# Real‐Time MRI With Deep Learning for Efficient Evaluation of Neuromuscular Breathing Impairment

## TL;DR

This study uses real-time MRI and deep learning to detect breathing issues in Pompe disease patients more effectively than standard tests.

## Contribution

The novel contribution is using real-time MRI with deep learning to identify early signs of respiratory muscle weakness in neuromuscular disorders.

## Key findings

- Pompe patients showed significantly reduced diaphragmatic motion compared to controls.
- Seven Pompe patients exhibited paradoxical diaphragmatic motion undetected by standard tests.
- Fatty involution of the diaphragm correlated with functional parameters from RT-MRI and pulmonary tests.

## 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 motion in Pompe patients (7 of 11). Reduced diaphragmatic sniff velocity and pathological diaphragmatic/thoracic synchronicity were detected in Pompe patients with still normal results in standard pulmonary function tests. Fatty involution of the diaphragm as quantified by fast T1 mapping correlated significantly with functional parameters from RT‐MRI and pulmonary function tests. RT‐MRI combined with deep learning‐based lung segmentation offers novel biomarkers for early detection of respiratory muscle weakness. This new technique provides useful outcome measures for clinical care as well as treatment studies in patients with neuromuscular breathing impairment. The technique can also be used to characterize physiologic breathing patterns in healthy individuals.

Breathing impairment is an important and common symptom in neuromuscular disorders. Reliable diagnostics are highly warranted. Real‐time MRI and deep learning‐based lung segmentation were used to analyze respiratory mechanics in patients with Pompe disease, a myopathy with diaphragmatic weakness, compared to healthy controls. Time‐resolved analysis enabled precise quantification of pathological breathing patterns and identified new, sensitive biomarkers for respiratory impairment.