Efficient GPU-Based Texture Interpolation using Uniform B-Splines

Ruijters, Daniël; Romeny, BM Bart ter Haar; Suetens, Paul

doi:10.1080/2151237x.2008.10129269

Cited by 66 publications

(44 citation statements)

References 7 publications

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“…Lensing is an expensive operation, even if easily computed in parallel, and there are a large number of maps to process until convergence is reached, and this can dominate the overall computational cost in typical runs. We found that porting the lensing on GPU, using a GPU-optimized implementation [28,29] can provide substantial speed-up. This is one of the implementations that we provide.…”

Section: B Lensing and Delensing Operationsmentioning

confidence: 99%

Maximum a posteriori CMB lensing reconstruction

2017

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Gravitational lensing of the cosmic microwave background (CMB) is a valuable cosmological signal that correlates to tracers of large-scale structure and acts as a important source of confusion for primordial B-mode polarization. State-of-the-art lensing reconstruction analyses use quadratic estimators, which are easily applicable to data. However, these estimators are known to be suboptimal, in particular for polarization, and large improvements are expected to be possible for high signal-to-noise polarization experiments. We develop a method and numerical code, LensIt, that is able to find efficiently the most probable lensing map, introducing no significant approximations to the lensed CMB likelihood, and applicable to beamed and masked data with inhomogeneous noise. It works by iteratively reconstructing the primordial unlensed CMB using a deflection estimate and its inverse, and removing residual lensing from these maps with quadratic estimator techniques. Roughly linear computational cost is maintained due to fast convergence of iterative searches, combined with the local nature of lensing. The method achieves the maximal improvement in signal to noise expected from analytical considerations on the unmasked parts of the sky. Delensing with this optimal map leads to forecast tensor-to-scalar ratio parameter errors improved by a factor ≃2 compared to the quadratic estimator in a CMB stage IV configuration.

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Section: B Lensing and Delensing Operationsmentioning

confidence: 99%

Maximum a posteriori CMB lensing reconstruction

2017

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“…The same is true for B-spline evaluation (Ruijters et al 2008). Therefore, the motion compensated backprojection can be carried out completely parallelised on the graphics card.…”

Section: Motion Compensated Reconstructionmentioning

confidence: 98%

Residual motion compensation in ECG-gated interventional cardiac vasculature reconstruction

Schwemmer¹,

Rohkohl²,

Lauritsch³

et al. 2013

Phys. Med. Biol.

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Abstract. 3-D reconstruction of cardiac vasculature from angiographic C-arm CT (rotational angiography) data is a major challenge. Motion artefacts corrupt image quality, reducing usability for diagnosis and guidance. Many state-of-theart approaches depend on retrospective ECG-gating of projection data for image reconstruction. A trade-off has to be made regarding the size of the ECG-gating window. A large temporal window is desirable to avoid undersampling. However, residual motion will occur in a large window, causing motion artefacts.We present an algorithm to correct for residual motion. Our approach is based on a deformable 2-D-2-D registration between the forward projection of an initial, ECG-gated reconstruction, and the original projection data. The approach is fully automatic and does not require any complex segmentation of vasculature, or landmarks. The estimated motion is compensated for during the backprojection step of a subsequent reconstruction. We evaluated the method using the publicly available cavarev platform and on six human clinical datasets. We found a better visibility of structure, reduced motion artefacts, and increased sharpness of the vessels in the compensated reconstructions compared to the initial reconstructions. At the time of writing, our algorithm outperforms the leading result of the cavarev ranking list. For the clinical datasets, we found an average reduction of motion artefacts by 13±6%. Vessel sharpness was improved by 25±12% on average.

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“…As shown by Sigg and Hadwiger [2005], even though the bicubic B-spline basis has the same 4×4 support, the fact that it is non-negative and separable allows it to be evaluated by combining just 4 bilinear reads at appropriately computed locations. Ruijters et al [2008] also describe a similar trick. Depending on the GPU, our implementation of this idea runs 3-6× slower than bilinear filtering, but about 2× faster than Catmull-Rom or Mitchell-Netravali filters.…”

Section: Efficient Use Of Piecewise-polynomial Kernelsmentioning

confidence: 99%