Real-Time MRI With Deep Learning for Efficient Evaluation of Neuromuscular Breathing Impairment.

ABSTRACT: Efficient detection of breathing impairment is critical for treatment and prognosis in neuromuscular disorders. However, standard pulmonary function tests often yield ambiguous results. This prospective study evaluates whether advanced real-time MRI (RT-MRI) combined with deep learning-based image segmentation provides sensitive outcome measures for respiratory dysfunction in late-onset Pompe disease (LOPD), a model disease for diaphragmatic weakness. Eleven Pompe patients (mean age 52.2 years; 55% female) and 11 controls (mean age 50.9 years; 55% female) were included. RT-MRI with a temporal resolution of 50 ms, combined with U-Net-supported lung segmentation, revealed significantly reduced diaphragmatic motion in Pompe patients compared to controls and unmasked paradoxical diaphragmatic

SUBMITTER: Zeng R 

PROVIDER: S-EPMC12932971 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature