Precise MRI-histology coregistration of paraffin-embedded tissue with blockface imaging

Magnetic resonance imaging (MRI) provides 3D spatial information on tissue, yet it lacks at the molecular level. In contrast, histology provides cellular and molecular information, but it lacks the 3D spatial context and direct <i>in vivo</i> translation. Coregistering the two is key for the 3D embedding of histological details, validating pathological MRI findings, and identifying quantitative imaging biomarkers of neurodegenerative diseases. However, coregistration is challenging due to non-linear distortions of the tissue from histological processing and sectioning leading to microscopic and macroscopic nonlinear 3D deformations between specimen MRI and stained histology sections. To address this, we developed a novel pipeline, named Brewster's Blockface Quantification (BBQ), integrating robust optical approaches with innovative 2D and 3D registration algorithms to achieve precise volumetric alignment of specimen MRI data with histological images. On a variety of brain tissue specimens from distinct anatomical regions and across multiple species, our methodology generated blockface volumes with minimal distortion and artifacts. Using these blockface volumes as an intermediary, we achieve a precise alignment between MRI and histology slides, yielding registration results with an overlapping Dice score of ~90% for whole tissue alignment between MRI and blockface volumes, and >95% for 2D MRI-histology registration. This correlative MRI-histology pipeline with robust 2D and 3D coregistration methods promises to enhance our understanding of neurodegenerative diseases and aid the development of MRI-based disease biomarkers.

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Publication Details

Journal
Imaging Neuroscience
Published
2025-01-01
DOI
https://doi.org/10.1162/imag.a.106
Primary Topic
Medical Image Segmentation Techniques
Type
article
Field-Weighted Citation Impact
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article

Precise MRI-histology coregistration of paraffin-embedded tissue with blockface imaging

Michael Zeineh, Inma Cobos, Marios Georgiadis, Lingzhi Tao et al.
Imaging Neuroscience
Medical Image Segmentation Techniques
article

Precise MRI-histology coregistration of paraffin-embedded tissue with blockface imaging

Michael Zeineh, Inma Cobos, Marios Georgiadis, Lingzhi Tao, Jeffrey Nirschl, Markus Axer, Wei Shao, Nhu Nguyen, Matthew Choi, Mirabela Rusu, David B. Camarillo, István N. Huszár, Phillip DiGiacomo, Philipp Schlömer, Hossein Moein Taghavi, Yixin Wang, William Ho, Samantha Leventis
article en

Abstract

Magnetic resonance imaging (MRI) provides 3D spatial information on tissue, yet it lacks at the molecular level. In contrast, histology provides cellular and molecular information, but it lacks the 3D spatial context and direct <i>in vivo</i> translation. Coregistering the two is key for the 3D embedding of histological details, validating pathological MRI findings, and identifying quantitative imaging biomarkers of neurodegenerative diseases. However, coregistration is challenging due to non-linear distortions of the tissue from histological processing and sectioning leading to microscopic and macroscopic nonlinear 3D deformations between specimen MRI and stained histology sections. To address this, we developed a novel pipeline, named Brewster's Blockface Quantification (BBQ), integrating robust optical approaches with innovative 2D and 3D registration algorithms to achieve precise volumetric alignment of specimen MRI data with histological images. On a variety of brain tissue specimens from distinct anatomical regions and across multiple species, our methodology generated blockface volumes with minimal distortion and artifacts. Using these blockface volumes as an intermediary, we achieve a precise alignment between MRI and histology slides, yielding registration results with an overlapping Dice score of ~90% for whole tissue alignment between MRI and blockface volumes, and >95% for 2D MRI-histology registration. This correlative MRI-histology pipeline with robust 2D and 3D coregistration methods promises to enhance our understanding of neurodegenerative diseases and aid the development of MRI-based disease biomarkers.

Imaging NeuroscienceVol. 3
Harvard University (US), University of Wuppertal (DE), Forschungszentrum Jülich (DE), University of Florida (US), Athinoula A. Martinos Center for Biomedical Imaging (US), Stanford Medicine (US), Stanford University (US)
Openalex Percentile: Top 83%
Medical Image Segmentation Techniques
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