Piecewise affine registration of biological images for volume reconstruction

Pitiot, Alain; Bardinet, Éric; Thompson, Paul M.; Malandain, Grégoire

doi:10.1016/j.media.2005.03.008

Cited by 72 publications

(49 citation statements)

References 46 publications

(59 reference statements)

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“…However, they allow for multiresolution registration, meaning that the user could define the level of complexity of the transformation through a specified number of degrees of freedom. Most common transformation models are splines [29], cosine basis [30], tetrahedral mesh [31,32], multi-affine [33][34][35] and free (one vector per voxel) [36][37][38].…”

Section: Registration Algorithm For Healthy Subjectmentioning

confidence: 99%

Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

Angelini¹,

Clatz²,

Mandonnet³

et al. 2007

CMIR

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Tracking gliomas dynamics on MRI has became more and more important for therapeutic management. Powerful computational tools have been recently developed in this context enabling in silico growth on a virtual brain that can be matched with real 3D segmented evolution through registration between atlases and patient brain MRI data. In this paper, we provide an extensive review of existing algorithms for the three computational tasks involved in patient-specific tumor modeling: image segmentation, image registration, and in silico growth modelling (with special emphasis on the proliferation-diffusion model). Accuracy and limits of the reviewed algorithms are systematically discussed. Finally applications of these methods for both clinical practice and fundamental research are also discussed.

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Section: Registration Algorithm For Healthy Subjectmentioning

confidence: 99%

Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

Angelini¹,

Clatz²,

Mandonnet³

et al. 2007

CMIR

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show abstract

“…Las imágenes de esta investigación se basan en la experiencia de otros autores 19,[22][23][24] . Se obtuvieron de imágenes médicas de tomografía computarizada, superpuestas y dibujadas en un programa para dibujo técnico (AUTOCAD).…”

Section: Características Del Modelounclassified

Traumas cerebrales en niños secundarios a cabeceo de balones en fútbol: Modelo de simulación matemática

Ponce

Pérez²,

Ponce³

et al. 2011

Rev. méd. Chile

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“…The final transformation is constructed by combining the effect of the local transformations, usually by employing an interpolation scheme. For example see the locally affine transformation discussed in [Commowick et al, 2006;Pitiot et al, 2006].…”

Section: Linear Transformationsmentioning

confidence: 99%

Information theoretic regularization in diffuse optical tomography

Panagiotou¹,

Somayajula²,

Gibson³

et al. 2009

J. Opt. Soc. Am. A

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Diffuse optical tomography (DOT) retrieves the spatially distributed optical characteristics of a medium from external measurements. Recovering these parameters of interest involves solving a non-linear and severely ill-posed inverse problem. In this thesis we propose methods towards the regularization of DOT via the introduction of spatially unregistered, a priori information from alternative high resolution anatomical modalities, using the information theory concepts of joint entropy (JE) and mutual information (MI). Such functionals evaluate the similarity between the reconstructed optical image and the prior image, while bypassing the multi-modality barrier manifested as the incommensurate relation between the gray value representations of corresponding anatomical features in the modalities involved. By introducing structural a priori information in the image reconstruction process, we aim to improve the spatial resolution and quantitative accuracy of the solution.A further condition for the accurate incorporation of a priori information is the establishment of correct alignment between the prior image and the probed anatomy in a common coordinate system. However, limited information regarding the probed anatomy is known prior to the reconstruction process.In this work we explore the potentiality of spatially registering the prior image simultaneously with the solution of the reconstruction process.We provide a thorough explanation of the theory from an imaging perspective, accompanied by preliminary results obtained by numerical simulations as well as experimental data. In addition we compare the performance of MI and JE. Finally, we propose a method for fast joint entropy evaluation and optimization, which we later employ for the information theoretic regularization of DOT. The main areas involved in this thesis are: inverse problems, image reconstruction & regularization, diffuse optical tomography and medical image registration. Statement of intellectual contributionI, Christos Panagiotou, declare that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been properly indicated in the thesis.

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Piecewise affine registration of biological images for volume reconstruction

Cited by 72 publications

References 46 publications

Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

Traumas cerebrales en niños secundarios a cabeceo de balones en fútbol: Modelo de simulación matemática

Information theoretic regularization in diffuse optical tomography

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