Evaluation of Registration Strategies for Multi-modality Images of Rat Brain Slices

  • 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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Metadaten
Author:Christoph PalmORCiDGND, Andreas Vieten, Dagmar Salber, Uwe Pietrzyk
DOI:https://doi.org/10.1088/0031-9155/54/10/021
Parent Title (English):Physics in Medicine and Biology
Document Type:Article
Language:English
Year of first Publication:2009
Release Date:2020/01/08
GND Keyword:Histologie; Bildgebendes Verfahren; Schnittdarstellung; Multimodales Verfahren
Volume:54
Issue:10
First Page:3269
Last Page:3289
Institutes:Fakult├Ąt Informatik und Mathematik
Regensburg Medical Image Computing - ReMIC
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke
Publication:Externe Publikationen