Grid Enabled Non-rigid Registration with a Dense Transformation and a priori Information

Stefanescu, Radu; Pennec, Xavier; Ayache, Nicholas

doi:10.1007/978-3-540-39903-2_98

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…We have exemplified the simultaneousneed of grid resources andi nteractivity on the volume reconstruction case. Alarge number of medical image analysis applications share the samerequirements,asdescribedin mored etaili n [ 9]: in high-degree-of-freedom, optimization problems such as nonlinear image registration [8]o rr adiation treatment planning,computational steering is useful to help algorithmsa void local minima.A nother very importanta reai s analysis of massive distributeddatasets. The example of the EU Mammogrid project,and itsr elation with the highe nergyp hysics (HEP) experiment ALICE [10] is significant.…”

Section: Resultsmentioning

confidence: 99%

Interactive Volume Reconstruction and Measurement on the Grid

Germain-Renaud

Osorio

Texier³

2005

Methods Inf Med

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Clinical experiments of a grid-enabled PTM3D become possible. Widespread adoption of grid technology in the medical images analysis field will benefit from this "early user" project. Convergences appear between two broadly different fields, high energy physics and medical image, towards the need of a smooth integration of the new resources offered by grid systems into the everyday tools of their respective end-users. It can be expected that the convergence will mature towards truly interactive grids, able to serve the needs of the medical community.

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Section: Resultsmentioning

confidence: 99%

Interactive Volume Reconstruction and Measurement on the Grid

Germain-Renaud

Osorio

Texier³

2005

Methods Inf Med

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“…We developed in [6] an original registration method that appear particularly well fitted to our current problem. The algorithm models the transformation as a dense deformation field, which enables it to recover fine details.…”

Section: A Grid-powered Registration Algorithmmentioning

confidence: 99%

“…We chose a more heuristic approach that only approximates a biomechanical behavior, but which is much faster. We use the same diffusion equation than above, now with U , and with a stiffness field D(p) (instead of 1 − k(p)) that now depends on the local nature of the tissues, as in [6]. Thus, combined with the above regularization, this realizes a good approximation of a visco-elastic material.…”

Section: General Description Of the Algorithmmentioning

confidence: 99%

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Non-rigid Atlas to Subject Registration with Pathologies for Conformal Brain Radiotherapy

Stefanescu

Commowick

Malandain

et al. 2004

Lecture Notes in Computer Science

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Abstract. Warping a digital atlas toward a patient image allows the simultaneous segmentation of several structures. This may be of great interest for cerebral images, since the brain contains a large number of small but important structures (optical nerves, grey nuclei, etc.). One important application is the conformal radiotherapy of cerebral tumor, where a precise delineation of all these structures is required. However, in this case, the variability induced by the tumor or a surgical resection, that are not present in the digital atlas, prevents an accurate registration between the atlas and the patient images. Since our registration method allows to locally control the amount of regularization, we are able to explicitly introduce those areas in the warping process. For computational efficiency, we have created a parallel implementation that can be used from the clinical environment through a grid interface.

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“…Grid computing technology is a rapidly evolving field of interest and has been experiments have been undertaken in a wide range of areas such as particle physics, multi-scale modeling, computational steering, finance, defense research, drug discovery, decision-making, and collaborative design. The Grid applications for medical imaging analysis have also been reported [4,5]. It has been shown that the Grid can tackle complex problems in reasonable time scales and with convincing performance and this has further push the development of Grid technologies.…”

Section: Introductionmentioning

confidence: 96%

Alignment of dynamic contrast-enhanced MR volumes of the breast for a multicenter trial: an exemplar grid application

Zheng

Tanner²,

Hill

et al. 2004

SPIE Proceedings

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Image registration is a very important procedure in medical imaging analysis. However, the intensive computations involved in image registration have to some extent made it impractical for interactive use as well as limiting its general availability. This paper presents our current Grid project to facilitate image registration tasks. We have set up an image registration Grid by combining the attractive features of both Globus and Condor distributed computing environments. In order to make it more convenient to use, we have also developed a web interface for potential clients to specify and submit their image registration jobs to the Grid. The initial experiments in 3D breast MR images have shown encouraging results and demonstrated the suitability of Grid technology to this type of computationally intensive applications. The image registration Grid makes it much more straightforward for different institutes to use the identical registration program and protocols to register images consistently, quickly and efficiently. This can greatly improve data sharing and comparative studies in multi-centre trials. The Grid presented here could be an important step for clinical applications of image registration. Future work will focus on refining the Grid with the aim of upgrading it to a Grid Service and testing the system more extensively with medical imaging dataset.Image registration is an important procedure in medical imaging analysis. The purpose of image registration is to align one image (source image) to another (target or reference image) so that the possible misalignments between them can be minimized or eliminated, thus establishing spatial correspondence. Proper registration enables us to have a better understanding of the features of interest and integration of useful information. Applications are wide: ranging from image-guided surgery to atlas construction, segmentation propagation, monitoring changes over time and dynamic sequence analysis. The general procedure of fully automated image registration typically requires optimization of a function of the similarity between the target and source images. A large variety of image registration methods have been proposed for medical applications. In practice, image registration can be further classified as rigid and non-rigid according to the underlying transformation model. The former registers images by assuming that images are misaligned by translations and rotations only while the latter allows many more degrees of freedom. Compared to rigid registration, non-rigid registration can theoretically model more complicated deformations but requires more computation [1,2]. Rigid registration is normally used to provide a starting estimate before non-rigid registration in order to reduce computational time. Even so non-rigid registration still requires massive computational time, e.g. an average computation time up to 5 hours has been reported to register 3-D MR images of a single breast with a typical region of

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Grid Enabled Non-rigid Registration with a Dense Transformation and a priori Information

Cited by 5 publications

References 6 publications

Interactive Volume Reconstruction and Measurement on the Grid

Interactive Volume Reconstruction and Measurement on the Grid

Non-rigid Atlas to Subject Registration with Pathologies for Conformal Brain Radiotherapy

Alignment of dynamic contrast-enhanced MR volumes of the breast for a multicenter trial: an exemplar grid application

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