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

Research output: Contribution to journalArticlepeer-review

Adrià Casamitjana, Marco Lorenzi, Sebastiano Ferraris, Loïc Peter, Marc Modat, Allison Stevens, Bruce Fischl, Tom Vercauteren, Juan Eugenio Iglesias

Original languageEnglish
Article number102265
Pages (from-to)102265
JournalMedical Image Analysis
Volume75
Early online date16 Oct 2021
DOIs
E-pub ahead of print16 Oct 2021
PublishedJan 2022

Bibliographical note

Funding Information: This work was primarily funded by the European Research Council (Starting Grant 677697, project ?BUNGEE-TOOLS?). SF is supported by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging (EP/L016478/1) and Doctoral Training Grant (EP/M506448/1). Support for this research was provided in part by the BRAIN Initiative (1RF1MH123195-01, U01MH117023), the National Institute for Biomedical Imaging and Bioengineering (P41EB015896, 1R01EB023281, R01EB006758, R21EB018907, R01EB019956, P41EB030006), the National Institute on Aging (1R01AG070988-01, 1R56AG064027, 1R01AG064027, 5R01AG008122, R01AG016495), the National Institute of Mental Health (R01 MH123195, R01 MH121885, 1RF1MH123195), the National Institute for Neurological Disorders and Stroke (R01NS0525851, R21NS072652, R01NS070963, R01NS083534, 5U01NS086625,5U24NS10059103, R01NS105820), and was made possible by the resources provided by Shared Instrumentation Grants 1S10RR023401, 1S10RR019307, and 1S10RR023043. Additional support was provided by the NIH Blueprint for Neuroscience Research (5U01-MH093765), part of the multi-institutional Human Connectome Project. In addition, BF has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies. BF's interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies. This work was also partly supported by core funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z; NS/A000049/1]. Additional support was provided by Alzheimer's Research UK (ARUK-IRG2019A-003) and the NIH (1RF1MH123195-01, 1R01AG070988-01). Funding Information: This work was primarily funded by the European Research Council (Starting Grant 677697, project “BUNGEE-TOOLS”). SF is supported by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging (EP/L016478/1) and Doctoral Training Grant (EP/M506448/1). Support for this research was provided in part by the BRAIN Initiative (1RF1MH123195-01, U01MH117023), the National Institute for Biomedical Imaging and Bioengineering (P41EB015896, 1R01EB023281, R01EB006758, R21EB018907, R01EB019956, P41EB030006), the National Institute on Aging (1R01AG070988-01, 1R56AG064027, 1R01AG064027, 5R01AG008122, R01AG016495), the National Institute of Mental Health ( R01 MH123195, R01 MH121885, 1RF1MH123195 ), the National Institute for Neurological Disorders and Stroke (R01NS0525851, R21NS072652, R01NS070963, R01NS083534, 5U01NS086625,5U24NS10059103, R01NS105820), and was made possible by the resources provided by Shared Instrumentation Grants 1S10RR023401, 1S10RR019307, and 1S10RR023043. Additional support was provided by the NIH Blueprint for Neuroscience Research (5U01-MH093765), part of the multi-institutional Human Connectome Project. In addition, BF has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies. BF’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies. This work was also partly supported by core funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z; NS/A000049/1]. Additional support was provided by Alzheimer’s Research UK (ARUK-IRG2019A-003) and the NIH (1RF1MH123195-01, 1R01AG070988-01). Publisher Copyright: © 2021 The Author(s)

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Abstract

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.

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