Explainable multi-contrast deep learning model with anomaly-aware attention for reduced gadolinium dose in CE brain MRI - a feasibility study

作者：Srivathsa Pasumarthi Venkata, Ben A. Duffy, Enhao Gong, Greg Zaharchuk, Keshav Datta · 发表于：Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 年份：2023 · DOI：10.58530/2022/2438 · 研究领域：Advanced MRI Techniques and Applications、Medical Imaging Techniques and Applications、Radiomics and Machine Learning in Medical Imaging

Complementary information from multi-contrast MRI data is used in deep learning algorithms for reducing contrast dosage in brain MRI. Though existing models produce clinically equivalent post-contrast images, they lack explainability in terms of mapping the source of contrast information from input to output. In this work we explore the feasibility of an explainable deep learning model for gadolinium dose reduction in contrast-enhanced brain MRI.