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Abstract #4828

Fully Automated Dynamic Contrast Enhanced MRI Image Processing Pipeline Reduces Variance in Ktrans

Lucas Saca1, Ararat Chakhoyan2, Raghav Gaggar2, Ioannis Pappas3, Arthur W. Toga3, Berislav V. Zlokovic2, Daniel A. Nation2, and Samuel Barnes1
1Radiology, Loma Linda University Medical Center, Loma Linda, CA, United States, 2Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, CA, United States, 3USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States

Synopsis

Keywords: Software Tools, DSC & DCE Perfusion

Motivation: Processing dynamic contrast-enhanced (DCE) MRI involves several preprocessing steps, making it very time-consuming and challenging to achieve consistency and reproducibility.

Goal(s): To develop an automated software pipeline for processing DCE MRI images, enhancing both the speed and consistency of DCE data processing.

Approach: We developed a DCE pipeline with preprocessing steps for motion correction, bias field removal, automatic arterial input function (AIF) selection, image alignment, z-slice normalization, segmentation, and quality control report generation.

Results: The pipeline successfully processed 400 out of 407 cases, showing significant reductions in population standard deviations for Ktrans values in gray and white matter, and AIF quality scores.

Impact: This software will facilitate more reproducible and consistent DCE processing across a variety of imaging sites and research labs.

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