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Kerkelä L, Nery F, Callaghan R, Zhou F, Gyori NG, Szczepankiewicz F, Palombo M, Parker GJM, Zhang H, Hall MG, Clark CA. Comparative analysis of signal models for microscopic fractional anisotropy estimation using q-space trajectory encoding. Neuroimage 2021; 242:118445. [PMID: 34375753 DOI: 10.1016/j.neuroimage.2021.118445] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 07/06/2021] [Accepted: 08/02/2021] [Indexed: 12/12/2022] Open
Abstract
Microscopic diffusion anisotropy imaging using diffusion-weighted MRI and multidimensional diffusion encoding is a promising method for quantifying clinically and scientifically relevant microstructural properties of neural tissue. Several methods for estimating microscopic fractional anisotropy (µFA), a normalized measure of microscopic diffusion anisotropy, have been introduced but the differences between the methods have received little attention thus far. In this study, the accuracy and precision of µFA estimation using q-space trajectory encoding and different signal models were assessed using imaging experiments and simulations. Three healthy volunteers and a microfibre phantom were imaged with five non-zero b-values and gradient waveforms encoding linear and spherical b-tensors. Since the ground-truth µFA was unknown in the imaging experiments, Monte Carlo random walk simulations were performed using axon-mimicking fibres for which the ground truth was known. Furthermore, parameter bias due to time-dependent diffusion was quantified by repeating the simulations with tuned waveforms, which have similar power spectra, and with triple diffusion encoding, which, unlike q-space trajectory encoding, is not based on the assumption of time-independent diffusion. The truncated cumulant expansion of the powder-averaged signal, gamma-distributed diffusivities assumption, and q-space trajectory imaging, a generalization of the truncated cumulant expansion to individual signals, were used to estimate µFA. The gamma-distributed diffusivities assumption consistently resulted in greater µFA values than the second order cumulant expansion, 0.1 greater when averaged over the whole brain. In the simulations, the generalized cumulant expansion provided the most accurate estimates. Importantly, although time-dependent diffusion caused significant overestimation of µFA using all the studied methods, the simulations suggest that the resulting bias in µFA is less than 0.1 in human white matter.
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Affiliation(s)
- Leevi Kerkelä
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
| | - Fabio Nery
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Ross Callaghan
- UCL Centre for Medical Image Computing, University College London, London, UK
| | - Fenglei Zhou
- UCL Centre for Medical Image Computing, University College London, London, UK; UCL School of Pharmacy, University College London, London, UK
| | - Noemi G Gyori
- UCL Centre for Medical Image Computing, University College London, London, UK; UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Filip Szczepankiewicz
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, US; Harvard Medical School, Boston, Massachusetts, US; Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Marco Palombo
- UCL Centre for Medical Image Computing, University College London, London, UK
| | - Geoff J M Parker
- UCL Centre for Medical Image Computing, University College London, London, UK; Bioxydyn Limited, Manchester, UK; UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Hui Zhang
- UCL Centre for Medical Image Computing, University College London, London, UK
| | - Matt G Hall
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK; National Physical Laboratory, Teddington, UK
| | - Chris A Clark
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK
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Arezza NJJ, Tse DHY, Baron CA. Rapid microscopic fractional anisotropy imaging via an optimized linear regression formulation. Magn Reson Imaging 2021; 80:132-143. [PMID: 33945859 DOI: 10.1016/j.mri.2021.04.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/01/2021] [Accepted: 04/29/2021] [Indexed: 02/06/2023]
Abstract
Water diffusion anisotropy in the human brain is affected by disease, trauma, and development. Microscopic fractional anisotropy (μFA) is a diffusion MRI (dMRI) metric that can quantify water diffusion anisotropy independent of neuron fiber orientation dispersion. However, there are several different techniques to estimate μFA and few have demonstrated full brain imaging capabilities within clinically viable scan times and resolutions. Here, we present an optimized spherical tensor encoding (STE) technique to acquire μFA directly from the 2nd order cumulant expansion of the powder averaged dMRI signal obtained from direct linear regression (i.e. diffusion kurtosis) which requires fewer powder-averaged signals than other STE fitting techniques and can be rapidly computed. We found that the optimal dMRI parameters for white matter μFA imaging were a maximum b-value of 2000 s/mm2 and a ratio of STE to LTE tensor encoded acquisitions of 1.7 for our system specifications. We then compared two implementations of the direct regression approach to the well-established gamma model in 4 healthy volunteers on a 3 Tesla system. One implementation used mean diffusivity (D) obtained from a 2nd order fit of the cumulant expansion, while the other used a linear estimation of D from the low b-values. Both implementations of the direct regression approach showed strong linear correlations with the gamma model (ρ = 0.97 and ρ = 0.90) but mean biases of -0.11 and - 0.02 relative to the gamma model were also observed, respectively. All three μFA measurements showed good test-retest reliability (ρ ≥ 0.79 and bias = 0). To demonstrate the potential scan time advantage of the direct approach, 2 mm isotropic resolution μFA was demonstrated over a 10 cm slab using a subsampled data set with fewer powder-averaged signals that would correspond to a 3.3-min scan. Accordingly, our results introduce an optimization procedure that has enabled nearly full brain μFA in only several minutes.
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Affiliation(s)
- N J J Arezza
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada.
| | - D H Y Tse
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Canada
| | - C A Baron
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
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He Y, Aznar S, Siebner HR, Dyrby TB. In vivo tensor-valued diffusion MRI of focal demyelination in white and deep grey matter of rodents. Neuroimage Clin 2021; 30:102675. [PMID: 34215146 DOI: 10.1016/j.nicl.2021.102675] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 03/22/2021] [Accepted: 03/24/2021] [Indexed: 02/02/2023]
Abstract
We performed in-vivo tensor-valued diffusion MRI in demyelinating rodents. Lysolecithin was injected in white and deep grey matter to cause focal demyelination. Focal demyelination reduced microscopic fractional anisotropy (µFA). Isotropic kurtosis may be particularly sensitive to deep grey matter lesions.
Background Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease leading to damage of white matter (WM) and grey matter (GM). Magnetic resonance imaging (MRI) is the modality of choice to assess brain damage in MS, but there is an unmet need in MRI for achieving higher sensitivity and specificity to MS-related microstructural alterations in WM and GM. Objective To explore whether tensor-valued diffusion MRI (dMRI) can yield sensitive microstructural read-outs for focal demyelination in cerebral WM and deep GM (DGM). Methods Eight rats underwent L-α-Lysophosphatidylcholine (LPC) injections in the WM and striatum to introduce focal demyelination. Multimodal MRI was performed at 7 Tesla after 7 days. Tensor-valued dMRI was complemented by diffusion tensor imaging, quantitative MRI and proton magnetic resonance spectroscopy (MRS). Results Quantitative MRI and MRS confirmed that LPC injections caused inflammatory demyelinating lesions in WM and DGM. Tensor-valued dMRI illustrated a significant decline of microscopic fractional anisotropy (µFA) in both LPC-treated WM and DGM (P < 0.005) along with a marked increase of isotropic kurtosis (MKI) in DGM (P < 0.0001). Conclusion Tensor-valued dMRI bears considerable potential for microstructural imaging in MS, suggesting a regional µFA decrease may be a sensitive indicator of MS lesions, while a regional MKI increase may be particularly sensitive in detecting DGM lesions of MS.
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Ikenouchi Y, Kamagata K, Andica C, Hatano T, Ogawa T, Takeshige-Amano H, Kamiya K, Wada A, Suzuki M, Fujita S, Hagiwara A, Irie R, Hori M, Oyama G, Shimo Y, Umemura A, Hattori N, Aoki S. Evaluation of white matter microstructure in patients with Parkinson's disease using microscopic fractional anisotropy. Neuroradiology 2019; 62:197-203. [PMID: 31680195 DOI: 10.1007/s00234-019-02301-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 10/03/2019] [Indexed: 10/25/2022]
Abstract
PURPOSE Micro fractional anisotropy (μFA) is more accurate than conventional fractional anisotropy (FA) for assessing microscopic tissue properties and can overcome limitations related to crossing white matter fibres. We compared μFA and FA for evaluating white matter changes in patients with Parkinson's disease (PD). METHODS We compared FA and μFA measures between 25 patients with PD and 25 age- and gender-matched healthy controls using tract-based spatial statistics (TBSS) analysis. We also examined potential correlations between changes, revealed by conventional FA or μFA, and disease duration or Unified Parkinson's Disease Rating Scale (UPDRS)-III scores. RESULTS Compared with healthy controls, patients with PD had significantly reduced μFA values, mainly in the anterior corona radiata (ACR). In the PD group, μFA values (primarily those from the ACR) were significantly negatively correlated with UPDRS-III motor scores. No significant changes or correlations with disease duration or UPDRS-III scores with tissue properties were detected using conventional FA. CONCLUSION μFA can evaluate microstructural changes that occur during white matter degeneration in patients with PD and may overcome a key limitation of FA.
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Affiliation(s)
- Yutaka Ikenouchi
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Taku Hatano
- Department of Neurology, Juntendo University School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Takashi Ogawa
- Department of Neurology, Juntendo University School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Haruka Takeshige-Amano
- Department of Neurology, Juntendo University School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Kouhei Kamiya
- Department of Radiology, The University of Tokyo Graduate School of Medicine, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Michimasa Suzuki
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Shohei Fujita
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Ryusuke Irie
- Department of Radiology, The University of Tokyo Graduate School of Medicine, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Masaaki Hori
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Genko Oyama
- Department of Neurology, Juntendo University School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yashushi Shimo
- Department of Neurology, Juntendo University Nerima Hospital, 3-1-10 Takanodai, Nerima-ku, Tokyo, 177-8521, Japan
| | - Atsushi Umemura
- Department of Neurosurgery, Juntendo University School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
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Szczepankiewicz F, Lasič S, van Westen D, Sundgren PC, Englund E, Westin CF, Ståhlberg F, Lätt J, Topgaard D, Nilsson M. Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: applications in healthy volunteers and in brain tumors. Neuroimage 2014; 104:241-52. [PMID: 25284306 DOI: 10.1016/j.neuroimage.2014.09.057] [Citation(s) in RCA: 179] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Revised: 08/28/2014] [Accepted: 09/25/2014] [Indexed: 12/11/2022] Open
Abstract
The anisotropy of water diffusion in brain tissue is affected by both disease and development. This change can be detected using diffusion MRI and is often quantified by the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI). Although FA is sensitive to anisotropic cell structures, such as axons, it is also sensitive to their orientation dispersion. This is a major limitation to the use of FA as a biomarker for "tissue integrity", especially in regions of complex microarchitecture. In this work, we seek to circumvent this limitation by disentangling the effects of microscopic diffusion anisotropy from the orientation dispersion. The microscopic fractional anisotropy (μFA) and the order parameter (OP) were calculated from the contrast between signal prepared with directional and isotropic diffusion encoding, where the latter was achieved by magic angle spinning of the q-vector (qMAS). These parameters were quantified in healthy volunteers and in two patients; one patient with meningioma and one with glioblastoma. Finally, we used simulations to elucidate the relation between FA and μFA in various micro-architectures. Generally, μFA was high in the white matter and low in the gray matter. In the white matter, the largest differences between μFA and FA were found in crossing white matter and in interfaces between large white matter tracts, where μFA was high while FA was low. Both tumor types exhibited a low FA, in contrast to the μFA which was high in the meningioma and low in the glioblastoma, indicating that the meningioma contained disordered anisotropic structures, while the glioblastoma did not. This interpretation was confirmed by histological examination. We conclude that FA from DTI reflects both the amount of diffusion anisotropy and orientation dispersion. We suggest that the μFA and OP may complement FA by independently quantifying the microscopic anisotropy and the level of orientation coherence.
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Affiliation(s)
- Filip Szczepankiewicz
- Clinical Sciences, Lund, Department of Medical Radiation Physics, Lund University, Lund, Sweden.
| | | | - Danielle van Westen
- Diagnostic Radiology, Department of Clinical Sciences, Lund, Lund University, Lund, Sweden; Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Pia C Sundgren
- Diagnostic Radiology, Department of Clinical Sciences, Lund, Lund University, Lund, Sweden; Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Elisabet Englund
- Division of Oncology and Pathology, Department of Clinical Sciences, Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Carl-Fredrik Westin
- Laboratory for Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Freddy Ståhlberg
- Clinical Sciences, Lund, Department of Medical Radiation Physics, Lund University, Lund, Sweden; Diagnostic Radiology, Department of Clinical Sciences, Lund, Lund University, Lund, Sweden; Lund University Bioimaging Center, Lund University, Lund, Sweden
| | - Jimmy Lätt
- Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund University, Lund, Sweden
| | - Markus Nilsson
- Lund University Bioimaging Center, Lund University, Lund, Sweden
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