Abstract #1753

A Denoising Diffusion Probability Model for T1W contrast-enhanced Synthesis based on multi-parametric MRI

Chen Lei¹, Jing Zhang², Peian Hu³, Ruimin Li⁴, Yi Li², Rong Luo¹, Zehua Zhang¹, Huijing Xiang¹, Yuqi Duan¹, Chunxiang Li¹, Zhengrong Zhou⁵, Shuying Jia¹, Mengzhou Sun⁶, and Xiaoyun Liang²

¹Department of Radiology, Minhang Branch, Fudan University Shanghai Cancer Center, Shanghai, China, ²Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Shanghai, China, ³Department of Radiology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China, ⁴Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China, ⁵Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China, ⁶Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Beijing, China

Synopsis

Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence, T1CE, Synthesis

Motivation: T1W Contrast-enhanced (T1CE) images are obtained with gadolinium administration, but there might be adverse effects related to gadolinium retention.

Goal(s): To generated T1CE images from multi-parametric MRI (mp-MRI) without contrast agents.

Approach: T1W, T1map, SWI and QSM were obtained from patients with brain metastasis. A novel approach for generating non-contrast enhanced images from mp-MRI was proposed based on the diffusion model, which was trained in 91 cases, evaluated and test 28 and 27 cases respectively.

Results: The proposed model achieves the highest SSIM of 0.78, and the synthetic images are capable of revealing the details of brain tissues.

Impact: Multi-parametric MRI based DDPM provides a feasible approach for generating contrast enhanced images from non-contrast multi-parametric MRI, therefore circumventing the issue of adverse effects of gadolinium retention, which will benefit patients who have to undergo contrast enhanced MRI scans.

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