Towards statistically optimal interpolation for 3D medical imaging

Parrott, R.W.; Stytz, Martin R.; Amburn, P.; Robinson, D. F.

doi:10.1109/51.232341

Cited by 42 publications

(21 citation statements)

References 20 publications

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“…Trilinear interpolation is considered the gold standard for calculating DRRs (Galwin 1995, McGee et al 1995. More expensive non-linear interpolation schemes have been studied (Maeland 1988, Grevera & Udupa 1998, Parrot et al 1993, Joliot & Mazoyer 1993, Grevera & Udupa 1996, but have not yet replaced tri-linear interpolation in clinical visualization nor received much attention in medical volume visualization research.…”

Section: Verification Visualization For Radiotherapymentioning

confidence: 99%

“…Parrot and coworkers compared different interpolation algorithms for MR image reformatting, and, continued by Metchik and coworkers, found kriging to be the best interpolation tool for 3d MR image data (Parrot et al 1993, Metchik et al 1994. Kriging uses a variogram, the correlation intensity as a function of distance, to calculate the interpolation weights.…”

Section: Preprocessing Of Magnetic Resonance Imagesmentioning

confidence: 99%

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Algorithms for modeling anatomic and target volumes in image‐guided neurosurgery and radiotherapy

Alakuijala¹

2002

Medical Physics

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The use of image-guidance in surgery and radiotherapy has significantly improved patient outcome in neurosurgery and radiotherapy treatments. This work developed volume definition and verification techniques for image-guided applications, using a number of algorithms ranging from image processing to visualization.Stereoscopic visualization, volumetric tumor model overlaid on an ultrasound image, and visualization of the treatment geometry were experimented with on a neurosurgical workstation. Visualization and volume definition tools were developed for radiotherapy treatment planning system.The magnetic resonance inhomogeneity correction developed in this work, possibly the first published data-driven method with wide applicability, automatically mitigates the RF field inhomogeneity artefact present in magnetic resonance images. Correcting the RF inhomogeneity improves the accuracy of the generated volumetric models.Various techniques to improve region growing are also presented. The simplex search method and combinatory similarity terms were used to improve the similarity function with a low additional computational cost and high yield in region correctness. Moreover, the effects of different priority queue implementations were studied.A fast algorithm for calculating high-quality digitally reconstructed radiographs has been developed and shown to better meet typical radiotherapy needs than the two alternative algorithms. A novel visualization method, beam's light view, is presented. It uses texture mapping for projecting the fluence of a radiation field on an arbitrary surface.This work suggests several improved algorithms for image processing, segmentation, and visualization used in image-guided treatment systems. The presented algorithms increase the accuracy of image-guidance, which can further improve the applicability and efficiency of imageguided treatments.

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Section: Verification Visualization For Radiotherapymentioning

confidence: 99%

Section: Preprocessing Of Magnetic Resonance Imagesmentioning

confidence: 99%

Algorithms for modeling anatomic and target volumes in image‐guided neurosurgery and radiotherapy

Alakuijala¹

2002

Medical Physics

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“…In this paper, we resort to the MM approach [30] as it is easy to comprehend: The idea, as described in [30], is to replace the original difficult task by several easy-to-optimize problems that will guarantee a monotonic decrease of the original cost. We briefly review from [30] the mathematical details underlying the MM philosophy in Section IV-B.1 and then apply it to the following two instances of convex nonquadratic cost 3 .…”

Section: B Nonquadratic Costsmentioning

confidence: 99%

Regularized Interpolation for Noisy Images

Ramani

Thévenaz

Unser

2010

IEEE Trans. Med. Imaging

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Abstract-Interpolation is the means by which a continuously defined model is fit to discrete data samples. When the data samples are exempt of noise, it seems desirable to build the model by fitting them exactly. In medical imaging, where quality is of paramount importance, this ideal situation unfortunately does not occur. In this paper, we propose a scheme that improves on the quality by specifying a tradeoff between fidelity to the data and robustness to the noise. We resort to variational principles, which allow us to impose smoothness constraints on the model for tackling noisy data. Based on shift-, rotation-, and scale-invariant requirements on the model, we show that the -norm of an appropriate vector derivative is the most suitable choice of regularization for this purpose. In addition to Tikhonov-like quadratic regularization, this includes edge-preserving total-variation-like (TV) regularization. We give algorithms to recover the continuously defined model from noisy samples and also provide a data-driven scheme to determine the optimal amount of regularization. We validate our method with numerical examples where we demonstrate its superiority over an exact fit as well as the benefit of TV-like nonquadratic regularization over Tikhonov-like quadratic regularization.

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“…This means that distant samples lose importance and eventually can be ignored in the estimator while closer samples, which are more likely to be part of the same body and to have similar realization values, increase their relative importance. Its application to the interpolation of 3D scalar medical images has been referred elsewhere [9]. Here the method is used in the interpolation of displacement fields considering each spatial component independently.…”

Section: Interpolation: the Kriging Estimatormentioning

confidence: 99%

“…Substituting them into (8) and making some manipulations its possible to derive the well-known correlation coefficient similarity measure (9), whose absolute value is to be maximized and where…”

Section: Similarity Functionsmentioning

confidence: 99%

Nonrigid Registration of 3D Scalar, Vector and Tensor Medical Data

Ruiz-Alzola

Westin

Warfield

et al. 2000

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000

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Abstract. New medical imaging modalities offering multi-valued data, such as phase contrast MRA and diffusion tensor MRI, require general representations for the development of automatized algorithms. In this paper we propose a unified framework for the registration of medical volumetric multi-valued data. The paper extends the usual concept of similarity in intensity (scalar) data to vector and tensor cases. A discussion on appropriate template selection and on the limitations of the template matching approach to incorporate the vector and tensor reorientation is also offered. Our approach to registration is based on a multiresolution scheme based on local matching of areas with a high degree of local structure and subsequent interpolation. Consequently we provide an algorithm to assess the amount of structure in generic multi-valued data by means of gradient and correlation computations. The interpolation step is carried out by means of the Kriging estimator that outperforms conventional polynomial methods for the interpolation of sparse vector fields. The feasibility of the approach is illustrated by results on synthetic and clinical data.

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Towards statistically optimal interpolation for 3D medical imaging

Cited by 42 publications

References 20 publications

Algorithms for modeling anatomic and target volumes in image‐guided neurosurgery and radiotherapy

Algorithms for modeling anatomic and target volumes in image‐guided neurosurgery and radiotherapy

Regularized Interpolation for Noisy Images

Nonrigid Registration of 3D Scalar, Vector and Tensor Medical Data

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