Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas

• Main Authors: Casamitjana, A. (Author), Ferraris, S. (Author), Fischl, B. (Author), Iglesias, J.E (Author), Lorenzi, M. (Author), Modat, M. (Author), Peter, L. (Author), Stevens, A. (Author), Vercauteren, T. (Author)
• Format: Article
• Language: English
• Published: Elsevier B.V. 2022
• Subjects: 3D histology reconstruction; 3D reconstruction; 3D Reconstruction; Article; artifact; Bayes theorem; Bayes Theorem; Bayesian networks; brain; Brain; Brain atlas; brain histology; coloring agent; Coloring Agents; deep learning; diagnostic imaging; Dynamic programming; Ex vivo MRI; ex vivo study; Ex-vivo; first cervical vertebra; Histological section; Histology; HTTP; human; Human brain; Humans; image reconstruction; Image reconstruction; Imaging, Three-Dimensional; Inference engines; kernel method; Linear programming; Linear-programming; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging; Markov random field; Multi-modal; neuroimaging; Nonlinear registration; nuclear magnetic resonance imaging; Statistics; three-dimensional imaging
• Online Access: View Fulltext in Publisher

 Joint registration of a stack of 2D histological sections to recover 3D structure (“3D histology reconstruction”) finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as “banana effect” (straightening of curved structures) and “z-shift” (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ2 norm, which can be minimised in closed form) and Laplacian (ℓ1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest. © 2021 The Author(s)