Parallel and memory efficient multimodal image registration for radiotherapy using normalized gradient fields

König, Lars; Derksen, Alexander; Hallmann, Marc; Papenberg, Nils

doi:10.1109/isbi.2015.7163977

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…As basis for the registration algorithm, a highly parallel registration framework with low memory consumption has been used [ 23 , 25 ]. In the following, a general description of the registration algorithm is given, a more detailed mathematical description of the presented framework is part of the Additional file 1 of this article.…”

Section: Methodsmentioning

confidence: 99%

Deformable image registration for adaptive radiotherapy with guaranteed local rigidity constraints

et al. 2016

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BackgroundDeformable image registration (DIR) is a key component in many radiotherapy applications. However, often resulting deformations are not satisfying, since varying deformation properties of different anatomical regions are not considered. To improve the plausibility of DIR in adaptive radiotherapy in the male pelvic area, this work integrates a local rigidity deformation model into a DIR algorithm.MethodsA DIR framework is extended by constraints, enforcing locally rigid deformation behavior for arbitrary delineated structures. The approach restricts those structures to rigid deformations, while surrounding tissue is still allowed to deform elastically. The algorithm is tested on ten CT/CBCT male pelvis datasets with active rigidity constraints on bones and prostate and compared to the Varian SmartAdapt deformable registration (VSA) on delineations of bladder, prostate and bones.ResultsThe approach with no rigid structures (REG0) obtains an average dice similarity coefficient (DSC) of 0.87 ± 0.06 and a Hausdorff-Distance (HD) of 8.74 ± 5.95 mm. The new approach with rigid bones (REG1) yields a DSC of 0.87 ± 0.07, HD 8.91 ± 5.89 mm. Rigid deformation of bones and prostate (REG2) obtains 0.87 ± 0.06, HD 8.73 ± 6.01 mm, while VSA yields a DSC of 0.86 ± 0.07, HD 10.22 ± 6.62 mm. No deformation grid foldings are observed for REG0 and REG1 in 7 of 10 cases; for REG2 in 8 of 10 cases, with no grid foldings in prostate, an average of 0.08 % in bladder (REG2: no foldings) and 0.01 % inside the body contour. VSA exhibits grid foldings in each case, with an average percentage of 1.81 % for prostate, 1.74 % for bladder and 0.12 % for the body contour. While REG1 and REG2 keep bones rigid, elastic bone deformations are observed with REG0 and VSA. An average runtime of 26.2 s was achieved with REG1; 31.1 s with REG2, compared to 10.5 s with REG0 and 10.7 s with VMS.ConclusionsWith accuracy in the range of VSA, the new approach with constraints delivers physically more plausible deformations in the pelvic area with guaranteed rigidity of arbitrary structures. Although the algorithm uses an advanced deformation model, clinically feasible runtimes are achieved.Electronic supplementary materialThe online version of this article (doi:10.1186/s13014-016-0697-4) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Deformable image registration for adaptive radiotherapy with guaranteed local rigidity constraints

et al. 2016

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“…To clearly demonstrate the procedure, let ( , ) represent a pixel sampling from , where , denotes as the coordinates of the corresponding pixel. Then the affine transformation can be expressed as： = 11 12 13 21 22 23…”

Section: A Affine Transformation Network -Atnetmentioning

confidence: 99%

“…ulti-modal medical imaging plays an important role in many clinical applications [1][2][3][4][5][6][7][8][9][10][11], such as image-guided intervention, disease diagnosis, and treatment planning. Among them, multi-contrast magnetic resonance (MR) imaging is one of the most prevalent techniques utilized in brain imaging as different MR imaging sequences can highlight different regions of interest.…”

Section: Introductionmentioning

confidence: 99%

A Coarse-to-Fine Deformable Transformation Framework for Unsupervised Multi-Contrast MR Image Registration with Dual Consistency Constraint

Huang

Yang

Liu

et al. 2021

IEEE Trans. Med. Imaging

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Multi-contrast magnetic resonance (MR) image registration is essential in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning. Nevertheless, the efficiency and performance of the existing registration algorithms can still be improved. In this paper, we propose a novel unsupervised learning-based framework to achieve accurate and efficient multi-contrast MR image registrations. Specifically, an end-to-end coarse-to-fine network architecture consisting of affine and deformable transformations is designed to get rid of both the multi-step iteration process and the complex image preprocessing operations. Furthermore, a dual consistency constraint and a new prior knowledge-based loss function are developed to enhance the registration performances. The proposed method has been evaluated on a clinical dataset that consists of 555 cases, with encouraging performances achieved. Compared to the commonly utilized registration methods, including Voxelmorph, SyN, and LDDMM, the proposed method achieves the best registration performance with a Dice score of 0.826 in identifying stroke lesions. More robust performance in low-signal areas is also observed. With regards to the registration speed, our method is about 17 times faster than the most competitive method of SyN when testing on a same CPU.

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“…The matrix ∇ 2 J should approximate the Hessian ∇ 2 J(y k ). Here we consider the Gauss-Newton scheme and the L-BFGS scheme, which have both been used in different image registration applications [50,25,43]. The minimization is embedded in a coarse-to-fine multi-level scheme, where the problem is solved on consecutively finer deformation and image grids.…”

Section: Numerical Optimizationmentioning

confidence: 99%

“…• We study the use of efficient matrix-free techniques for derivative calculations in order to improve computational efficiency (Section 4). In particular, we present fully matrix-free computation rules, based on the work on affinelinear image registration in [44] and deformable registration in [28,25], for objective function gradient computations (Section 4.1.1) and Gauss-Newton Hessian-vector multiplications (Section 4.1.2). • We perform a theoretical analysis of the proposed approach in terms of computational effort and memory usage (Section 5).…”

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confidence: 99%

A Matrix-Free Approach to Parallel and Memory-Efficient Deformable Image Registration

König¹,

Rühaak²,

Derksen³

et al. 2018

SIAM J. Sci. Comput.

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We present a novel computational approach to fast and memory-efficient deformable image registration. In the variational registration model, the computation of the objective function derivatives is the computationally most expensive operation, both in terms of runtime and memory requirements. In order to target this bottleneck, we analyze the matrix structure of gradient and Hessian computations for the case of the normalized gradient fields distance measure and curvature regularization. Based on this analysis, we derive equivalent matrix-free closed-form expressions for derivative computations, eliminating the need for storing intermediate results and the costs of sparse matrix arithmetic. This has further benefits: (1) matrix computations can be fully parallelized, (2) memory complexity for derivative computation is reduced from linear to constant, and (3) overall computation times are substantially reduced.In comparison with an optimized matrix-based reference implementation, the CPU implementation achieves speedup factors between 3.1 and 9.7, and we are able to handle substantially higher resolutions. Using a GPU implementation, we achieve an additional speedup factor of up to 9.2.Furthermore, we evaluated the approach on real-world medical datasets. On ten publicly available lung CT images from the DIR-Lab 4DCT dataset, we achieve the best mean landmark error of 0.93 mm compared to other submissions on the DIR-Lab website, with an average runtime of only 9.23 s. Complete non-rigid registration of full-size 3D thorax-abdomen CT volumes from oncological follow-up is achieved in 12.6 s. The experimental results show that the proposed matrix-free algorithm enables the use of variational registration models also in applications which were previously impractical due to memory or runtime restrictions.

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Parallel and memory efficient multimodal image registration for radiotherapy using normalized gradient fields

Cited by 6 publications

References 24 publications

Deformable image registration for adaptive radiotherapy with guaranteed local rigidity constraints

Deformable image registration for adaptive radiotherapy with guaranteed local rigidity constraints

A Coarse-to-Fine Deformable Transformation Framework for Unsupervised Multi-Contrast MR Image Registration with Dual Consistency Constraint

A Matrix-Free Approach to Parallel and Memory-Efficient Deformable Image Registration

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