoised scans for femur and tibia, at each spatial resolution. Differences were evaluated using Wilcox-on-signed ranked test and Bland-Altman plots. When comparing DL versus non-denoised diffu-sion metrics in femur and tibia using the 2 mm x 2mm x 3 mm voxel dimension there were no significant differences between tract count (p = 0.1, p =0.14) tract volume (p = 0.14, p = 0.29), or tibial tract length (p=0.16); femur tract length exhibited a significant difference (p

Background: UTE T2* cartilage mapping use in patients undergoing femoroacetabular impingement (FAI) has been lacking but may allow the detection of early cartilage damage. Purpose: To assess the reproducibility of UTE T2* cartilage mapping and determine the difference in UTE T2* values between FAI and asymptomatic patients and to evaluate the correlation between UTE T2* values and patient-reported symptoms. Material and Methods: Prospective evaluation of both hips (7 FAI and 7 asymptomatic patients). Bilateral hip 3-T MRI scans with UTE T2* cartilage maps were acquired. A second MRI scan was acquired 1–9 months later. Cartilage was segmented into anterosuperior, superior, and posterosuperior regions. Assessment was made of UTE T2* reproducibility (ICC). Mean UTE T2* values in patients were compared ( t-tests) and correlation was made with patient-reported outcomes (Spearman's). Results: ICCs of mean UTE T2* were as follows: acetabular, 0.82 (95% CI=0.50–0.95); femoral, 0.76 (95% CI=0.35–0.92). Significant strong correlation was found between mean acetabular UTE T2* values and iHOT12 (ρ = −0.63) and moderate correlation with mHHS (ρ = −0.57). There was no difference in mean UTE T2* values between affected vs. non-affected FAI hips. FAI-affected hips had significantly higher values in acetabulum vs. asymptomatic patients (13.47 vs. 12.55 ms). There was no difference in mean femoral cartilage values between the FAI-affected hips vs. asymptomatic patients. The posterosuperior femoral region had a higher mean value in non-affected FAI hips vs. asymptomatic patients (12.60 vs. 11.53 ms). Conclusion: UTE T2* cartilage mapping had excellent reproducibility. Affected FAI hips had higher mean acetabular UTE T2* values than asymptomatic patients. Severity of patient-reported symptoms correlates with UTE T2* acetabular cartilage values.

Objective: Imaging is crucial in the assessment of head and neck cancers for site, extension, and enlarged lymph nodes. Restriction spectrum imaging (RSI) is a new diffusion-weighted magnetic resonance imaging (MRI) technique that enhances the ability to differentiate aggressive cancer from low-grade or benign tumors and helps guide treatment and biopsy. Its contribution to imaging of brain and prostate tumors has been previously published. However, there are no prior studies using RSI sequence in head and neck tumors. The purpose of this study was to evaluate the feasibility of performing RSI in head and neck cancer. Methods: An additional RSI sequence was added in the routine MRI neck protocol for 13 patients diagnosed with head and neck cancer between November 2018 and April 2019. Restriction spectrum imaging sequence was performed with b values of 0, 500, 1500, and 3000 s/mm² and 29 directions on 1.5T magnetic resonance scanners. Diffusion-weighted imaging (DWI) images and RSI images were compared according to their ability to detect the primary malignancy and possible metastatic lymph nodes. Results: In 71% of the patients, RSI outperformed DWI in detecting the primary malignancy and possible metastatic lymph nodes, whereas in the remaining cases, the 2 were comparable. In 66% of the patients, RSI detected malignant lymph nodes that DWI/apparent diffusion coefficient failed to detect. Conclusions: This is the first study of RSI in head and neck imaging and showed its superiority over the conventional DWI sequence. Because of its ability to differentiate benign and malignant lymph nodes in some cases, the addition of RSI to routine head and neck MRI should be considered.