Elastic registration of brain images on large PC-Clusters

Modersitzki, Jan; Lustig, Gunther; Schmitt, Oliver; Obelöer, Wolfgang

doi:10.1016/s0167-739x(00)00081-9

Cited by 18 publications

(1 citation statement)

References 10 publications

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“…The whole processing system is a combination of partial reconfigurable DSP clusters and an embedded LEON2 processor, targeting high-performance payload computing and data transmission in outer space [2] . Modersitzki et al used a large PC-Cluster of 48 Dual SMP platforms connected via a Myrinet network to reconstruct the essential information out of linear and non-linear deformed images of human brain by correcting these deformations based on elastic matching of the images [3] . Delves and Pryde utilized a processor array of 9 T800 processors to achieving super-resolution of synthetic aperture radar image [4] .…”

Section: Introductionmentioning

confidence: 99%

Research on the Architectures of Parallel Image Processing Systems

Jin-zong

Zhu

et al. 2008

2008 Second International Symposium on Intelligent Information Technology Application

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Image processing is considered a good candidate for the application of parallel processing because of the large volumes of data and the complex algorithms commonly encountered. Research on the architectures of parallel image processing systems is the basis of system designing. It plays an important role in achieving optimal conversion from algorithm to structure so that it is helpful to design a highly efficient system. Parallel image processing systems can be classified into three categories: computer-based dedicated systems, computer-based general systems and DSP-based systems. According to this classification, analysis and comparison of many kinds of realization technologies and structure characteristics were carried out. We also gave some application examples. In particular, research on the two most generic parallel image processing architectures of DSP and Cluster was executed. Then we performed image restoration and super-resolution algorithms on each of them. The experiment results show that DSP is adapted to small images' fast processing and Cluster is good at processing vast volumes and large scale images on the contrary.

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Section: Introductionmentioning

confidence: 99%

Research on the Architectures of Parallel Image Processing Systems

Jin-zong

Zhu

et al. 2008

2008 Second International Symposium on Intelligent Information Technology Application

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Generative Modelling of Cortical Receptor Distributions from Cytoarchitectonic Images in the Macaque Brain

Nebli,

Schiffer,

Niu

et al. 2024

Neuroinform

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Neurotransmitter receptor densities are relevant for understanding the molecular architecture of brain regions. Quantitative in vitro receptor autoradiography, has been introduced to map neurotransmitter receptor distributions of brain areas. However, it is very time and cost-intensive, which makes it challenging to obtain whole-brain distributions. At the same time, high-throughput light microscopy and 3D reconstructions have enabled high-resolution brain maps capturing measures of cell density across the whole human brain. Aiming to bridge gaps in receptor measurements for building detailed whole-brain atlases, we study the feasibility of predicting realistic neurotransmitter density distributions from cell-body stainings. Specifically, we utilize conditional Generative Adversarial Networks (cGANs) to predict the density distributions of the M2 receptor of acetylcholine and the kainate receptor for glutamate in the macaque monkey’s primary visual (V1) and motor cortex (M1), based on light microscopic scans of cell-body stained sections. Our model is trained on corresponding patches from aligned consecutive sections that display cell-body and receptor distributions, ensuring a mapping between the two modalities. Evaluations of our cGANs, both qualitative and quantitative, show their capability to predict receptor densities from cell-body stained sections while maintaining cortical features such as laminar thickness and curvature. Our work underscores the feasibility of cross-modality image translation problems to address data gaps in multi-modal brain atlases.

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Numerical Methods for Image Registration

Modersitzki

2003

636

779

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In this paper we introduce a new framework for image registration. Our formulation is based on consistent discretization of the optimization problem coupled with a multigrid solution of the linear system which evolve in a Gauss-Newton iteration. We show that our discretization is h-elliptic independent of parameter choice and therefore a simple multigrid implementation can be used. To overcome potential large nonlinearities and to further speed up computation, we use a multilevel continuation technique. We demonstrate the efficiency of our method on a realistic highly nonlinear registration problem.

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Elastic registration of brain images on large PC-Clusters

Cited by 18 publications

References 10 publications

Research on the Architectures of Parallel Image Processing Systems

Research on the Architectures of Parallel Image Processing Systems

Generative Modelling of Cortical Receptor Distributions from Cytoarchitectonic Images in the Macaque Brain

Numerical Methods for Image Registration

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