Robust left ventricular myocardium segmentation for multi-protocol MR

A. Groth; J. Weese; H. Lehmann

doi:10.1117/12.911201

14 February 2012 Robust left ventricular myocardium segmentation for multi-protocol MR

A. Groth, J. Weese, H. Lehmann

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83142S (2012) https://doi.org/10.1117/12.911201
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

For a number of cardiac procedures like the treatments of ventricular tachycardia (VT), coronary artery disease (CAD) and heart failure (HF) both anatomical as well as vitality information about the left ventricular myocardium are required. To this end, two images for the anatomical and functional information, respectively, must be acquired and analyzed, e.g. using two different 3D MR protocols. To enable automatic analysis, a workflow has been proposed1 which allows to integrate the vitality information extracted from the functional image data into a patient-specific anatomical model generated from the anatomical image. However, in the proposed workflow the extraction of accurate vitality information from the functional image depends to a large extend on the accuracy of both the anatomical model and the mapping of the model to the functional image. In this paper we propose and evaluate methods for improving these two aspects. More specifically, on one hand we aim to improve the segmentation of the often low-contrast left ventricular epicardium in the anatomical 3D MR images by introducing a patient-specific shape-bias. On the other hand, we introduce a registration approach that facilitates the mapping of the anatomical model to images acquired by different protocols and modalities, such as functional 3D MR. The new methods are evaluated on clinical MR data, for which considerable improvements can be achieved.

Citation Download Citation

A. Groth, J. Weese, and H. Lehmann "Robust left ventricular myocardium segmentation for multi-protocol MR", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142S (14 February 2012); https://doi.org/10.1117/12.911201

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