Abstract #4344

Iterative De-contrasted Image Registration Improves Quantification of Extracellular Volume and Fractional Myocardial Blood Volume

Meng Lu¹, Mostafa Mahaoudi^2,3, Kim-Lien Nguyen^2,3, and Yibin Xie¹

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Department of Radiology and Bioengineering, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States, ³Division of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles, Los Angeles, CA, United States

Synopsis

Keywords: Myocardium, Multi-Contrast, fMBV, ECV

Motivation: Myocardial Extracellular Volume (ECV) and Fractional Myocardial Blood Volume (fMBV) maps, derived from T1 mapping before and after exogenous contrast agent administration, are highly susceptible to image mis-registration.

Goal(s): We propose a de-contrast preprocessing step that minimizes contrast variance in regions of interest to improve overall group-wise image registration and mapping accuracy.

Approach: An optimized de-contrasting and image registration framework was designed and applied to 11 ECV and 9 fMBV datasets. Registration results and resultant maps with and without de-contrast preprocessing were compared for accuracy and precision.

Results: De-contrasting improved image registration accuracy and reduced parametric map variance in myocardial ECV and fMBV.

Impact: Iterative de-contrast preprocessing is an effective approach for improving the accuracy of group-wise image registration and quality of parametric mapping in the myocardium in the setting of contrast enhancement.

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