Model-less multi-input analysis of pulmonary blood flow using deep learning convolution

Abstract

The study investigates two categories of perfusion-based pulmonary blood flow analysis: model-based and model-less methods. The model-based approach yields plausible results, but requires strict parameter settings and presents challenges in handling. On the other hand, the model-less approach is simpler but limited to a single input analysis, necessitating an inverse problem to estimate the impulse response from input–output relationships. To overcome these limitations, this article proposes a model-less method that combines simplicity and accuracy, enabling multi-input system analysis and aiming for standardized analysis. They leverage deep learning convolution to directly estimate the impulse response, allowing for multi-input analysis. Comparative experiments demonstrate that the proposed method is easy to implement and exhibits a low estimation error within the measured signal-to-noise ratio (SNR) range, even though it is sensitive to noise. Furthermore, the proposed method is evaluated through waveform analysis, specifically Delay and Dispersion in Experiment 1, where it is compared with conventional methods. In Experiment 2, blood flow analysis is performed on a patient with a defect in the left pulmonary artery. The results indicate high convergence, independence from input waveforms, and effective analysis of cases with vascular stenosis. Moreover, the method enables multi-input system analysis, consistently yielding results consistent with medical findings, even for patients with left pulmonary artery defects.