Cross-vender, cross-tracer, and cross-protocol deep transfer learning for attenuation map generation of cardiac SPECT.

ABSTRACT: It has been proved feasible to generate attenuation maps (μ-maps) from cardiac SPECT using deep learning. However, this assumed that the training and testing datasets were acquired using the same scanner, tracer, and protocol. We investigated a robust generation of CT-derived μ-maps from cardiac SPECT acquired by different scanners, tracers, and protocols from the training data. We first pre-trained a network using 120 studies injected with ^99mTc-tetrofosmin acquired from a GE 850 SPECT/CT with 360-degree gantry rotation, which was then fine-tuned and tested using 80 studies injected with ^99mTc-sestamibi acquired from a Philips BrightView SPECT/CT with 180-degree gantry rotation. The error between ground-truth and predicted μ-maps by transfer learning was 5.13 ± 7.02%

SUBMITTER: Chen X 

PROVIDER: S-EPMC11407548 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature