Pavlos Panos | Searcharxiv

arXiv2026

Free-breathing Pulmonary Relaxometry at 0.55T

Purpose: To evaluate the feasibility of an integrated, free-breathing workflow for automated 2D pulmonary relaxometry (T1, T2) at 0.55T. Methods: A 2D inversion recovery ultra-fast balanced steady-state free precession (IR-uf-bSSFP) sequence was adapted to achieve high-temporal sampling of the transient phase at 0.55T. The technique was validated in a phantom and tested in eight healthy volunteers as well as one patient. A fully automated pipeline was developed, featuring multi-contrast registration for motion correction and deep learning based lung segmentation to enable voxel-wise nonlinear fitting for T1 and T2 map generation. Results: Phantom results were in close agreement with reference scans. In-vivo, the proposed free-breathing framework effectively mitigated respiratory motion, yielding quantitative maps in close agreement with breath-hold references. Healthy lung parenchyma relaxation times were T1 = (930+-40)ms and T2 = (90+-8)ms. In a patient case, the method successfully distinguished a solid lung mass from healthy parenchyma, with the lesion showing elevated T1 (960ms vs 810ms in the surrounding parenchyma). Conclusions: Simultaneous free-breathing T1 and T2 mapping of the lung is feasible at 0.55T using a fully automated pipeline. By eliminating breath-holds and external gating, this approach improves patient compliance and potentially facilitates the use of quantitative lung MRI in routine clinical practice.

physics.med-ph↗

arXiv2026

VQ-Wave: A physics-driven spatio-temporal deep learning approach for non-contrast-enhanced lung ventilation and perfusion MRI

Purpose: To develop a robust deep learning framework for non-contrast-enhanced functional lung MRI, overcoming the limitations of spectral decomposition in the presence of physiological non-stationarity. Methods: We introduce VQ-Wave (Ventilation/Q-perfusion Waveform-based Assessment of Variable Evolutions), a physics-driven spatio-temporal inception neural network trained on synthetic signal models to estimate ventilation and perfusion parameters. By processing local spatial context alongside temporal evolution, the network learns to decouple physiological signals from noise. The training generator simulated non-stationary dynamics, including amplitude modulations, frequency drifts, and noise. Performance was validated against matrix pencil (MP) decomposition using numerical phantoms and in-vivo lung MRI acquired in four healthy volunteers and two children with cystic fibrosis (CF) at 1.5T. Results: In numerical benchmarks, VQ-Wave demonstrated superior robustness to non-stationarity, maintaining low global and regional error rates where MP exhibited stochastic instability due to spectral leakage. In-vivo, VQ-Wave accurately captured functional defects in patients with CF yielding ventilation and perfusion maps with high quantitative stability (mean variation < 12%) even when scan time was reduced from 45s to 15s. Conversely, under irregular physiology and short scan lengths, MP decomposition severely degraded, exhibiting systematic amplitude instability, overestimation bias, and regional signal dropouts. Conclusion: VQ-Wave offers a robust, physics-driven neural network-based alternative to spectral decomposition. By effectively handling physiological irregularity and noise, it enables reliable functional lung imaging with substantially shortened acquisition protocols.

physics.med-ph↗