Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans.

ABSTRACT:

Purpose

To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF).

Methods

A total of 273 [¹⁸F]-FDG PET scans were used, including data from 6 centres equipped with GE Discovery MI ToF scanners. PET data were reconstructed using the block-sequential-regularised-expectation-maximisation (BSREM) algorithm with and without ToF. The images were then split into training (n = 208), validation (n = 15), and testing (n = 50) sets. Three DL-ToF models were trained to transform non-ToF BSREM images to their target ToF images with different levels of DL-ToF strength (low, medium, high). The models were objectively evaluated using the testing set

SUBMITTER: Mehranian A 

PROVIDER: S-EPMC9399038 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature