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Fuchs C, Dessain Q, Delinte N, Dausort M, Macq B. Sparse Blind Spherical Deconvolution of diffusion weighted MRI. Front Neurosci 2024; 18:1385975. [PMID: 38846718 PMCID: PMC11155299 DOI: 10.3389/fnins.2024.1385975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 04/19/2024] [Indexed: 06/09/2024] Open
Abstract
Diffusion-weighted magnetic resonance imaging provides invaluable insights into in-vivo neurological pathways. However, accurate and robust characterization of white matter fibers microstructure remains challenging. Widely used spherical deconvolution algorithms retrieve the fiber Orientation Distribution Function (ODF) by using an estimation of a response function, i.e., the signal arising from individual fascicles within a voxel. In this paper, an algorithm of blind spherical deconvolution is proposed, which only assumes the axial symmetry of the response function instead of its exact knowledge. This algorithm provides a method for estimating the peaks of the ODF in a voxel without any explicit response function, as well as a method for estimating signals associated with the peaks of the ODF, regardless of how those peaks were obtained. The two stages of the algorithm are tested on Monte Carlo simulations, as well as compared to state-of-the-art methods on real in-vivo data for the orientation retrieval task. Although the proposed algorithm was shown to attain lower angular errors than the state-of-the-art constrained spherical deconvolution algorithm on synthetic data, it was outperformed by state-of-the-art spherical deconvolution algorithms on in-vivo data. In conjunction with state-of-the art methods for axon bundles direction estimation, the proposed method showed its potential for the derivation of per-voxel per-direction metrics on synthetic as well as in-vivo data.
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Affiliation(s)
- Clément Fuchs
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
| | - Quentin Dessain
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
- Institute of NeuroScience, UCLouvain, Brussels, Belgium
| | - Nicolas Delinte
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
- Institute of NeuroScience, UCLouvain, Brussels, Belgium
| | - Manon Dausort
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
| | - Benoît Macq
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
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Filipiak P, Sajitha TA, Shepherd TM, Clarke K, Goldman H, Placantonakis DG, Zhang J, Chan KC, Boada FE, Baete SH. Improved reconstruction of crossing fibers in the mouse optic pathways with orientation distribution function fingerprinting. Magn Reson Med 2024; 91:1075-1086. [PMID: 37927121 DOI: 10.1002/mrm.29911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/10/2023] [Accepted: 10/14/2023] [Indexed: 11/07/2023]
Abstract
PURPOSE The accuracy of diffusion MRI tractography reconstruction decreases in the white matter regions with crossing fibers. The optic pathways in rodents provide a challenging structure to test new diffusion tractography approaches because of the small crossing volume within the optic chiasm and the unbalanced 9:1 proportion between the contra- and ipsilateral neural projections from the retina to the lateral geniculate nucleus, respectively. METHODS Common approaches based on Orientation Distribution Function (ODF) peak finding or statistical inference were compared qualitatively and quantitatively to ODF Fingerprinting (ODF-FP) for reconstruction of crossing fibers within the optic chiasm using in vivo diffusion MRI (n = 18 $$ n=18 $$ healthy C57BL/6 mice). Manganese-Enhanced MRI (MEMRI) was obtained after intravitreal injection of manganese chloride and used as a reference standard for the optic pathway anatomy. RESULTS ODF-FP outperformed by over 100% all the tested methods in terms of the ratios between the contra- and ipsilateral segments of the reconstructed optic pathways as well as the spatial overlap between tractography and MEMRI. CONCLUSION In this challenging model system, ODF-Fingerprinting reduced uncertainty of diffusion tractography for complex structural formations of fiber bundles.
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Affiliation(s)
- Patryk Filipiak
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
| | | | - Timothy M Shepherd
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
| | - Kamri Clarke
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
| | - Hannah Goldman
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
| | - Dimitris G Placantonakis
- Department of Neurosurgery, Perlmutter Cancer Center, Neuroscience Institute, Kimmel Center for Stem Cell Biology, NYU Langone Health, New York, New York, USA
| | - Jiangyang Zhang
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
| | - Kevin C Chan
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
- Department of Ophthalmology, NYU Langone Health, New York, New York, USA
| | - Fernando E Boada
- Radiological Sciences Laboratory and Molecular Imaging Program at Stanford, Department of Radiology, Stanford University, Stanford, California, USA
| | - Steven H Baete
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
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Menzel M, Gräßel D, Rajkovic I, Zeineh MM, Georgiadis M. Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI. eLife 2023; 12:e84024. [PMID: 37166005 PMCID: PMC10259419 DOI: 10.7554/elife.84024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 05/02/2023] [Indexed: 05/12/2023] Open
Abstract
Disentangling human brain connectivity requires an accurate description of nerve fiber trajectories, unveiled via detailed mapping of axonal orientations. However, this is challenging because axons can cross one another on a micrometer scale. Diffusion magnetic resonance imaging (dMRI) can be used to infer axonal connectivity because it is sensitive to axonal alignment, but it has limited spatial resolution and specificity. Scattered light imaging (SLI) and small-angle X-ray scattering (SAXS) reveal axonal orientations with microscopic resolution and high specificity, respectively. Here, we apply both scattering techniques on the same samples and cross-validate them, laying the groundwork for ground-truth axonal orientation imaging and validating dMRI. We evaluate brain regions that include unidirectional and crossing fibers in human and vervet monkey brain sections. SLI and SAXS quantitatively agree regarding in-plane fiber orientations including crossings, while dMRI agrees in the majority of voxels with small discrepancies. We further use SAXS and dMRI to confirm theoretical predictions regarding SLI determination of through-plane fiber orientations. Scattered light and X-ray imaging can provide quantitative micrometer 3D fiber orientations with high resolution and specificity, facilitating detailed investigations of complex fiber architecture in the animal and human brain.
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Affiliation(s)
- Miriam Menzel
- Department of Imaging Physics, Faculty of Applied Sciences, Delft University of TechnologyDelftNetherlands
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbHJülichGermany
| | - David Gräßel
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbHJülichGermany
| | - Ivan Rajkovic
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator LaboratoryStandfordUnited States
| | - Michael M Zeineh
- Department of Radiology, Stanford School of MedicineStanfordUnited States
| | - Marios Georgiadis
- Department of Radiology, Stanford School of MedicineStanfordUnited States
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Filipiak P, Shepherd T, Basler L, Zuccolotto A, Placantonakis DG, Schneider W, Boada FE, Baete SH. Stepwise Stochastic Dictionary Adaptation Improves Microstructure Reconstruction with Orientation Distribution Function Fingerprinting. COMPUTATIONAL DIFFUSION MRI : 13TH INTERNATIONAL WORKSHOP, CDMRI 2022, HELD IN CONJUNCTION WITH MICCAI 2022, SINGAPORE, SINGAPORE, SEPTEMBER 22, 2022, PROCEEDINGS. CDMRI (WORKSHOP) (13TH : 2022 : SINGAPORE, SINGAPORE) 2022; 13722:89-100. [PMID: 36695675 PMCID: PMC9870046 DOI: 10.1007/978-3-031-21206-2_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Fitting of the multicompartment biophysical model of white matter is an ill-posed optimization problem. One approach to make it computationally tractable is through Orientation Distribution Function (ODF) Fingerprinting. However, the accuracy of this method relies solely on ODF dictionary generation mechanisms which either sample the microstructure parameters on a multidimensional grid or draw them randomly with a uniform distribution. In this paper, we propose a stepwise stochastic adaptation mechanism to generate ODF dictionaries tailored specifically to the diffusion-weighted images in hand. The results we obtained on a diffusion phantom and in vivo human brain images show that our reconstructed diffusivities are less noisy and the separation of a free water fraction is more pronounced than for the prior (uniform) distribution of ODF dictionaries.
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Affiliation(s)
- Patryk Filipiak
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, NYU Langone Health, New York, NY, USA
| | - Timothy Shepherd
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, NYU Langone Health, New York, NY, USA
| | - Lee Basler
- Psychology Software Tools, Inc., Pittsburgh, PA, USA
| | | | - Dimitris G. Placantonakis
- Department of Neurosurgery, Perlmutter Cancer Center, Neuroscience Institute, Kimmel Center for Stem Cell Biology, NYU Langone Health, New York, NY, USA
| | | | - Fernando E. Boada
- Radiological Sciences Laboratory and Molecular Imaging Program at Stanford, Department of Radiology, Stanford University, Stanford, CA, USA
| | - Steven H. Baete
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, NYU Langone Health, New York, NY, USA
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Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI. Neuroimage 2022; 257:119327. [PMID: 35636227 PMCID: PMC9453851 DOI: 10.1016/j.neuroimage.2022.119327] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/06/2022] [Accepted: 05/19/2022] [Indexed: 01/25/2023] Open
Abstract
Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.
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Sammartino F, Marsh R, Yeh FC, Sondergaard A, Changizi BK, Krishna V. Radiological identification of the globus pallidus motor subregion in Parkinson's disease. J Neurosurg 2022; 137:175-183. [PMID: 34740190 DOI: 10.3171/2021.7.jns21858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 07/01/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Globus pallidus (GP) lesioning improves motor symptoms of Parkinson's disease (PD) and is occasionally associated with nonmotor side effects. Although these variable clinical effects were shown to be site-specific within the GP, the motor and nonmotor subregions have not been distinguished radiologically in patients with PD. The GP was recently found to have a distinct radiological signature on diffusion MRI (dMRI), potentially related to its unique cellular content and organization (or tissue architecture). In this study, the authors hypothesize that the magnitude of water diffusivity, a surrogate for tissue architecture, will radiologically distinguish motor from nonmotor GP subregions in patients with PD. They also hypothesize that the therapeutic focused ultrasound pallidotomy lesions will preferentially overlap the motor subregion. METHODS Diffusion MRI from healthy subjects (n = 45, test-retest S1200 cohort) and PD patients (n = 33) was parcellated based on the magnitude of water diffusivity in the GP, as measured orientation distribution function (ODF). A clustering algorithm was used to identify GP parcels with distinct ODF magnitude. The individual parcels were used as seeds for tractography to distinguish motor from nonmotor subregions. The locations of focused ultrasound lesions relative to the GP parcels were also analyzed in 11 patients with PD. RESULTS Radiologically, three distinct parcels were identified within the GP in healthy controls and PD patients: posterior, central, and anterior. The posterior and central parcels comprised the motor subregion and the anterior parcel was classified as a nonmotor subregion based on their tractography connections. The focused ultrasound lesions preferentially overlapped with the motor subregion (posterior more than central). The hotspots for motor improvement were localized in the posterior GP parcel. CONCLUSIONS Using a data-driven approach of ODF-based parcellation, the authors radiologically distinguished GP motor subregions in patients with PD. This method can aid stereotactic targeting in patients with PD undergoing surgical treatments, especially focused ultrasound ablation.
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Affiliation(s)
| | | | - Fang-Cheng Yeh
- 2Department of Neurological Surgery, University of Pittsburgh, Pennsylvania
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Filipiak P, Shepherd T, Lin YC, Placantonakis DG, Boada FE, Baete SH. Performance of orientation distribution function-fingerprinting with a biophysical multicompartment diffusion model. Magn Reson Med 2022; 88:418-435. [PMID: 35225365 PMCID: PMC9142101 DOI: 10.1002/mrm.29208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 01/31/2022] [Accepted: 02/07/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE Orientation Distribution Function (ODF) peak finding methods typically fail to reconstruct fibers crossing at shallow angles below 40°, leading to errors in tractography. ODF-Fingerprinting (ODF-FP) with the biophysical multicompartment diffusion model allows for breaking this barrier. METHODS A randomized mechanism to generate a multidimensional ODF-dictionary that covers biologically plausible ranges of intra- and extra-axonal diffusivities and fraction volumes is introduced. This enables ODF-FP to address the high variability of brain tissue. The performance of the proposed approach is evaluated on both numerical simulations and a reconstruction of major fascicles from high- and low-resolution in vivo diffusion images. RESULTS ODF-FP with the suggested modifications correctly identifies fibers crossing at angles as shallow as 10 degrees in the simulated data. In vivo, our approach reaches 56% of true positives in determining fiber directions, resulting in visibly more accurate reconstruction of pyramidal tracts, arcuate fasciculus, and optic radiations than the state-of-the-art techniques. Moreover, the estimated diffusivity values and fraction volumes in corpus callosum conform with the values reported in the literature. CONCLUSION The modified ODF-FP outperforms commonly used fiber reconstruction methods at shallow angles, which improves deterministic tractography outcomes of major fascicles. In addition, the proposed approach allows for linearization of the microstructure parameters fitting problem.
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Affiliation(s)
- Patryk Filipiak
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, NYU Langone Health, New York, NY, USA
| | - Timothy Shepherd
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, NYU Langone Health, New York, NY, USA
| | - Ying-Chia Lin
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, NYU Langone Health, New York, NY, USA
| | - Dimitris G. Placantonakis
- Department of Neurosurgery, Perlmutter Cancer Center, Neuroscience Institute, Kimmel Center for Stem Cell Biology, NYU Langone Health, New York, NY, USA
| | - Fernando E. Boada
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, NYU Langone Health, New York, NY, USA,Radiological Sciences Laboratory and Molecular Imaging Program at Stanford, Department of Radiology, Stanford University, Stanford, CA
| | - Steven H. Baete
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, NYU Langone Health, New York, NY, USA
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De Luca A, Karayumak SC, Leemans A, Rathi Y, Swinnen S, Gooijers J, Clauwaert A, Bahr R, Sandmo SB, Sochen N, Kaufmann D, Muehlmann M, Biessels GJ, Koerte I, Pasternak O. Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH). Neuroimage 2022; 259:119439. [PMID: 35788044 DOI: 10.1016/j.neuroimage.2022.119439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 06/23/2022] [Accepted: 06/30/2022] [Indexed: 11/25/2022] Open
Abstract
Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the in-vivo brain. Compared to single-shell data for diffusion tensor imaging (DTI), multi-shell data allows to apply more complex models such as diffusion kurtosis imaging (DKI), which attempts to capture both diffusion hindrance and restriction effects, or biophysical models such as NODDI, which attempt to increase specificity by separating biophysical components. Because of the strong dependence of the dMRI signal on the measurement hardware, DKI and NODDI metrics show scanner and site differences, much like other dMRI metrics. These effects limit the implementation of multi-shell approaches in multicenter studies, which are needed to collect large sample sizes for robust analyses. Recently, a post-processing technique based on rotation invariant spherical harmonics (RISH) features was introduced to mitigate cross-scanner differences in DTI metrics. Unlike statistical harmonization methods, which require repeated application to every dMRI metric of choice, RISH harmonization is applied once on the raw data, and can be followed by any analysis. RISH features harmonization has been tested on DTI features but not its generalizability to harmonize multi-shell dMRI. In this work, we investigated whether performing the RISH features harmonization of multi-shell dMRI data removes cross-site differences in DKI and NODDI metrics while retaining longitudinal effects. To this end, 46 subjects underwent a longitudinal (up to 3 time points) two-shell dMRI protocol at 3 imaging sites. DKI and NODDI metrics were derived before and after harmonization and compared both at the whole brain level and at the voxel level. Then, the harmonization effects on cross-sectional and on longitudinal group differences were evaluated. RISH features averaged for each of the 3 sites exhibited prominent between-site differences in the frontal and posterior part of the brain. Statistically significant differences in fractional anisotropy, mean diffusivity and mean kurtosis were observed both at the whole brain and voxel level between all the acquisition sites before harmonization, but not after. The RISH method also proved effective to harmonize NODDI metrics, particularly in white matter. The RISH based harmonization maintained the magnitude and variance of longitudinal changes as compared to the non-harmonized data of all considered metrics. In conclusion, the application of RISH feature based harmonization to multi-shell dMRI data can be used to remove cross-site differences in DKI metrics and NODDI analyses, while retaining inherent relations between longitudinal acquisitions.
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Affiliation(s)
- Alberto De Luca
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands; Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia.
| | | | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Stephan Swinnen
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium; KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Jolien Gooijers
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium; KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Amanda Clauwaert
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium; KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Roald Bahr
- Oslo Sports Trauma Research Center, Norwegian School of Sport Sciences, Oslo, Norway
| | - Stian Bahr Sandmo
- Oslo Sports Trauma Research Center, Norwegian School of Sport Sciences, Oslo, Norway
| | - Nir Sochen
- Department of Applied Mathematics, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - David Kaufmann
- Radiology Department, Charite University Hospital, Berlin, Germany
| | - Marc Muehlmann
- Department of Radiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Geert-Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Inga Koerte
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; cBRAIN, Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Adil SM, Calabrese E, Charalambous LT, Cook JJ, Rahimpour S, Atik AF, Cofer GP, Parente BA, Johnson GA, Lad SP, White LE. A high-resolution interactive atlas of the human brainstem using magnetic resonance imaging. Neuroimage 2021; 237:118135. [PMID: 33951517 PMCID: PMC8480283 DOI: 10.1016/j.neuroimage.2021.118135] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/15/2021] [Accepted: 04/28/2021] [Indexed: 12/30/2022] Open
Abstract
Conventional atlases of the human brainstem are limited by the inflexible, sparsely-sampled, two-dimensional nature of histology, or the low spatial resolution of conventional magnetic resonance imaging (MRI). Postmortem high-resolution MRI circumvents the challenges associated with both modalities. A single human brainstem specimen extending from the rostral diencephalon through the caudal medulla was prepared for imaging after the brain was removed from a 65-year-old male within 24 h of death. The specimen was formalin-fixed for two weeks, then rehydrated and placed in a custom-made MRI compatible tube and immersed in liquid fluorocarbon. MRI was performed in a 7-Tesla scanner with 120 unique diffusion directions. Acquisition time for anatomic and diffusion images were 14 h and 208 h, respectively. Segmentation was performed manually. Deterministic fiber tractography was done using strategically chosen regions of interest and avoidance, with manual editing using expert knowledge of human neuroanatomy. Anatomic and diffusion images were rendered with isotropic resolutions of 50 μm and 200 μm, respectively. Ninety different structures were segmented and labeled, and 11 different fiber bundles were rendered with tractography. The complete atlas is available online for interactive use at https://www.civmvoxport.vm.duke.edu/voxbase/login.php?return_url=%2Fvoxbase%2F. This atlas presents multiple contrasting datasets and selected tract reconstruction with unprecedented resolution for MR imaging of the human brainstem. There are immediate applications in neuroanatomical education, with the potential to serve future applications for neuroanatomical research and enhanced neurosurgical planning through "safe" zones of entry into the human brainstem.
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Affiliation(s)
- Syed M Adil
- Department of Neurosurgery, Duke University Medical Center, Durham, NC, United States; Center for In Vivo Microscopy, Duke University Medical Center, Durham, NC, United States.
| | - Evan Calabrese
- University of California San Francisco, Department of Radiology & Biomedical Imaging, San Francisco, CA, United States.
| | - Lefko T Charalambous
- Department of Neurosurgery, Duke University Medical Center, Durham, NC, United States; Center for In Vivo Microscopy, Duke University Medical Center, Durham, NC, United States.
| | - James J Cook
- Center for In Vivo Microscopy, Duke University Medical Center, Durham, NC, United States.
| | - Shervin Rahimpour
- Department of Neurosurgery, Duke University Medical Center, Durham, NC, United States.
| | - Ahmet F Atik
- Department of Neurosurgery, Cleveland Clinic, Cleveland, OH, United States.
| | - Gary P Cofer
- Center for In Vivo Microscopy, Duke University Medical Center, Durham, NC, United States.
| | - Beth A Parente
- Department of Neurosurgery, Duke University Medical Center, Durham, NC, United States.
| | - G Allan Johnson
- Center for In Vivo Microscopy, Duke University Medical Center, Durham, NC, United States.
| | - Shivanand P Lad
- Department of Neurosurgery, Duke University Medical Center, Durham, NC, United States.
| | - Leonard E White
- Department of Neurology, Duke University Medical Center, Durham, NC, United States; Duke Institute for Brain Sciences, Duke University, Durham NC, United States.
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