MS-VMANet: A Multi-Scale VMamba Attention Registration Network for Efficient Assessment of Regional Pulmonary Ventilation Function from 4DCT

Abstract

The evaluation of regional pulmonary ventilation function is of significant clinical value, particularly in the initial diagnosis of pulmonary disorders, staging assessment, and personalized treatment planning. This study proposes a multi-scale VMamba attention registration network (MS-VMANet) to predict 4DCT pulmonary ventilation changes using unsupervised learning registration. MS-VMANet primarily integrates the efficient visual mamba attention, which captures long-range feature information globally, and the multi-head dilated regional attention improves deformation field prediction via aggregating multi-scale contextual features through dilated convolutions and attention mechanisms. Then, the deformation fields were calculated using the Jacobian determinant to generate images that reflect lung ventilation distribution to assess regional lung ventilation function. According to the experimental findings, the MS-VMANet performs better in terms of registration accuracy and performance, providing a reliable technical means for assessing regional pulmonary ventilation function.