Abstract #1046

A Reconstruction Compatible, Fast and Memory Efficient Visualization Framework for Large-scale Volumetric Dynamic MRI

Cedric Yue Sik Kin¹, Frank Ong², Jonathan I Tamir^3,4, Michael Lustig³, John M Pauly², and Shreyas S Vasanawala¹

¹Radiology, Stanford University, Stanford, CA, United States, ²Electrical Engineering, Stanford University, Stanford, CA, United States, ³Electrical and Computer Sciences, UC Berkeley, Berkeley, CA, United States, ⁴Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States

We addressed the speed and memory shortcomings of conventional visualization consoles when processing high-dimensional MRI datasets by proposing a novel approach that leverages compressed representations of such datasets. We considered low rank reconstructions and operated on them directly for visualization, unlike traditional viewers which load entire uncompressed image datasets. We built a web viewer that utilizes this approach to demonstrate real time reformatting and slicing. We were able to achieve more than 15x reduction in both memory usage and loading times.

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