Abstract #1964

A deep learning based approach to generate synthetic CT images from multi-modal MRI data

Zhuoyao Xin¹, Christopher Wu², Dong Liu³, Chunming Gu^1,4,5, Jia Guo², and Jun Hua^1,4,5

¹F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ²Department of Biomedical Engineering, Columbia University, New York City, NY, United States, ³Department of Neuroscience, Columbia University, New York City, NY, United States, ⁴Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁵Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States

Synopsis

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, multi-modal MRI

Motivation: Synthetic CT is a useful technique to generate CT images from MR images. Most existing methods exploit only one single MRI modality such as T1-weighted (T1w) images.

Goal(s): We aim to develop a synthetic CT method integrating dual-channel T1w+FLAIR input images.

Approach: A dual-channel, multi-task deep learning approach based on the 3D Transformer U-net was tested using a public human brain MRI-CT dataset. Its performance was compared to single-modal T1w-based CT synthesis.

Results: Our results indicate that dual-modal T1w+FLAIR images can provide richer details, particularly in pixel-level predictions compared to single-modal synthetic CT. The improvement in morphology was moderate.

Impact: The proposed framework may be used to integrate two or more MRI modalities to improve the performance of CT image synthesis.

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