1
|
Gomolka RS, Hablitz LM, Mestre H, Giannetto M, Du T, Hauglund NL, Xie L, Peng W, Martinez PM, Nedergaard M, Mori Y. Loss of aquaporin-4 results in glymphatic system dysfunction via brain-wide interstitial fluid stagnation. eLife 2023; 12:82232. [PMID: 36757363 PMCID: PMC9995113 DOI: 10.7554/elife.82232] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 02/08/2023] [Indexed: 02/10/2023] Open
Abstract
The glymphatic system is a fluid transport network of cerebrospinal fluid (CSF) entering the brain along arterial perivascular spaces, exchanging with interstitial fluid (ISF), ultimately establishing directional clearance of interstitial solutes. CSF transport is facilitated by the expression of aquaporin-4 (AQP4) water channels on the perivascular endfeet of astrocytes. Mice with genetic deletion of AQP4 (AQP4 KO) exhibit abnormalities in the brain structure and molecular water transport. Yet, no studies have systematically examined how these abnormalities in structure and water transport correlate with glymphatic function. Here, we used high-resolution 3D magnetic resonance (MR) non-contrast cisternography, diffusion-weighted MR imaging (MR-DWI) along with intravoxel-incoherent motion (IVIM) DWI, while evaluating glymphatic function using a standard dynamic contrast-enhanced MR imaging to better understand how water transport and glymphatic function is disrupted after genetic deletion of AQP4. AQP4 KO mice had larger interstitial spaces and total brain volumes resulting in higher water content and reduced CSF space volumes, despite similar CSF production rates and vascular density compared to wildtype mice. The larger interstitial fluid volume likely resulted in increased slow but not fast MR diffusion measures and coincided with reduced glymphatic influx. This markedly altered brain fluid transport in AQP4 KO mice may result from a reduction in glymphatic clearance, leading to enlargement and stagnation of fluid in the interstitial space. Overall, diffusion MR is a useful tool to evaluate glymphatic function and may serve as valuable translational biomarker to study glymphatics in human disease.
Collapse
Affiliation(s)
| | - Lauren M Hablitz
- Center for Translational Neuromedicine, University of Rochester Medical CenterRochesterUnited States
| | - Humberto Mestre
- Center for Translational Neuromedicine, University of Rochester Medical CenterRochesterUnited States
- Department of Neurology, University of PennsylvaniaPhiladelphiaUnited States
| | - Michael Giannetto
- Center for Translational Neuromedicine, University of Rochester Medical CenterRochesterUnited States
| | - Ting Du
- Center for Translational Neuromedicine, University of Rochester Medical CenterRochesterUnited States
- School of Pharmacy, China Medical UniversityShenyangChina
| | | | - Lulu Xie
- Center for Translational Neuromedicine, University of Rochester Medical CenterRochesterUnited States
| | - Weiguo Peng
- Center for Translational Neuromedicine, University of CopenhagenCopenhagenDenmark
- Center for Translational Neuromedicine, University of Rochester Medical CenterRochesterUnited States
| | | | - Maiken Nedergaard
- Center for Translational Neuromedicine, University of CopenhagenCopenhagenDenmark
- Center for Translational Neuromedicine, University of Rochester Medical CenterRochesterUnited States
| | - Yuki Mori
- Center for Translational Neuromedicine, University of CopenhagenCopenhagenDenmark
| |
Collapse
|
2
|
Cai TX, Williamson NH, Ravin R, Basser PJ. Disentangling the effects of restriction and exchange with diffusion exchange spectroscopy. FRONTIERS IN PHYSICS 2022; 10:805793. [PMID: 37063496 PMCID: PMC10104504 DOI: 10.3389/fphy.2022.805793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Diffusion exchange spectroscopy (DEXSY) is a multidimensional NMR technique that can reveal how water molecules exchange between compartments within heterogeneous media, such as biological tissue. Data from DEXSY experiments is typically processed using numerical inverse Laplace transforms (ILTs) to produce a diffusion-diffusion spectrum. A tacit assumption of this ILT approach is that the signal behavior is Gaussian - i.e., the spin echo intensity decays exponentially with the degree of diffusion weighting. The assumptions that underlie Gaussian signal behavior may be violated, however, depending on the gradient strength applied and the sample under study. We argue that non-Gaussian signal behavior due to restrictions is to be expected in the study of biological tissue using diffusion NMR. Further, we argue that this signal behavior can produce confounding features in the diffusion-diffusion spectra obtained from numerical ILTs of DEXSY data - entangling the effects of restriction and exchange. Specifically, restricted signal behavior can result in broadening of peaks and in the appearance of illusory exchanging compartments with distributed diffusivities, which pearl into multiple peaks if not highly regularized. We demonstrate these effects on simulated data. That said, we suggest the use of features in the signal acquisition domain that can be used to rapidly probe exchange without employing an ILT. We also propose a means to characterize the non-Gaussian signal behavior due to restrictions within a sample using DEXSY measurements with a near zero mixing time or storage interval. We propose a combined acquisition scheme to independently characterize restriction and exchange with various DEXSY measurements, which we term Restriction and Exchange from Equally-weighted Double and Single Diffusion Encodings (REEDS-DE). We test this method on ex vivo neonatal mouse spinal cord - a sample consisting primarily of gray matter - using a low-field, static gradient NMR system. In sum, we highlight critical shortcomings of prevailing DEXSY analysis methods that conflate the effects of restriction and exchange, and suggest a viable experimental approach to disentangle them.
Collapse
Affiliation(s)
- Teddy X. Cai
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Nathan H. Williamson
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA
- National Institute of General Medical Sciences, National Institutes of Health, Bethesda, Maryland, USA
| | - Rea Ravin
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA
- Celoptics, Rockville, Maryland, USA
| | - Peter J. Basser
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA
- Correspondence: Peter J. Basser, Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Building 13, Room 3W16, 13 South Drive, Bethesda, Maryland 20892-5772, USA,
| |
Collapse
|
3
|
Benjamini D, Iacono D, Komlosh ME, Perl DP, Brody DL, Basser PJ. Diffuse axonal injury has a characteristic multidimensional MRI signature in the human brain. Brain 2021; 144:800-816. [PMID: 33739417 DOI: 10.1093/brain/awaa447] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/11/2020] [Accepted: 10/11/2020] [Indexed: 02/01/2023] Open
Abstract
Axonal injury is a major contributor to the clinical symptomatology in patients with traumatic brain injury. Conventional neuroradiological tools, such as CT and MRI, are insensitive to diffuse axonal injury (DAI) caused by trauma. Diffusion tensor MRI parameters may change in DAI lesions; however, the nature of these changes is inconsistent. Multidimensional MRI is an emerging approach that combines T1, T2, and diffusion, and replaces voxel-averaged values with distributions, which allows selective isolation of specific potential abnormal components. By performing a combined post-mortem multidimensional MRI and histopathology study, we aimed to investigate T1-T2-diffusion changes linked to DAI and to define their histopathological correlates. Corpora callosa derived from eight subjects who had sustained traumatic brain injury, and three control brain donors underwent post-mortem ex vivo MRI at 7 T. Multidimensional, diffusion tensor, and quantitative T1 and T2 MRI data were acquired and processed. Following MRI acquisition, slices from the same tissue were tested for amyloid precursor protein (APP) immunoreactivity to define DAI severity. A robust image co-registration method was applied to accurately match MRI-derived parameters and histopathology, after which 12 regions of interest per tissue block were selected based on APP density, but blind to MRI. We identified abnormal multidimensional T1-T2, diffusion-T2, and diffusion-T1 components that are strongly associated with DAI and used them to generate axonal injury images. We found that compared to control white matter, mild and severe DAI lesions contained significantly larger abnormal T1-T2 component (P = 0.005 and P < 0.001, respectively), and significantly larger abnormal diffusion-T2 component (P = 0.005 and P < 0.001, respectively). Furthermore, within patients with traumatic brain injury the multidimensional MRI biomarkers differentiated normal-appearing white matter from mild and severe DAI lesions, with significantly larger abnormal T1-T2 and diffusion-T2 components (P = 0.003 and P < 0.001, respectively, for T1-T2; P = 0.022 and P < 0.001, respectively, for diffusion-T2). Conversely, none of the conventional quantitative MRI parameters were able to differentiate lesions and normal-appearing white matter. Lastly, we found that the abnormal T1-T2, diffusion-T1, and diffusion-T2 components and their axonal damage images were strongly correlated with quantitative APP staining (r = 0.876, P < 0.001; r = 0.727, P < 0.001; and r = 0.743, P < 0.001, respectively), while producing negligible intensities in grey matter and in normal-appearing white matter. These results suggest that multidimensional MRI may provide non-invasive biomarkers for detection of DAI, which is the pathological substrate for neurological disorders ranging from concussion to severe traumatic brain injury.
Collapse
Affiliation(s)
- Dan Benjamini
- Section on Quantitative Imaging and Tissue Sciences, The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
| | - Diego Iacono
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA.,Department of Neurology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA.,Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA.,Neuroscience Graduate Program, Department of Anatomy, Physiology, and Genetics, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA.,Motor Neuron Disorders Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Michal E Komlosh
- Section on Quantitative Imaging and Tissue Sciences, The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
| | - Daniel P Perl
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA
| | - David L Brody
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,Department of Neurology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA.,Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences, The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| |
Collapse
|
4
|
Avram AV, Sarlls JE, Basser PJ. Whole-Brain Imaging of Subvoxel T1-Diffusion Correlation Spectra in Human Subjects. Front Neurosci 2021; 15:671465. [PMID: 34177451 PMCID: PMC8232058 DOI: 10.3389/fnins.2021.671465] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/14/2021] [Indexed: 12/12/2022] Open
Abstract
T1 relaxation and water mobility generate eloquent MRI tissue contrasts with great diagnostic value in many neuroradiological applications. However, conventional methods do not adequately quantify the microscopic heterogeneity of these important biophysical properties within a voxel, and therefore have limited biological specificity. We describe a new correlation spectroscopic (CS) MRI method for measuring how T1 and mean diffusivity (MD) co-vary in microscopic tissue environments. We develop a clinical pulse sequence that combines inversion recovery (IR) with single-shot isotropic diffusion encoding (IDE) to efficiently acquire whole-brain MRIs with a wide range of joint T1-MD weightings. Unlike conventional diffusion encoding, the IDE preparation ensures that all subvoxel water pools are weighted by their MDs regardless of the sizes, shapes, and orientations of their corresponding microscopic diffusion tensors. Accordingly, IR-IDE measurements are well-suited for model-free, quantitative spectroscopic analysis of microscopic water pools. Using numerical simulations, phantom experiments, and data from healthy volunteers we demonstrate how IR-IDE MRIs can be processed to reconstruct maps of two-dimensional joint probability density functions, i.e., correlation spectra, of subvoxel T1-MD values. In vivo T1-MD spectra show distinct cerebrospinal fluid and parenchymal tissue components specific to white matter, cortical gray matter, basal ganglia, and myelinated fiber pathways, suggesting the potential for improved biological specificity. The one-dimensional marginal distributions derived from the T1-MD correlation spectra agree well with results from other relaxation spectroscopic and quantitative MRI studies, validating the T1-MD contrast encoding and the spectral reconstruction. Mapping subvoxel T1-diffusion correlations in patient populations may provide a more nuanced, comprehensive, sensitive, and specific neuroradiological assessment of the non-specific changes seen on fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted MRIs (DWIs) in cancer, ischemic stroke, or brain injury.
Collapse
Affiliation(s)
- Alexandru V Avram
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States.,Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States
| | - Joelle E Sarlls
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Peter J Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
| |
Collapse
|
5
|
Slator PJ, Hutter J, Marinescu RV, Palombo M, Jackson LH, Ho A, Chappell LC, Rutherford M, Hajnal JV, Alexander DC. Data-Driven multi-Contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping. Med Image Anal 2021; 71:102045. [PMID: 33934005 PMCID: PMC8543043 DOI: 10.1016/j.media.2021.102045] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 02/08/2021] [Accepted: 03/16/2021] [Indexed: 11/19/2022]
Abstract
Unsupervised learning technique for spectroscopic analysis of quantitative MRI. Shares information across voxels to improve estimation of multi-dimensional or single-dimensional spectra. Spectral maps are dramatically improved compared to existing approaches. Can potentially identify and map tissue environments; in placental diffusion-relaxometry MRI we demonstrate that it identifies components that correspond to distinct tissue types.
We introduce and demonstrate an unsupervised machine learning technique for spectroscopic analysis of quantitative MRI experiments. Our algorithm supports estimation of one-dimensional spectra from single-contrast data, and multidimensional correlation spectra from simultaneous multi-contrast data. These spectrum-based approaches allow model-free investigation of tissue properties, but require regularised inversion of a Laplace transform or Fredholm integral, which is an ill-posed calculation. Here we present a method that addresses this limitation in a data-driven way. The algorithm simultaneously estimates a canonical basis of spectral components and voxelwise maps of their weightings, thereby pooling information across whole images to regularise the ill-posed problem. We show in simulations that our algorithm substantially outperforms current voxelwise spectral approaches. We demonstrate the method on multi-contrast diffusion-relaxometry placental MRI scans, revealing anatomically-relevant sub-structures, and identifying dysfunctional placentas. Our algorithm vastly reduces the data required to reliably estimate spectra, opening up the possibility of quantitative MRI spectroscopy in a wide range of new applications. Our InSpect code is available at github.com/paddyslator/inspect.
Collapse
Affiliation(s)
- Paddy J Slator
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK.
| | - Jana Hutter
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Razvan V Marinescu
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Laurence H Jackson
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Alison Ho
- Women's Health Department, King's College London, London, UK
| | - Lucy C Chappell
- Women's Health Department, King's College London, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, Kings College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| |
Collapse
|
6
|
Reymbaut A, Caron AV, Gilbert G, Szczepankiewicz F, Nilsson M, Warfield SK, Descoteaux M, Scherrer B. Magic DIAMOND: Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding. Med Image Anal 2021; 70:101988. [PMID: 33611054 DOI: 10.1016/j.media.2021.101988] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 01/25/2021] [Accepted: 01/29/2021] [Indexed: 01/05/2023]
Abstract
Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distributions of diffusion tensors. It models each sub-voxel fascicle separately, resolving crossing white-matter pathways and allowing for a fascicle-element (fixel) based analysis of microstructural features. Alternatively, specific features of the intra-voxel diffusion tensor distribution can be selectively measured using tensor-valued diffusion-weighted acquisition schemes. However, the impact of such schemes on estimating brain microstructural features has only been studied in a handful of parametric single-fascicle models. In this work, we derive a general Laplace transform for the non-central matrix-variate Gamma distribution, which enables the extension of DIAMOND to tensor-valued encoded data. We then evaluate this "Magic DIAMOND" model in silico and in vivo on various combinations of tensor-valued encoded data. Assessing uncertainty on parameter estimation via stratified bootstrap, we investigate both voxel-based and fixel-based metrics by carrying out multi-peak tractography. We demonstrate using in silico evaluations that tensor-valued diffusion encoding significantly improves Magic DIAMOND's accuracy. Most importantly, we show in vivo that our estimated metrics can be robustly mapped along tracks across regions of fiber crossing, which opens new perspectives for tractometry and microstructure mapping along specific white-matter tracts.
Collapse
Affiliation(s)
| | | | - Guillaume Gilbert
- MR Clinical Science, Philips Healthcare Canada, Markham, ON L6C 2S3, Canada
| | - Filip Szczepankiewicz
- Department of Clinical Sciences, Lund University, 22184, Lund, Sweden; Random Walk Imaging AB, 22224, Lund, Sweden
| | - Markus Nilsson
- Department of Clinical Sciences, Lund University, 22184, Lund, Sweden
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Boston, MA 02115, United States
| | | | - Benoit Scherrer
- Department of Radiology, Boston Children's Hospital, Boston, MA 02115, United States
| |
Collapse
|
7
|
Benjamini D, Basser PJ. Multidimensional correlation MRI. NMR IN BIOMEDICINE 2020; 33:e4226. [PMID: 31909516 PMCID: PMC11062766 DOI: 10.1002/nbm.4226] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 10/24/2019] [Accepted: 10/28/2019] [Indexed: 05/23/2023]
Abstract
Multidimensional correlation spectroscopy is emerging as a novel MRI modality that is well suited for microstructure and microdynamic imaging studies, especially of biological specimens. Conventional MRI methods only provide voxel-averaged and mostly macroscopically averaged information; these methods cannot disentangle intra-voxel heterogeneity on the basis of both water mobility and local chemical interactions. By correlating multiple MR contrast mechanisms and processing the data in an integrated manner, correlation spectroscopy is able to resolve the distribution of water populations according to their chemical and physical interactions with the environment. The use of a non-parametric, phenomenological representation of the multidimensional MR signal makes no assumptions about tissue structure, thereby allowing the study of microscopic structure and composition of complex heterogeneous biological systems. However, until recently, vast data requirements have confined these types of measurement to non-localized NMR applications and prevented them from being widely and successfully used in conjunction with imaging. Recent groundbreaking advancements have allowed this powerful NMR methodology to be migrated to MRI, initiating its emergence as a promising imaging approach. This review is not intended to cover the entire field of multidimensional MR; instead, it focuses on pioneering imaging applications and the challenges involved. In addition, the background and motivation that have led to multidimensional correlation MR development are discussed, along with the basic underlying mathematical concepts. The goal of the present work is to provide the reader with a fundamental understanding of the techniques developed and their potential benefits, and to provide guidance to help refine future applications of this technology.
Collapse
Affiliation(s)
- Dan Benjamini
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD, USA
| | - Peter J. Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| |
Collapse
|
8
|
Reymbaut A, Mezzani P, de Almeida Martins JP, Topgaard D. Accuracy and precision of statistical descriptors obtained from multidimensional diffusion signal inversion algorithms. NMR IN BIOMEDICINE 2020; 33:e4267. [PMID: 32067322 DOI: 10.1002/nbm.4267] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 01/15/2020] [Accepted: 01/16/2020] [Indexed: 05/22/2023]
Abstract
In biological tissues, typical MRI voxels comprise multiple microscopic environments, the local organization of which can be captured by microscopic diffusion tensors. The measured diffusion MRI signal can, therefore, be written as the multidimensional Laplace transform of an intravoxel diffusion tensor distribution (DTD). Tensor-valued diffusion encoding schemes have been designed to probe specific features of the DTD, and several algorithms have been introduced to invert such data and estimate statistical descriptors of the DTD, such as the mean diffusivity, the variance of isotropic diffusivities, and the mean squared diffusion anisotropy. However, the accuracy and precision of these estimations have not been assessed systematically and compared across methods. In this article, we perform and compare such estimations in silico for a one-dimensional Gamma fit, a generalized two-term cumulant approach, and two-dimensional and four-dimensional Monte-Carlo-based inversion techniques, using a clinically feasible tensor-valued acquisition scheme. In particular, we compare their performance at different signal-to-noise ratios (SNRs) for voxel contents varying in terms of the aforementioned statistical descriptors, orientational order, and fractions of isotropic and anisotropic components. We find that all inversion techniques share similar precision (except for a lower precision of the two-dimensional Monte Carlo inversion) but differ in terms of accuracy. While the Gamma fit exhibits infinite-SNR biases when the signal deviates strongly from monoexponentiality and is unaffected by orientational order, the generalized cumulant approach shows infinite-SNR biases when this deviation originates from the variance in isotropic diffusivities or from the low orientational order of anisotropic diffusion components. The two-dimensional Monte Carlo inversion shows remarkable accuracy in all systems studied, given that the acquisition scheme possesses enough directions to yield a rotationally invariant powder average. The four-dimensional Monte Carlo inversion presents no infinite-SNR bias, but suffers significantly from noise in the data, while preserving good contrast in most systems investigated.
Collapse
Affiliation(s)
- Alexis Reymbaut
- Physical Chemistry Department, Lund University, Lund, Sweden
- Random Walk Imaging AB, Lund, Sweden
| | - Paolo Mezzani
- Physical Chemistry Department, Lund University, Lund, Sweden
- Physics Department, Università degli Studi di Milano, Milan, Italy
| | | | - Daniel Topgaard
- Physical Chemistry Department, Lund University, Lund, Sweden
- Random Walk Imaging AB, Lund, Sweden
| |
Collapse
|
9
|
Benjamini D, Hutchinson EB, Komlosh ME, Comrie CJ, Schwerin SC, Zhang G, Pierpaoli C, Basser PJ. Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging. Neuroimage 2020; 221:117195. [PMID: 32726643 PMCID: PMC7805019 DOI: 10.1016/j.neuroimage.2020.117195] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 07/17/2020] [Accepted: 07/21/2020] [Indexed: 12/17/2022] Open
Abstract
We describe a practical two-dimensional (2D) diffusion MRI framework to deliver specificity and improve sensitivity to axonal injury in the spinal cord. This approach provides intravoxel distributions of correlations of water mobilities in orthogonal directions, revealing sub-voxel diffusion components. Here we use it to investigate water diffusivities along axial and radial orientations within spinal cord specimens with confirmed, tract-specific axonal injury. First, we show using transmission electron microscopy and immunohistochemistry that tract-specific axonal beading occurs following Wallerian degeneration in the cortico-spinal tract as direct sequelae to closed head injury. We demonstrate that although some voxel-averaged diffusion tensor imaging (DTI) metrics are sensitive to this axonal injury, they are non-specific, i.e., they do not reveal an underlying biophysical mechanism of injury. Then we employ 2D diffusion correlation imaging (DCI) to improve discrimination of different water microenvironments by measuring and mapping the joint water mobility distributions perpendicular and parallel to the spinal cord axis. We determine six distinct diffusion spectral components that differ according to their microscopic anisotropy and mobility. We show that at the injury site a highly anisotropic diffusion component completely disappears and instead becomes more isotropic. Based on these findings, an injury-specific MR image of the spinal cord was generated, and a radiological-pathological correlation with histological silver staining % area was performed. The resulting strong and significant correlation (r=0.70,p < 0.0001) indicates the high specificity with which DCI detects injury-induced tissue alterations. We predict that the ability to selectively image microstructural changes following axonal injury in the spinal cord can be useful in clinical and research applications by enabling specific detection and increased sensitivity to injury-induced microstructural alterations. These results also encourage us to translate DCI to higher spatial dimensions to enable assessment of traumatic axonal injury, and possibly other diseases and disorders in the brain.
Collapse
Affiliation(s)
- Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20817, USA; The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD 20814, USA.
| | - Elizabeth B Hutchinson
- The Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona 85721, USA
| | - Michal E Komlosh
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20817, USA; The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD 20814, USA
| | - Courtney J Comrie
- The Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona 85721, USA
| | - Susan C Schwerin
- The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD 20814, USA; Department of Anatomy, Physiology, and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
| | - Guofeng Zhang
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD 20817, USA
| | - Carlo Pierpaoli
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD 20817, USA
| | - Peter J Basser
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20817, USA
| |
Collapse
|
10
|
Fricke SN, Seymour JD, Battistel MD, Freedberg DI, Eads CD, Augustine MP. Data processing in NMR relaxometry using the matrix pencil. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2020; 313:106704. [PMID: 32179433 DOI: 10.1016/j.jmr.2020.106704] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 02/12/2020] [Accepted: 02/28/2020] [Indexed: 06/10/2023]
Abstract
The matrix pencil method (MPM) is explored for stable, reproducible data processing in nuclear magnetic resonance (NMR) relaxometry. Data from one-dimensional and two-dimensional relaxometry experiments designed to measure transverse relaxation T2, longitudinal relaxation T1, diffusion coefficient D values, and their correlations in a standard olive oil/water mixture serve as a platform available to any NMR spectroscopist to compare the performance of the MPM to the benchmark inverse Laplace transform (ILT). The data from two practical examples, including the drying of a solvent polymer system and the enzymatic digestion of polysialic acid, were also explored with the MPM and ILT. In the cases considered here, the MPM appears to outperform the ILT in terms of resolution and stability in the determination of fundamental constants for complex materials and mixtures.
Collapse
Affiliation(s)
- S N Fricke
- Department of Chemistry, 69 Chemistry Building, University of California, Davis, CA 95616, USA
| | - J D Seymour
- Department of Chemical and Biological Engineering, 306 Cobleigh Hall, Montana State University, Bozeman, MT 59717, USA
| | - M D Battistel
- Laboratory of Bacterial Polysaccharides, Center for Biologics Evaluation and Research, United States Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD 20993, USA
| | - D I Freedberg
- Laboratory of Bacterial Polysaccharides, Center for Biologics Evaluation and Research, United States Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD 20993, USA
| | - C D Eads
- The Procter & Gamble Company, 8700 S. Mason-Montgomery Road, Mason, OH 45040, USA
| | - M P Augustine
- Department of Chemistry, 69 Chemistry Building, University of California, Davis, CA 95616, USA.
| |
Collapse
|
11
|
Pas K, Komlosh ME, Perl DP, Basser PJ, Benjamini D. Retaining information from multidimensional correlation MRI using a spectral regions of interest generator. Sci Rep 2020; 10:3246. [PMID: 32094400 PMCID: PMC7040019 DOI: 10.1038/s41598-020-60092-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Accepted: 02/07/2020] [Indexed: 11/09/2022] Open
Abstract
Multidimensional correlation magnetic resonance imaging (MRI) is an emerging imaging modality that is capable of disentangling highly heterogeneous and opaque systems according to chemical and physical interactions of water within them. Using this approach, the conventional three dimensional MR scalar images are replaced with spatially resolved multidimensional spectra. The ensuing abundance in microstructural and chemical information is a blessing that incorporates a real challenge: how does one distill and refine it into images while retaining its significant components? In this paper we introduce a general framework that preserves the spectral information from spatially resolved multidimensional data. Equal weight is given to significant spectral components at the single voxel level, resulting in a summarized image spectrum. This spectrum is then used to define spectral regions of interest that are utilized to reconstruct images of sub-voxel components. Using numerical simulations we first show that, contrary to the conventional approach, the proposed framework preserves spectral resolution, and in turn, sensitivity and specificity of the reconstructed images. The retained spectral resolution allows, for the first time, to observe an array of distinct [Formula: see text]-[Formula: see text]-[Formula: see text] components images of the human brain. The robustly generated images of sub-voxel components overcome the limited spatial resolution of MRI, thus advancing multidimensional correlation MRI to fulfilling its full potential.
Collapse
Affiliation(s)
- Kristofor Pas
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, 20817, USA
- The Department of Biomedical Engineering, University of Texas at Arlington, Arlington, TX, 76010, USA
| | - Michal E Komlosh
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA
- The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD, 20814, USA
| | - Daniel P Perl
- The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD, 20814, USA
| | - Peter J Basser
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA
| | - Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA.
- The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD, 20814, USA.
| |
Collapse
|
12
|
Williamson NH, Ravin R, Benjamini D, Merkle H, Falgairolle M, O'Donovan MJ, Blivis D, Ide D, Cai TX, Ghorashi NS, Bai R, Basser PJ. Magnetic resonance measurements of cellular and sub-cellular membrane structures in live and fixed neural tissue. eLife 2019; 8:51101. [PMID: 31829935 PMCID: PMC6977971 DOI: 10.7554/elife.51101] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 12/11/2019] [Indexed: 12/21/2022] Open
Abstract
We develop magnetic resonance (MR) methods for real-time measurement of tissue microstructure and membrane permeability of live and fixed excised neonatal mouse spinal cords. Diffusion and exchange MR measurements are performed using the strong static gradient produced by a single-sided permanent magnet. Using tissue delipidation methods, we show that water diffusion is restricted solely by lipid membranes. Most of the diffusion signal can be assigned to water in tissue which is far from membranes. The remaining 25% can be assigned to water restricted on length scales of roughly a micron or less, near or within membrane structures at the cellular, organelle, and vesicle levels. Diffusion exchange spectroscopy measures water exchanging between membrane structures and free environments at 100 s-1.
Collapse
Affiliation(s)
- Nathan H Williamson
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States
| | - Rea Ravin
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States.,Celoptics, Rockville, United States
| | - Dan Benjamini
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States.,Center for Neuroscience and Regenerative Medicine, Henry Jackson Foundation, Bethesda, United States
| | - Hellmut Merkle
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
| | - Melanie Falgairolle
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
| | - Michael James O'Donovan
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
| | - Dvir Blivis
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
| | - Dave Ide
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States.,National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Teddy X Cai
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States
| | - Nima S Ghorashi
- Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, United States
| | - Ruiliang Bai
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States.,Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Peter J Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States
| |
Collapse
|
13
|
Slator PJ, Hutter J, Palombo M, Jackson LH, Ho A, Panagiotaki E, Chappell LC, Rutherford MA, Hajnal JV, Alexander DC. Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta. Magn Reson Med 2019; 82:95-106. [PMID: 30883915 PMCID: PMC6519240 DOI: 10.1002/mrm.27733] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 01/04/2019] [Accepted: 01/27/2019] [Indexed: 12/25/2022]
Abstract
PURPOSE A combined diffusion-relaxometry MR acquisition and analysis pipeline for in vivo human placenta, which allows for exploration of coupling between T 2 * and apparent diffusion coefficient (ADC) measurements in a sub 10-minute scan time. METHODS We present a novel acquisition combining a diffusion prepared spin echo with subsequent gradient echoes. The placentas of 17 pregnant women were scanned in vivo, including both healthy controls and participants with various pregnancy complications. We estimate the joint T 2 * -ADC spectra using an inverse Laplace transform. RESULTS T 2 * -ADC spectra demonstrate clear quantitative separation between normal and dysfunctional placentas. CONCLUSIONS Combined T 2 * -diffusivity MRI is promising for assessing fetal and maternal health during pregnancy. The T 2 * -ADC spectrum potentially provides additional information on tissue microstructure, compared to measuring these two contrasts separately. The presented method is immediately applicable to the study of other organs.
Collapse
Affiliation(s)
- Paddy J. Slator
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
| | - Jana Hutter
- Biomedical Engineering DepartmentKing’s College LondonLondonUnited Kingdom
- Centre for the Developing BrainKing’s College LondonLondonUnited Kingdom
| | - Marco Palombo
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
| | - Laurence H. Jackson
- Biomedical Engineering DepartmentKing’s College LondonLondonUnited Kingdom
- Centre for the Developing BrainKing’s College LondonLondonUnited Kingdom
| | - Alison Ho
- Women’s Health DepartmentKing’s College LondonLondonUnited Kingdom
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
| | - Lucy C. Chappell
- Women’s Health DepartmentKing’s College LondonLondonUnited Kingdom
| | - Mary A. Rutherford
- Centre for the Developing BrainKing’s College LondonLondonUnited Kingdom
| | - Joseph V. Hajnal
- Biomedical Engineering DepartmentKing’s College LondonLondonUnited Kingdom
- Centre for the Developing BrainKing’s College LondonLondonUnited Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
| |
Collapse
|
14
|
Lampinen B, Szczepankiewicz F, Novén M, van Westen D, Hansson O, Englund E, Mårtensson J, Westin C, Nilsson M. Searching for the neurite density with diffusion MRI: Challenges for biophysical modeling. Hum Brain Mapp 2019; 40:2529-2545. [PMID: 30802367 PMCID: PMC6503974 DOI: 10.1002/hbm.24542] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 01/17/2019] [Accepted: 02/03/2019] [Indexed: 12/19/2022] Open
Abstract
In vivo mapping of the neurite density with diffusion MRI (dMRI) is a high but challenging aim. First, it is unknown whether all neurites exhibit completely anisotropic ("stick-like") diffusion. Second, the "density" of tissue components may be confounded by non-diffusion properties such as T2 relaxation. Third, the domain of validity for the estimated parameters to serve as indices of neurite density is incompletely explored. We investigated these challenges by acquiring data with "b-tensor encoding" and multiple echo times in brain regions with low orientation coherence and in white matter lesions. Results showed that microscopic anisotropy from b-tensor data is associated with myelinated axons but not with dendrites. Furthermore, b-tensor data together with data acquired for multiple echo times showed that unbiased density estimates in white matter lesions require data-driven estimates of compartment-specific T2 values. Finally, the "stick" fractions of different biophysical models could generally not serve as neurite density indices across the healthy brain and white matter lesions, where outcomes of comparisons depended on the choice of constraints. In particular, constraining compartment-specific T2 values was ambiguous in the healthy brain and had a large impact on estimated values. In summary, estimating neurite density generally requires accounting for different diffusion and/or T2 properties between axons and dendrites. Constrained "index" parameters could be valid within limited domains that should be delineated by future studies.
Collapse
Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Medical Radiation PhysicsLund UniversityLundSweden
| | - Filip Szczepankiewicz
- Clinical Sciences Lund, Medical Radiation PhysicsLund UniversityLundSweden
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUS
| | - Mikael Novén
- Centre for Languages and LiteratureLund UniversityLundSweden
| | | | - Oskar Hansson
- Clinical Sciences Malmö, Clinical Memory Research UnitLund UniversityLundSweden
| | - Elisabet Englund
- Clinical Sciences Lund, Oncology and PathologyLund UniversityLundSweden
| | - Johan Mårtensson
- Clinical Sciences Lund, Department of Logopedics, Phoniatrics and AudiologyLund UniversityLundSweden
| | | | - Markus Nilsson
- Clinical Sciences Lund, RadiologyLund UniversityLundSweden
| |
Collapse
|
15
|
Topgaard D. Diffusion tensor distribution imaging. NMR IN BIOMEDICINE 2019; 32:e4066. [PMID: 30730586 PMCID: PMC6593682 DOI: 10.1002/nbm.4066] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 11/28/2018] [Accepted: 12/19/2018] [Indexed: 05/30/2023]
Abstract
Conventional diffusion MRI yields voxel-averaged parameters that suffer from ambiguities for heterogeneous anisotropic materials such as brain tissue. Using principles from solid-state NMR spectroscopy, we have previously introduced the shape of the diffusion encoding tensor as a separate acquisition dimension that disentangles isotropic and anisotropic contributions to the observed diffusivities, thereby allowing for unconstrained data inversion into diffusion tensor distributions with "size," "shape," and orientation dimensions. Here we combine our recent non-parametric data inversion algorithm and data acquisition protocol with an imaging pulse sequence to demonstrate spatial mapping of diffusion tensor distributions using a previously developed composite phantom with multiple isotropic and anisotropic components. We propose a compact format for visualizing two-dimensional arrays of the distributions, new scalar parameters quantifying intra-voxel heterogeneity, and a binning procedure giving maps of all relevant parameters for each of the components resolved in the multidimensional distribution space.
Collapse
Affiliation(s)
- Daniel Topgaard
- Physical Chemistry, Department of ChemistryLund UniversityLundSweden
| |
Collapse
|
16
|
Szczepankiewicz F, Sjölund J, Ståhlberg F, Lätt J, Nilsson M. Tensor-valued diffusion encoding for diffusional variance decomposition (DIVIDE): Technical feasibility in clinical MRI systems. PLoS One 2019; 14:e0214238. [PMID: 30921381 PMCID: PMC6438503 DOI: 10.1371/journal.pone.0214238] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 03/08/2019] [Indexed: 11/18/2022] Open
Abstract
Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularity within the MRI research community. Unlike conventional diffusion encoding-applied along a single direction in each shot-tensor-valued encoding employs diffusion encoding along multiple directions within a single preparation of the signal. The benefit is that such encoding may probe tissue features that are not accessible by conventional encoding. For example, diffusional variance decomposition (DIVIDE) takes advantage of tensor-valued encoding to probe microscopic diffusion anisotropy independent of orientation coherence. The drawback is that tensor-valued encoding generally requires gradient waveforms that are more demanding on hardware; it has therefore been used primarily in MRI systems with relatively high performance. The purpose of this work was to explore tensor-valued diffusion encoding on clinical MRI systems with varying performance to test its technical feasibility within the context of DIVIDE. We performed whole-brain imaging with linear and spherical b-tensor encoding at field strengths between 1.5 and 7 T, and at maximal gradient amplitudes between 45 and 80 mT/m. Asymmetric gradient waveforms were optimized numerically to yield b-values up to 2 ms/μm2. Technical feasibility was assessed in terms of the repeatability, SNR, and quality of DIVIDE parameter maps. Variable system performance resulted in echo times between 83 to 115 ms and total acquisition times of 6 to 9 minutes when using 80 signal samples and resolution 2×2×4 mm3. As expected, the repeatability, signal-to-noise ratio and parameter map quality depended on hardware performance. We conclude that tensor-valued encoding is feasible for a wide range of MRI systems-even at 1.5 T with maximal gradient waveform amplitudes of 33 mT/m-and baseline experimental design and quality parameters for all included configurations. This demonstrates that tissue features, beyond those accessible by conventional diffusion encoding, can be explored on a wide range of MRI systems.
Collapse
Affiliation(s)
- Filip Szczepankiewicz
- Lund University, Department of Clinical Sciences Lund, Medical Radiation Physics, Lund, Sweden
| | - Jens Sjölund
- Elekta Instrument AB, Kungstensgatan 18, Stockholm, Sweden
- Linköping University, Department of Biomedical Engineering, Linköping, Sweden
- Linköping University, Center for Medical Image Science and Visualization (CMIV), Linköping, Sweden
| | - Freddy Ståhlberg
- Lund University, Department of Clinical Sciences Lund, Medical Radiation Physics, Lund, Sweden
- Lund University, Department of Clinical Sciences Lund, Diagnostic Radiology, Lund, Sweden
| | - Jimmy Lätt
- Skåne University Hospital, Department of Imaging and Function, Lund, Sweden
| | - Markus Nilsson
- Lund University, Department of Clinical Sciences Lund, Diagnostic Radiology, Lund, Sweden
- Lund University, Lund University Bioimaging Center, Lund, Sweden
| |
Collapse
|
17
|
Benjamini D, Komlosh ME, Williamson NH, Basser PJ. Generalized Mean Apparent Propagator MRI to Measure and Image Advective and Dispersive Flows in Medicine and Biology. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:11-20. [PMID: 30010549 PMCID: PMC6345276 DOI: 10.1109/tmi.2018.2852259] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Water transport in biological systems spans different regimes with distinct physical behaviors: diffusion, advection, and dispersion. Identifying these regimes is of paramount importance in many in vivo applications, among them, measuring microcirculation of blood in capillary networks and cerebrospinal fluid transport in the glymphatic system. Diffusion magnetic resonance imaging (dMRI) can be used to encode water displacements, and a Fourier transform of the acquired signal furnishes a displacement probability density function known as the propagator. This transformation normally requires the use of a fast Fourier transform (FFT), which presents major feasibility challenges when scanning in vivo, mainly because of dense signal sampling, resulting in long acquisition times. A second approach to reconstruct the propagator is by using analytical representation of the signal, overcoming many of the FFT's limitations. In all analytical implementations of dMRI to date, the translational motion of water has been assumed to be exclusively diffusive, which is the case only in the absence of flow. However, retaining the phase information from the diffusion signal provides the ability to measure both mean coherent velocity and random diffusion from a single experiment. We implement and extend an analytical framework, mean apparent propagator (MAP), which can account for non-zero flow conditions. We call this method generalized MAP or GMAP. We describe a numerical optimization scheme and implement it on data from an MRI flow phantom constructed from a pack of 10- [Formula: see text] beads. The advantages of GMAP over the FFT-based method in the context of sampling density and low-flow detection were demonstrated, and analytically derived propagator moments were shown to agree with theoretical values even after data subsampling. GMAP would enable the detection of microflow in vivo that could help elucidate many important biological processes.
Collapse
|
18
|
Water mobility spectral imaging of the spinal cord: Parametrization of model-free Laplace MRI. Magn Reson Imaging 2018; 56:187-193. [PMID: 30584915 DOI: 10.1016/j.mri.2018.12.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Revised: 12/06/2018] [Accepted: 12/06/2018] [Indexed: 11/22/2022]
Abstract
Diffusion magnetic resonance imaging (dMRI) of biological systems most often results in non-monoexponential signal, due to their complexity and heterogeneity. One approach to interpreting dMRI data without imposing tissue microstructural models is to fit the signal to a multiexponential function, which is sometimes referred to as an inverse Laplace transformation, and to display the coefficients as a distribution of the diffusivities, or water mobility spectra. Until recently, this method has not been used in a voxelwise manner, mainly because of heavy data requirements. With recent advancements in processing and experimental design, voxelwise Laplace MRI approaches are becoming feasible and attractive. The rich spectral information, combined with a three-dimensional image, presents a challenge because it tremendously increases the dimensionality of the data and requires a robust method for interpretation and analysis. In this work, we suggest parameterizing the empirically measured water mobility spectra using a bimodal lognormal function. This approach allows for a compact representation of the spectrum, and it also resolves overlapping spectral peaks, which allows for a robust extraction of their signal fraction. We apply the method on a fixed spinal cord sample and use it to generate robust intensity images of slow- and fast-diffusion components. Using the parametric variables, we create novel image contrasts, among them the information entropy of the water mobility spectrum, which pack unique features of the individual diffusion regimes in the investigated system.
Collapse
|
19
|
Nielsen JS, Dyrby TB, Lundell H. Magnetic resonance temporal diffusion tensor spectroscopy of disordered anisotropic tissue. Sci Rep 2018; 8:2930. [PMID: 29440724 PMCID: PMC5811563 DOI: 10.1038/s41598-018-19475-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 12/17/2017] [Indexed: 01/09/2023] Open
Abstract
Molecular diffusion measured with diffusion weighted MRI (DWI) offers a probe for tissue microstructure. However, inferring microstructural properties from conventional DWI data is a complex inverse problem and has to account for heterogeneity in sizes, shapes and orientations of the tissue compartments contained within an imaging voxel. Alternative experimental means for disentangling the signal signatures of such features could provide a stronger link between the data and its interpretation. Double diffusion encoding (DDE) offers the possibility to factor out variation in compartment shapes from orientational dispersion of anisotropic domains by measuring the correlation between diffusivity in multiple directions. Time dependence of the diffusion is another effect reflecting the dimensions and distributions of barriers. In this paper we extend on DDE with a modified version of the oscillating gradient spin echo (OGSE) experiment, giving a basic contrast mechanism closely linked to both the temporal diffusion spectrum and the compartment anisotropy. We demonstrate our new method on post mortem brain tissue and show that we retrieve the correct temporal diffusion tensor spectrum in synthetic data from Monte Carlo simulations of random walks in a range of disordered geometries of different sizes and shapes.
Collapse
Affiliation(s)
- Jonathan Scharff Nielsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark.
| |
Collapse
|
20
|
Benjamini D, Basser PJ. Magnetic resonance microdynamic imaging reveals distinct tissue microenvironments. Neuroimage 2017; 163:183-196. [PMID: 28943412 DOI: 10.1016/j.neuroimage.2017.09.033] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2017] [Revised: 09/12/2017] [Accepted: 09/18/2017] [Indexed: 10/18/2022] Open
Abstract
Magnetic resonance imaging (MRI) provides a powerful set of tools with which to investigate biological tissues noninvasively and in vivo. Tissues are heterogeneous in nature; an imaging voxel contains an ensemble of different cells and extracellular matrix components. A long-standing challenge has been to infer the content of and interactions among these microscopic tissue components within a macroscopic imaging voxel. Spatially resolved multidimensional relaxation-diffusion correlation (REDCO) spectroscopy holds the potential to deliver such microdynamic information. However, to date, vast data requirements have mostly relegated these type of measurements to nuclear magnetic resonance applications and prevented them from being widely and successfully used in conjunction with imaging. By using a novel data acquisition and processing strategy in this study, spatially resolved REDCO could be performed in reasonable scanning times with excellent prospects for clinical applications. This new MR imaging framework-which we term "magnetic resonance microdynamic imaging (MRMI)"-permits the simultaneous noninvasive and model-free quantification of multiple subcellular, cellular, and interstitial tissue microenvironments within a voxel. MRMI is demonstrated with a fixed spinal cord specimen, enabling the quantification of microscopic tissue components with unprecedented specificity. Tissue components, such as axons, neuronal and glial soma, and myelin were identified on the basis of their multispectral signature within individual imaging voxels. These tissue elements could then be composed into images and be correlated with immunohistochemistry findings. MRMI provides novel image contrasts of tissue components and a new family of microdynamic biomarkers that could lead to new diagnostic imaging approaches to probe biological tissue alterations accompanied by pathological or developmental changes.
Collapse
Affiliation(s)
- Dan Benjamini
- Section on Quantitative Imaging and Tissue Sciences, NICHD, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences, NICHD, National Institutes of Health, Bethesda, MD 20892, USA
| |
Collapse
|
21
|
Topgaard D. Multidimensional diffusion MRI. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2017; 275:98-113. [PMID: 28040623 DOI: 10.1016/j.jmr.2016.12.007] [Citation(s) in RCA: 129] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2016] [Revised: 12/13/2016] [Accepted: 12/15/2016] [Indexed: 05/12/2023]
Abstract
Principles from multidimensional NMR spectroscopy, and in particular solid-state NMR, have recently been transferred to the field of diffusion MRI, offering non-invasive characterization of heterogeneous anisotropic materials, such as the human brain, at an unprecedented level of detail. Here we revisit the basic physics of solid-state NMR and diffusion MRI to pinpoint the origin of the somewhat unexpected analogy between the two fields, and provide an overview of current diffusion MRI acquisition protocols and data analysis methods to quantify the composition of heterogeneous materials in terms of diffusion tensor distributions with size, shape, and orientation dimensions. While the most advanced methods allow estimation of the complete multidimensional distributions, simpler methods focus on various projections onto lower-dimensional spaces as well as determination of means and variances rather than actual distributions. Even the less advanced methods provide simple and intuitive scalar parameters that are directly related to microstructural features that can be observed in optical microscopy images, e.g. average cell eccentricity, variance of cell density, and orientational order - properties that are inextricably entangled in conventional diffusion MRI. Key to disentangling all these microstructural features is MRI signal acquisition combining isotropic and directional dimensions, just as in the field of multidimensional solid-state NMR from which most of the ideas for the new methods are derived.
Collapse
Affiliation(s)
- Daniel Topgaard
- Physical Chemistry, Lund University, P.O.B. 124, SE-22100 Lund, Sweden.
| |
Collapse
|
22
|
Gupta R, Reneaux M. Role of Heterogeneous Macromolecular Crowding and Geometrical Irregularity at Central Excitatory Synapses in Shaping Synaptic Transmission. PLoS One 2016; 11:e0167505. [PMID: 27907112 PMCID: PMC5131996 DOI: 10.1371/journal.pone.0167505] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Accepted: 11/15/2016] [Indexed: 01/24/2023] Open
Abstract
Besides the geometrical tortousity due to the extrasynaptic structures, macromolecular crowding and geometrical irregularities constituting the cleft composition at central excitatory synapses has a major and direct role in retarding the glutamate diffusion within the cleft space. However, the cleft composition may not only coarsely reduce the overall diffusivity of the glutamate but may also lead to substantial spatial variation in the diffusivity across the cleft space. Decrease in the overall diffusivity of the glutamate may have straightforward consequences to the glutamate transients in the cleft. However, how spatial variation in the diffusivity may further affect glutamate transients is an intriguing aspect. Therefore, to understand the role of cleft heterogeneity, the present study adopts a novel approach of glutamate diffusion which considers a gamma statistical distribution of the diffusion coefficient of glutamate (Dglut) across the cleft space, such that its moments discernibly capture the dual impacts of the cleft composition, and further applies the framework of superstatistics. The findings reveal a power law behavior in the glutamate transients, akin to the long-range anomalous subdiffusion, which leads to slower decay profile of cleft glutamate at higher intensity of cleft heterogeneity. Moreover, increase in the cleft heterogeneity is seen to eventually cause slower-rising excitatory postsynaptic currents with higher amplitudes, lesser noise, and prolonged duration of charge transfer across the postsynaptic membrane. Further, with regard to the conventional standard diffusion approach, the study suggests that the effective Dglut essentially derives from the median of the Dglut distribution and does not necessarily need to be the mean Dglut. Together, the findings indicate a strong implication of cleft heterogeneity to the metabolically cost-effective tuning of synaptic response during the phenomenon of plasticity at individual synapses and also provide an additional factor of variability in transmission across identical synapses.
Collapse
Affiliation(s)
- Rahul Gupta
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India 110067
| | - Melissa Reneaux
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India 110067
| |
Collapse
|
23
|
McClymont D, Teh I, Carruth E, Omens J, McCulloch A, Whittington HJ, Kohl P, Grau V, Schneider JE. Evaluation of non-Gaussian diffusion in cardiac MRI. Magn Reson Med 2016; 78:1174-1186. [PMID: 27670633 PMCID: PMC5366286 DOI: 10.1002/mrm.26466] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 08/18/2016] [Accepted: 08/23/2016] [Indexed: 12/20/2022]
Abstract
PURPOSE The diffusion tensor model assumes Gaussian diffusion and is widely applied in cardiac diffusion MRI. However, diffusion in biological tissue deviates from a Gaussian profile as a result of hindrance and restriction from cell and tissue microstructure, and may be quantified better by non-Gaussian modeling. The aim of this study was to investigate non-Gaussian diffusion in healthy and hypertrophic hearts. METHODS Thirteen rat hearts (five healthy, four sham, four hypertrophic) were imaged ex vivo. Diffusion-weighted images were acquired at b-values up to 10,000 s/mm2 . Models of diffusion were fit to the data and ranked based on the Akaike information criterion. RESULTS The diffusion tensor was ranked best at b-values up to 2000 s/mm2 but reflected the signal poorly in the high b-value regime, in which the best model was a non-Gaussian "beta distribution" model. Although there was considerable overlap in apparent diffusivities between the healthy, sham, and hypertrophic hearts, diffusion kurtosis and skewness in the hypertrophic hearts were more than 20% higher in the sheetlet and sheetlet-normal directions. CONCLUSION Non-Gaussian diffusion models have a higher sensitivity for the detection of hypertrophy compared with the Gaussian model. In particular, diffusion kurtosis may serve as a useful biomarker for characterization of disease and remodeling in the heart. Magn Reson Med 78:1174-1186, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
Collapse
Affiliation(s)
- Darryl McClymont
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Irvin Teh
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Eric Carruth
- Department of Bioengineering, University of California-San Diego, La Jolla, California, USA
| | - Jeffrey Omens
- Department of Bioengineering, University of California-San Diego, La Jolla, California, USA.,Department of Medicine, University of California-San Diego, La Jolla, California, USA
| | - Andrew McCulloch
- Department of Bioengineering, University of California-San Diego, La Jolla, California, USA
| | - Hannah J Whittington
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Peter Kohl
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.,Institute for Experimental Cardiovascular Medicine, University Heart Centre Freiburg, Bad Krozingen, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Vicente Grau
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Jürgen E Schneider
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
24
|
The role of tissue microstructure and water exchange in biophysical modelling of diffusion in white matter. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2013; 26:345-70. [PMID: 23443883 PMCID: PMC3728433 DOI: 10.1007/s10334-013-0371-x] [Citation(s) in RCA: 100] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2012] [Revised: 01/28/2013] [Accepted: 02/01/2013] [Indexed: 12/27/2022]
Abstract
Biophysical models that describe the outcome of white matter diffusion MRI experiments have various degrees of complexity. While the simplest models assume equal-sized and parallel axons, more elaborate ones may include distributions of axon diameters and axonal orientation dispersions. These microstructural features can be inferred from diffusion-weighted signal attenuation curves by solving an inverse problem, validated in several Monte Carlo simulation studies. Model development has been paralleled by microscopy studies of the microstructure of excised and fixed nerves, confirming that axon diameter estimates from diffusion measurements agree with those from microscopy. However, results obtained in vivo are less conclusive. For example, the amount of slowly diffusing water is lower than expected, and the diffusion-encoded signal is apparently insensitive to diffusion time variations, contrary to what may be expected. Recent understandings of the resolution limit in diffusion MRI, the rate of water exchange, and the presence of microscopic axonal undulation and axonal orientation dispersions may, however, explain such apparent contradictions. Knowledge of the effects of biophysical mechanisms on water diffusion in tissue can be used to predict the outcome of diffusion tensor imaging (DTI) and of diffusion kurtosis imaging (DKI) studies. Alterations of DTI or DKI parameters found in studies of pathologies such as ischemic stroke can thus be compared with those predicted by modelling. Observations in agreement with the predictions strengthen the credibility of biophysical models; those in disagreement could provide clues of how to improve them. DKI is particularly suited for this purpose; it is performed using higher b-values than DTI, and thus carries more information about the tissue microstructure. The purpose of this review is to provide an update on the current understanding of how various properties of the tissue microstructure and the rate of water exchange between microenvironments are reflected in diffusion MRI measurements. We focus on the use of biophysical models for extracting tissue-specific parameters from data obtained with single PGSE sequences on clinical MRI scanners, but results obtained with animal MRI scanners are also considered. While modelling of white matter is the central theme, experiments on model systems that highlight important aspects of the biophysical models are also reviewed.
Collapse
|
25
|
Eriksson S, Lasic S, Topgaard D. Isotropic diffusion weighting in PGSE NMR by magic-angle spinning of the q-vector. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2013. [PMID: 23178533 DOI: 10.1016/j.jmr.2012.10.015] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
When PGSE NMR is applied to water in microheterogeneous materials such as liquid crystals, foodstuffs, porous rocks, and biological tissues, the signal attenuation is often multi-exponential, indicating the presence of pores having a range of sizes or anisotropic domains having a spread of orientations. Here we modify the standard PGSE experiment by introducing low-amplitude harmonically modulated gradients, which effectively make the q-vector perform magic-angle spinning (MAS) about an axis fixed in the laboratory frame. With this new technique, denoted q-MAS PGSE, the signal attenuation depends on the isotropic average of the local diffusion tensor. The capability of q-MAS PGSE to distinguish between pore size and domain orientation dispersion is demonstrated by experiments on a yeast cell suspension and a polydomain anisotropic liquid crystal. In the latter case, the broad distribution of apparent diffusivities observed with PGSE is narrowed to its isotropic average with q-MAS PGSE in a manner that is analogous to the narrowing of chemical shift anisotropy powder patterns using magic-angle sample spinning in solid-state NMR. The new q-MAS PGSE technique could be useful for resolving size/orientation ambiguities in the interpretation of PGSE data from, e.g., water confined within the axons of human brain tissue.
Collapse
|
26
|
Grinberg F, Ciobanu L, Farrher E, Shah NJ. Diffusion kurtosis imaging and log-normal distribution function imaging enhance the visualisation of lesions in animal stroke models. NMR IN BIOMEDICINE 2012; 25:1295-304. [PMID: 22461260 DOI: 10.1002/nbm.2802] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2011] [Revised: 02/22/2012] [Accepted: 02/29/2012] [Indexed: 05/16/2023]
Abstract
In this work, we report a case study of a stroke model in animals using two methods of quantification of the deviations from Gaussian behaviour: diffusion kurtosis imaging (DKI) and log-normal distribution function imaging (LNDFI). The affected regions were predominantly in grey rather than in white matter. The parameter maps were constructed for metrics quantifying the apparent diffusivity (evaluated from conventional diffusion tensor imaging, DKI and LNDFI) and for those quantifying the degree of deviations (mean kurtosis and a parameter σ characterising the width of the distribution). We showed that both DKI and LNDFI were able to dramatically enhance the visualisation of ischaemic lesions in comparison with conventional methods. The largest relative change in the affected versus healthy regions was observed in the mean kurtosis values. The average changes in the mean kurtosis and σ values in the lesions were a factor of two to three larger than the relative changes observed in the mean diffusivity. In conclusion, the applied methods promise valuable perspectives in the assessment of stroke.
Collapse
Affiliation(s)
- Farida Grinberg
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany.
| | | | | | | |
Collapse
|
27
|
De Santis S, Assaf Y, Jones DK. Using the biophysical CHARMED model to elucidate the underpinnings of contrast in diffusional kurtosis analysis of diffusion-weighted MRI. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2011; 25:267-76. [DOI: 10.1007/s10334-011-0292-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2011] [Revised: 10/14/2011] [Accepted: 10/19/2011] [Indexed: 10/15/2022]
|
28
|
De Santis S, Gabrielli A, Palombo M, Maraviglia B, Capuani S. Non-Gaussian diffusion imaging: a brief practical review. Magn Reson Imaging 2011; 29:1410-6. [PMID: 21601404 DOI: 10.1016/j.mri.2011.04.006] [Citation(s) in RCA: 72] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2010] [Revised: 02/15/2011] [Accepted: 04/03/2011] [Indexed: 11/30/2022]
Abstract
The departure from purely mono-exponential decay of the signal, as observed from brain tissue following a diffusion-sensitized sequence, has prompted the search for alternative models to characterize these unconventional water diffusion dynamics. Several approaches have been proposed in the last few years. While multi-exponential models have been applied to characterize brain tissue, several unresolved controversies about the interpretations of the results have motivated the search for alternative models that do not rely on the Gaussian diffusion hypothesis. In this brief review, diffusional kurtosis imaging (DKI) and anomalous diffusion imaging (ADI) techniques are addressed and compared with diffusion tensor imaging. Theoretical and experimental issues are briefly described to allow readers to understand similarities, differences and limitations of these two non-Gaussian models. However, since the ultimate goal is to improve specificity, sensitivity and spatial localization of diffusion MRI for the detection of brain diseases, special attention will be paid on the clinical feasibility of the proposed techniques as well as on the context of brain pathology investigations.
Collapse
Affiliation(s)
- Silvia De Santis
- Physics Department, Sapienza University of Rome, P.le A. Moro 5, 00185 Rome, Italy.
| | | | | | | | | |
Collapse
|
29
|
Steier R, Aradi M, Pál J, Perlaki G, Orsi G, Bogner P, Galyas F, Bukovics P, Janszky J, Dóczi T, Schwarcz A. A biexponential DWI study in rat brain intracellular oedema. Eur J Radiol 2011; 81:1758-65. [PMID: 21497469 DOI: 10.1016/j.ejrad.2011.03.058] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2011] [Revised: 03/15/2011] [Accepted: 03/16/2011] [Indexed: 11/25/2022]
Abstract
PURPOSE To examine the changes in MR parameters derived from diffusion weighted imaging (DWI) biexponential analysis in an in vivo intracellular brain oedema model, and to apply electron microscopy (EM) to shed more light on the morphological background of MR-related observations. MATERIALS AND METHODS Intracellular oedema was induced in ten male Wistar rats (380-450g) by way of water load, using a 20% body weight intraperitoneal injection of 140mmol/L dextrose solution. A 3T MRI instrument was used to perform serial DWI, and MR specroscopy (water signal) measurements. Following the MR examination the brains of the animals were analyzed for EM. RESULTS Following the water load induction, apparent diffusion coefficient (ADC) values started declining from 724±43μm(2)/s to 682±26μm(2)/s (p<0.0001). ADC-fast values dropped from 948±122 to 840±66μm(2)/s (p<0.001). ADC-slow showed a decrease from 226±66 to 191±74μm(2)/s (p<0.05). There was a shift from the slow to the fast component at 110min time point. The percentage of the fast component demonstrated moderate, yet significant increase from 76.56±7.79% to 81.2±7.47% (p<0.05). The water signal was increasing by 4.98±3.52% compared to the base line (p<0.01). The results of the E.M. revealed that water was detected intracellularly, within astrocytic preivascular end-feet and cell bodies. CONCLUSION The unexpected volume fraction changes (i.e. increase in fast component) detected in hypotonic oedema appear to be substantially different from those observed in stroke. It may suggest that ADC decrease in stroke, in contrast to general presumptions, cannot be explained only by water shift from extra to intracellular space (i.e. intracellular oedema).
Collapse
Affiliation(s)
- Roy Steier
- Department of Neurosurgery, Faculty of Medicine University of Pécs, H-7623 Pécs, Rét street 2, Hungary.
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
30
|
De Santis S, Gabrielli A, Bozzali M, Maraviglia B, Macaluso E, Capuani S. Anisotropic anomalous diffusion assessed in the human brain by scalar invariant indices. Magn Reson Med 2010; 65:1043-52. [DOI: 10.1002/mrm.22689] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2010] [Revised: 08/27/2010] [Accepted: 09/26/2010] [Indexed: 11/07/2022]
|
31
|
Mulkern RV, Haker SJ, Maier SE. On high b diffusion imaging in the human brain: ruminations and experimental insights. Magn Reson Imaging 2009; 27:1151-62. [PMID: 19520535 PMCID: PMC2894527 DOI: 10.1016/j.mri.2009.05.003] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2008] [Revised: 02/20/2009] [Accepted: 05/06/2009] [Indexed: 01/23/2023]
Abstract
Interest in the manner in which brain tissue signal decays with b factor in diffusion imaging schemes has grown in recent years following the observation that the decay curves depart from purely monoexponential decay behavior. Regardless of the model or fitting function proposed for characterizing sufficiently sampled decay curves (vide infra), the departure from monoexponentiality spells increased tissue characterization potential. The degree to which this potential can be harnessed to improve specificity, sensitivity and spatial localization of diseases in brain, and other tissues, largely remains to be explored. Furthermore, the degree to which currently popular diffusion tensor imaging methods, including visually impressive white matter fiber "tractography" results, have almost completely ignored the nonmonoexponential nature of the basic signal decay with b factor is worthy of communal introspection. Here we limit our attention to a review of the basic experimental features associated with brain water signal diffusion decay curves as measured over extended b-factor ranges, the simple few parameter fitting functions that have been proposed to characterize these decays and the more involved models, e.g.,"ruminations," which have been proposed to account for the nonmonoexponentiality to date.
Collapse
Affiliation(s)
- Robert V. Mulkern
- Department of Radiology, Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Steven J. Haker
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Stephan E. Maier
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|