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Fusion of Serial 2D Section Images and MRI Reference – an Overview
Abstract
Serial 2D section images with high resolution, resulting from innovative imaging methods become even more valuable, if they are fused with in vivo volumes. Achieving this goal, the 3D context of the sections would be restored, the deformations would be corrected and the artefacts would be eliminated. However, the registration in this field faces big challenges and is not solved in general. On the other hand, several approaches have been introduced dealing at least with some of these difficulties. Here, a brief overview of the topic is given and some of the solutions are presented. It does not constitute the claim to be a complete review, but could be a starting point for those who are interested in this field.
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Introduction - Fusion of histology and MRI is frequently demanded in biomedical research to study in vitro tissue properties in an in vivo reference space. Distortions and artifacts caused by cutting and staining of histological slices as well as differences in spatial resolution make even the rigid fusion a difficult task. State-of- the-art methods start with a mono-modal restacking yielding a histological pseudo-3D volume. The 3D information of the MRI reference is considered subsequently. However, consistency of the histology volume and consistency due to the corresponding MRI seem to be diametral goals. Therefore, we propose a novel fusion framework optimizing histology/histology and histology/MRI consistency at the same time finding a balance between both goals. Method - Direct slice-to-slice correspondence even in irregularly-spaced cutting sequences is achieved by registration-based interpolation of the MRI. Introducing a weighted multi-image mutual information metric (WI), adjacent histology and corresponding MRI are taken into account at the same time. Therefore, the reconstruction of the histological volume as well as the fusion with the MRI is done in a single step. Results - Based on two data sets with more than 110 single registrations in all, the results are evaluated quantitatively based on Tanimoto overlap measures and qualitatively showing the fused volumes. In comparison to other multi-image metrics, the reconstruction based on WI is significantly improved. We evaluated different parameter settings with emphasis on the weighting term steering the balance between intra- and inter-modality consistency.
Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been developed and established as an emerging technique in the generation of quantitative images of metal distributions in thin tissue sections of brain samples (such as human, rat and mouse brain), with applications in research related to neurodegenerative disorders. A new analytical protocol is described which includes sample preparation by cryo-cutting of thin tissue sections and matrix-matched laboratory standards, mass spectrometric measurements, data acquisition, and quantitative analysis. Specific examples of the bioimaging of metal distributions in normal rodent brains are provided. Differences to the normal were assessed in a Parkinson's disease and a stroke brain model. Furthermore, changes during normal aging were studied. Powerful analytical techniques are also required for the determination and characterization of metal-containing proteins within a large pool of proteins, e.g., after denaturing or non-denaturing electrophoretic separation of proteins in one-dimensional and two-dimensional gels. LA-ICP-MS can be employed to detect metalloproteins in protein bands or spots separated after gel electrophoresis. MALDI-MS can then be used to identify specific metal-containing proteins in these bands or spots. The combination of these techniques is described in the second section.
Our current understanding of neuroanatomical abnormalities in neuropsychiatric diseases is based largely on magnetic resonance imaging (MRI) and post mortem histological analyses of the brain. Further advances in elucidating altered brain structure in these human conditions might emerge from combining MRI and histological methods. We propose a multistage method for registering 3D volumes reconstructed from histological sections to corresponding in vivo MRI volumes from the same subjects: (1) manual segmentation of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) compartments in histological sections, (2) alignment of consecutive histological sections using 2D rigid transformation to construct a 3D histological image volume from the aligned sections, (3) registration of reconstructed 3D histological volumes to the corresponding 3D MRI volumes using 3D affine transformation, (4) intensity normalization of images via histogram matching, and (5) registration of the volumes via intensity based large deformation diffeomorphic metric (LDDMM) image matching algorithm. Here we demonstrate the utility of our method in the transfer of cytoarchitectonic information from histological sections to identify regions of interest in MRI scans of nine adult macaque brains for morphometric analyses. LDDMM improved the accuracy of the registration via decreased distances between GM/CSF surfaces after LDDMM (0.39 +/- 0.13 mm) compared to distances after affine registration (0.76 +/- 0.41 mm). Similarly, WM/GM distances decreased to 0.28 +/- 0.16 mm after LDDMM compared to 0.54 +/- 0.39 mm after affine registration. The multistage registration method may find broad application for mapping histologically based information, for example, receptor distributions, gene expression, onto MRI volumes.
Polarised light imaging (PLI) utilises the birefringence of the myelin sheaths in order to visualise the orientation of nerve fibres in microtome sections of adult human post-mortem brains at ultra-high spatial resolution. The preparation of post-mortem brains for PLI involves fixation, freezing and cutting into 100-mum-thick sections. Hence, geometrical distortions of histological sections are inevitable and have to be removed for 3D reconstruction and subsequent fibre tracking. We here present a processing pipeline for 3D reconstruction of these sections using PLI derived multimodal images of post-mortem brains. Blockface images of the brains were obtained during cutting; they serve as reference data for alignment and elimination of distortion artefacts. In addition to the spatial image transformation, fibre orientation vectors were reoriented using the transformation fields, which consider both affine and subsequent non-linear registration. The application of this registration and reorientation approach results in a smooth fibre vector field, which reflects brain morphology. PLI combined with 3D reconstruction and fibre tracking is a powerful tool for human brain mapping. It can also serve as an independent method for evaluating in vivo fibre tractography.
To validate and add value to non-invasive imaging techniques, corresponding histology is required to establish biological correlates. We present an efficient semi-automated image-processing pipeline that uses immunohistochemically-stained sections to reconstruct a 3D brain volume from 2D histological images before registering these with the corresponding 3D in vivo magnetic resonance images (MRI). A multistep registration procedure that first aligns the "global" volume by using the center of mass and then applies a rigid and affine alignment based on signal intensities is described. This is applied to a training set of three rat brain volumes before being validated on three more normal brains. Application of the approach to register "abnormal" images from a rat model of stroke allows the neurobiological correlates of the variations in the hyper-intense MRI signal intensity caused by infarction to be investigated. For evaluation, corresponding anatomical landmarks in MR and histology were defined to measure the accuracy of registration. A registration error of 0.249mm (approximately one in-plane voxel dimension) was evident in healthy rat brains and 0.323mm in a rodent model of stroke. The proposed reconstruction and registration pipeline allows precise analysis of non-invasive MRI and corresponding microstructural histological features in 3D. We were thus able to interrogate histology to deduce the cause of MRI signal variations in the lesion cavity and the peri-infarct area.
This book really shows how registration works: the flip-book appearing at the top right corners shows a registration of a human knee from bent to straight position (keeping bones rigid). Of course, the book also provides insight into concepts and practical tools. The presented framework exploits techniques from various fields such as image processing, numerical linear algebra, and optimization. Therefore, a brief overview of some preliminary literature in those fields is presented in the introduction (references [1–51]), and registration-specific literature is assembled at the end of the book (references [52–212]). Examples and results are based on the FAIR software, a package written in MATLAB. The FAIR software, as well as a PDF version of this entire book, can be freely downloaded from www.siam.org/books/fa06.
This book would not have been possible without the help of Bernd Fischer, Eldad Haber, Claudia Kremling, Jim Nagy, and the Safir Research Group from Lübeck: Sven Barendt, Björn Beuthin, Konstantin Ens, Stefan Heldmann, Sven Kabus, Janine Olesch, Nils Papenberg, Hanno Schumacher, and Stefan Wirtz. I'm also indebted to Sahar Alipour, Reza Heydarian, Raya Horesh, Ramin Mafi, and Bahram Marami Dizaji for improving the manuscript.
By performing registration of preoperative multiprotocol in vivo magnetic resonance (MR) images of the prostate with corresponding whole-mount histology (WMH) sections from postoperative radical prostatectomy specimens, an accurate estimate of the spatial extent of prostate cancer (CaP) on in vivo MR imaging (MRI) can be retrospectively established. This could allow for definition of quantitative image-based disease signatures and lead to development of classifiers for disease detection on multiprotocol in vivo MRI. Automated registration of MR and WMH images of the prostate is complicated by dissimilar image intensities, acquisition artifacts, and nonlinear shape differences.
The authors present a method for automated elastic registration of multiprotocol in vivo MRI and WMH sections of the prostate. The method, multiattribute combined mutual information (MACMI), leverages all available multiprotocol image data to drive image registration using a multivariate formulation of mutual information.
Elastic registration using the multivariate MI formulation is demonstrated for 150 corresponding sets of prostate images from 25 patient studies with T2-weighted and dynamic-contrast enhanced MRI and 85 image sets from 15 studies with an additional functional apparent diffusion coefficient MRI series. Qualitative results of MACMI evaluation via visual inspection suggest that an accurate delineation of CaP extent on MRI is obtained. Results of quantitative evaluation on 150 clinical and 20 synthetic image sets indicate improved registration accuracy using MACMI compared to conventional pairwise mutual information-based approaches.
The authors' approach to the registration of in vivo multiprotocol MRI and ex vivo WMH of the prostate using MACMI is unique, in that (1) information from all available image protocols is utilized to drive the registration with histology, (2) no additional, intermediate ex vivo radiology or gross histology images need be obtained in addition to the routinely acquired in vivo MRI series, and (3) no corresponding anatomical landmarks are required to be identified manually or automatically on the images.
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.
In the past years, the development of 3-D medical imaging has enabled the 3-D imaging of in vivo tissues, from an anatomical (MR, CT) or even functional (fMRI, PET, SPECT) point of view. However, despite immense technological progress, the resolution of these images is still short of the level of anatomical or functional details that in vitro imaging (e.g., histology, autoradiography) permits. The motivation of this work is to compare fMRI activations to activations observed in autoradiographic images from the same animals. We aim to fuse post-mortem autoradiographic data with a pre-mortem anatomical MR image. We first reconstruct a 3-D volume from the 2-D autoradiographic sections, coherent both in geometry and intensity. Then, this volume is fused with the MR image. This way, we ensure that the reconstructed 3-D volume can be superimposed onto the MR image that represents the reference anatomy. We demonstrate that this fusion can be achieved by using only simple global transformations (rigid and/or affine, 2-D and 3-D), while yielding very satisfactory results.