Creating consistent 3D multi-modality data sets from autoradiographic and histological images of the rat brain

Pietrzyk, U.; Bauer, Dominik; Vieten, Andrea; Bauer, Andreas; Langen, K.-J.; Zilles, Karl; Palm, Christoph

doi:10.1109/nssmic.2004.1466754

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Nikou et al, 1 Ourselin et al 2 and Pietryzk et al 3 ). They base either on extrinsic fiducial markers, 4 intrinsic features like landmarks and contours 5, 6 principal axes 7 or image intensities.…”

Section: Introductionmentioning

confidence: 96%

Fusion of rat brain histology and MRI using weighted multi-image mutual information

et al. 2008

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Introduction -Fusion of histology and MRI is frequently demanded in biomedical research to study in vitro tissue properties in an in vivo reference space. Distortions and artifacts caused by cutting and staining of histological slices as well as differences in spatial resolution make even the rigid fusion a difficult task. Stateof-the-art methods start with a mono-modal restacking yielding a histological pseudo-3D volume. The 3D information of the MRI reference is considered subsequently. However, consistency of the histology volume and consistency due to the corresponding MRI seem to be diametral goals. Therefore, we propose a novel fusion framework optimizing histology/histology and histology/MRI consistency at the same time finding a balance between both goals.Method -Direct slice-to-slice correspondence even in irregularly-spaced cutting sequences is achieved by registration-based interpolation of the MRI. Introducing a weighted multi-image mutual information metric (WI), adjacent histology and corresponding MRI are taken into account at the same time. Therefore, the reconstruction of the histological volume as well as the fusion with the MRI is done in a single step.Results -Based on two data sets with more than 110 single registrations in all, the results are evaluated quantitatively based on Tanimoto overlap measures and qualitatively showing the fused volumes. In comparison to other multi-image metrics, the reconstruction based on WI is significantly improved. We evaluated different parameter settings with emphasis on the weighting term steering the balance between intra-and inter-modality consistency.

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Section: Introductionmentioning

confidence: 96%

Fusion of rat brain histology and MRI using weighted multi-image mutual information

et al. 2008

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Evaluation of registration strategies for multi-modality images of rat brain slices

Palm¹,

Vieten²,

Salber³

et al. 2009

Phys. Med. Biol.

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In neuroscience, small-animal studies frequently involve dealing with series of images from multiple modalities such as histology and autoradiography. The consistent and bias-free restacking of multi-modality image series is obligatory as a starting point for subsequent non-rigid registration procedures and for quantitative comparisons with positron emission tomography (PET) and other in vivo data. Up to now, consistency between 2D slices without cross validation using an inherent 3D modality is frequently presumed to be close to the true morphology due to the smooth appearance of the contours of anatomical structures. However, in multi-modality stacks consistency is difficult to assess. In this work, consistency is defined in terms of smoothness of neighboring slices within a single modality and between different modalities. Registration bias denotes the distortion of the registered stack in comparison to the true 3D morphology and shape. Based on these metrics, different restacking strategies of multi-modality rat brain slices are experimentally evaluated. Experiments based on MRI-simulated and real dual-tracer autoradiograms reveal a clear bias of the restacked volume despite quantitatively high consistency and qualitatively smooth brain structures. However, different registration strategies yield different inter-consistency metrics. If no genuine 3D modality is available, the use of the so-called SOP (slice-order preferred) or MOSOP (modality-and-slice-order preferred) strategy is recommended.

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Nerve fiber mapping of the human visual cortex using Polarized Light Imaging (PLI)

Axer¹,

Axer

Gräßel

et al. 2007

2007 IEEE Nuclear Science Symposium Conference Record

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Creating consistent 3D multi-modality data sets from autoradiographic and histological images of the rat brain

Cited by 3 publications

References 2 publications

Fusion of rat brain histology and MRI using weighted multi-image mutual information

Fusion of rat brain histology and MRI using weighted multi-image mutual information

Evaluation of registration strategies for multi-modality images of rat brain slices

Nerve fiber mapping of the human visual cortex using Polarized Light Imaging (PLI)

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