Deep-Learning Generation of Synthetic Intermediate Projections Improves ¹⁷⁷Lu SPECT Images Reconstructed with Sparsely Acquired Projections.

ABSTRACT: The aims of this study were to decrease the ¹⁷⁷Lu-SPECT acquisition time by reducing the number of projections and to circumvent image degradation by adding deep-learning-generated synthesized projections. Methods: We constructed a deep convolutional U-net-shaped neural network for generation of synthetic intermediate projections (CUSIPs). The number of SPECT investigations was 352 for training, 37 for validation, and 15 for testing. The input was every fourth projection of 120 acquired SPECT projections, that is, 30 projections. The output was 30 synthetic intermediate projections (SIPs) per CUSIP. SPECT images were reconstructed with 120 or 30 projections, or with 120 projections when 90 SIPs were generated from 30 projections (30-120SIPs), using 3 CUSIPs. The reconstru

SUBMITTER: Ryden T 

PROVIDER: S-EPMC8049368 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature