Title
Multi-modal image registration: matching MRI with histology
Author
TNO Defensie en Veiligheid
Alić, L.
Haeck, J.C.
Klein, S.
Bol, K.
van Tiel, S.T.
Wielopolski, P.A.
Bijster, M.
Niessen, W.J.
Bernsen, M.
Veenland, J.F.
de Jong, M.
Contributor
Molthen, R.C. (editor)
Weaver, J.B. (editor)
Publication year
2010
Abstract
Spatial correspondence between histology and multi sequence MRI can provide information about the capabilities of non-invasive imaging to characterize cancerous tissue. However, shrinkage and deformation occurring during the excision of the tumor and the histological processing complicate the co registration of MR images with histological sections. This work proposes a methodology to establish a detailed 3D relation between histology sections and in vivo MRI tumor data. The key features of the methodology are a very dense histological sampling (up to 100 histology slices per tumor), mutual information based non-rigid B-spline registration, the utilization of the whole 3D data sets, and the exploitation of an intermediate ex vivo MRI. In this proof of concept paper, the methodology was applied to one tumor. We found that, after registration, the visual alignment of tumor borders and internal structures was fairly accurate. Utilizing the intermediate ex vivo MRI, it was possible to account for changes caused by the excision of the tumor: we observed a tumor expansion of 20%. Also the effects of fixation, dehydration and histological sectioning could be determined: 26% shrinkage of the tumor was found. The annotation of viable tissue, performed in histology and transformed to the in vivo MRI, matched clearly with high intensity regions in MRI. With this methodology, histological annotation can be directly related to the corresponding in vivo MRI. This is a vital step for the evaluation of the feasibility of multi-spectral MRI to depict histological groundtruth.
Subject
TS - Technical Sciences
Physics & Electronics
Multi-modal Registration,
Tissue Characterization
Image registration
Magnetic Resonance Imaging
Medical Image Processing
Tissues
Histology
Tumors
II - Intelligent Imaging
To reference this document use:
http://resolver.tudelft.nl/uuid:d399a0b6-70d0-49e7-b4d2-f2687350d8e2
DOI
https://doi.org/10.1117/12.844123
TNO identifier
485127
Publisher
SPIE, Bellingham, WA
Source
Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging, 13 February 2010, San Diego, CA, USA
Series
Proceedings of SPIE
Document type
conference paper