Histological Validation of MRI: A Review of Challenges in Registration of Imaging and Whole‐Mount Histopathology

Abstract

Rigorous validation with ground truth information such as histology is needed to reliably assess the current and potential value of MRI techniques to characterize tissue and identify disease‐related tissue alterations. Commonly used methods that aim to directly correlate histology and MRI data generally fall short of this goal due to spatial errors that preclude direct matching. Errors result from tissue deformation, differences in spatial resolution and slice thickness, non‐coplanar and/or nonintersecting plane orientations, and different image contrast mechanisms. Some of these problems arise from limitations in standard protocols for clinical tissue processing and histology‐based pathology reporting, and to some extent can be addressed by modifications to standard protocols without compromising the clinical process. Typical modifications include ex vivo specimen MRI, block‐face photography, addition of fiducial markers, and 3D printed molds to constrain tissue deformation and guide sectioning. This review summarizes the advantages and limitations of MRI validation techniques based on coregistration of MRI with whole‐mount histology of tissue specimens. Level of Evidence 4 Technical Efficacy Stage 1

Elastic transformation of histological slices allows precise co-registration with microCT data sets for a refined virtual histology approach

“…In an earlier study we showed already that histological sections from para n embedded samples experience some non-uniform deformations during their preparation caused by the mechanical sectioning with a microtome, as well as the depara nisation and the staining procedure 7 . Similar ndings were stated previously [18][19][20] . Furthermore, we demonstrated that this problem can be circumvented by using resin embedded samples.…”

Integrating multimodality magnetic resonance imaging to the Allen Mouse Brain Common Coordinate Framework

“…Integrating 3D histology images and various 3D dMRI metrics in CCFv3 space allows for the confirmation of noninvasive MRI measurements with the baseline information of the actual tissue properties. 22 Histological sectioning is the most common method with which to study normal and diseased brains by characterizing tissue properties with staining at the cellular level. 48,49 Compared with traditional histology, MRI routinely generates images of intact whole brain, enabling straightforward 3D characterization of tissue properties with quantitative measurements.…”

“…[19][20][21] Conventional histology is crucial for exploring the cellular components of the central nervous system, brain structure and connectivity, and the pathologies of neurodegenerative diseases. 22 Despite the maturity and versatility of histology for identifying tissue cellular properties, this technique can be labor-intensive and difficult to implement across whole organs. 23 The tissue structure and topological integrity can also be distorted by the processing of fixing, sectioning, mounting, staining, and dehydration.…”

Tensor Image Registration Library: Automated Deformable Registration of Stand-Alone Histology Images to Whole-Brain Post-Mortem MRI Data

“…Depending on the aim, MRI-microscopy datasets can vary along many axes: 1) whole brain [4] vs tissue blocks [5], 2) serial histological sectioning [6] vs single-section sampling [7], 3) large [8] vs small [9] histology sections, 4) ex-vivo [10] vs post-mortem MRI [11], and 5) the exact combination of MRI and microscopy modalities used. This diversity of the input data presents unique challenges [12] for the alignment of MRI-microscopy images (e.g., extreme contrast differences, vastly different spatial resolutions, 2D vs 3D image domains), which are difficult to overcome with existing registration software that was not optimised for this task. This is especially true if the source code is closed, or an inflexible implementation prohibits the customisation of the core algorithm.…”