Abstract #0867

A general low-rank tensor framework for high-dimensional cardiac imaging: Application to time-resolved T1 mapping

Anthony G. Christodoulou^1,2, Jaime L. Shaw^2,3, Behzad Sharif^2,4, and Debiao Li^2,3

¹Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ³Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ⁴Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States

We present a general low-rank tensor framework for high-dimensional cardiac imaging, modeling the underlying image as partially separable in all relevant dimensions: space, cardiac phase, respiratory phase, wall-clock time (e.g., for contrast agent dynamics), variable sequence parameters (e.g., inversion time), etc. An explicit-subspace variant of the framework is demonstrated, with subspaces estimated from navigator data and a signal recovery dictionary of solutions to the Bloch equations (similar to MR fingerprinting). This variant is used to perform ECG-less cardiac- and time-resolved T₁ mapping during first-pass perfusion, as well as free-breathing, ECG-less native T₁ mapping at multiple cardiac phases. The framework shows promise for time-resolved T₁ mapping and other high-dimensional applications.

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