Dataset Information

In-vivo probabilistic atlas of human thalamic nuclei based on diffusion- weighted magnetic resonance imaging.

ABSTRACT: The thalamic nuclei are involved in many neurodegenerative diseases and therefore, their identification is of key importance in numerous clinical treatments. Automated segmentation of thalamic subparts is currently achieved by exploring diffusion-weighted magnetic resonance imaging (DW-MRI), but in absence of such data, atlas-based segmentation can be used as an alternative. Currently, there is a limited number of available digital atlases of the thalamus. Moreover, all atlases are created using a few subjects only, thus are prone to errors due to the inter-subject variability of the thalamic morphology. In this work, we present a probabilistic atlas of anatomical subparts of the thalamus built upon a relatively large dataset where the individual thalamic parcellation was done by employing a recently proposed automatic diffusion-based clustering method. Our analyses, comparing the segmentation performance between the atlas-based and the clustering method, demonstrate the ability of the provided atlas to substitute the automated diffusion-based subdivision in the individual space when the DW-MRI is not available.

SUBMITTER: Najdenovska E

PROVIDER: S-EPMC6257045 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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In-vivo probabilistic atlas of human thalamic nuclei based on diffusion- weighted magnetic resonance imaging.

Najdenovska Elena E Alemán-Gómez Yasser Y Battistella Giovanni G Descoteaux Maxime M Hagmann Patric P Jacquemont Sebastien S Maeder Philippe P Thiran Jean-Philippe JP Fornari Eleonora E Bach Cuadra Meritxell M

Scientific data 20181127

The thalamic nuclei are involved in many neurodegenerative diseases and therefore, their identification is of key importance in numerous clinical treatments. Automated segmentation of thalamic subparts is currently achieved by exploring diffusion-weighted magnetic resonance imaging (DW-MRI), but in absence of such data, atlas-based segmentation can be used as an alternative. Currently, there is a limited number of available digital atlases of the thalamus. Moreover, all atlases are created using ...[more]

PMID: 30480664

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Project description:The thalamus is an essential relay station in the cortical-subcortical connections. It is characterized by a complex anatomical architecture composed of numerous small nuclei, which mediate the involvement of the thalamus in a wide range of neurological functions. We present a novel framework for segmenting the thalamic nuclei, which explores the orientation distribution functions (ODFs) from diffusion magnetic resonance images at 3 T. The differentiation of the complex intra-thalamic microstructure is improved by using the spherical harmonic (SH) representation of the ODFs, which provides full angular characterization of the diffusion process in each voxel. The clustering was performed using the k-means algorithm initialized in a data-driven manner. The method was tested on 35 healthy volunteers and our results show a robust, reproducible and accurate segmentation of the thalamus in seven nuclei groups. Six of them closely matched the anatomy and were labeled as anterior, ventral anterior, medio-dorsal, ventral latero-ventral, ventral latero-dorsal and pulvinar, while the seventh cluster included the centro-lateral and the latero-posterior nuclei. Results were evaluated both qualitatively, by comparing the segmented nuclei to the histological atlas of Morel, and quantitatively, by measuring the clusters' extent and the clusters' spatial distribution across subjects and hemispheres. We also showed the robustness of our approach across different sequences and scanners, as well as intra-subject reproducibility of the segmented clusters using additional two scan-rescan datasets. We also observed an overlap between the path of the main long-connection tracts passing through the thalamus and the spatial distribution of the nuclei identified with our clustering algorithm. Our approach, based on SH representations of the ODFs, outperforms the one based on angular differences between the principle diffusion directions, which is considered so far as state-of-the-art method. Our findings show an anatomically reliable segmentation of the main groups of thalamic nuclei that could be of potential use in many clinical applications.

Project description:OBJECTIVES:Diffusion-weighted imaging (DWI) and the introduction of the intravoxel incoherent motion (IVIM) model have provided a unique method for evaluating perfusion and diffusion within a tissue without the need for a contrast agent. Despite its relevance, cardiac DWI has thus far been limited by low b values because of signal loss induced by physiological motion. The goal of this study was to develop a methodology for estimating IVIM parameters of in vivo cardiac magnetic resonance imaging using an efficient DWI acquisition framework. This was achieved by investigating various acquisition strategies (principal component analysis [PCA] filtering and temporal maximum intensity projection [PCATMIP] and single trigger delay [TD]) and fitting methods. MATERIAL AND METHODS:Simulations were performed on a synthetic dataset of diffusion-weighted signal intensity (SI) to determine the fitting method that would yield IVIM parameters with the greatest accuracy. The required number of b values to correctly estimate IVIM parameters was also investigated. Breath-hold DWI scans were performed for 12 volunteers to collect several TD values during diastole. Thirteen b values ranging from 0 to 550 s/mm were used. The IVIM parameters derived using the data from all the acquired TDs (PCATMIP technique) were compared with those derived using a single acquisition performed at an optimized diastolic time point (1TD). RESULTS:The main result of this study was that PCATMIP, when combined with a fitting model that accounted for T1 and T2 relaxation, provided IVIM parameters with less variability. However, an acquisition performed with 1 optimized diastolic TD provided results that were as good as those provided using PCATMIP if the R-R variability during the acquisition was sufficiently low (± 5%). Furthermore, the use of only 9 b values (that could be acquired in 2 breath-holds), instead of 13 b values (requiring 3 breath-holds), was sufficient to determine the IVIM parameters. CONCLUSIONS:This study demonstrates that IVIM is technically feasible in vivo and reports for the first time the perfusion fraction, f, and the diffusion coefficients, D and D*, for the cardiac DWI of healthy volunteers. Motion-induced signal loss, which is the main problem associated with cardiac DWI, could be avoided with the combined use of sliding acquisition during the cardiac cycle and image postprocessing with the PCATMIP algorithm. This study provides new perspectives for perfusion imaging without a contrast agent and demonstrates that IVIM parameters can act as promising tools to further characterize microvascular abnormalities or dysfunction.

Project description:The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing segmentation of 25 thalamic labels per hemisphere informed by both modalities. We present an improved probabilistic atlas, incorporating thalamic nuclei identified from histology and 45 white matter tracts surrounding the thalamus identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclear groups versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimer's disease (AUROC 81.98%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer (https://freesurfer.net/fswiki/ThalamicNucleiDTI).

Dataset Information

In-vivo probabilistic atlas of human thalamic nuclei based on diffusion- weighted magnetic resonance imaging.

Publications

In-vivo probabilistic atlas of human thalamic nuclei based on diffusion- weighted magnetic resonance imaging.

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