Mr. Yudi Sang Profile

Yudi Sang

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SPIE Journal Paper | 28 December 2020

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

Yudi Sang, Xianglei Xing, YingNian Wu, Dan Ruan

JMI, Vol. 7, Issue 06, 064005, (December 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.6.064005

KEYWORDS: Image registration, 3D modeling, Statistical modeling, Image segmentation, Cardiovascular magnetic resonance imaging, 3D image processing, Lung, Computed tomography, Tissues, Digital imaging

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Purpose: Deformable registration problems are conventionally posed in a regularized optimization framework, where balance between fidelity and prescribed regularization usually needs to be 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 proposed to incorporate a spatially variant deformation prior into image registration framework using a statistical generative model. Approach: 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 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 SimpleElastix, DIRNet, and Voxelmorph. Results: Experiments with synthetic images and simulated CTs showed that our method yielded registration errors significantly lower than SimpleElastix and DIRNet. Experiments with cardiac magnetic resonance images showed that the method encouraged physical and physiological feasibility of deformation. Evaluation with left ventricle contours showed that our method achieved a dice of (0.93 ± 0.03) with significant improvement over all SimpleElastix options, DIRNet, and VoxelMorph. Mean average surface distance was on millimeter level, comparable to the best SimpleElastix setting. The average 3D registration time was 12.78 s, faster than 24.70 s in SimpleElastix. Conclusions: The learned implicit parametrization could be an efficacious alternative to regularized B-spline model, more flexible in admitting spatial heterogeneity.

Proceedings Article | 10 March 2020 Paper

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

Yudi Sang, Xianglei Xing, Yingnian Wu, Dan Ruan

Proceedings Volume 11313, 113132V (2020) https://doi.org/10.1117/12.2549193

KEYWORDS: Image registration, Statistical modeling, Tissues, Motion models, Magnetic resonance imaging, Deconvolution, Digital imaging, Cardiovascular magnetic resonance imaging, Optimization (mathematics)

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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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