Pelvic PET/MR attenuation correction in the image space using deep learning.

ABSTRACT:

Introduction

The five-class Dixon-based PET/MR attenuation correction (AC) model, which adds bone information to the four-class model by registering major bones from a bone atlas, has been shown to be error-prone. In this study, we introduce a novel method of accounting for bone in pelvic PET/MR AC by directly predicting the errors in the PET image space caused by the lack of bone in four-class Dixon-based attenuation correction.

Methods

A convolutional neural network was trained to predict the four-class AC error map relative to CT-based attenuation correction. Dixon MR images and the four-class attenuation correction µ-map were used as input to the models. CT and PET/MR examinations for 22 patients ([¹⁸F]FDG) were used for training and validation, and 17

SUBMITTER: Abrahamsen BS 

PROVIDER: S-EPMC10484800 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature