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Abstract #0881

Non-contrast Cerebral Blood Volume Images Synthesis from Arterial Spin Labeling and Non-contrast Standard MRI Using Deep Learning Network

Bao Wang1, Yongsheng Pan2, Jiaxiang Xin3, Cuiyang Wang4, and Jingzhen He5
1Qilu Hospital of Shandong University, Jinan, China, 2School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China, 3MR Research Collaboration, Siemens Healthineers Ltd, Shanghai, China, 4Radiology, The Affiliated Hospital of Qingdao University, Jinan, China, 5Radiology, Qilu Hospital of Shandong University, Jinan, China

Synopsis

Keywords: Tumors (Post-Treatment), DSC & DCE Perfusion

Motivation: High quality brain perfusion maps without contrast agent has potential values in in clinical scenarios.

Goal(s): To explore the possibility of deep learning model for synthesizing non-contrast cerebral blood volume (CBV) maps from arterial spin labeling (ASL) and non-contrast standard MRI sequences.

Approach: Quantitative CBV maps of all participants were acquired by using the DSC-PWI sequence and synthesized by training a 3D incrementable encoder-decoder network on small sample size.

Results: Deep learning model produced high-quality non-contrast CBV maps and the synthetic non-contrast CBV maps have better performance in glioma grading, prognosis prediction and differential diagnosis between tumor recurrence and treatment response than ASL.

Impact: Patients undergoing radiochemotherapy with fragile vessels or adverse reactions to gadolinium contrast could benefit from synthetic non-contrast CBV methods.

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Keywords