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Objectives: To explore the use of deep learning–constrained compressed sensing (DLCS) in improving image quality and acquisition time for 3D MRI of the brachial plexus. Methods: Fifty-four participants who underwent contrast-enhanced imaging and forty-one participants who underwent unenhanced imaging were included. Sensitivity encoding with an acceleration of 2 × 2 (SENSE4x), CS with an acceleration of 4 (CS4x), and DLCS with acceleration of 4 (DLCS4x) and 8 (DLCS8x) were used for MRI of the brachial plexus. Apparent signal-to-noise ratios (aSNRs), apparent contrast-to-noise ratios (aCNRs), and qualitative scores on a 4-point scale were evaluated and compared by ANOVA and the Friedman test. Interobserver agreement was evaluated by calculating the intraclass correlation coefficients. Results: DLCS4x achieved higher aSNR and aCNR than SENSE4x, CS4x, and DLCS8x (all p < 0.05). For the root segment of the brachial plexus, no statistically significant differences in the qualitative scores were found among the four sequences. For the trunk segment, DLCS4x had higher scores than SENSE4x (p = 0.04) in the contrast-enhanced group and had higher scores than SENSE4x and DLCS8x in the unenhanced group (all p < 0.05). For the divisions, cords, and branches, DLCS4x had higher scores than SENSE4x, CS4x, and DLCS8x (all p ≤ 0.01). No overt difference was found among SENSE4x, CS4x, and DLCS8x in any segment of the brachial plexus (all p > 0.05). Conclusions: In three-dimensional MRI for the brachial plexus, DLCS4x can improve image quality compared with SENSE4x and CS4x, and DLCS8x can maintain the image quality compared to SENSE4x and CS4x. Clinical relevance statement: Deep learning–constrained compressed sensing can improve the image quality or accelerate acquisition of 3D MRI of the brachial plexus, which should be benefit in evaluating the brachial plexus and its branches in clinical practice. Key Points: •Deep learning–constrained compressed sensing showed higher aSNR, aCNR, and qualitative scores for the brachial plexus than SENSE and CS at the same acceleration factor with similar scanning time. •Deep learning–constrained compressed sensing at acceleration factor of 8 had comparable aSNR, aCNR, and qualitative scores to SENSE4x and CS4x with approximately half the examination time. •Deep learning–constrained compressed sensing may be helpful in clinical practice for improving image quality and acquisition time in three-dimensional MRI of the brachial plexus. [ABSTRACT FROM AUTHOR]

Purpose: The characteristics of surface-based morphological patterns to primary trigeminal neuralgia (PTN) are still not well understood. This study aims to screen the useful cortical indices for the prediction of PTN and the quantification of pain severity. Methods: Fifty PTN patients and 48 matched healthy subjects enrolled in the study from March 2016 to August 2021. High-resolution T1 data were performed at 3.0 Tesla scanner and were analyzed with FreeSurfer software to detect the abnormalities of cortical mean curve (CMC), cortical thickness (CT), surface area (SA), and cortical volume (CV) in PTN patients compared to healthy controls. Logistic regression analysis was conducted to determine whether certain morphological patterns could predict PTN disorder. Then, the relationships of cortical indices to the pain characteristics in patient group were examined using linear regression model. Results: Distinctive cortical alterations were discovered through surface-based analysis, including increased temporal CMC, decreased insular CT and fusiform SA, along with decreased CV in several temporal and occipital areas. Moreover, the difference of temporal CMC was greater than other cortical parameters between the two groups, and the combination of certain morphological indices was of good value in the diagnosis for PTN. Besides, CT of left insula was negatively associated with the pain intensity in PTN patients. Conclusion: The patients with PTN demonstrate distinctive morphological patterns in several cortical regions, which may contribute to the imaging diagnosis of this refractory disorder and be useful for the quantification of the orofacial pain. Clinical trials: The registry name of this study in https://clinicaltrials.gov/: Magnetic Resonance Imaging Study on Patients with Trigeminal Neuralgia (MRI-TN) https://clinicaltrials.gov/ ID: NCT02713646 A link to the full application: https://clinicaltrials.gov/ct2/results?cond=&term=NCT02713646&cntry=&state=&city=&dist= The first patient with primary trigeminal neuralgia was recruited on November 28, 2016. [ABSTRACT FROM AUTHOR]