Synthetic PET via Domain Translation of 3D MRI

by   Abhejit Rajagopal, et al.

Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this paper we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly-available whole-body MRI. Specifically, we use a dataset of 56 ^18F-FDG-PET/MRI exams to train a 3D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI. In training we implemented a balanced loss function to generate realistic uptake across a large dynamic range and computed losses along tomographic lines of response to mimic the PET acquisition. The predicted PET images are forward projected to produce synthetic PET time-of-flight (ToF) sinograms that can be used with vendor-provided PET reconstruction algorithms, including using CT-based attenuation correction (CTAC) and MR-based attenuation correction (MRAC). The resulting synthetic data recapitulates physiologic ^18F-FDG uptake, e.g. high uptake localized to the brain and bladder, as well as uptake in liver, kidneys, heart and muscle. To simulate abnormalities with high uptake, we also insert synthetic lesions. We demonstrate that this synthetic PET data can be used interchangeably with real PET data for the PET quantification task of comparing CT and MR-based attenuation correction methods, achieving ≤ 7.6% error in mean-SUV compared to using real data. These results together show that the proposed synthetic PET data pipeline can be reasonably used for development, evaluation, and validation of PET/MRI reconstruction methods.


page 1

page 2

page 4

page 5

page 7

page 8


Deep Boosted Regression for MR to CT Synthesis

Attenuation correction is an essential requirement of positron emission ...

Deep Learning Body Region Classification of MRI and CT examinations

Standardized body region labelling of individual images provides data th...

SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy

Purpose: Medical imaging has become increasingly important in diagnosing...

A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images

CBCT images suffer from acute shading artifacts primarily due to scatter...

Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network

In this work, we present a method for synthetic CT (sCT) generation from...

Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning

The ability to synthesise Computed Tomography images - commonly known as...

A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation using Deep Learning

In PET/CT imaging, CT is used for PET attenuation correction (AC). Misma...

Please sign up or login with your details

Forgot password? Click here to reset