A New Variational Model for Joint Image Reconstruction and Motion Estimation in Spatiotemporal Imaging

Chen, Chong; Gris, Barbara; Öktem, Ozan

doi:10.1137/18m1234047

Cited by 23 publications

(34 citation statements)

References 57 publications

(147 reference statements)

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“…The variational model is among the most successful and accurate approaches to calculate a deformation between two images [3]. Given a specific regularization term, such a model has a clear mathematical structure and it is also well understood which mathematical space the solution lies in, e.g., Hilbert space [4]- [6], bounded variation [7], [8], etc. However, the variational model has limitations: (1) For each image pair, the hyper-parameter λ needs to be tuned carefully to deliver a precise deformation.…”

Section: Take Down Policymentioning

confidence: 99%

“…This is followed by a deformable transformation which has more degrees of freedom as well as higher capability to describe local deformations. There is a wide range of classical variational methods to account for local deformations such as diffusion models [4], total variation models [7], [8], fluid models [5], [6], elastic models [24]- [26], biharmonic (linear curvature) models [27], [28], mean curvature models [29], [30], optical flow models [3], [31], [32], fractional-order variation models [33], [34], non-local graph models [35]- [37], etc. The free-form deformation (FFD) methods based on B-splines model [38], [39] are able to accurately model global and local deformations with fewer degrees of freedom parameterized by control points.…”

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confidence: 99%

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Learning a Model-Driven Variational Network for Deformable Image Registration

Jia

Thorley

Chen

et al. 2022

IEEE Trans. Med. Imaging

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Data-driven deep learning approaches to image registration can be less accurate than conventional iterative approaches, especially when training data is limited. To address this issue and meanwhile retain the fast inference speed of deep learning, we propose VR-Net, a novel cascaded variational network for unsupervised deformable image registration. Using a variable splitting optimization scheme, we first convert the image registration problem, established in a generic variational framework, into two sub-problems, one with a point-wise, closed-form solution and the other one being a denoising problem. We then propose two neural layers (i.e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net (termed generalized denoising layer) to formulate the denoising problem. Finally, we cascade the three neural layers multiple times to form our VR-Net. Extensive experiments on three (two 2D and one 3D) cardiac magnetic resonance imaging datasets show that VR-Net outperforms state-of-the-art deep learning methods on registration accuracy, whilst maintaining the fast inference speed of deep learning and the data-efficiency of variational models.

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Section: Take Down Policymentioning

confidence: 99%

Section: Take Down Policymentioning

confidence: 99%

Learning a Model-Driven Variational Network for Deformable Image Registration

Jia

Thorley

Chen

et al. 2022

IEEE Trans. Med. Imaging

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show abstract

“…However, the cost function is still nonconvex in the intensity-based non-rigid image registration problem, so the OT algorithm can be trapped in a local optimum and the solution is highly influenced by noise in the images. Burger et al proposed a variational model for joint motion estimation and image reconstruction [12], and Chen et al further extended this method to simulated tomographic images [13].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Joint PET Image Reconstruction and Motion Estimation

Zhang

et al. 2022

IEEE Trans. Med. Imaging

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Section: Introductionmentioning

confidence: 99%

Learning a Model-Driven Variational Network for Deformable Image Registration

Jia¹,

Thorley²,

Chen³

et al. 2021

Preprint

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Data-driven deep learning approaches to image registration can be less accurate than conventional iterative approaches, especially when training data is limited. To address this whilst retaining the fast inference speed of deep learning, we propose VR-Net, a novel cascaded variational network for unsupervised deformable image registration. Using the variable splitting optimization scheme, we first convert the image registration problem, established in a generic variational framework, into two sub-problems, one with a point-wise, closed-form solution while the other one is a denoising problem. We then propose two neural layers (i.e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i.e. generalized denoising layer). Finally, we cascade the warping layer, intensity consistency layer, and generalized denoising layer to form the VR-Net. Extensive experiments on three (two 2D and one 3D) cardiac magnetic resonance imaging datasets show that VR-Net outperforms state-of-the-art deep learning methods on registration accuracy, while maintains the fast inference speed of deep learning and the data-efficiency of variational model.

show abstract

A New Variational Model for Joint Image Reconstruction and Motion Estimation in Spatiotemporal Imaging

Cited by 23 publications

References 57 publications

Learning a Model-Driven Variational Network for Deformable Image Registration

Learning a Model-Driven Variational Network for Deformable Image Registration

Deep Learning Based Joint PET Image Reconstruction and Motion Estimation

Learning a Model-Driven Variational Network for Deformable Image Registration

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