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Uddin MN, Singh MV, Faiyaz A, Szczepankiewicz F, Nilsson M, Boodoo ZD, Sutton KR, Tivarus ME, Zhong J, Wang L, Qiu X, Weber MT, Schifitto G. Tensor-valued diffusion MRI detects brain microstructure changes in HIV infected individuals with cognitive impairment. RESEARCH SQUARE 2024:rs.3.rs-4482269. [PMID: 38946952 PMCID: PMC11213220 DOI: 10.21203/rs.3.rs-4482269/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Despite advancements, the prevalence of HIV-associated neurocognitive impairment remains at approximately 40%, attributed to factors like pre-cART (combination antiretroviral therapy) irreversible brain injury. People with HIV (PWH) treated with cART do not show significant neurocognitive changes over relatively short follow-up periods. However, quantitative neuroimaging may be able to detect ongoing subtle microstructural changes. This study aimed to investigate the sensitivity of tensor-valued diffusion encoding in detecting such changes in brain microstructural integrity in cART-treated PWH. Additionally, it explored relationships between these metrics, neurocognitive scores, and plasma levels of neurofilament light (NFL) chain and glial fibrillary acidic protein (GFAP). Using MRI at 3T, 24 PWH and 31 healthy controls underwent cross-sectional examination. The results revealed significant variations in b-tensor encoding metrics across white matter regions, with associations observed between these metrics, cognitive performance, and blood markers of neuronal and glial injury (NFL and GFAP). Moreover, a significant interaction between HIV status and imaging metrics was observed, particularly impacting total cognitive scores in both gray and white matter. These findings suggest that b-tensor encoding metrics offer heightened sensitivity in detecting subtle changes associated with axonal injury in HIV infection, underscoring their potential clinical relevance in understanding neurocognitive impairment in PWH.
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Boito D, Eklund A, Tisell A, Levi R, Özarslan E, Blystad I. MRI with generalized diffusion encoding reveals damaged white matter in patients previously hospitalized for COVID-19 and with persisting symptoms at follow-up. Brain Commun 2023; 5:fcad284. [PMID: 37953843 PMCID: PMC10638510 DOI: 10.1093/braincomms/fcad284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/25/2023] [Accepted: 10/26/2023] [Indexed: 11/14/2023] Open
Abstract
There is mounting evidence of the long-term effects of COVID-19 on the central nervous system, with patients experiencing diverse symptoms, often suggesting brain involvement. Conventional brain MRI of these patients shows unspecific patterns, with no clear connection of the symptomatology to brain tissue abnormalities, whereas diffusion tensor studies and volumetric analyses detect measurable changes in the brain after COVID-19. Diffusion MRI exploits the random motion of water molecules to achieve unique sensitivity to structures at the microscopic level, and new sequences employing generalized diffusion encoding provide structural information which are sensitive to intravoxel features. In this observational study, a total of 32 persons were investigated: 16 patients previously hospitalized for COVID-19 with persisting symptoms of post-COVID condition (mean age 60 years: range 41-79, all male) at 7-month follow-up and 16 matched controls, not previously hospitalized for COVID-19, with no post-COVID symptoms (mean age 58 years, range 46-69, 11 males). Standard MRI and generalized diffusion encoding MRI were employed to examine the brain white matter of the subjects. To detect possible group differences, several tissue microstructure descriptors obtainable with the employed diffusion sequence, the fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, microscopic anisotropy, orientational coherence (Cc) and variance in compartment's size (CMD) were analysed using the tract-based spatial statistics framework. The tract-based spatial statistics analysis showed widespread statistically significant differences (P < 0.05, corrected for multiple comparisons using the familywise error rate) in all the considered metrics in the white matter of the patients compared to the controls. Fractional anisotropy, microscopic anisotropy and Cc were lower in the patient group, while axial diffusivity, radial diffusivity, mean diffusivity and CMD were higher. Significant changes in fractional anisotropy, microscopic anisotropy and CMD affected approximately half of the analysed white matter voxels located across all brain lobes, while changes in Cc were mainly found in the occipital parts of the brain. Given the predominant alteration in microscopic anisotropy compared to Cc, the observed changes in diffusion anisotropy are mostly due to loss of local anisotropy, possibly connected to axonal damage, rather than white matter fibre coherence disruption. The increase in radial diffusivity is indicative of demyelination, while the changes in mean diffusivity and CMD are compatible with vasogenic oedema. In summary, these widespread alterations of white matter microstructure are indicative of vasogenic oedema, demyelination and axonal damage. These changes might be a contributing factor to the diversity of central nervous system symptoms that many patients experience after COVID-19.
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Affiliation(s)
- Deneb Boito
- Department of Biomedical Engineering, Linköping University, S-58183 Linköping, Sweden
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, S-58183 Linköping, Sweden
| | - Anders Eklund
- Department of Biomedical Engineering, Linköping University, S-58183 Linköping, Sweden
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, S-58183 Linköping, Sweden
- Division of Statistics and Machine learning, Department of Computer and Information Science, Linköping University, S-58183 Linköping, Sweden
| | - Anders Tisell
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, S-58183 Linköping, Sweden
- Department of Radiation Physics, Linköping University, S-58185 Linköping, Sweden
- Department of Health, Medicine and Caring Sciences, Linköping University, S58183 Linköping, Sweden
| | - Richard Levi
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, S-58183 Linköping, Sweden
- Department of Health, Medicine and Caring Sciences, Linköping University, S58183 Linköping, Sweden
- Department of Rehabilitation Medicine in Linköping, Linköping University, S-58185 Linköping, Sweden
| | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, S-58183 Linköping, Sweden
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, S-58183 Linköping, Sweden
| | - Ida Blystad
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, S-58183 Linköping, Sweden
- Department of Health, Medicine and Caring Sciences, Linköping University, S58183 Linköping, Sweden
- Department of Radiology in Linköping, Linköping University, S-58185 Linköping, Sweden
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Morez J, Szczepankiewicz F, den Dekker AJ, Vanhevel F, Sijbers J, Jeurissen B. Optimal experimental design and estimation for q-space trajectory imaging. Hum Brain Mapp 2023; 44:1793-1809. [PMID: 36564927 PMCID: PMC9921251 DOI: 10.1002/hbm.26175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 11/10/2022] [Accepted: 11/14/2022] [Indexed: 12/25/2022] Open
Abstract
Tensor-valued diffusion encoding facilitates data analysis by q-space trajectory imaging. By modeling the diffusion signal of heterogeneous tissues with a diffusion tensor distribution (DTD) and modulating the encoding tensor shape, this novel approach allows disentangling variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To facilitate the estimation of the DTD parameters, a parsimonious acquisition scheme coupled with an accurate and precise estimation of the DTD is needed. In this work, we create two precision-optimized acquisition schemes: one that maximizes the precision of the raw DTD parameters, and another that maximizes the precision of the scalar measures derived from the DTD. The improved precision of these schemes compared to a naïve sampling scheme is demonstrated in both simulations and real data. Furthermore, we show that the weighted linear least squares (WLLS) estimator that uses the squared reciprocal of the noisy signal as weights can be biased, whereas the iteratively WLLS estimator with the squared reciprocal of the predicted signal as weights outperforms the conventional unweighted linear LS and nonlinear LS estimators in terms of accuracy and precision. Finally, we show that the use of appropriate constraints can considerably increase the precision of the estimator with only a limited decrease in accuracy.
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Affiliation(s)
- Jan Morez
- imec‐Vision Lab, Department of PhysicsUniversity of AntwerpAntwerpBelgium
- μNEURO Research Centre of ExcellenceUniversity of AntwerpAntwerpBelgium
| | | | - Arnold J. den Dekker
- imec‐Vision Lab, Department of PhysicsUniversity of AntwerpAntwerpBelgium
- μNEURO Research Centre of ExcellenceUniversity of AntwerpAntwerpBelgium
| | - Floris Vanhevel
- Department of RadiologyUniversity Hospital AntwerpAntwerpBelgium
| | - Jan Sijbers
- imec‐Vision Lab, Department of PhysicsUniversity of AntwerpAntwerpBelgium
- μNEURO Research Centre of ExcellenceUniversity of AntwerpAntwerpBelgium
| | - Ben Jeurissen
- imec‐Vision Lab, Department of PhysicsUniversity of AntwerpAntwerpBelgium
- μNEURO Research Centre of ExcellenceUniversity of AntwerpAntwerpBelgium
- Lab for Equilibrium Investigations and Aerospace, Department of PhysicsUniversity of AntwerpAntwerpBelgium
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Syed Nasser N, Rajan S, Venugopal VK, Lasič S, Mahajan V, Mahajan H. A review on investigation of the basic contrast mechanism underlying multidimensional diffusion MRI in assessment of neurological disorders. J Clin Neurosci 2022; 102:26-35. [PMID: 35696817 DOI: 10.1016/j.jocn.2022.05.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 05/20/2022] [Accepted: 05/30/2022] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Multidimensional diffusion MRI (MDD MRI) is a novel diffusion technique that uses advanced gradient waveforms for microstructural tissue characterization to provide information about average rate, anisotropy and orientation of the diffusion and to disentangle the signal fraction from specific cell types i.e., elongated cells, isotropic cells and free water. AIM To review the diagnostic potential of MDD MRI in the clinical setting for microstructural tissue characterization in patients with neurological disorders to aid in patient care and treatment. METHOD A scoping review on the clinical applications of MDD MRI was conducted from original articles published in PubMed and Scopus from 2015 to 2021 using the keywords "Multidimensional diffusion MRI" OR "diffusion tensor distribution" OR "Tensor-Valued Diffusion" OR "b-tensor encoding" OR "microscopic diffusion anisotropy" OR "microscopic anisotropy" OR "microscopic fractional anisotropy" OR "double diffusion encoding" OR "triple diffusion encoding" OR "double pulsed field gradients" OR "double wave vector" OR "correlation tensor imaging" AND "brain" OR "axons". RESULTS Initially 145 articles were screened and after applying inclusion and exclusion criteria, nine articles were included in the final analysis. In most of these studies, microscopic diffusion anisotropy within the lesion showed deviation from the normal-appearing tissue. CONCLUSION Multidimensional diffusion MRI can provide better quantification and visualization of tissue microstructure than conventional diffusion MRI and can be used in the clinical setting for diagnosis of neurological disorders.
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Affiliation(s)
| | - Sriram Rajan
- Department of Radiology, Mahajan Imaging, New Delhi, India
| | | | | | | | - Harsh Mahajan
- CARPL.ai, New Delhi, India; Department of Radiology, Mahajan Imaging, New Delhi, India
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Karan P, Reymbaut A, Gilbert G, Descoteaux M. Bridging the gap between constrained spherical deconvolution and diffusional variance decomposition via tensor-valued diffusion MRI. Med Image Anal 2022; 79:102476. [DOI: 10.1016/j.media.2022.102476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 03/29/2022] [Accepted: 05/03/2022] [Indexed: 10/18/2022]
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Kornaropoulos EN, Winzeck S, Rumetshofer T, Wikstrom A, Knutsson L, Correia MM, Sundgren PC, Nilsson M. Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus. Front Neurol 2022; 13:837385. [PMID: 35557624 PMCID: PMC9087851 DOI: 10.3389/fneur.2022.837385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.
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Affiliation(s)
- Evgenios N. Kornaropoulos
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
| | - Stefan Winzeck
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
- BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom
| | | | - Anna Wikstrom
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Marta M. Correia
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Pia C. Sundgren
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
- Lund University BioImaging Center, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
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Novello L, Henriques RN, Ianuş A, Feiweier T, Shemesh N, Jovicich J. In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner. Neuroimage 2022; 254:119137. [PMID: 35339682 DOI: 10.1016/j.neuroimage.2022.119137] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 02/17/2022] [Accepted: 03/22/2022] [Indexed: 12/15/2022] Open
Abstract
Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusional Kurtosis MRI (DKI) quantifies the degree of non-gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mapping in neural tissue. However, the specificity of DKI is limited as different sources can contribute to the total intravoxel diffusional kurtosis, including: variance in diffusion tensor magnitudes (Kiso), variance due to diffusion anisotropy (Kaniso), and microscopic kurtosis (μK) related to restricted diffusion, microstructural disorder, and/or exchange. Interestingly, μK is typically ignored in diffusion MRI signal modeling as it is assumed to be negligible in neural tissues. However, recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation, revealing non negligible μK in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the sources of total kurtosis in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of μK (and the other kurtosis sources) in the healthy brain. Using this clinical CTI approach, we find that μK significantly contributes to total diffusional kurtosis both in gray and white matter tissue but, as expected, not in the ventricles. The first μK maps of the human brain are presented, revealing that the spatial distribution of μK provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. Moreover, group average templates of these kurtosis sources have been generated for the first time, which corroborated our findings at the underlying individual-level maps. We further show that the common practice of ignoring μK and assuming the multiple gaussian component approximation for kurtosis source estimation introduces significant bias in the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue.
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Affiliation(s)
- Lisa Novello
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy.
| | | | - Andrada Ianuş
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | | | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Jorge Jovicich
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy
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Li X, Sawamura D, Hamaguchi H, Urushibata Y, Feiweier T, Ogawa K, Tha KK. Microscopic Fractional Anisotropy Detects Cognitive Training-Induced Microstructural Brain Changes. Tomography 2022; 8:33-44. [PMID: 35076639 PMCID: PMC8788549 DOI: 10.3390/tomography8010004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 12/17/2021] [Accepted: 12/23/2021] [Indexed: 11/17/2022] Open
Abstract
Cognitive training-induced neuroplastic brain changes have been reported. This prospective study evaluated whether microscopic fractional anisotropy (μFA) derived from double diffusion encoding (DDE) MRI could detect brain changes following a 4 week cognitive training. Twenty-nine healthy volunteers were recruited and randomly assigned into the training (n = 21) and control (n = 8) groups. Both groups underwent brain MRI including DDE MRI and 3D-T1-weighted imaging twice at an interval of 4–6 weeks, during which the former underwent the training. The training consisted of hour-long dual N-back and attention network tasks conducted five days per week. Training and time-related changes of DDE MRI indices (μFA, fractional anisotropy (FA), and mean diffusivity (MD)) and the gray and white matter volume were evaluated using mixed-design analysis of variance. In addition, any significant imaging indices were tested for correlation with cognitive training-induced task performance changes, using partial correlation analyses. μFA in the left middle frontal gyrus decreased upon the training (53 voxels, uncorrected p < 0.001), which correlated moderately with response time changes in the orienting component of attention (r = −0.521, uncorrected p = 0.032). No significant training and time-related changes were observed for other imaging indices. Thus, μFA can become a sensitive index to detect cognitive training-induced neuroplastic changes.
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Affiliation(s)
- Xinnan Li
- Laboratory for Biomarker Imaging Science, Hokkaido University Graduate School of Biomedical Science and Engineering, Sapporo 060-8638, Japan; (X.L.); (H.H.)
| | - Daisuke Sawamura
- Department of Rehabilitation Science, Hokkaido University Faculty of Health Sciences, Sapporo 060-0812, Japan;
| | - Hiroyuki Hamaguchi
- Laboratory for Biomarker Imaging Science, Hokkaido University Graduate School of Biomedical Science and Engineering, Sapporo 060-8638, Japan; (X.L.); (H.H.)
| | | | | | - Keita Ogawa
- Department of Rehabilitation, Hokkaido University Hospital, Sapporo 060-8648, Japan;
| | - Khin Khin Tha
- Laboratory for Biomarker Imaging Science, Hokkaido University Graduate School of Biomedical Science and Engineering, Sapporo 060-8638, Japan; (X.L.); (H.H.)
- Global Center for Biomedical Science and Engineering, Hokkaido University Faculty of Medicine, Sapporo 060-8638, Japan
- Correspondence: ; Tel.: +81-11-706-8183
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9
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Yoo J, Kerkelä L, Hales PW, Seunarine KK, Clark CA. High-resolution microscopic diffusion anisotropy imaging in the human hippocampus at 3T. Magn Reson Med 2021; 87:1903-1913. [PMID: 34841566 DOI: 10.1002/mrm.29104] [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: 05/26/2021] [Revised: 11/10/2021] [Accepted: 11/10/2021] [Indexed: 11/06/2022]
Abstract
PURPOSE Several neurological conditions are associated with microstructural changes in the hippocampus that can be observed using DWI. Imaging studies often use protocols with whole-brain coverage, imposing limits on image resolution and worsening partial-volume effects. Also, conventional single-diffusion-encoding methods confound microscopic diffusion anisotropy with size variance of microscopic diffusion environments. This study addresses these issues by implementing a multidimensional diffusion-encoding protocol for microstructural imaging of the hippocampus at high resolution. METHODS The hippocampus of 8 healthy volunteers was imaged at 1.5-mm isotropic resolution with a multidimensional diffusion-encoding sequence developed in house. Microscopic fractional anisotropy (µFA) and normalized size variance (CMD ) were estimated using q-space trajectory imaging, and their values were compared with DTI metrics. The overall scan time was 1 hour. The reproducibility of the protocol was confirmed with scan-rescan experiments, and a shorter protocol (14 minutes) was defined for situations with time constraints. RESULTS Mean µFA (0.47) was greater than mean FA (0.20), indicating orientation dispersion in hippocampal tissue microstructure. Mean CMD was 0.17. The reproducibility of q-space trajectory imaging metrics was comparable to DTI, and microstructural metrics in the healthy hippocampus are reported. CONCLUSION This work shows the feasibility of high-resolution microscopic anisotropy imaging in the human hippocampus at 3 T and provides reference values for microstructural metrics in a healthy hippocampus.
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Affiliation(s)
- Jiyoon Yoo
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Leevi Kerkelä
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Patrick W Hales
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Kiran K Seunarine
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Christopher A Clark
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
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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] [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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11
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Henriques RN, Jespersen SN, Shemesh N. Evidence for microscopic kurtosis in neural tissue revealed by correlation tensor MRI. Magn Reson Med 2021; 86:3111-3130. [PMID: 34329509 PMCID: PMC9290035 DOI: 10.1002/mrm.28938] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 07/01/2021] [Accepted: 07/04/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE The impact of microscopic diffusional kurtosis (µK), arising from restricted diffusion and/or structural disorder, remains a controversial issue in contemporary diffusion MRI (dMRI). Recently, correlation tensor imaging (CTI) was introduced to disentangle the sources contributing to diffusional kurtosis, without relying on a-priori multi-gaussian component (MGC) or other microstructural assumptions. Here, we investigated µK in in vivo rat brains and assessed its impact on state-of-the-art methods ignoring µK. THEORY AND METHODS CTI harnesses double diffusion encoding (DDE) experiments, which were here improved for speed and minimal bias using four different sets of acquisition parameters. The robustness of the improved CTI protocol was assessed via simulations. In vivo CTI acquisitions were performed in healthy rat brains using a 9.4T pre-clinical scanner equipped with a cryogenic coil, and targeted the estimation of µK, anisotropic kurtosis, and isotropic kurtosis. RESULTS The improved CTI acquisition scheme substantially reduces scan time and importantly, also minimizes higher-order-term biases, thus enabling robust µK estimation, alongside Kaniso and Kiso metrics. Our CTI experiments revealed positive µK both in white and gray matter of the rat brain in vivo; µK is the dominant kurtosis source in healthy gray matter tissue. The non-negligible µK substantially were found to bias prior MGC analyses of Kiso and Kaniso . CONCLUSIONS Correlation Tensor MRI offers a more accurate and robust characterization of kurtosis sources than its predecessors. µK is non-negligible in vivo in healthy white and gray matter tissues and could be an important biomarker for future studies. Our findings thus have both theoretical and practical implications for future dMRI research.
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Affiliation(s)
| | - Sune N Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Clinical Institute, Aarhus University, Aarhus, Denmark.,Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
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Herberthson M, Boito D, Haije TD, Feragen A, Westin CF, Özarslan E. Q-space trajectory imaging with positivity constraints (QTI+). Neuroimage 2021; 238:118198. [PMID: 34029738 PMCID: PMC9596133 DOI: 10.1016/j.neuroimage.2021.118198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 05/02/2021] [Accepted: 05/20/2021] [Indexed: 01/18/2023] Open
Abstract
Q-space trajectory imaging (QTI) enables the estimation of useful scalar measures indicative of the local tissue structure. This is accomplished by employing generalized gradient waveforms for diffusion sensitization alongside a diffusion tensor distribution (DTD) model. The first two moments of the underlying DTD are made available by acquisitions at low diffusion sensitivity (b-values). Here, we show that three independent conditions have to be fulfilled by the mean and covariance tensors associated with distributions of symmetric positive semidefinite tensors. We introduce an estimation framework utilizing semi-definite programming (SDP) to guarantee that these conditions are met. Applying the framework on simulated signal profiles for diffusion tensors distributed according to non-central Wishart distributions demonstrates the improved noise resilience of QTI+ over the commonly employed estimation methods. Our findings on a human brain data set also reveal pronounced improvements, especially so for acquisition protocols featuring few number of volumes. Our method’s robustness to noise is expected to not only improve the accuracy of the estimates, but also enable a meaningful interpretation of contrast in the derived scalar maps. The technique’s performance on shorter acquisitions could make it feasible in routine clinical practice.
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Affiliation(s)
| | - Deneb Boito
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
| | - Tom Dela Haije
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
| | - Aasa Feragen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.
| | - Carl-Fredrik Westin
- Laboratory for Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
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Kamagata K, Andica C, Kato A, Saito Y, Uchida W, Hatano T, Lukies M, Ogawa T, Takeshige-Amano H, Akashi T, Hagiwara A, Fujita S, Aoki S. Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases. Int J Mol Sci 2021; 22:ijms22105216. [PMID: 34069159 PMCID: PMC8155849 DOI: 10.3390/ijms22105216] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/10/2021] [Accepted: 05/10/2021] [Indexed: 12/27/2022] Open
Abstract
There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.
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Affiliation(s)
- Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
- Correspondence:
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
| | - Ayumi Kato
- Department of Multidisciplinary Internal Medicine, Faculty of Medicine, Tottori University, Yonago 683-8504, Japan;
| | - Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
| | - Taku Hatano
- Department of Neurology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (T.H.); (T.O.); (H.T.-A.)
| | - Matthew Lukies
- Department of Diagnostic and Interventional Radiology, Alfred Health, Melbourne, VIC 3004, Australia;
| | - Takashi Ogawa
- Department of Neurology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (T.H.); (T.O.); (H.T.-A.)
| | - Haruka Takeshige-Amano
- Department of Neurology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (T.H.); (T.O.); (H.T.-A.)
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
| | - Shohei Fujita
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
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Naranjo ID, Reymbaut A, Brynolfsson P, Lo Gullo R, Bryskhe K, Topgaard D, Giri DD, Reiner JS, Thakur SB, Pinker-Domenig K. Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study. Cancers (Basel) 2021; 13:1606. [PMID: 33807205 PMCID: PMC8037718 DOI: 10.3390/cancers13071606] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 03/29/2021] [Indexed: 12/19/2022] Open
Abstract
Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting. Data acquired via custom MDD sequences was processed using an algorithm estimating non-parametric diffusion tensor distributions. The statistical descriptors of these distributions allow us to quantify tissue composition in terms of metrics informing on cell densities, shapes, and orientations. Additionally, signal fractions from specific cell types, such as elongated cells (bin1), isotropic cells (bin2), and free water (bin3), were teased apart. Histogram analysis in cancers and healthy breast tissue showed that cancers exhibited lower mean values of "size" (1.43 ± 0.54 × 10-3 mm2/s) and higher mean values of "shape" (0.47 ± 0.15) corresponding to bin1, while FGT (fibroglandular breast tissue) presented higher mean values of "size" (2.33 ± 0.22 × 10-3 mm2/s) and lower mean values of "shape" (0.27 ± 0.11) corresponding to bin3 (p < 0.001). Invasive carcinomas showed significant differences in mean signal fractions from bin1 (0.64 ± 0.13 vs. 0.4 ± 0.25) and bin3 (0.18 ± 0.08 vs. 0.42 ± 0.21) compared to ductal carcinomas in situ (DCIS) and invasive carcinomas with associated DCIS (p = 0.03). MDD enabled qualitative and quantitative evaluation of the composition of breast cancers and healthy glands.
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Affiliation(s)
- Isaac Daimiel Naranjo
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th Street, New York, NY 10065, USA; (I.D.N.); (R.L.G.); (J.S.R.); (S.B.T.)
- Department of Radiology, Breast Imaging Service, Guy’s and St. Thomas’ NHS Trust, Great Maze Pond, London SE1 9RT, UK
| | - Alexis Reymbaut
- Random Walk Imaging AB, SE-22002 Lund, Sweden; (A.R.); (P.B.); (K.B.)
| | - Patrik Brynolfsson
- Random Walk Imaging AB, SE-22002 Lund, Sweden; (A.R.); (P.B.); (K.B.)
- NONPI Medical AB, SE-90738 Umeå, Sweden
| | - Roberto Lo Gullo
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th Street, New York, NY 10065, USA; (I.D.N.); (R.L.G.); (J.S.R.); (S.B.T.)
| | - Karin Bryskhe
- Random Walk Imaging AB, SE-22002 Lund, Sweden; (A.R.); (P.B.); (K.B.)
| | - Daniel Topgaard
- Department of Chemistry, Lund University, SE-22100 Lund, Sweden;
| | - Dilip D. Giri
- Memorial Sloan Kettering Cancer Center, Department of Pathology, 1275 York Ave, New York, NY 10065, USA;
| | - Jeffrey S. Reiner
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th Street, New York, NY 10065, USA; (I.D.N.); (R.L.G.); (J.S.R.); (S.B.T.)
| | - Sunitha B. Thakur
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th Street, New York, NY 10065, USA; (I.D.N.); (R.L.G.); (J.S.R.); (S.B.T.)
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Ave, New York, NY 10065, USA
| | - Katja Pinker-Domenig
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th Street, New York, NY 10065, USA; (I.D.N.); (R.L.G.); (J.S.R.); (S.B.T.)
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