A deep learning method for translating 3DCT to SPECT ventilation imaging: First comparison with ^81m Kr-gas SPECT ventilation imaging.

ABSTRACT:

Purpose

This study aimed to evaluate the accuracy of deep learning (DL)-based computed tomography (CT) ventilation imaging (CTVI).

Methods

A total of 71 cases that underwent single-photon emission CT ^81m Kr-gas ventilation (SPECT V) and CT imaging were included. Sixty cases were assigned to the training and validation sets, and the remaining 11 cases were assigned to the test set. To directly transform three-dimensional (3D) CT (free-breathing CT) images to SPECT V images, a DL-based model was implemented based on the U-Net architecture. The input and output data were 3DCT- and SPECT V-masked, respectively, except for whole-lung volumes. These data were rearranged in voxel size, registered rigidly, cropped, and normalized in preprocessing. In addition to a standar

SUBMITTER: Kajikawa T 

PROVIDER: S-EPMC9545310 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature