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Cardiac diffusion tensor imaging (cDTI) is an emerging meth-od capable of characterizing the microstructural organization of both healthy and diseased myocardium. One of the challenging aspects of a cDTI study is the associated data processing due to various acquisition imperfections that can corrupt the acquired data. We sought to investigate the role of various data processing steps by evaluating an open-source cDTI data processing software, Cardiac Diffusion in Python (CarDpy). In order to achieve this goal, healthy volunteers (N = 40) were imaged. Imaging data was evaluated at six incremental post-processing steps (POSTs) using the CarDpy pipeline. cDTI metrics such as mean diffusivity (MD), fractional anisotropy (FA), and helix angle range (HAR) were evaluated after each POST. Additionally, the uncertainties of MD (dMD), FA (dFA), and the primary eigenvector ( \(d\textbf{e}_1\) ) were evaluated to quantify the data precision. Statistical testing was performed after each POST in a comparison with the final POST. Empirical measurements of MD displayed stable trends across all POSTs, while a decrease in FA was observed with each incremental step. HAR remained stable after the integration of the POST that incorporated image registration into the data processing pipeline. Uncertainties decreased for all metrics as each incremental POST was added. dMD, dFA, and the \(d\textbf{e}_1\) had minimal improvements after the POST that incorporated shot-rejection into the data processing pipeline. Overall, this study provides an in-depth analysis pertaining to the impact of image processing on cDTI metrics and their corresponding uncertainties.

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Evaluating the Effect of Post-processing Steps When Analyzing Cardiac Diffusion Tensor Data

  • 会议论文

  • 发表日期：

    2025年

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  • Tyler E. Cork,
  • Ariel J. Hannum,
  • Michael Loecher,
  • Luigi E. Perotti,
  • Daniel B. Ennis

摘要

Cardiac diffusion tensor imaging (cDTI) is an emerging meth-od capable of characterizing the microstructural organization of both healthy and diseased myocardium. One of the challenging aspects of a cDTI study is the associated data processing due to various acquisition imperfections that can corrupt the acquired data. We sought to investigate the role of various data processing steps by evaluating an open-source cDTI data processing software, Cardiac Diffusion in Python (CarDpy). In order to achieve this goal, healthy volunteers (N = 40) were imaged. Imaging data was evaluated at six incremental post-processing steps (POSTs) using the CarDpy pipeline. cDTI metrics such as mean diffusivity (MD), fractional anisotropy (FA), and helix angle range (HAR) were evaluated after each POST. Additionally, the uncertainties of MD (dMD), FA (dFA), and the primary eigenvector ( \(d\textbf{e}_1\) ) were evaluated to quantify the data precision. Statistical testing was performed after each POST in a comparison with the final POST. Empirical measurements of MD displayed stable trends across all POSTs, while a decrease in FA was observed with each incremental step. HAR remained stable after the integration of the POST that incorporated image registration into the data processing pipeline. Uncertainties decreased for all metrics as each incremental POST was added. dMD, dFA, and the \(d\textbf{e}_1\) had minimal improvements after the POST that incorporated shot-rejection into the data processing pipeline. Overall, this study provides an in-depth analysis pertaining to the impact of image processing on cDTI metrics and their corresponding uncertainties.