Abstract #4129

On-the-fly 3D images generation from a single k-space readout using a pre-learned spatial subspace: Towards MR-guided therapy and intervention

Pei Han^1,2, Fei Han³, Debiao Li^1,2,4, Anthony Christodoulou², and Zhaoyang Fan^1,2,4

¹Department of Bioengineering, UCLA, Los Angeles, CA, United States, ²Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ³Siemens Healthineers, Los Angeles, CA, United States, ⁴Department of Medicine, UCLA, Los Angeles, CA, United States

A new real-time imaging scheme is proposed based on low-rank spatiotemporal decomposition. Briefly, a high-quality spatial subspace and a direct linear mapping from k-space navigator data to subspace coordinates are first learned from a “demo” scan. In the subsequent “live” scan, successive real-time images can be generated by a fast matrix multiplication procedure on a single instance of the k-space navigator readout (e.g. a single k-space line), which can be acquired at a high temporal rate. The method was demonstrated in vivo using a T1-T2 abdominal multitasking sequence.

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