Abstract #4444

A unified framework of tumor segmentation and response assessment in patients with malignant gliomas using structural and APT-weighted MRI

Puyang Wang¹, Qianqi Huang², Jingpu Wu³, Mingchao Liu⁴, Keyi Chai⁵, Yunfan Zou⁵, Jinyuan Zhou¹, and Shanshan Jiang¹

¹Department of Radiology, Johns Hopkins University, Baltimore, MD, United States, ²Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States, ³Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States, ⁴Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States, ⁵Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States

Synopsis

Keywords: CEST / APT / NOE, CEST & MT

Motivation: Amide protein transfer-weighted (APTw) MRI has been validated to accurately detect recurrent malignant gliomas across different studies. However, APTw image interpretation is time consuming and requires professional knowledge.

Goal(s): Our goal was to develop a reliable, automated imaging diagnostic tool to assess malignant glioma response to therapies are urgently needed.

Approach: We developed and verified a unified CNN-based deep-learning framework for both tumor segmentation and tumor progression assessment by adding APTw MRI data to structural MR images as model input.

Results: The use of APTw images can improve not only diagnostic accuracy but also segmentation performance to structural MRIs.

Impact: The proposed deep-learning method could be a highly efficient solution that could help clinical experts to make precise diagnoses for patients with post-treatment gliomas.

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