Imposing implicit feasibility constraints on deformable image registration using a statistical generative model

Yudi Sang; Xianglei Xing; Yingnian Wu; Dan Ruan

doi:10.1117/12.2549193

10 March 2020 Imposing implicit feasibility constraints on deformable image registration using a statistical generative model

Yudi Sang, Xianglei Xing, Yingnian Wu, Dan Ruan

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113132V (2020) https://doi.org/10.1117/12.2549193
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Deformable registration problems are conventionally posed in a regularized optimization framework, where balance between fidelity and prescribed regularization usually needs to be manually tuned for each case. Even so, using a single weight to control regularization strength may be insufficient to reflect spatially variant tissue properties and limit registration performance. In this study, we propose to incorporate a spatially variant deformation prior into image registration framework using a statistical generative model. A generator network is trained in an unsupervised setting to maximize the likelihood of observing the moving and fixed image pairs, using an alternating back-propagation approach. The trained generative model imposes constraints on deformation and serves as an effective low dimensional deformation parametrization. During registration, optimization is performed over this learned parametrization, eliminating the need for explicit regularization and tuning. The proposed method was tested against a B-spline optimization method SimpleElastix, and an end-to-end learning method DIRNet. Experiment with synthetic images shows that our method yielded a registration error of (0.70±0.05) pixels, significantly lower than (0.86±0.12) pixels in SimpleElastix and (0.81±0.06) pixels in DIRNet. Experiment with 2D cardiac MR images demonstrates that the method completed registration with physically and physiologically more feasible deformations and the performance was close to the best of manually tuned results when evaluated with segmentation masks. The average registration time was 1.72 s, faster than 5.63 s in SimpleElastix.

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Yudi Sang, Xianglei Xing, Yingnian Wu, and Dan Ruan "Imposing implicit feasibility constraints on deformable image registration using a statistical generative model", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132V (10 March 2020); https://doi.org/10.1117/12.2549193

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KEYWORDS

Image registration

Statistical modeling

Motion models

Tissues

Cardiovascular magnetic resonance imaging

Deconvolution

Digital imaging

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