Accurate Regression-Based 4D Mitral Valve Surface Reconstruction from 2D+t MRI Slices View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011

AUTHORS

Dime Vitanovski , Alexey Tsymbal , Razvan Ioan Ionasec , Michaela Schmidt , Andreas Greiser , Edgar Mueller , Xiaoguang Lu , Gareth Funka-Lea , Joachim Hornegger , Dorin Comaniciu

ABSTRACT

Cardiac MR (CMR) imaging is increasingly accepted as the gold standard for the evaluation of cardiac anatomy, function and mass. The multi-plan ability of CMR makes it a well suited modality for evaluation of the complex anatomy of the mitral valve (MV). However, the 2D slice-based acquisition paradigm of CMR limits the 4D capabilities for precise and accurate morphological and pathological analysis due to long through-put times and protracted study. In this paper we propose a new CMR protocol for acquiring MR images for 4D MV analysis. The proposed protocol is optimized regarding the number and spatial configuration of the 2D CMR slices. Furthermore, we present a learning- based framework for patient-specific 4D MV segmentation from 2D CMR slices (sparse data). The key idea with our Regression-based Surface Reconstruction (RSR) algorithm is the use of available MV models from other imaging modalities (CT, US) to train a dynamic regression model which will then be able to infer the absent information pertinent to CMR. Extensive experiments on 200 transesophageal echochardiographic (TEE) US and 20 cardiac CT sequences are performed to train the regression model and to define the CMR acquisition protocol. With the proposed acquisition protocol, a stack of 6 parallel long-axis (LA) planes, we acquired CMR patient images and regressed 4D patient-specific MV model with an accuracy of 1.5±0.2 mm and average speed of 10 sec per volume. More... »

PAGES

282-290

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-24319-6_35

DOI

http://dx.doi.org/10.1007/978-3-642-24319-6_35

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1053420121


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