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Fast GPU 3D Diffeomorphic Image Registration
3D image registration is one of the most fundamental and computationally...
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Adaptive Regularization of Ill-Posed Problems: Application to Non-rigid Image Registration
We introduce an adaptive regularization approach. In contrast to convent...
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Constraining Volume Change in Learned Image Registration for Lung CTs
Deep-learning-based registration methods emerged as a fast alternative t...
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Fast geodesic shooting for landmark matching using CUDA
Landmark matching via geodesic shooting is a prerequisite task for numer...
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Deep Learning for Regularization Prediction in Diffeomorphic Image Registration
This paper presents a predictive model for estimating regularization par...
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A matrix-free approach to parallel and memory-efficient deformable image registration
We present a novel computational approach to fast and memory-efficient d...
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Practical and Verifiable Electronic Sortition
Existing verifiable e-sortition systems are impractical due to computati...
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A Generalized Framework for Analytic Regularization of Uniform Cubic B-spline Displacement Fields
Image registration is an inherently ill-posed problem that lacks the constraints needed for a unique mapping between voxels of the two images being registered. As such, one must regularize the registration to achieve physically meaningful transforms. The regularization penalty is usually a function of derivatives of the displacement-vector field, and can be calculated either analytically or numerically. The numerical approach, however, is computationally expensive depending on the image size, and therefore a computationally efficient analytical framework has been developed. Using cubic B-splines as the registration transform, we develop a generalized mathematical framework that supports five distinct regularizers: diffusion, curvature, linear elastic, third-order, and total displacement. We validate our approach by comparing each with its numerical counterpart in terms of accuracy. We also provide benchmarking results showing that the analytic solutions run significantly faster – up to two orders of magnitude – than finite differencing based numerical implementations.
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