Machine-learning-based prediction of respiratory flow and lung volume from real-time cardiac MRI using MR-compatible spirometry

• Published in: Medical physics (Lancaster) Vol. 52; no. 8; p. e18019
• Main Authors: Malik, Halima, Uelwer, Tobias, Röwer, Lena Maria, Hußmann, Janina, Verde, Pablo Emilio, Harmeling, Stefan, Voit, Dirk, Frahm, Jens, Klee, Dirk, Pillekamp, Frank
• Format: Journal Article
• Language: English
• Published: United States 01.08.2025
• Subjects: Adult; Female; Heart - diagnostic imaging; Heart - physiology; Humans; Image Processing, Computer-Assisted - methods; Lung - diagnostic imaging; Lung - physiology; Lung Volume Measurements; Machine Learning; Magnetic Resonance Imaging; Male; Respiration; Spirometry - methods; Time Factors; respiration; cardiac MRI; machine learning; real‐time MRI
• ISSN: 2473-4209
• DOI: 10.1002/mp.18019

Cardiac real-time MRI (RT-MRI) in combination with MR-compatible spirometry (MRcS) offers unique opportunities to study heart-lung interactions. In contrast to other techniques that monitor respiration during MRI, MRcS provides quantitative respiratory data. Though MRcS is well tolerated, shortening of the scanning time with MRcS would be desirable, especially in young and sick patients. The aim of the study was to predict airflow and lung volume based on RT-MR images after a short learning phase of combined RT-MRI and MRcS to provide respiratory data for subsequent short axis stack-based volumetries. Cardiac RT-MRI (1.5 T; short axis; 30 frames/s) was acquired during free breathing in combination with MRcS in adult healthy subjects (n = 10). MR images with MRcS were recorded during a learning phase to collect training data. The iterative Lucas-Kanade method was applied to estimate optical flow from the captured MR images. A ridge regression model was fitted to predict airflow and thus also the lung volume from the estimated optical flow. Hyperparameters were estimated using leave-one-out cross validation and the performance was assessed on a held-out test dataset. Different durations and compositions of the learning phase were investigated to develop the most efficient measurement protocol. Coefficient of determination (R ), relative mean squared error (rMSE), Bland-Altman analysis on absolute tidal volume difference (aTVD), and absolute maximal airflow difference (aMFD) were used to validate the predictions on held-out test data. MRI combined with MRcS can train a machine learning algorithm to provide excellent predictive quantitative respiratory volume and flow for the remaining study. The optimal trade-off between predictive power and time necessary for training was reached with a shortened cardiac volumetry protocol covering only about two breaths per slice and every second slice (airflow: mean R : 0.984, mean rMSE: 0.015, Bias aMFD: -0.01 L/s with +0.084/-0.1 95% CI and volume: mean R : 0.990, mean rMSE: 0.003, Bias aTVD: 4.27 mL with +33/-24 95% CI) at a total duration of 100 s. Shorter protocols or application of the algorithm to subsequent studies in the same subject or even in different subjects still provided useful qualitative data. Machine-learning-based prediction of respiratory flow and lung volume from cardiac RT-MR images after a short training phase with MRcS is feasible and can help to shorten the time with MRcS while providing accurate respiratory data during RT-MRI.