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27 February 2009 Efficient cross-modality cardiac four-dimensional active appearance model construction
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Abstract
The efficiency of constructing an active appearance model (AAM) is limited by establishing the independent standard via time-consuming and tedious manual tracing. It is more challenging for 3D and 4D (3D+time) datasets as the smoothness of shape and motion is essential. In this paper, a three-stage pipeline is designed for efficient cross-modality model construction. It utilizes existing AAM and active shape model (ASM) of the left ventricle (LV) for magnetic resonance (MR) datasets to build 4D AAM of the LV for real-time 3D echocardiography (RT3DE) datasets. The first AAM fitting stage uses AAM for MR to fit valid shapes onto the intensity-transformed RT3DE data that resemble low-quality MR data. The fitting is implemented in a 3D phase-by-phase fashion to prevent introducing bias due to different motion patterns related to the two modalities and patient groups. The second global-scale editing stage adjusts fitted shapes by tuning modes of ASM for MR data. The third local-scale editing stage adjusts the fitted volumes at small local regions and produces the final accurate independent standard. By visual inspection, the AAM fitting stage successfully produces results that capture the LV motion - especially its base movement - within the cardiac cycle on 29 of the 32 RT3DE datasets tested. This multi-stage approach can reduce the human effort of the manual tracing by at least 50%. With the model built for a modality A available, this approach is generalizable to constructing the model of the same organ for any other modality B.
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Honghai Zhang, Ademola K. Abiose, Elisabeth J. Buettner, Emily K. Birrer, Milan Sonka, James B. Martins, and Andreas Wahle "Efficient cross-modality cardiac four-dimensional active appearance model construction", Proc. SPIE 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 726214 (27 February 2009); https://doi.org/10.1117/12.811175
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