Facilitating Tumor Functional Assessment by Spatially Relating 3D Tumor Histology and in Vivo MRI: Image Registration Approach

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2011 Sep 8

PMID 21897840

Citations 17

Authors

Lejla Alic

Joost C Haeck

Karin Bol

Stefan Klein

Sandra T van Tiel

Piotr A Wielepolski

Marion de Jong

Wiro J Niessen

Monique Bernsen

Jifke F Veenland

Affiliations

Soon will be listed here.

Abstract

Background: Magnetic resonance imaging (MRI), together with histology, is widely used to diagnose and to monitor treatment in oncology. Spatial correspondence between these modalities provides information about the ability of MRI to characterize cancerous tissue. However, registration is complicated by deformations during pathological processing, and differences in scale and information content.

Methodology/principal Findings: This study proposes a methodology for establishing an accurate 3D relation between histological sections and high resolution in vivo MRI tumor data. The key features of the methodology are: 1) standardized acquisition and processing, 2) use of an intermediate ex vivo MRI, 3) use of a reference cutting plane, 4) dense histological sampling, 5) elastic registration, and 6) use of complete 3D data sets. Five rat pancreatic tumors imaged by T2*-w MRI were used to evaluate the proposed methodology. The registration accuracy was assessed by root mean squared (RMS) distances between manually annotated landmark points in both modalities. After elastic registration the average RMS distance decreased from 1.4 to 0.7 mm. The intermediate ex vivo MRI and the reference cutting plane shared by all three 3D images (in vivo MRI, ex vivo MRI, and 3D histology data) were found to be crucial for the accurate co-registration between the 3D histological data set and in vivo MRI. The MR intensity in necrotic regions, as manually annotated in 3D histology, was significantly different from other histologically confirmed regions (i.e., viable and hemorrhagic). However, the viable and the hemorrhagic regions showed a large overlap in T2(*)-w MRI signal intensity.

Conclusions: The established 3D correspondence between tumor histology and in vivo MRI enables extraction of MRI characteristics for histologically confirmed regions. The proposed methodology allows the creation of a tumor database of spatially registered multi-spectral MR images and multi-stained 3D histology.

Citing Articles

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Multiparametric Analysis of Longitudinal Quantitative MRI data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer.

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Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse Models.

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Montelius M, Jalnefjord O, Spetz J, Nilsson O, Forssell-Aronsson E, Ljungberg M NMR Biomed. 2019; 32(3):e4060.

PMID: 30693592 PMC: 6590232. DOI: 10.1002/nbm.4060.

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