A framework of whole heart extracellular volume fraction estimation for low dose cardiac CT images

Xinjian Chen; Ronald M. Summers; Marcelo Souto Nacif; Songtao Liu; David A. Bluemke; Jianhua Yao

doi:10.1117/12.911475

14 February 2012 A framework of whole heart extracellular volume fraction estimation for low dose cardiac CT images

Xinjian Chen, Ronald M. Summers, Marcelo Souto Nacif, Songtao Liu, David A. Bluemke, Jianhua Yao

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 831429 (2012) https://doi.org/10.1117/12.911475
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Cardiac magnetic resonance imaging (CMRI) has been well validated and allows quantification of myocardial fibrosis in comparison to overall mass of the myocardium. Unfortunately, CMRI is relatively expensive and is contraindicated in patients with intracardiac devices. Cardiac CT (CCT) is widely available and has been validated for detection of scar and myocardial stress/rest perfusion. In this paper, we sought to evaluate the potential of low dose CCT for the measurement of myocardial whole heart extracellular volume (ECV) fraction. A novel framework was proposed for CCT whole heart ECV estimation, which consists of three main steps. First, a shape constrained graph cut (GC) method was proposed for myocardium and blood pool segmentation for post-contrast image. Second, the symmetric Demons deformable registrations method was applied to register pre-contrast to post-contrast images. Finally, the whole heart ECV value was computed. The proposed method was tested on 7 clinical low dose CCT datasets with pre-contrast and post-contrast images. The preliminary results demonstrated the feasibility and efficiency of the proposed method.

Citation Download Citation

Xinjian Chen, Ronald M. Summers, Marcelo Souto Nacif, Songtao Liu, David A. Bluemke, and Jianhua Yao "A framework of whole heart extracellular volume fraction estimation for low dose cardiac CT images", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831429 (14 February 2012); https://doi.org/10.1117/12.911475

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