Surface-based parcellation and vertex-wise analysis of ultra high-resolution ex vivo 7 tesla MRI in Alzheimer's disease and related dementias
- URL: http://arxiv.org/abs/2403.19497v2
- Date: Wed, 3 Jul 2024 00:09:31 GMT
- Title: Surface-based parcellation and vertex-wise analysis of ultra high-resolution ex vivo 7 tesla MRI in Alzheimer's disease and related dementias
- Authors: Pulkit Khandelwal, Michael Tran Duong, Lisa Levorse, Constanza Fuentes, Amanda Denning, Winifred Trotman, Ranjit Ittyerah, Alejandra Bahena, Theresa Schuck, Marianna Gabrielyan, Karthik Prabhakaran, Daniel Ohm, Gabor Mizsei, John Robinson, Monica Munoz, John Detre, Edward Lee, David Irwin, Corey McMillan, M. Dylan Tisdall, Sandhitsu Das, David Wolk, Paul A. Yushkevich,
- Abstract summary: We present one-of-its-kind dataset of 82 ex vivo T2w whole brain hemispheres MRI at 0.3 mm isotropic resolution spanning Alzheimer's disease and related dementias.
We adapted and developed a fast and easy-to-use automated surface-based pipeline to parcellate, for the first time, ultra high-resolution ex vivo brain tissue at the native subject space resolution using the Desikan-Killiany-Tourville (DKT) brain atlas.
- Score: 32.61675068837929
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Magnetic resonance imaging (MRI) is the standard modality to understand human brain structure and function in vivo (antemortem). Decades of research in human neuroimaging has led to the widespread development of methods and tools to provide automated volume-based segmentations and surface-based parcellations which help localize brain functions to specialized anatomical regions. Recently ex vivo (postmortem) imaging of the brain has opened-up avenues to study brain structure at sub-millimeter ultra high-resolution revealing details not possible to observe with in vivo MRI. Unfortunately, there has been limited methodological development in ex vivo MRI primarily due to lack of datasets and limited centers with such imaging resources. Therefore, in this work, we present one-of-its-kind dataset of 82 ex vivo T2w whole brain hemispheres MRI at 0.3 mm isotropic resolution spanning Alzheimer's disease and related dementias. We adapted and developed a fast and easy-to-use automated surface-based pipeline to parcellate, for the first time, ultra high-resolution ex vivo brain tissue at the native subject space resolution using the Desikan-Killiany-Tourville (DKT) brain atlas. This allows us to perform vertex-wise analysis in the template space and thereby link morphometry measures with pathology measurements derived from histology. We will open-source our dataset docker container, Jupyter notebooks for ready-to-use out-of-the-box set of tools and command line options to advance ex vivo MRI clinical brain imaging research on the project webpage.
Related papers
- NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping [9.423808859117122]
We introduce NeuroBOLT, i.e., Neuro-to-BOLD Transformer, to translate raw EEG data to fMRI activity signals across the brain.
Our experiments demonstrate that NeuroBOLT effectively reconstructs unseen resting-state fMRI signals from primary sensory, high-level cognitive areas, and deep subcortical brain regions.
arXiv Detail & Related papers (2024-10-07T02:47:55Z) - BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation [6.5388528484686885]
This study introduces a novel approach towards the creation of medical foundation models.
Our method involves a novel two-stage pretraining approach using vision transformers.
BrainFounder demonstrates a significant performance gain, surpassing the achievements of previous winning solutions.
arXiv Detail & Related papers (2024-06-14T19:49:45Z) - NeuroCine: Decoding Vivid Video Sequences from Human Brain Activties [23.893490180665996]
We introduce NeuroCine, a novel dual-phase framework to targeting the inherent challenges of decoding fMRI data.
tested on a publicly available fMRI dataset, our method shows promising results.
Our attention analysis suggests that the model aligns with existing brain structures and functions, indicating its biological plausibility and interpretability.
arXiv Detail & Related papers (2024-02-02T17:34:25Z) - fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for
Multi-Subject Brain Activity Decoding [54.17776744076334]
We propose fMRI-PTE, an innovative auto-encoder approach for fMRI pre-training.
Our approach involves transforming fMRI signals into unified 2D representations, ensuring consistency in dimensions and preserving brain activity patterns.
Our contributions encompass introducing fMRI-PTE, innovative data transformation, efficient training, a novel learning strategy, and the universal applicability of our approach.
arXiv Detail & Related papers (2023-11-01T07:24:22Z) - UniBrain: Universal Brain MRI Diagnosis with Hierarchical
Knowledge-enhanced Pre-training [66.16134293168535]
We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain.
Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
arXiv Detail & Related papers (2023-09-13T09:22:49Z) - Automated deep learning segmentation of high-resolution 7 T postmortem
MRI for quantitative analysis of structure-pathology correlations in
neurodegenerative diseases [33.191270998887326]
We present a high resolution of 135 postmortem human brain tissue specimens imaged at 0.3 mm$3$ isotropic using a T2w sequence on a 7T whole-body MRI scanner.
We show generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm3 and 0.16 mm3 isotropic T2*w FLASH sequence at 7T.
arXiv Detail & Related papers (2023-03-21T23:44:02Z) - Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy.
We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
arXiv Detail & Related papers (2023-03-07T09:40:22Z) - fMRI from EEG is only Deep Learning away: the use of interpretable DL to
unravel EEG-fMRI relationships [68.8204255655161]
We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data.
We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
arXiv Detail & Related papers (2022-10-23T15:11:37Z) - Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with
Multi-Task Brain Age Prediction [53.122045119395594]
Unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results.
We propose deep learning for UAD in 3D brain MRI considering additional age information.
Based on our analysis, we propose a novel deep learning approach for UAD with multi-task age prediction.
arXiv Detail & Related papers (2022-01-31T09:39:52Z) - Gray Matter Segmentation in Ultra High Resolution 7 Tesla ex vivo T2w
MRI of Human Brain Hemispheres [9.196429840458629]
We present a high resolution 7 Tesla dataset of 32 ex vivo human brain specimens.
We benchmark the cortical mantle segmentation performance of nine neural network architectures.
We show excellent generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at different magnetic field strength and imaging sequences.
arXiv Detail & Related papers (2021-10-14T21:01:18Z) - Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation [81.30750944868142]
We are able to track changes in cerebral blood volume over time and identify spontaneous arterial dilations that propagate towards the pial surface.
This new imaging capability is a promising step towards characterizing the hemodynamic response function upon which functional magnetic resonance imaging (fMRI) is based.
arXiv Detail & Related papers (2020-01-14T22:55:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.