Improvement of peripheral nerve visualization using a deep learning-based MR reconstruction algorithm

Abstract

Objective: To assess a new deep learning-based MR reconstruction method, "DLRecon," for clinical evaluation of peripheral nerves.

Methods: Sixty peripheral nerves were prospectively evaluated in 29 patients (mean age: 49 ± 16 years, 17 female) undergoing standard-of-care (SOC) MR neurography for clinically suspected neuropathy. SOC-MRIs and DLRecon-MRIs were obtained through conventional and DLRecon reconstruction methods, respectively. Two radiologists randomly evaluated blinded images for outer epineurium conspicuity, fascicular architecture visualization, pulsation artifact, ghosting artifact, and bulk motion.

Results: DLRecon-MRIs were likely to score better than SOC-MRIs for outer epineurium conspicuity (OR = 1.9, p = 0.007) and visualization of fascicular architecture (OR = 1.8, p < 0.001) and were likely to score worse for ghosting (OR = 2.8, p = 0.004) and pulsation artifacts (OR = 1.6, p = 0.004). There was substantial to almost-perfect inter-reconstruction method agreement (AC = 0.73-1.00) and fair to almost-perfect interrater agreement (AC = 0.34-0.86) for all features evaluated. DLRecon-MRI had improved interrater agreement for outer epineurium conspicuity (AC = 0.71, substantial agreement) compared to SOC-MRIs (AC = 0.34, fair agreement). In >80% of images, the radiologist correctly identified an image as SOC- or DLRecon-MRI.

Discussion: Outer epineurium and fascicular architecture conspicuity, two key morphological features critical to evaluating a nerve injury, were improved in DLRecon-MRIs compared to SOC-MRIs. Although pulsation and ghosting artifacts increased in DLRecon images, image interpretation was unaffected.

Keywords: Artificial intelligence; Deep learning; Humans; Magnetic resonance imaging; Peripheral nerves.