Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT.

ABSTRACT:

Purpose

Deep-learning-based attenuation correction (AC) for SPECT includes both indirect and direct approaches. Indirect approaches generate attenuation maps (μ-maps) from emission images, while direct approaches predict AC images directly from non-attenuation-corrected (NAC) images without μ-maps. For dedicated cardiac SPECT scanners with CZT detectors, indirect approaches are challenging due to the limited field-of-view (FOV). In this work, we aim to 1) first develop novel indirect approaches to improve the AC performance for dedicated SPECT; and 2) compare the AC performance between direct and indirect approaches for both general purpose and dedicated SPECT.

Methods

For dedicated SPECT, we developed strategies to predict truncated μ-maps from NAC images reconstructed with

SUBMITTER: Chen X 

PROVIDER: S-EPMC9253078 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature