Abstract #0232

Cardiac Diffusion in Python (CarDpy): An Open-Source Toolbox for Cardiac Diffusion Tensor Data Processing

Tyler E. Cork^1,2,3,4, Ariel J. Hannum^1,2,3,4, Michael Loecher^1,3,4, and Daniel B. Ennis^1,3,4

¹Department of Radiology, Stanford University, Stanford, CA, United States, ²Department of Bioengineering, Stanford University, Stanford, CA, United States, ³Division of Radiology, Veterans Administration Health Care System, Palo Alto, CA, United States, ⁴Cardiovascular Institute, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Software Tools, Tissue Characterization, Cardiac Diffusion Tensor Imaging, cDTI, Heart, Data Processing

Motivation: cDTI provides several new and useful MRI biomarkers, but robust and reliable data processing pipelines are still needed to adequately handle cDTI data.

Goal(s): Goal: To demonstrate the benefits of an open-source Python cDTI data processing toolbox and its impact on measurement accuracy and precision.

Approach: A direct averaging and tensor-fitting data processing technique was compared to our open-source data processing pipeline. Data from healthy subjects was used to demonstrate improvements in the accuracy and uncertainty of cDTI metrics.

Results: Our open-source cDTI data processing toolbox provides smoother parametric maps that are more accurate with less uncertainty compared to direct averaging and tensor-fitting.

Impact: Development of Cardiac Diffusion in Python (CarDpy), an open-source python toolbox for cardiac diffusion tensor imaging (cDTI) data processing to facilitate reproducible cDTI research for new and established researchers. A strong foundation, plus software modularity encourages contributions from the community.

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