A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI.

ABSTRACT:

Background

Hyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co-registered functional hyperpolarized gas and structural proton (¹ H)-MRI. Although acquired at similar lung inflation levels, they are frequently misaligned, requiring a lung cavity estimation (LCE). Recently, single-channel, mono-modal deep learning (DL)-based methods have shown promise for pulmonary image segmentation problems. Multichannel, multimodal approaches may outperform single-channel alternatives.

Purpose

We hypothesized that a DL-based dual-channel approach, leveraging both ¹ H-MRI and Xenon-129-MRI (¹²⁹ Xe-MRI), can generate LCEs mo

SUBMITTER: Astley JR 

PROVIDER: S-EPMC10947587 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature