scholarly journals Tensor Image Registration Library: Automated Non-Linear Registration of Sparsely Sampled Histological Specimens to Post-Mortem MRI of the Whole Human Brain

2019 ◽  
Author(s):  
Istvan N. Huszar ◽  
Menuka Pallebage-Gamarallage ◽  
Sean Foxley ◽  
Benjamin C. Tendler ◽  
Anna Leonte ◽  
...  

AbstractThere is a need to understand the histopathological basis of MRI signal characteristics in complex biological matter. Microstructural imaging holds promise for sensitive and specific indicators of the early stages of human neurodegeneration but requires validation against traditional histological markers before it can be reliably applied in the clinical setting. Validation relies on a precise and preferably automatic method to align MRI and histological images of the same tissue, which poses unique challenges compared to more conventional MRI-to-MRI registration.A customisable open-source platform, Tensor Image Registration Library (TIRL) is presented. Based on TIRL, a fully automated pipeline was implemented to align small stained histological images with dissection photographs of corresponding tissue blocks and coronal brain slices, and further with high-resolution (0.5 mm) whole-brain post-mortem MRI data. The pipeline performed three separate deformable registrations to achieve accurate mapping between whole-brain MRI and small-slide histology coordinates. The robustness and accuracy of the individual registration steps were evaluated using both simulated data and real-life images from 6 different anatomical locations of one post-mortem human brain.The automated registration method demonstrated sub-millimetre accuracy in all steps, robustness against tissue damage, and good reproducibility between experiments. The method also outperformed manual landmark-based slice-to-volume registration, also correcting for curvatures in the slicing plane. Due to the customisability of TIRL, the pipeline can be conveniently adapted for other research needs and is therefore suitable for the large-scale comparison of routinely collected histology and MRI data.HighlightsTIRL: new framework for prototyping bespoke image registration pipelinesPipeline for automated registration of small-slide histology to whole-brain MRISlice-to-volume registration accounting for through-plane deformationsNo need for serial histological sampling

2018 ◽  
Vol 60 (10) ◽  
pp. 1089-1092 ◽  
Author(s):  
Max Scheffler ◽  
Enrique Maturana ◽  
Rares Salomir ◽  
Sven Haller ◽  
Enikö Kövari

2021 ◽  
Vol 11 (8) ◽  
pp. 960
Author(s):  
Mina Kheirkhah ◽  
Philipp Baumbach ◽  
Lutz Leistritz ◽  
Otto W. Witte ◽  
Martin Walter ◽  
...  

Studies investigating human brain response to emotional stimuli—particularly high-arousing versus neutral stimuli—have obtained inconsistent results. The present study was the first to combine magnetoencephalography (MEG) with the bootstrapping method to examine the whole brain and identify the cortical regions involved in this differential response. Seventeen healthy participants (11 females, aged 19 to 33 years; mean age, 26.9 years) were presented with high-arousing emotional (pleasant and unpleasant) and neutral pictures, and their brain responses were measured using MEG. When random resampling bootstrapping was performed for each participant, the greatest differences between high-arousing emotional and neutral stimuli during M300 (270–320 ms) were found to occur in the right temporo-parietal region. This finding was observed in response to both pleasant and unpleasant stimuli. The results, which may be more robust than previous studies because of bootstrapping and examination of the whole brain, reinforce the essential role of the right hemisphere in emotion processing.


2001 ◽  
Vol 915 (1) ◽  
pp. 47-57 ◽  
Author(s):  
Katarina Varnäs ◽  
Håkan Hall ◽  
Pascal Bonaventure ◽  
Göran Sedvall
Keyword(s):  

NeuroImage ◽  
2021 ◽  
pp. 118551
Author(s):  
J.A. Galadí ◽  
S. Silva Pereira ◽  
Y. Sanz Perl ◽  
M.L. Kringelbach ◽  
I. Gayte ◽  
...  

2012 ◽  
Author(s):  
Takahiro Kawamura ◽  
Norihiro Omae ◽  
Masahiko Yamada ◽  
Wataru Ito ◽  
Kiyosumi Kawamoto ◽  
...  

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