Cedars-sinai Medical Center

Siemens Healthcare Gmbh

Christopher Nguyen

Debiao Li

Zhaoyang Fan

Anthony Christodoulou

Yibin Xie

Jaime Shaw

Zhengwei Zhou

Zixin Deng

Sen Ma

Nan Wang

Gerhard Laub

Xiaoming Bi

Yutaka Natsuaki

A method of performing multidimensional magnetic resonance imaging on a subject comprises collecting imaging data for a region of interest of the subject, the imaging data related to one or more spatially-varying parameters of the subject within the region of interest; collecting auxiliary data for the region of interest in the subject, the auxiliary data related to one or more time-varying parameters of the subject within the region of interest; linking the imaging data and the auxiliary data; and constructing an image tensor with one or more temporal dimensions based on at least a portion of the linked imaging data and at least a portion of the linked auxiliary data.

Low-rank tensor imaging for quantitative MRI

Embodiments can provide a computer-implemented method for free breathing three dimensional diffusion imaging, the method comprising initiating, via a k-space component processor, diffusion/T2 preparation, comprising generating diffusion contrast; and adjusting one or more of amplitude, duration, and polarity to set a first order moment; applying, via an image data processor, a stack of stars k-space ordering, comprising acquiring a radial/spiral view for all members of a plurality of partitions in a partition-encoding direction; increasing an azimuthal angle until a complete set of radial/spiral views are sampled; and applying diffusion gradients along each of three axis simultaneously; and calculating, via the image data processor, an apparent diffusion coefficient map.

Free-breathing 3D body diffusion imaging using motion-compensated diffusion preparation and motion robust readout

A new low rank tensor (LRT) imaging strategy/methodology, specifically for quantitative cardiovascular magnetic resonance (CMR) multitasking, includes performing a low-rank tensor image model exploiting image correlation along multiple physiological and physical time dimensions, a non-ECG data acquisition strategy featuring minimal gaps in acquisition and frequent collection of auxiliary subspace training data, and a factored tensor reconstruction approach which enforces the LRT model.

Low-rank tensor imaging for multidimensional cardiovascular MRI

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Hollywood, CA, United States of America

Christopher Nguyen