Abstract #5250

Evaluation and Robustness Assessment of Cardiac Diffusion in Python (CarDpy)

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

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

Synopsis

Keywords: Myocardium, Software Tools

Motivation: Cardiac diffusion tensor imaging (cDTI) can estimate several biomarkers of myocardial remodeling. We evaluated an open-source software package for cDTI processing to enable robust data processing and promote rigorous science.

Goal(s): The goal of this study was to evaluate the open-source Cardiac Diffusion in Python (CarDpy) toolbox and identify which post-processing step (POST) most impact improved tensor metric quality.

Approach: CarDpy was evaluated at six POSTs. cDTI metrics and their corresponding uncertainties were measured across slices and assessed for significance.

Results: cDTI metrics and corresponding uncertainties displayed minimal improvements beyond shot-rejection.

Impact: CarDpy is an open-source cardiac diffusion data processing toolbox that enables community development and promotes rigorous science. Each post-processing step was evaluated to estimate the impact on improving the quality of several tensor metrics.

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