Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms.

ABSTRACT:

Background

¹⁸ F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is valuable for determining presence of viable tumor, but is limited by geographical restrictions, radiation exposure, and high cost.

Purpose

To generate diagnostic-quality PET equivalent imaging for patients with brain neoplasms by deep learning with multi-contrast MRI.

Study type

Retrospective.

Subjects

Patients (59 studies from 51 subjects; age 56 ± 13 years; 29 males) who underwent ¹⁸ F-FDG PET and MRI for determining recurrent brain tumor.

Field strength/sequence

3T; 3D GRE T1, 3D GRE T1c, 3D FSE T2-FLAIR, and 3D FSE ASL, ¹⁸ F-FDG PET imaging.

Assessment

Convolutional neural networks were trained using four MRIs as inputs and acquired

SUBMITTER: Ouyang J 

PROVIDER: S-EPMC10689577 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature