Leland Stanford Junior University

Yan Wu

Lei Xing

Shreyas S Vasanawala

A method for magnetic resonance imaging performs chemical shift encoded imaging to produce complex dual-echo images which are then applied (with imaging parameters) as input to a deep neural network to produce as output water-only and fat-only images. The deep neural network can be trained with ground truth water/fat images derived from chemical shift encoded images using a conventional water-fat separation algorithm such as projected power approach, IDEAL, or VARPRO. The chemical shift encoded imaging comprises performing an image acquisition with the MRI scanner via a spoiled-gradient echo sequence or a spin-echo sequence.

Deep learning-based water-fat separation from dual-echo chemical shift encoded imaging

A method for quantitative magnetic resonance imaging (MRI) includes [] performing an MRI scan using a conventional pulse sequence to obtain a qualitative MR image; and [] applying the qualitative MR image as input to a deep convolutional neural network (CNN) to produce a quantitative magnetic resonance (MR) relaxation parametric map. The qualitative MR image is the only image input to the deep neural network to produce the quantitative MR relaxation parametric map. The conventional pulse sequence may be a Spoiled Gradient Echo sequence, a Fast Spin Echo sequence, a Steady-State Free Precession sequence, or other sequence that is commonly used in current clinical practice.

Qualitative and quantitative MRI using deep learning

Retrospective magnetic resonance imaging (MRI) uses a deep neural network framework [] to generate from MRI imaging data [] acquired by an MRI apparatus using a predetermined imaging protocol tissue relaxation parametric maps and magnetic/radiofrequency field maps [] which are then used to generate using the Bloch equations [] predicted MRI images [] corresponding to imaging protocols distinct from the predetermined imaging protocol. This allows obtaining a wide spectrum of tissue contrasts distinct from those of the acquired MRI imaging data.

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Los Altos, CA, United States of America

Yan Wu