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Narvaez O, Yon M, Jiang H, Bernin D, Forssell-Aronsson E, Sierra A, Topgaard D. Nonparametric distributions of tensor-valued Lorentzian diffusion spectra for model-free data inversion in multidimensional diffusion MRI. J Chem Phys 2024; 161:084201. [PMID: 39171708 DOI: 10.1063/5.0213252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/09/2024] [Indexed: 08/23/2024] Open
Abstract
Magnetic resonance imaging (MRI) is the method of choice for noninvasive studies of micrometer-scale structures in biological tissues via their effects on the time- and frequency-dependent (restricted) and anisotropic self-diffusion of water. While new designs of time-dependent magnetic field gradient waveforms have enabled disambiguation between different aspects of translational motion that are convolved in traditional MRI methods relying on single pairs of field gradient pulses, data analysis for complex heterogeneous materials remains a challenge. Here, we propose and demonstrate nonparametric distributions of tensor-valued Lorentzian diffusion spectra, or "D(ω) distributions," as a general representation with sufficient flexibility to describe the MRI signal response from a wide range of model systems and biological tissues investigated with modulated gradient waveforms separating and correlating the effects of restricted and anisotropic diffusion.
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Affiliation(s)
- Omar Narvaez
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Maxime Yon
- Department of Chemistry, Lund University, Lund, Sweden
| | - Hong Jiang
- Department of Chemistry, Lund University, Lund, Sweden
| | - Diana Bernin
- Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Eva Forssell-Aronsson
- Department of Medical Radiation Sciences, University of Gothenburg, Gothenburg, Sweden
- Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
- Sahlgrenska Center for Cancer Research, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Alejandra Sierra
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
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Lasič S, Chakwizira A, Lundell H, Westin CF, Nilsson M. Tuned exchange imaging: Can the filter exchange imaging pulse sequence be adapted for applications with thin slices and restricted diffusion? NMR IN BIOMEDICINE 2024:e5208. [PMID: 38961745 DOI: 10.1002/nbm.5208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 06/04/2024] [Accepted: 06/06/2024] [Indexed: 07/05/2024]
Abstract
Filter exchange imaging (FEXI) is a double diffusion-encoding (DDE) sequence that is specifically sensitive to exchange between sites with different apparent diffusivities. FEXI uses a diffusion-encoding filtering block followed by a detection block at varying mixing times to map the exchange rate. Long mixing times enhance the sensitivity to exchange, but they pose challenges for imaging applications that require a stimulated echo sequence with crusher gradients. Thin imaging slices require strong crushers, which can introduce significant diffusion weighting and bias exchange rate estimates. Here, we treat the crushers as an additional encoding block and consider FEXI as a triple diffusion-encoding sequence. This allows the bias to be corrected in the case of multi-Gaussian diffusion, but not easily in the presence of restricted diffusion. Our approach addresses challenges in the presence of restricted diffusion and relies on the ability to independently gauge sensitivities to exchange and restricted diffusion for arbitrary gradient waveforms. It follows two principles: (i) the effects of crushers are included in the forward model using signal cumulant expansion; and (ii) timing parameters of diffusion gradients in filter and detection blocks are adjusted to maintain the same level of restriction encoding regardless of the mixing time. This results in the tuned exchange imaging (TEXI) protocol. The accuracy of exchange mapping with TEXI was assessed through Monte Carlo simulations in spheres of identical sizes and gamma-distributed sizes, and in parallel hexagonally packed cylinders. The simulations demonstrate that TEXI provides consistent exchange rates regardless of slice thickness and restriction size, even with strong crushers. However, the accuracy depends on b-values, mixing times, and restriction geometry. The constraints and limitations of TEXI are discussed, including suggestions for protocol adaptations. Further studies are needed to optimize the precision of TEXI and assess the approach experimentally in realistic, heterogeneous substrates.
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Affiliation(s)
- Samo Lasič
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Arthur Chakwizira
- Department of Medical Radiation Physics, Lund, Lund University, Lund, Sweden
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
- MR Section, DTU Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Markus Nilsson
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
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Zhou M, Stobbe R, Szczepankiewicz F, Budde M, Buck B, Kate M, Lloret M, Fairall P, Butcher K, Shuaib A, Emery D, Nilsson M, Westin CF, Beaulieu C. Tensor-valued diffusion MRI of human acute stroke. Magn Reson Med 2024; 91:2126-2141. [PMID: 38156813 DOI: 10.1002/mrm.29975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/18/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE Tensor-valued diffusion encoding can disentangle orientation dispersion and subvoxel anisotropy, potentially offering insight into microstructural changes after cerebral ischemia. The purpose was to evaluate tensor-valued diffusion MRI in human acute ischemic stroke, assess potential confounders from diffusion time dependencies, and compare to Monte Carlo diffusion simulations of axon beading. METHODS Linear (LTE) and spherical (STE) b-tensor encoding with inherently different effective diffusion times were acquired in 21 acute ischemic stroke patients between 3 and 57 h post-onset at 3 T in 2.5 min. In an additional 10 patients, STE with 2 LTE yielding different effective diffusion times were acquired for comparison. Diffusional variance decomposition (DIVIDE) was used to estimate microscopic anisotropy (μFA), as well as anisotropic, isotropic, and total diffusional variance (MKA , MKI , MKT ). DIVIDE parameters, and diffusion tensor imaging (DTI)-derived mean diffusivity and fractional anisotropy (FA) were compared in lesion versus contralateral white matter. Monte Carlo diffusion simulations of various cylindrical geometries for all b-tensor protocols were used to interpret parameter measurements. RESULTS MD was ˜40% lower in lesions for all LTE/STE protocols. The DIVIDE parameters varied with effective diffusion time: higher μFA and MKA in lesion versus contralateral white matter for STE with longer effective diffusion time LTE, whereas the shorter effective diffusion time LTE protocol yielded lower μFA and MKA in lesions. Both protocols, regardless of diffusion time, were consistent with simulations of greater beading amplitude and intracellular volume fraction. CONCLUSION DIVIDE parameters depend on diffusion time in acute stroke but consistently indicate neurite beading and larger intracellular volume fraction.
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Affiliation(s)
- Mi Zhou
- Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Robert Stobbe
- Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
- Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | | | - Matthew Budde
- Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Brian Buck
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Mahesh Kate
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Mar Lloret
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Paige Fairall
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Ken Butcher
- School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Ashfaq Shuaib
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Derek Emery
- Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Markus Nilsson
- Clinical Sciences Lund, Lund University, Lund, Scania, Sweden
| | - Carl-Fredrik Westin
- Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian Beaulieu
- Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
- Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
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Magdoom KN, Avram AV, Witzel TE, Huang SY, Basser PJ. Water Diffusion in the Live Human Brain is Gaussian at the Mesoscale. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588939. [PMID: 38645264 PMCID: PMC11030434 DOI: 10.1101/2024.04.10.588939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Imaging the live human brain at the mesoscopic scale is a desideratum in basic and clinical neurosciences. Despite the promise of diffusion MRI, the lack of an accurate model relating the measured signal and the associated microstructure has hampered its success. The widely used diffusion tensor MRI (DTI) model assumes an anisotropic Gaussian diffusion process in each voxel, but lacks the ability to capture intravoxel heterogeneity. This study explores the extension of the DTI model to mesoscopic length scales by use of the diffusion tensor distribution (DTD) model, which assumes a Gaussian diffusion process in each subvoxel. DTD MRI has shown promise in addressing some limitations of DTI, particularly in distinguishing among different types of brain cancers and elucidating multiple fiber populations within a voxel. However, its validity in live brain tissue has never been established. Here, multiple diffusion-encoded (MDE) data were acquired in the living human brain using a 3 Tesla MRI scanner with large diffusion weighting factors. Two different diffusion times (Δ = 37, 74 ms) were employed, with other scanning parameters fixed to assess signal decay differences. In vivo diffusion-weighted signals in gray and white matter were nearly identical at the two diffusion times. Fitting the signals to the DTD model yielded indistinguishable results, except in the cerebrospinal fluid (CSF)-filled voxels likely due to pulsatile flow. Overall, the study supports the time invariance of water diffusion at the mesoscopic scale in live brain parenchyma, extending the validity of the anisotropic Gaussian diffusion model in clinical brain imaging.
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Lampinen B, Szczepankiewicz F, Lätt J, Knutsson L, Mårtensson J, Björkman-Burtscher IM, van Westen D, Sundgren PC, Ståhlberg F, Nilsson M. Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding. Neuroimage 2023; 282:120338. [PMID: 37598814 DOI: 10.1016/j.neuroimage.2023.120338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 06/30/2023] [Accepted: 08/17/2023] [Indexed: 08/22/2023] Open
Abstract
Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T2 relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden.
| | | | - Jimmy Lätt
- Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Linda Knutsson
- Clinical Sciences Lund, 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 for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Johan Mårtensson
- Clinical Sciences Lund, Logopedics, Phoniatrics and Audiology, Lund University, Lund, Sweden
| | - Isabella M Björkman-Burtscher
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Danielle van Westen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Pia C Sundgren
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden; Lund University BioImaging Centre (LBIC), Lund University, Lund, Sweden
| | - Freddy Ståhlberg
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden
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Teh I, Shelley D, Boyle JH, Zhou F, Poenar A, Sharrack N, Foster RJ, Yuldasheva NY, Parker GJM, Dall'Armellina E, Plein S, Schneider JE, Szczepankiewicz F. Cardiac q-space trajectory imaging by motion-compensated tensor-valued diffusion encoding in human heart in vivo. Magn Reson Med 2023; 90:150-165. [PMID: 36941736 PMCID: PMC10952623 DOI: 10.1002/mrm.29637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 01/25/2023] [Accepted: 02/23/2023] [Indexed: 03/23/2023]
Abstract
PURPOSE Tensor-valued diffusion encoding can probe more specific features of tissue microstructure than what is available by conventional diffusion weighting. In this work, we investigate the technical feasibility of tensor-valued diffusion encoding at high b-values with q-space trajectory imaging (QTI) analysis, in the human heart in vivo. METHODS Ten healthy volunteers were scanned on a 3T scanner. We designed time-optimal gradient waveforms for tensor-valued diffusion encoding (linear and planar) with second-order motion compensation. Data were analyzed with QTI. Normal values and repeatability were investigated for the mean diffusivity (MD), fractional anisotropy (FA), microscopic FA (μFA), isotropic, anisotropic and total mean kurtosis (MKi, MKa, and MKt), and orientation coherence (Cc ). A phantom, consisting of two fiber blocks at adjustable angles, was used to evaluate sensitivity of parameters to orientation dispersion and diffusion time. RESULTS QTI data in the left ventricular myocardium were MD = 1.62 ± 0.07 μm2 /ms, FA = 0.31 ± 0.03, μFA = 0.43 ± 0.07, MKa = 0.20 ± 0.07, MKi = 0.13 ± 0.03, MKt = 0.33 ± 0.09, and Cc = 0.56 ± 0.22 (mean ± SD across subjects). Phantom experiments showed that FA depends on orientation dispersion, whereas μFA was insensitive to this effect. CONCLUSION We demonstrated the first tensor-valued diffusion encoding and QTI analysis in the heart in vivo, along with first measurements of myocardial μFA, MKi, MKa, and Cc . The methodology is technically feasible and provides promising novel biomarkers for myocardial tissue characterization.
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Affiliation(s)
- Irvin Teh
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - David Shelley
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
- Leeds Teaching Hospitals TrustLeedsUK
| | - Jordan H. Boyle
- Faculty of Industrial Design EngineeringDelft University of TechnologyDelftNetherlands
| | - Fenglei Zhou
- Center for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of NeuroinflammationUniversity College LondonLondonUK
- Astrea BioseparationCombertonUK
| | - Ana‐Maria Poenar
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Noor Sharrack
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Richard J. Foster
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Nadira Y. Yuldasheva
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Geoff J. M. Parker
- Center for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of NeuroinflammationUniversity College LondonLondonUK
- Bioxydyn LimitedManchesterUK
| | - Erica Dall'Armellina
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Sven Plein
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Jürgen E. Schneider
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
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Brabec J, Englund E, Bengzon J, Szczepankiewicz F, van Westen D, Sundgren PC, Nilsson M. Coregistered histology sections with diffusion tensor imaging data at 200 µm resolution in meningioma tumors. Data Brief 2023; 48:109261. [PMID: 37383742 PMCID: PMC10294079 DOI: 10.1016/j.dib.2023.109261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/08/2023] [Accepted: 05/17/2023] [Indexed: 06/30/2023] Open
Abstract
A significant problem in diffusion MRI (dMRI) is the lack of understanding regarding which microstructural features account for the variability in the diffusion tensor imaging (DTI) parameters observed in meningioma tumors. A common assumption is that mean diffusivity (MD) and fractional anisotropy (FA) from DTI are inversely proportional to cell density and proportional to tissue anisotropy, respectively. Although these associations have been established across a wide range of tumors, they have been challenged for interpreting within-tumor variations where several additional microstructural features have been suggested as contributing to MD and FA. To facilitate the investigation of the biological underpinnings of DTI parameters, we performed ex-vivo DTI at 200 µm isotropic resolution on sixteen excised meningioma tumor samples. The samples exhibit a variety of microstructural features because the dataset includes meningiomas of six different meningioma types and two different grades. Diffusion-weighted signal (DWI) maps, DWI maps averaged over all directions for given b-value, signal intensities without diffusion encoding (S0) as well as DTI parameters: MD, FA, in-plane FA (FAIP), axial diffusivity (AD) and radial diffusivity (RD), were coregistered to Hematoxylin & Eosin- (H&E) and Elastica van Gieson-stained (EVG) histological sections by a non-linear landmark-based approach. Here, we provide DWI signal and DTI maps coregistered to histology sections and describe the pipeline for processing the raw DTI data and the coregistration. The raw, processed, and coregistered data are hosted by Analytic Imaging Diagnostics Arena (AIDA) data hub registry, and software tools for processing are provided via GitHub. We hope that data can be used in research and education concerning the link between the meningioma microstructure and parameters obtained by DTI.
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Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | | | - Johan Bengzon
- Division of Neurosurgery, Skane University Hospital, Lund, Sweden
- Stem Cell Center, Department of Clinical Sciences, Lund, Sweden
| | | | | | - Pia C. Sundgren
- Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden
- Lund University Bioimaging Centre, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Markus Nilsson
- Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden
- Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden
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Golkar E, Parker D, Brem S, Almairac F, Verma R. CrOssing fiber Modeling in the Peritumoral Area using dMRI (COMPARI). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.07.539770. [PMID: 37215003 PMCID: PMC10197585 DOI: 10.1101/2023.05.07.539770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Visualization of fiber tracts around the tumor is critical for neurosurgical planning and preservation of crucial structural connectivity during tumor resection. Biophysical modeling approaches estimate fiber tract orientations from differential water diffusivity information of diffusion MRI. However, the presence of edema and tumor infiltration presents a challenge to visualize crossing fiber tracts in the peritumoral region. Previous approaches proposed free water modeling to compensate for the effect of water diffusivity in edema, but those methods were limited in estimating complex crossing fiber tracts. We propose a new cascaded multi-compartment model to estimate tissue microstructure in the presence of edema and pathological contaminants in the area surrounding brain tumors. In our model (COMPARI), the isotropic components of diffusion signal, including free water and hindered water, were eliminated, and the fiber orientation distribution (FOD) of the remaining signal was estimated. In simulated data, COMPARI accurately recovered fiber orientations in the presence of extracellular water. In a dataset of 23 patients with highly edematous brain tumors, the amplitudes of FOD and anisotropic index distribution within the peritumoral region were higher with COMPARI than with a recently proposed multi-compartment constrained deconvolution model. In a selected patient with metastatic brain tumor, we demonstrated COMPARI's ability to effectively model and eliminate water from the peritumoral region. The white matter bundles reconstructed with our model were qualitatively improved compared to those of other models, and allowed the identification of crossing fibers. In conclusion, the removal of isotropic components as proposed with COMPARI improved the bio-physical modeling of dMRI in edema, thus providing information on crossing fibers, thereby enabling improved tractography in a highly edematous brain tumor. This model may improve surgical planning tools to help achieve maximal safe resection of brain tumors.
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Affiliation(s)
- Ehsan Golkar
- DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Perelman School of Medicine,University of Pennsylvania, Philadelphia, PA, USA
| | - Drew Parker
- DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Perelman School of Medicine,University of Pennsylvania, Philadelphia, PA, USA
| | - Steven Brem
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania,Philadelphia, PA
| | - Fabien Almairac
- Neurosurgery department, Pasteur 2 Hospital, University Hospital of Nice, France
- UR2CA PIN, Université Côte d’Azur, France
| | - Ragini Verma
- DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Perelman School of Medicine,University of Pennsylvania, Philadelphia, PA, USA
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Figini M, Castellano A, Bailo M, Callea M, Cadioli M, Bouyagoub S, Palombo M, Pieri V, Mortini P, Falini A, Alexander DC, Cercignani M, Panagiotaki E. Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology. Cancers (Basel) 2023; 15:2490. [PMID: 37173965 PMCID: PMC10177485 DOI: 10.3390/cancers15092490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
The aim of this work was to extend the VERDICT-MRI framework for modelling brain tumours, enabling comprehensive characterisation of both intra- and peritumoural areas with a particular focus on cellular and vascular features. Diffusion MRI data were acquired with multiple b-values (ranging from 50 to 3500 s/mm2), diffusion times, and echo times in 21 patients with brain tumours of different types and with a wide range of cellular and vascular features. We fitted a selection of diffusion models that resulted from the combination of different types of intracellular, extracellular, and vascular compartments to the signal. We compared the models using criteria for parsimony while aiming at good characterisation of all of the key histological brain tumour components. Finally, we evaluated the parameters of the best-performing model in the differentiation of tumour histotypes, using ADC (Apparent Diffusion Coefficient) as a clinical standard reference, and compared them to histopathology and relevant perfusion MRI metrics. The best-performing model for VERDICT in brain tumours was a three-compartment model accounting for anisotropically hindered and isotropically restricted diffusion and isotropic pseudo-diffusion. VERDICT metrics were compatible with the histological appearance of low-grade gliomas and metastases and reflected differences found by histopathology between multiple biopsy samples within tumours. The comparison between histotypes showed that both the intracellular and vascular fractions tended to be higher in tumours with high cellularity (glioblastoma and metastasis), and quantitative analysis showed a trend toward higher values of the intracellular fraction (fic) within the tumour core with increasing glioma grade. We also observed a trend towards a higher free water fraction in vasogenic oedemas around metastases compared to infiltrative oedemas around glioblastomas and WHO 3 gliomas as well as the periphery of low-grade gliomas. In conclusion, we developed and evaluated a multi-compartment diffusion MRI model for brain tumours based on the VERDICT framework, which showed agreement between non-invasive microstructural estimates and histology and encouraging trends for the differentiation of tumour types and sub-regions.
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Affiliation(s)
- Matteo Figini
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Michele Bailo
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Marcella Callea
- Pathology Unit, IRCCS Ospedale San Raffaele, 20132 Milan, Italy
| | | | - Samira Bouyagoub
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton BN1 9RR, UK
| | - Marco Palombo
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
| | - Valentina Pieri
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Pietro Mortini
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Daniel C. Alexander
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Mara Cercignani
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton BN1 9RR, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
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10
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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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Rahman N, Xu K, Budde MD, Brown A, Baron CA. A longitudinal microstructural MRI dataset in healthy C57Bl/6 mice at 9.4 Tesla. Sci Data 2023; 10:94. [PMID: 36788251 PMCID: PMC9929084 DOI: 10.1038/s41597-023-01942-5] [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: 07/07/2022] [Accepted: 01/06/2023] [Indexed: 02/16/2023] Open
Abstract
Multimodal microstructural MRI has shown increased sensitivity and specificity to changes in various brain disease and injury models in the preclinical setting. Here, we present an in vivo longitudinal dataset, including a subset of ex vivo data, acquired as control data and to investigate microstructural changes in the healthy mouse brain. The dataset consists of structural T2-weighted imaging, magnetization transfer ratio and saturation imaging, and advanced quantitative diffusion MRI (dMRI) methods. The dMRI methods include oscillating gradient spin echo (OGSE) dMRI and microscopic anisotropy (μA) dMRI, which provide additional insight by increasing sensitivity to smaller spatial scales and disentangling fiber orientation dispersion from true microstructural changes, respectively. The technical skills required to analyze microstructural MRI data are complex and include MRI sequence development, acquisition, and computational neuroimaging expertise. Here, we share unprocessed and preprocessed data, and scalar maps of quantitative MRI metrics. We envision utility of this dataset in the microstructural MRI field to develop and test biophysical models, methods that model temporal brain dynamics, and registration and preprocessing pipelines.
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Affiliation(s)
- Naila Rahman
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Ontario, Canada.
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada.
| | - Kathy Xu
- Translational Neuroscience Group, Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Matthew D Budde
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Arthur Brown
- Translational Neuroscience Group, Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
- Department of Anatomy and Cell Biology, University of Western Ontario, London, Ontario, Canada
| | - Corey A Baron
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
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Borsos KB, Tse DHY, Dubovan PI, Baron CA. Tuned bipolar oscillating gradients for mapping frequency dispersion of diffusion kurtosis in the human brain. Magn Reson Med 2023; 89:756-766. [PMID: 36198030 DOI: 10.1002/mrm.29473] [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: 04/05/2022] [Revised: 09/06/2022] [Accepted: 09/08/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE Oscillating gradient spin-echo (OGSE) sequences have demonstrated an ability to probe time-dependent microstructural features, although they often suffer from low SNR due to increased TEs. In this work we introduce frequency-tuned bipolar (FTB) gradients as a variation of oscillating gradients with reduced TE and demonstrate their utility by mapping the frequency dispersion of kurtosis in human subjects. METHODS An FTB oscillating gradient waveform is presented that provides encoding of 1.5 net oscillation periods, thereby reducing the TE of the acquisition. Simulations were performed to determine an optimal protocol based on the SNR of kurtosis frequency dispersion-defined as the difference in kurtosis between pulsed and oscillating gradient acquisitions. Healthy human subjects were scanned at 7T using pulsed gradient and an optimized 23 Hz FTB protocol, which featured a maximum b-value of 2500 s/mm2 . In addition, to directly compare existing methods, measurements using traditional cosine OGSE were also acquired. RESULTS FTB oscillating gradients demonstrated equivalent frequency-dependent diffusion measurements compared with cosine-modulated OGSE while enabling a significant reduction in TE. Optimization and in vivo results suggest that FTB gradients provide increased SNR of kurtosis dispersion maps compared with traditional cosine OGSE. The optimized FTB gradient protocol demonstrated consistent reductions in apparent kurtosis values and increased diffusivity in generated frequency dispersion maps. CONCLUSIONS This work presents an alternative to traditional cosine OGSE sequences, enabling more time-efficient acquisitions of frequency-dependent diffusion quantities as demonstrated through in vivo kurtosis frequency dispersion maps.
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Affiliation(s)
- Kevin B Borsos
- Department of Medical Biophysics, Western University, London, Ontario, Canada.,Center for Functional and Metabolic Mapping, Western University, London, Ontario, Canada
| | - Desmond H Y Tse
- Center for Functional and Metabolic Mapping, Western University, London, Ontario, Canada
| | - Paul I Dubovan
- Department of Medical Biophysics, Western University, London, Ontario, Canada.,Center for Functional and Metabolic Mapping, Western University, London, Ontario, Canada
| | - Corey A Baron
- Department of Medical Biophysics, Western University, London, Ontario, Canada.,Center for Functional and Metabolic Mapping, Western University, London, Ontario, Canada.,Imaging Laboratories, Robarts Research Institute, London, Ontario, Canada
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Brabec J, Friedjungová M, Vašata D, Englund E, Bengzon J, Knutsson L, Szczepankiewicz F, van Westen D, Sundgren PC, Nilsson M. Meningioma microstructure assessed by diffusion MRI: An investigation of the source of mean diffusivity and fractional anisotropy by quantitative histology. Neuroimage Clin 2023; 37:103365. [PMID: 36898293 PMCID: PMC10020119 DOI: 10.1016/j.nicl.2023.103365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/08/2023] [Accepted: 02/25/2023] [Indexed: 03/06/2023]
Abstract
BACKGROUND Mean diffusivity (MD) and fractional anisotropy (FA) from diffusion MRI (dMRI) have been associated with cell density and tissue anisotropy across tumors, but it is unknown whether these associations persist at the microscopic level. PURPOSE To quantify the degree to which cell density and anisotropy, as determined from histology, account for the intra-tumor variability of MD and FA in meningioma tumors. Furthermore, to clarify whether other histological features account for additional intra-tumor variability of dMRI parameters. MATERIALS AND METHODS We performed ex-vivo dMRI at 200 μm isotropic resolution and histological imaging of 16 excised meningioma tumor samples. Diffusion tensor imaging (DTI) was used to map MD and FA, as well as the in-plane FA (FAIP). Histology images were analyzed in terms of cell nuclei density (CD) and structure anisotropy (SA; obtained from structure tensor analysis) and were used separately in a regression analysis to predict MD and FAIP, respectively. A convolutional neural network (CNN) was also trained to predict the dMRI parameters from histology patches. The association between MRI and histology was analyzed in terms of out-of-sample (R2OS) on the intra-tumor level and within-sample R2 across tumors. Regions where the dMRI parameters were poorly predicted from histology were analyzed to identify features apart from CD and SA that could influence MD and FAIP, respectively. RESULTS Cell density assessed by histology poorly explained intra-tumor variability of MD at the mesoscopic level (200 μm), as median R2OS = 0.04 (interquartile range 0.01-0.26). Structure anisotropy explained more of the variation in FAIP (median R2OS = 0.31, 0.20-0.42). Samples with low R2OS for FAIP exhibited low variations throughout the samples and thus low explainable variability, however, this was not the case for MD. Across tumors, CD and SA were clearly associated with MD (R2 = 0.60) and FAIP (R2 = 0.81), respectively. In 37% of the samples (6 out of 16), cell density did not explain intra-tumor variability of MD when compared to the degree explained by the CNN. Tumor vascularization, psammoma bodies, microcysts, and tissue cohesivity were associated with bias in MD prediction based solely on CD. Our results support that FAIP is high in the presence of elongated and aligned cell structures, but low otherwise. CONCLUSION Cell density and structure anisotropy account for variability in MD and FAIP across tumors but cell density does not explain MD variations within the tumor, which means that low or high values of MD locally may not always reflect high or low tumor cell density. Features beyond cell density need to be considered when interpreting MD.
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Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Magda Friedjungová
- Faculty of Information Technology, Czech Technical University in Prague, Prague, Czech Republic
| | - Daniel Vašata
- Faculty of Information Technology, Czech Technical University in Prague, Prague, Czech Republic
| | | | - Johan Bengzon
- Neurosurgery, Clinical Sciences, Lund University, Lund, Sweden
| | - Linda Knutsson
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | - Pia C Sundgren
- Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden; Lund University Bioimaging Centre, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Markus Nilsson
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden
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Arezza NJJ, Santini T, Omer M, Baron CA. Estimation of free water-corrected microscopic fractional anisotropy. Front Neurosci 2023; 17:1074730. [PMID: 36960165 PMCID: PMC10027922 DOI: 10.3389/fnins.2023.1074730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 02/16/2023] [Indexed: 03/09/2023] Open
Abstract
Water diffusion anisotropy MRI is sensitive to microstructural changes in the brain that are hallmarks of various neurological conditions. However, conventional metrics like fractional anisotropy are confounded by neuron fiber orientation dispersion, and the relatively low resolution of diffusion-weighted MRI gives rise to significant free water partial volume effects in many brain regions that are adjacent to cerebrospinal fluid. Microscopic fractional anisotropy is a recent metric that can report water diffusion anisotropy independent of neuron fiber orientation dispersion but is still susceptible to free water contamination. In this paper, we present a free water elimination (FWE) technique to estimate microscopic fractional anisotropy and other related diffusion indices by implementing a signal representation in which the MRI signal within a voxel is assumed to come from two distinct sources: a tissue compartment and a free water compartment. A two-part algorithm is proposed to rapidly fit a set of diffusion-weighted MRI volumes containing both linear- and spherical-tensor encoding acquisitions to the representation. Simulations and in vivo acquisitions with four healthy volunteers indicated that the FWE method may be a feasible technique for measuring microscopic fractional anisotropy and other indices with greater specificity to neural tissue characteristics than conventional methods.
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Affiliation(s)
- Nico J. J. Arezza
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, Western University, London, ON, Canada
- *Correspondence: Nico J. J. Arezza,
| | - Tales Santini
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, Western University, London, ON, Canada
| | - Mohammad Omer
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Corey A. Baron
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, Western University, London, ON, Canada
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Chakwizira A, Westin C, Brabec J, Lasič S, Knutsson L, Szczepankiewicz F, Nilsson M. Diffusion MRI with pulsed and free gradient waveforms: Effects of restricted diffusion and exchange. NMR IN BIOMEDICINE 2023; 36:e4827. [PMID: 36075110 PMCID: PMC10078514 DOI: 10.1002/nbm.4827] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 08/27/2022] [Accepted: 09/06/2022] [Indexed: 05/06/2023]
Abstract
Monitoring time dependence with diffusion MRI yields observables sensitive to compartment sizes (restricted diffusion) and membrane permeability (water exchange). However, restricted diffusion and exchange have opposite effects on the diffusion-weighted signal, which can lead to errors in parameter estimates. In this work, we propose a signal representation that incorporates the effects of both restricted diffusion and exchange up to second order in b-value and is compatible with gradient waveforms of arbitrary shape. The representation features mappings from a gradient waveform to two scalars that separately control the sensitivity to restriction and exchange. We demonstrate that these scalars span a two-dimensional space that can be used to choose waveforms that selectively probe restricted diffusion or exchange, eliminating the correlation between the two phenomena. We found that waveforms with specific but unconventional shapes provide an advantage over conventional pulsed and oscillating gradient acquisitions. We also show that parametrization of waveforms into a two-dimensional space can be used to understand protocols from other approaches that probe restricted diffusion and exchange. For example, we found that the variation of mixing time in filter-exchange imaging corresponds to variation of our exchange-weighting scalar at a fixed value of the restriction-weighting scalar. The proposed signal representation was evaluated using Monte Carlo simulations in identical parallel cylinders with hexagonal and random packing as well as parallel cylinders with gamma-distributed radii. Results showed that the approach is sensitive to sizes in the interval 4-12 μm and exchange rates in the simulated range of 0 to 20 s - 1 , but also that there is a sensitivity to the extracellular geometry. The presented theory constitutes a simple and intuitive description of how restricted diffusion and exchange influence the signal as well as a guide to protocol design capable of separating the two effects.
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Affiliation(s)
- Arthur Chakwizira
- Department of Medical Radiation Physics, LundLund UniversityLundSweden
| | - Carl‐Fredrik Westin
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jan Brabec
- Department of Medical Radiation Physics, LundLund UniversityLundSweden
| | - Samo Lasič
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital ‐ Amager and HvidovreCopenhagenDenmark
- Random Walk Imaging ABLundSweden
| | - Linda Knutsson
- Department of Medical Radiation Physics, LundLund UniversityLundSweden
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- F. M. Kirby Research Center for Functional Brain ImagingKennedy Krieger InstituteBaltimoreMarylandUSA
| | | | - Markus Nilsson
- Department of Clinical Sciences Lund, RadiologyLund UniversityLundSweden
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Avram AV, Saleem KS, Basser PJ. COnstrained Reference frame diffusion TEnsor Correlation Spectroscopic (CORTECS) MRI: A practical framework for high-resolution diffusion tensor distribution imaging. Front Neurosci 2022; 16:1054509. [PMID: 36590291 PMCID: PMC9798222 DOI: 10.3389/fnins.2022.1054509] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
High-resolution imaging studies have consistently shown that in cortical tissue water diffuses preferentially along radial and tangential orientations with respect to the cortical surface, in agreement with histology. These dominant orientations do not change significantly even if the relative contributions from microscopic water pools to the net voxel signal vary across experiments that use different diffusion times, b-values, TEs, and TRs. With this in mind, we propose a practical new framework for imaging non-parametric diffusion tensor distributions (DTDs) by constraining the microscopic diffusion tensors of the DTD to be diagonalized using the same orthonormal reference frame of the mesoscopic voxel. In each voxel, the constrained DTD (cDTD) is completely determined by the correlation spectrum of the microscopic principal diffusivities associated with the axes of the voxel reference frame. Consequently, all cDTDs are inherently limited to the domain of positive definite tensors and can be reconstructed efficiently using Inverse Laplace Transform methods. Moreover, the cDTD reconstruction can be performed using only data acquired efficiently with single diffusion encoding, although it also supports datasets with multiple diffusion encoding. In tissues with a well-defined architecture, such as the cortex, we can further constrain the cDTD to contain only cylindrically symmetric diffusion tensors and measure the 2D correlation spectra of principal diffusivities along the radial and tangential orientation with respect to the cortical surface. To demonstrate this framework, we perform numerical simulations and analyze high-resolution dMRI data from a fixed macaque monkey brain. We estimate 2D cDTDs in the cortex and derive, in each voxel, the marginal distributions of the microscopic principal diffusivities, the corresponding distributions of the microscopic fractional anisotropies and mean diffusivities along with their 2D correlation spectra to quantify the cDTD shape-size characteristics. Signal components corresponding to specific bands in these cDTD-derived spectra show high specificity to cortical laminar structures observed with histology. Our framework drastically simplifies the measurement of non-parametric DTDs in high-resolution datasets with mesoscopic voxel sizes much smaller than the radius of curvature of the underlying anatomy, e.g., cortical surface, and can be applied retrospectively to analyze existing diffusion MRI data from fixed cortical tissues.
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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, Bethesda, MD, United States
- Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD, United States
| | - Kadharbatcha S. Saleem
- 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, Bethesda, MD, United States
- Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., 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
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Brynolfsson P, Lerner M, Sundgren PC, Jamtheim Gustafsson C, Nilsson M, Szczepankiewicz F, Olsson LE. Tensor-valued diffusion magnetic resonance imaging in a radiotherapy setting. Phys Imaging Radiat Oncol 2022; 24:144-151. [DOI: 10.1016/j.phro.2022.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 11/11/2022] Open
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Afzali M, Mueller L, Sakaie K, Hu S, Chen Y, Szczepankiewicz F, Griswold MA, Jones DK, Ma D. MR Fingerprinting with b-Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan. Magn Reson Med 2022; 88:2043-2057. [PMID: 35713357 PMCID: PMC9420788 DOI: 10.1002/mrm.29352] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 11/08/2022]
Abstract
PURPOSE Although both relaxation and diffusion imaging are sensitive to tissue microstructure, studies have reported limited sensitivity and robustness of using relaxation or conventional diffusion alone to characterize tissue microstructure. Recently, it has been shown that tensor-valued diffusion encoding and joint relaxation-diffusion quantification enable more reliable quantification of compartment-specific microstructural properties. However, scan times to acquire such data can be prohibitive. Here, we aim to simultaneously quantify relaxation and diffusion using MR fingerprinting (MRF) and b-tensor encoding in a clinically feasible time. METHODS We developed multidimensional MRF scans (mdMRF) with linear and spherical b-tensor encoding (LTE and STE) to simultaneously quantify T1, T2, and ADC maps from a single scan. The image quality, accuracy, and scan efficiency were compared between the mdMRF using LTE and STE. Moreover, we investigated the robustness of different sequence designs to signal errors and their impact on the maps. RESULTS T1 and T2 maps derived from the mdMRF scans have consistently high image quality, while ADC maps are sensitive to different sequence designs. Notably, the fast imaging steady state precession (FISP)-based mdMRF scan with peripheral pulse gating provides the best ADC maps that are free of image distortion and shading artifacts. CONCLUSION We demonstrated the feasibility of quantifying T1, T2, and ADC maps simultaneously from a single mdMRF scan in around 24 s/slice. The map quality and quantitative values are consistent with the reference scans.
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Affiliation(s)
- Maryam Afzali
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of Leeds
LeedsUK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff UniversityCardiffUK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of Leeds
LeedsUK
| | - Ken Sakaie
- Imaging Institute, Cleveland ClinicClevelandOhioUSA
| | - Siyuan Hu
- Biomedical EngineeringCase Western Reserve UniversityClevelandOhioUSA
| | - Yong Chen
- RadiologyCase Western Reserve UniversityClevelandOhioUSA
| | | | | | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff UniversityCardiffUK
| | - Dan Ma
- Biomedical EngineeringCase Western Reserve UniversityClevelandOhioUSA
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Rosenberg JT, Grant SC, Topgaard D. Nonparametric 5D D-R 2 distribution imaging with single-shot EPI at 21.1 T: Initial results for in vivo rat brain. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 341:107256. [PMID: 35753184 PMCID: PMC9339475 DOI: 10.1016/j.jmr.2022.107256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/27/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
In vivo human diffusion MRI is by default performed using single-shot EPI with greater than 50-ms echo times and associated signal loss from transverse relaxation. The individual benefits of the current trends of increasing B0 to boost SNR and employing more advanced signal preparation schemes to improve the specificity for selected microstructural properties eventually may be cancelled by increased relaxation rates at high B0 and echo times with advanced encoding. Here, initial attempts to translate state-of-the-art diffusion-relaxation correlation methods from 3 T to 21.1 T are made to identify hurdles that need to be overcome to fulfill the promises of both high SNR and readily interpretable microstructural information.
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Affiliation(s)
- Jens T Rosenberg
- National High Magnetic Field Laboratory, Florida State University, Tallahassee FL, United States.
| | - Samuel C Grant
- National High Magnetic Field Laboratory, Florida State University, Tallahassee FL, United States; Chemical and Biomedical Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL, United States.
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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: 4] [Impact Index Per Article: 2.0] [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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Brabec J, Durmo F, Szczepankiewicz F, Brynolfsson P, Lampinen B, Rydelius A, Knutsson L, Westin CF, Sundgren PC, Nilsson M. Separating Glioma Hyperintensities From White Matter by Diffusion-Weighted Imaging With Spherical Tensor Encoding. Front Neurosci 2022; 16:842242. [PMID: 35527815 PMCID: PMC9069143 DOI: 10.3389/fnins.2022.842242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 03/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Tumor-related hyperintensities in high b-value diffusion-weighted imaging (DWI) are radiologically important in the workup of gliomas. However, the white matter may also appear as hyperintense, which may conflate interpretation. Purpose To investigate whether DWI with spherical b-tensor encoding (STE) can be used to suppress white matter and enhance the conspicuity of glioma hyperintensities unrelated to white matter. Materials and Methods Twenty-five patients with a glioma tumor and at least one pathology-related hyperintensity on DWI underwent conventional MRI at 3 T. The DWI was performed both with linear and spherical tensor encoding (LTE-DWI and STE-DWI). The LTE-DWI here refers to the DWI obtained with conventional diffusion encoding and averaged across diffusion-encoding directions. Retrospectively, the differences in contrast between LTE-DWI and STE-DWI, obtained at a b-value of 2,000 s/mm2, were evaluated by comparing hyperintensities and contralateral normal-appearing white matter (NAWM) both visually and quantitatively in terms of the signal intensity ratio (SIR) and contrast-to-noise ratio efficiency (CNReff). Results The spherical tensor encoding DWI was more effective than LTE-DWI at suppressing signals from white matter and improved conspicuity of pathology-related hyperintensities. The median SIR improved in all cases and on average by 28%. The median (interquartile range) SIR was 1.9 (1.6 – 2.1) for STE and 1.4 (1.3 – 1.7) for LTE, with a significant difference of 0.4 (0.3 –0.5) (p < 10–4, paired U-test). In 40% of the patients, the SIR was above 2 for STE-DWI, but with LTE-DWI, the SIR was below 2 for all patients. The CNReff of STE-DWI was significantly higher than of LTE-DWI: 2.5 (2 – 3.5) vs. 2.3 (1.7 – 3.1), with a significant difference of 0.4 (−0.1 –0.6) (p < 10–3, paired U-test). The STE improved CNReff in 70% of the cases. We illustrate the benefits of STE-DWI in three patients, where STE-DWI may facilitate an improved radiological description of tumor-related hyperintensity, including one case that could have been missed out if only LTE-DWI was inspected. Conclusion The contrast mechanism of high b-value STE-DWI results in a stronger suppression of white matter than conventional LTE-DWI, and may, therefore, be more sensitive and specific for assessment of glioma tumors and DWI-hyperintensities.
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Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Lund University, Lund, Sweden
- *Correspondence: Jan Brabec,
| | - Faris Durmo
- Diagnostic Radiology, Lund University, Lund, Sweden
| | - Filip Szczepankiewicz
- Diagnostic Radiology, Lund University, Lund, Sweden
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Patrik Brynolfsson
- Division of Medical Radiation Physics, Department of Translational Medicine, Lund University, Lund, Sweden
| | - Björn Lampinen
- Medical Radiation Physics, Lund University, Lund, Sweden
| | - Anna Rydelius
- Department of Neurology, Lund University, Lund, Sweden
| | - Linda Knutsson
- 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 for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Pia C. Sundgren
- Diagnostic Radiology, Lund University, Lund, Sweden
- Lund University Bioimaging Center, Lund University, Lund, Sweden
- Department of Imaging and Physiology, Skåne University Hospital, 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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25
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Brabec J, Szczepankiewicz F, Lennartsson F, Englund E, Pebdani H, Bengzon J, Knutsson L, Westin CF, Sundgren PC, Nilsson M. Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type. Neuroimage Clin 2022; 33:102912. [PMID: 34922122 PMCID: PMC8688887 DOI: 10.1016/j.nicl.2021.102912] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 11/30/2021] [Accepted: 12/08/2021] [Indexed: 01/18/2023]
Abstract
Tensor-valued dMRI facilitates prediction of meningioma consistency, grade and type. Tensor-valued dMRI corroborates findings of diffusion tensor and kurtosis imaging. MK and MKA is associated with firm and MD with variable meningioma consistency. Variability of MKI in the vicinity of the tumor is associated with meningioma grade. MKA 50 and MKI 50 separates psammomatous meningiomas from other meningioma types.
Background Preoperative radiological assessment of meningioma characteristics is of value for pre- and post-operative patient management, counselling, and surgical approach. Purpose To investigate whether tensor-valued diffusion MRI can add to the preoperative prediction of meningioma consistency, grade and type. Materials and methods 30 patients with intracranial meningiomas (22 WHO grade I, 8 WHO grade II) underwent MRI prior to surgery. Diffusion MRI was performed with linear and spherical b-tensors with b-values up to 2000 s/mm2. The data were used to estimate mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and its components—the anisotropic and isotropic kurtoses (MKA and MKI). Meningioma consistency was estimated for 16 patients during resection based on ultrasonic aspiration intensity, ease of resection with instrumentation or suction. Grade and type were determined by histopathological analysis. The relation between consistency, grade and type and dMRI parameters was analyzed inside the tumor (“whole-tumor”) and within brain tissue in the immediate periphery outside the tumor (“rim”) by histogram analysis. Results Lower 10th percentiles of MK and MKA in the whole-tumor were associated with firm consistency compared with pooled soft and variable consistency (n = 7 vs 9; U test, p = 0.02 for MKA 10 and p = 0.04 for MK10) and lower 10th percentile of MD with variable against soft and firm (n = 5 vs 11; U test, p = 0.02). Higher standard deviation of MKI in the rim was associated with lower grade (n = 22 vs 8; U test, p = 0.04) and in the MKI maps we observed elevated rim-like structure that could be associated with grade. Higher median MKA and lower median MKI distinguished psammomatous type from other pooled meningioma types (n = 5 vs 25; U test; p = 0.03 for MKA 50 and p = 0.03 and p = 0.04 for MKI 50). Conclusion Parameters from tensor-valued dMRI can facilitate prediction of consistency, grade and type.
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Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden.
| | | | - Finn Lennartsson
- Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden
| | | | - Houman Pebdani
- Department of Neurosurgery, Clinical Sciences, Lund University, Lund, Sweden
| | - Johan Bengzon
- Department of Neurosurgery, Clinical Sciences, Lund University, Lund, Sweden; Lund Stem Cell Center, Clinical Sciences, Lund University, Lund, Sweden
| | - Linda Knutsson
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Pia C Sundgren
- Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden; Lund University Bioimaging Center, Lund University, Lund, Sweden; Department for Imaging and Function, Skåne University Hospital, Lund University, Lund, Sweden
| | - Markus Nilsson
- Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden
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Correlation Tensor MRI deciphers underlying kurtosis sources in stroke. Neuroimage 2021; 247:118833. [PMID: 34929382 DOI: 10.1016/j.neuroimage.2021.118833] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 12/15/2021] [Accepted: 12/16/2021] [Indexed: 02/06/2023] Open
Abstract
Noninvasively detecting and characterizing modulations in cellular scale micro-architecture remains a desideratum for contemporary neuroimaging. Diffusion MRI (dMRI) has become the mainstay methodology for probing microstructure, and, in ischemia, its contrasts have revolutionized stroke management. Diffusion kurtosis imaging (DKI) has been shown to significantly enhance the sensitivity of stroke detection compared to its diffusion tensor imaging (DTI) counterparts. However, the interpretation of DKI remains ambiguous as its contrast may arise from competing kurtosis sources related to the anisotropy of tissue components, diffusivity variance across components, and microscopic kurtosis (e.g., arising from cross-sectional variance, structural disorder, and restriction). Resolving these sources may be fundamental for developing more specific imaging techniques for stroke management, prognosis, and understanding its pathophysiology. In this study, we apply Correlation Tensor MRI (CTI) - a double diffusion encoding (DDE) methodology recently introduced for deciphering kurtosis sources based on the unique information captured in DDE's diffusion correlation tensors - to investigate the underpinnings of kurtosis measurements in acute ischemic lesions. Simulations for the different kurtosis sources revealed specific signatures for cross-sectional variance (representing neurite beading), edema, and cell swelling. Ex vivo CTI experiments at 16.4 T were then performed in an experimental photothrombotic stroke model 3 h post-stroke (N = 10), and successfully separated anisotropic, isotropic, and microscopic non-Gaussian diffusion sources in the ischemic lesions. Each of these kurtosis sources provided unique contrasts in the stroked area. Particularly, microscopic kurtosis was shown to be a primary "driver" of total kurtosis upon ischemia; its large increases, coupled with decreases in anisotropic kurtosis, are consistent with the expected elevation in cross-sectional variance, likely linked to beading effects in small objects such as neurites. In vivo experiments at 9.4 T at the same time point (3 h post ischemia, N = 5) demonstrated the stability and relevance of the findings and showed that fixation is not a dominant confounder in our findings. In future studies, the different CTI contrasts may be useful to address current limitations of stroke imaging, e.g., penumbra characterization, distinguishing lesion progression form tissue recovery, and elucidating pathophysiological correlates.
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Nonparametric D-R 1-R 2 distribution MRI of the living human brain. Neuroimage 2021; 245:118753. [PMID: 34852278 DOI: 10.1016/j.neuroimage.2021.118753] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/17/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R1 and D-R2 correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R2 or R1 in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R1-R2 correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R1-R2 correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R1-R2 distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R1-, and R2-weightings based on the rich information contained in the D-R1-R2 distributions.
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28
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Multi-tissue spherical deconvolution of tensor-valued diffusion MRI. Neuroimage 2021; 245:118717. [PMID: 34775006 DOI: 10.1016/j.neuroimage.2021.118717] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/28/2021] [Accepted: 11/08/2021] [Indexed: 12/18/2022] Open
Abstract
Multi-tissue constrained spherical deconvolution (MT-CSD) leverages the characteristic b-value dependency of each tissue type to estimate both the apparent tissue densities and the white matter fiber orientation distribution function from diffusion MRI data. In this work, we generalize MT-CSD to tensor-valued diffusion encoding with arbitrary b-tensor shapes. This enables the use of data encoded with mixed b-tensors, rather than being limited to the subset of linear (conventional) b-tensors. Using the complete set of data, including all b-tensor shapes, provides a categorical improvement in the estimation of apparent tissue densities, fiber ODF, and resulting tractography. Furthermore, we demonstrate that including multiple b-tensor shapes in the analysis provides improved contrast between tissue types, in particular between gray matter and white matter. We also show that our approach provides high-quality apparent tissue density maps and high-quality fiber tracking from data, even with sparse sampling across b-tensors that yield whole-brain coverage at 2 mm isotropic resolution in approximately 5:15 min.
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Langbein BJ, Szczepankiewicz F, Westin CF, Bay C, Maier SE, Kibel AS, Tempany CM, Fenness FM. A Pilot Study of Multidimensional Diffusion MRI for Assessment of Tissue Heterogeneity in Prostate Cancer. Invest Radiol 2021; 56:845-853. [PMID: 34049334 PMCID: PMC8626531 DOI: 10.1097/rli.0000000000000796] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES The objectives of this exploratory study were to investigate the feasibility of multidimensional diffusion magnetic resonance imaging (MddMRI) in assessing diffusion heterogeneity at both a macroscopic and microscopic level in prostate cancer (PCa). MATERIALS AND METHODS Informed consent was obtained from 46 subjects who underwent 3.0-T prostate multiparametric MRI, complemented with a prototype spin echo-based MddMRI sequence in this institutional review board-approved study. Prostate cancer tumors and comparative normal tissue from each patient were contoured on both apparent diffusion coefficient and MddMRI-derived mean diffusivity (MD) maps (from which microscopic diffusion heterogeneity [MKi] and microscopic diffusion anisotropy were derived) using 3D Slicer. The discriminative ability of MddMRI-derived parameters to differentiate PCa from normal tissue was determined using the Friedman test. To determine if tumor diffusion heterogeneity is similar on macroscopic and microscopic scales, the linear association between SD of MD and mean MKi was estimated using robust regression (bisquare weighting). Hypothesis testing was 2 tailed; P values less than 0.05 were considered statistically significant. RESULTS All MddMRI-derived parameters could distinguish tumor from normal tissue in the fixed-effects analysis (P < 0.0001). Tumor MKi was higher (P < 0.05) compared with normal tissue (median, 0.40; interquartile range, 0.29-0.52 vs 0.20-0.18; 0.25), as was tumor microscopic diffusion anisotropy (0.55; 0.36-0.81 vs 0.20-0.15; 0.28). The MKi could not be predicted (no significant association) by SD of MD. There was a significant correlation between tumor volume and SD of MD (R2 = 0.50, slope = 0.008 μm2/ms per millimeter, P < 0.001) but not between tumor volume and MKi. CONCLUSIONS This explorative study demonstrates that MddMRI provides novel information on MKi and microscopic anisotropy, which differ from measures at the macroscopic level. MddMRI has the potential to characterize tumor tissue heterogeneity at different spatial scales.
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Affiliation(s)
- Björn J. Langbein
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA
- University Clinic Magdeburg, Otto von Guericke University, Magdeburg, Germany
- Harvard Medical School, Boston, MA
| | - Filip Szczepankiewicz
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Camden Bay
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Stephan E. Maier
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Adam S. Kibel
- Harvard Medical School, Boston, MA
- Department of Urology, Brigham and Women’s Hospital, Boston, MA
| | - Clare M. Tempany
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Fiona M. Fenness
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA
- Harvard Medical School, Boston, MA
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30
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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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31
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Rahman N, Xu K, Omer M, Budde MD, Brown A, Baron CA. Test-retest reproducibility of in vivo oscillating gradient and microscopic anisotropy diffusion MRI in mice at 9.4 Tesla. PLoS One 2021; 16:e0255711. [PMID: 34739479 PMCID: PMC8570471 DOI: 10.1371/journal.pone.0255711] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 10/22/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND AND PURPOSE Microstructure imaging with advanced diffusion MRI (dMRI) techniques have shown increased sensitivity and specificity to microstructural changes in various disease and injury models. Oscillating gradient spin echo (OGSE) dMRI, implemented by varying the oscillating gradient frequency, and microscopic anisotropy (μA) dMRI, implemented via tensor valued diffusion encoding, may provide additional insight by increasing sensitivity to smaller spatial scales and disentangling fiber orientation dispersion from true microstructural changes, respectively. The aims of this study were to characterize the test-retest reproducibility of in vivo OGSE and μA dMRI metrics in the mouse brain at 9.4 Tesla and provide estimates of required sample sizes for future investigations. METHODS Twelve adult C57Bl/6 mice were scanned twice (5 days apart). Each imaging session consisted of multifrequency OGSE and μA dMRI protocols. Metrics investigated included μA, linear diffusion kurtosis, isotropic diffusion kurtosis, and the diffusion dispersion rate (Λ), which explores the power-law frequency dependence of mean diffusivity. The dMRI metric maps were analyzed with mean region-of-interest (ROI) and whole brain voxel-wise analysis. Bland-Altman plots and coefficients of variation (CV) were used to assess the reproducibility of OGSE and μA metrics. Furthermore, we estimated sample sizes required to detect a variety of effect sizes. RESULTS Bland-Altman plots showed negligible biases between test and retest sessions. ROI-based CVs revealed high reproducibility for most metrics (CVs < 15%). Voxel-wise CV maps revealed high reproducibility for μA (CVs ~ 10%), but low reproducibility for OGSE metrics (CVs ~ 50%). CONCLUSION Most of the μA dMRI metrics are reproducible in both ROI-based and voxel-wise analysis, while the OGSE dMRI metrics are only reproducible in ROI-based analysis. Given feasible sample sizes (10-15), μA metrics and OGSE metrics may provide sensitivity to subtle microstructural changes (4-8%) and moderate changes (> 6%), respectively.
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Affiliation(s)
- Naila Rahman
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Kathy Xu
- Translational Neuroscience Group, Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Mohammad Omer
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Matthew D. Budde
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Arthur Brown
- Translational Neuroscience Group, Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
- Department of Anatomy and Cell Biology, University of Western Ontario, London, Ontario, Canada
| | - Corey A. Baron
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
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Nilsson M, Eklund G, Szczepankiewicz F, Skorpil M, Bryskhe K, Westin CF, Lindh C, Blomqvist L, Jäderling F. Mapping prostatic microscopic anisotropy using linear and spherical b-tensor encoding: A preliminary study. Magn Reson Med 2021; 86:2025-2033. [PMID: 34056750 PMCID: PMC9272946 DOI: 10.1002/mrm.28856] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 04/12/2021] [Accepted: 05/05/2021] [Indexed: 12/24/2022]
Abstract
Purpose: Tensor-valued diffusion encoding provides more specific information than conventional diffusion-weighted imaging (DWI), but has mainly been applied in neuroimaging studies. This study aimed to assess its potential for the imaging of prostate cancer (PCa). Methods: Seventeen patients with histologically proven PCa were enrolled. DWI of the prostate was performed with linear and spherical tensor encoding using a maximal b-value of 1.5 ms/μm2 and a voxel size of 3 × 3 × 4 mm3. The gamma-distribution model was used to estimate the mean diffusivity (MD), the isotropic kurtosis (MKI), and the anisotropic kurtosis (MKA). Regions of interest were placed in MR-defined cancerous tissues, as well as in apparently healthy tissues in the peripheral and transitional zones (PZs and TZs). Results: DWI with linear and spherical encoding yielded different image contrasts at high b-values, which enabled the estimation of MKA and MKI. Compared with healthy tissue (PZs and TZs combined) the cancers displayed a significantly lower MD (P < .05), higher MKI (P < 10−5), and lower MKA (P < .05). Compared with the TZ, tissue in the PZ showed lower MD (P < 10−3) and higher MKA (P < 10−3). No significant differences were found between cancers of different Gleason scores, possibly because of the limited sample size. Conclusion: Tensor-valued diffusion encoding enabled mapping of MKA and MKI in the prostate. The elevated MKI in PCa compared with normal tissues suggests an elevated heterogeneity in the cancers. Increased in-plane resolution could improve tumor delineation in future studies.
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Affiliation(s)
- Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | | | | | - Mikael Skorpil
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Solna, Stockholm, Sweden
| | | | - Carl-Fredrik Westin
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Claes Lindh
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Lennart Blomqvist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Diagnostic Radiology, Karolinska University Hospital, Solna, Sweden
| | - Fredrik Jäderling
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Diagnostic Radiology, Karolinska University Hospital, Solna, Sweden.,Department of Radiology, Capio S:t Görans Hospital, Stockholm, Sweden
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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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Li S, Zheng Y, Sun W, Lasič S, Szczepankiewicz F, Wei Q, Han S, Zhang S, Zhong X, Wang L, Li H, Cai Y, Xu D, Li Z, He Q, van Westen D, Bryskhe K, Topgaard D, Xu H. Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging. Eur Radiol 2021; 31:8197-8207. [PMID: 33914116 DOI: 10.1007/s00330-021-07959-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/10/2021] [Accepted: 03/29/2021] [Indexed: 12/19/2022]
Abstract
OBJECTIVE To evaluate the potential of diffusional variance decomposition (DIVIDE) for grading, molecular feature classification, and microstructural characterization of gliomas. MATERIALS AND METHODS Participants with suspected gliomas underwent DIVIDE imaging, yielding parameter maps of fractional anisotropy (FA), mean diffusivity (MD), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), total mean kurtosis (MKT), MKA/MKT, and microscopic fractional anisotropy (μFA). Tumor type and grade, isocitrate dehydrogenase (IDH) 1/2 mutant status, and the Ki-67 labeling index (Ki-67 LI) were determined after surgery. Statistical analysis included 33 high-grade gliomas (HGG) and 17 low-grade gliomas (LGG). Tumor diffusion metrics were compared between HGG and LGG, among grades, and between wild and mutated IDH types using appropriate tests according to normality assessment results. Receiver operating characteristic and Spearman correlation analysis were also used for statistical evaluations. RESULTS FA, MD, MKA, MKI, MKT, μFA, and MKA/MKT differed between HGG and LGG (FA: p = 0.047; MD: p = 0.037, others p < 0.001), and among glioma grade II, III, and IV (FA: p = 0.048; MD: p = 0.038, others p < 0.001). All diffusion metrics differed between wild-type and mutated IDH tumors (MKI: p = 0.003; others: p < 0.001). The metrics that best discriminated between HGG and LGGs and between wild-type and mutated IDH tumors were MKT and FA respectively (area under the curve 0.866 and 0.881). All diffusion metrics except FA showed significant correlation with Ki-67 LI, and MKI had the highest correlation coefficient (rs = 0.618). CONCLUSION DIVIDE is a promising technique for glioma characterization and diagnosis. KEY POINTS • DIVIDE metrics MKI is related to cell density heterogeneity while MKA and μFA are related to cell eccentricity. • DIVIDE metrics can effectively differentiate LGG from HGG and IDH mutation from wild-type tumor, and showed significant correlation with the Ki-67 labeling index. • MKI was larger than MKA which indicates predominant cell density heterogeneity in gliomas. • MKA and MKI increased with grade or degree of malignancy, however with a relatively larger increase in the cell eccentricity metric MKA in relation to the cell density heterogeneity metric MKI.
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Affiliation(s)
- Sirui Li
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | | | - Wenbo Sun
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | | | | | - Qing Wei
- United Imaging Healthcare, Shanghai, China
| | | | | | - Xiaoli Zhong
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Liang Wang
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Huan Li
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Yuxiang Cai
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Dan Xu
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Zhiqiang Li
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Qiang He
- United Imaging Healthcare, Shanghai, China
| | | | | | | | - Haibo Xu
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
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In vivo tensor-valued diffusion MRI of focal demyelination in white and deep grey matter of rodents. NEUROIMAGE-CLINICAL 2021; 30:102675. [PMID: 34215146 PMCID: PMC8100629 DOI: 10.1016/j.nicl.2021.102675] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 03/22/2021] [Accepted: 03/24/2021] [Indexed: 02/02/2023]
Abstract
We performed in-vivo tensor-valued diffusion MRI in demyelinating rodents. Lysolecithin was injected in white and deep grey matter to cause focal demyelination. Focal demyelination reduced microscopic fractional anisotropy (µFA). Isotropic kurtosis may be particularly sensitive to deep grey matter lesions.
Background Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease leading to damage of white matter (WM) and grey matter (GM). Magnetic resonance imaging (MRI) is the modality of choice to assess brain damage in MS, but there is an unmet need in MRI for achieving higher sensitivity and specificity to MS-related microstructural alterations in WM and GM. Objective To explore whether tensor-valued diffusion MRI (dMRI) can yield sensitive microstructural read-outs for focal demyelination in cerebral WM and deep GM (DGM). Methods Eight rats underwent L-α-Lysophosphatidylcholine (LPC) injections in the WM and striatum to introduce focal demyelination. Multimodal MRI was performed at 7 Tesla after 7 days. Tensor-valued dMRI was complemented by diffusion tensor imaging, quantitative MRI and proton magnetic resonance spectroscopy (MRS). Results Quantitative MRI and MRS confirmed that LPC injections caused inflammatory demyelinating lesions in WM and DGM. Tensor-valued dMRI illustrated a significant decline of microscopic fractional anisotropy (µFA) in both LPC-treated WM and DGM (P < 0.005) along with a marked increase of isotropic kurtosis (MKI) in DGM (P < 0.0001). Conclusion Tensor-valued dMRI bears considerable potential for microstructural imaging in MS, suggesting a regional µFA decrease may be a sensitive indicator of MS lesions, while a regional MKI increase may be particularly sensitive in detecting DGM lesions of MS.
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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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Lampinen B, Lätt J, Wasselius J, van Westen D, Nilsson M. Time dependence in diffusion MRI predicts tissue outcome in ischemic stroke patients. Magn Reson Med 2021; 86:754-764. [PMID: 33755261 PMCID: PMC8445077 DOI: 10.1002/mrm.28743] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 01/29/2021] [Accepted: 01/30/2021] [Indexed: 12/18/2022]
Abstract
Purpose: Reperfusion therapy enables effective treatment of ischemic stroke presenting within 4–6 hours. However, tissue progression from ischemia to infarction is variable, and some patients benefit from treatment up until 24 hours. Improved imaging techniques are needed to identify these patients. Here, it was hypothesized that time dependence in diffusion MRI may predict tissue outcome in ischemic stroke. Methods: Diffusion MRI data were acquired with multiple diffusion times in five non-reperfused patients at 2, 9, and 100 days after stroke onset. Maps of “rate of kurtosis change” (k), mean kurtosis, ADC, and fractional anisotropy were derived. The ADC maps defined lesions, normal-appearing tissue, and the lesion tissue that would either be infarcted or remain viable by day 100. Diffusion parameters were compared (1) between lesions and normal-appearing tissue, and (2) between lesion tissue that would be infarcted or remain viable. Results: Positive values of k were observed within stroke lesions on day 2 (P = .001) and on day 9 (P = .023), indicating diffusional exchange. On day 100, high ADC values indicated infarction of 50 ± 20% of the lesion volumes. Tissue infarction was predicted by high k values both on day 2 (P = .026) and on day 9 (P = .046), by low mean kurtosis values on day 2 (P = .043), and by low fractional anisotropy values on day 9 (P = .029), but not by low ADC values. Conclusions: Diffusion time dependence predicted tissue outcome in ischemic stroke more accurately than the ADC, and may be useful for predicting reperfusion benefit.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Jimmy Lätt
- Center for Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Johan Wasselius
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | | | - Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
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Magdoom KN, Pajevic S, Dario G, Basser PJ. A new framework for MR diffusion tensor distribution. Sci Rep 2021; 11:2766. [PMID: 33531530 PMCID: PMC7854653 DOI: 10.1038/s41598-021-81264-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 11/19/2020] [Indexed: 12/25/2022] Open
Abstract
The ability to characterize heterogeneous and anisotropic water diffusion processes within macroscopic MRI voxels non-invasively and in vivo is a desideratum in biology, neuroscience, and medicine. While an MRI voxel may contain approximately a microliter of tissue, our goal is to examine intravoxel diffusion processes on the order of picoliters. Here we propose a new theoretical framework and efficient experimental design to describe and measure such intravoxel structural heterogeneity and anisotropy. We assume that a constrained normal tensor-variate distribution (CNTVD) describes the variability of positive definite diffusion tensors within a voxel which extends its applicability to a wide range of b-values while preserving the richness of diffusion tensor distribution (DTD) paradigm unlike existing models. We introduce a new Monte Carlo (MC) scheme to synthesize realistic 6D DTD numerical phantoms and invert the MR signal. We show that the signal inversion is well-posed and estimate the CNTVD parameters parsimoniously by exploiting the different symmetries of the mean and covariance tensors of CNTVD. The robustness of the estimation pipeline is assessed by adding noise to calculated MR signals and compared with the ground truth. A family of invariant parameters and glyphs which characterize microscopic shape, size and orientation heterogeneity within a voxel are also presented.
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Affiliation(s)
- Kulam Najmudeen Magdoom
- Division on Translational Imaging and Genomic Integrity, Eunice Kennedy Shriver, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Sinisa Pajevic
- Division on Translational Imaging and Genomic Integrity, Eunice Kennedy Shriver, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Gasbarra Dario
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Peter J Basser
- Division on Translational Imaging and Genomic Integrity, Eunice Kennedy Shriver, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
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Szczepankiewicz F, Westin CF, Nilsson M. Gradient waveform design for tensor-valued encoding in diffusion MRI. J Neurosci Methods 2021; 348:109007. [PMID: 33242529 PMCID: PMC8443151 DOI: 10.1016/j.jneumeth.2020.109007] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 11/17/2020] [Accepted: 11/19/2020] [Indexed: 12/13/2022]
Abstract
Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b-tensor. The benefit of tensor-valued diffusion encoding is that it unlocks the 'shape of the b-tensor' as a new encoding dimension. By modulating the b-tensor shape, we can control the sensitivity to microscopic diffusion anisotropy which can be used as a contrast mechanism; a feature that is inaccessible by conventional diffusion encoding. Since imaging methods based on tensor-valued diffusion encoding are finding an increasing number of applications we are prompted to highlight the challenge of designing the optimal gradient waveforms for any given application. In this review, we first establish the basic design objectives in creating field gradient waveforms for tensor-valued diffusion MRI. We also survey additional design considerations related to limitations imposed by hardware and physiology, potential confounding effects that cannot be captured by the b-tensor, and artifacts related to the diffusion encoding waveform. Throughout, we discuss the expected compromises and tradeoffs with an aim to establish a more complete understanding of gradient waveform design and its impact on accurate measurements and interpretations of data.
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Affiliation(s)
- Filip Szczepankiewicz
- Radiology, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Clinical Sciences, Lund University, Lund, Sweden.
| | - Carl-Fredrik Westin
- Radiology, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
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Probing tissue microstructure by diffusion skewness tensor imaging. Sci Rep 2021; 11:135. [PMID: 33420140 PMCID: PMC7794496 DOI: 10.1038/s41598-020-79748-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 11/30/2020] [Indexed: 01/29/2023] Open
Abstract
Probing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a theoretical proof that shows that, to estimate the skewness of a diffusion tensor distribution, the encoding protocol needs to include full-rank tensor diffusion encoding. This proof provides a guideline for the application of this technique. The properties of the proposed indices are illustrated using both synthetic data and in vivo data acquired from a human brain.
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Henriques RN, Palombo M, Jespersen SN, Shemesh N, Lundell H, Ianuş A. Double diffusion encoding and applications for biomedical imaging. J Neurosci Methods 2020; 348:108989. [PMID: 33144100 DOI: 10.1016/j.jneumeth.2020.108989] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/25/2020] [Accepted: 10/20/2020] [Indexed: 12/11/2022]
Abstract
Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important contemporary non-invasive modalities for probing tissue structure at the microscopic scale. The majority of dMRI techniques employ standard single diffusion encoding (SDE) measurements, covering different sequence parameter ranges depending on the complexity of the method. Although many signal representations and biophysical models have been proposed for SDE data, they are intrinsically limited by a lack of specificity. Advanced dMRI methods have been proposed to provide additional microstructural information beyond what can be inferred from SDE. These enhanced contrasts can play important roles in characterizing biological tissues, for instance upon diseases (e.g. neurodegenerative, cancer, stroke), aging, learning, and development. In this review we focus on double diffusion encoding (DDE), which stands out among other advanced acquisitions for its versatility, ability to probe more specific diffusion correlations, and feasibility for preclinical and clinical applications. Various DDE methodologies have been employed to probe compartment sizes (Section 3), decouple the effects of microscopic diffusion anisotropy from orientation dispersion (Section 4), probe displacement correlations, study exchange, or suppress fast diffusing compartments (Section 6). DDE measurements can also be used to improve the robustness of biophysical models (Section 5) and study intra-cellular diffusion via magnetic resonance spectroscopy of metabolites (Section 7). This review discusses all these topics as well as important practical aspects related to the implementation and contrast in preclinical and clinical settings (Section 9) and aims to provide the readers a guide for deciding on the right DDE acquisition for their specific application.
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Affiliation(s)
- Rafael N Henriques
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Marco Palombo
- Centre for Medical Image Computing and Dept. of Computer Science, University College London, London, UK
| | - Sune N Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark
| | - Andrada Ianuş
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.
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Kamiya K, Kamagata K, Ogaki K, Hatano T, Ogawa T, Takeshige-Amano H, Murata S, Andica C, Murata K, Feiweier T, Hori M, Hattori N, Aoki S. Brain White-Matter Degeneration Due to Aging and Parkinson Disease as Revealed by Double Diffusion Encoding. Front Neurosci 2020; 14:584510. [PMID: 33177985 PMCID: PMC7594529 DOI: 10.3389/fnins.2020.584510] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 09/22/2020] [Indexed: 11/16/2022] Open
Abstract
Microstructure imaging by means of multidimensional diffusion encoding is increasingly applied in clinical research, with expectations that it yields a parameter that better correlates with clinical disability than current methods based on single diffusion encoding. Under the assumption that diffusion within a voxel can be well described by a collection of diffusion tensors, several parameters of this diffusion tensor distribution can be derived, including mean size, variance of sizes, orientational dispersion, and microscopic anisotropy. The information provided by multidimensional diffusion encoding also enables us to decompose the sources of the conventional fractional anisotropy and mean kurtosis. In this study, we explored the utility of the diffusion tensor distribution approach for characterizing white-matter degeneration in aging and in Parkinson disease by using double diffusion encoding. Data from 23 healthy older subjects and 27 patients with Parkinson disease were analyzed. Advanced age was associated with greater mean size and size variances, as well as smaller microscopic anisotropy. By analyzing the parameters underlying diffusion kurtosis, we found that the reductions of kurtosis in aging and Parkinson disease reported in the literature are likely driven by the reduction in microscopic anisotropy. Furthermore, microscopic anisotropy correlated with the severity of motor impairment in the patients with Parkinson disease. The present results support the use of multidimensional diffusion encoding in clinical studies and are encouraging for its future clinical implementation.
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Affiliation(s)
- Kouhei Kamiya
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.,Department of Radiology, Toho University, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Kotaro Ogaki
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Taku Hatano
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Takashi Ogawa
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | | | - Syo Murata
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Christina Andica
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | | | | | - Masaaki Hori
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.,Department of Radiology, Toho University, Tokyo, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
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43
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Jelescu IO, Palombo M, Bagnato F, Schilling KG. Challenges for biophysical modeling of microstructure. J Neurosci Methods 2020; 344:108861. [PMID: 32692999 PMCID: PMC10163379 DOI: 10.1016/j.jneumeth.2020.108861] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/10/2020] [Accepted: 07/14/2020] [Indexed: 02/07/2023]
Abstract
The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifying both hurdles that have been already overcome and outstanding issues. First, we describe the critical initial task of selecting which features of tissue microstructure can be estimated using a model and which acquisition protocol needs to be implemented to make the estimation possible. The model performance should necessarily be tested in realistic numerical simulations and in experimental data - adapting the fitting strategy accordingly, and parameter estimates should be validated against complementary techniques, when/if available. Secondly, the model performance and validity should be explored in pathological conditions, and, if appropriate, dedicated models for pathology should be developed. We build on examples from tumors, ischemia and demyelinating diseases. We then discuss the challenges associated with clinical translation and added value. Finally, we single out four major unresolved challenges that are related to: the availability of a microstructural ground truth, the validation of model parameters which cannot be accessed with complementary techniques, the development of a generalized standard model for any brain region and pathology, and the seamless communication between different parties involved in the development and application of biophysical models of diffusion.
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44
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Lampinen B, Zampeli A, Björkman-Burtscher IM, Szczepankiewicz F, Källén K, Compagno Strandberg M, Nilsson M. Tensor-valued diffusion MRI differentiates cortex and white matter in malformations of cortical development associated with epilepsy. Epilepsia 2020; 61:1701-1713. [PMID: 32667688 PMCID: PMC7963222 DOI: 10.1111/epi.16605] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 05/21/2020] [Accepted: 06/15/2020] [Indexed: 12/24/2022]
Abstract
Objective: Delineation of malformations of cortical development (MCD) is central in presurgical evaluation of drug-resistant epilepsy. Delineation using magnetic resonance imaging (MRI) can be ambiguous, however, because the conventional T1- and T2-weighted contrasts depend strongly on myelin for differentiation of cortical tissue and white matter. Variations in myelin content within both cortex and white matter may cause MCD findings on MRI to change size, become undetectable, or disagree with histopathology. The novel tensor-valued diffusion MRI (dMRI) technique maps microscopic diffusion anisotropy, which is sensitive to axons rather than myelin. This work investigated whether tensor-valued dMRI may improve differentiation of cortex and white matter in the delineation of MCD. Methods: Tensor-valued dMRI was performed on a 7 T MRI scanner in 13 MCD patients (age = 32 ± 13 years) featuring periventricular heterotopia, subcortical heterotopia, focal cortical dysplasia, and polymicrogyria. Data analysis yielded maps of microscopic anisotropy that were compared with T1-weighted and T2-fluid-attenuated inversion recovery images and with the fractional anisotropy from diffusion tensor imaging. Results: Maps of microscopic anisotropy revealed large white matter-like regions within MCD that were uniformly cortex-like in the conventional MRI contrasts. These regions were seen particularly in the deep white matter parts of subcortical heterotopias and near the gray-white boundaries of focal cortical dysplasias and polymicrogyrias. Significance: By being sensitive to axons rather than myelin, mapping of microscopic anisotropy may yield a more robust differentiation of cortex and white matter and improve MCD delineation in presurgical evaluation of epilepsy.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Ariadne Zampeli
- Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | | | - Filip Szczepankiewicz
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden.,Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Kristina Källén
- Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden.,Clinical Sciences Lund, Department of Clinical Sciences Helsingborg, Lund University, Lund, Sweden
| | | | - Markus Nilsson
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden
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45
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Andersen KW, Lasič S, Lundell H, Nilsson M, Topgaard D, Sellebjerg F, Szczepankiewicz F, Siebner HR, Blinkenberg M, Dyrby TB. Disentangling white-matter damage from physiological fibre orientation dispersion in multiple sclerosis. Brain Commun 2020; 2:fcaa077. [PMID: 32954329 PMCID: PMC7472898 DOI: 10.1093/braincomms/fcaa077] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 04/20/2020] [Accepted: 05/07/2020] [Indexed: 01/23/2023] Open
Abstract
Multiple sclerosis leads to diffuse damage of the central nervous system, affecting also the normal-appearing white matter. Demyelination and axonal degeneration reduce regional fractional anisotropy in normal-appearing white matter, which can be routinely mapped with diffusion tensor imaging. However, the standard fractional anisotropy metric is also sensitive to physiological variations in orientation dispersion of white matter fibres. This complicates the detection of disease-related damage in large parts of cerebral white matter where microstructure physiologically displays a high degree of fibre dispersion. To resolve this ambiguity, we employed a novel tensor-valued encoding method for diffusion MRI, which yields a microscopic fractional anisotropy metric that is unaffected by regional variations in orientation dispersion. In 26 patients with relapsing-remitting multiple sclerosis, 14 patients with primary-progressive multiple sclerosis and 27 age-matched healthy controls, we compared standard fractional anisotropy mapping with the novel microscopic fractional anisotropy mapping method, focusing on normal-appearing white matter. Mean microscopic fractional anisotropy and standard fractional anisotropy of normal-appearing white matter were significantly reduced in both patient groups relative to healthy controls, but microscopic fractional anisotropy yielded a better reflection of disease-related white-matter alterations. The reduction in mean microscopic fractional anisotropy showed a significant positive linear relationship with physical disability, as reflected by the expanded disability status scale. Mean reduction of microscopic fractional anisotropy in normal-appearing white matter also scaled positively with individual cognitive dysfunction, as measured with the symbol digit modality test. Mean microscopic fractional anisotropy reduction in normal-appearing white matter also showed a positive relationship with total white-matter lesion load as well as lesion load in specific tract systems. None of these relationships between normal-appearing white-matter microstructure and clinical, cognitive or structural measures emerged when using mean fractional anisotropy. Together, the results provide converging evidence that microscopic fractional anisotropy mapping substantially advances the assessment of cerebral white matter in multiple sclerosis by disentangling microstructure damage from variations in physiological fibre orientation dispersion at the stage of data acquisition. Since tensor-valued encoding can be implemented in routine diffusion MRI, microscopic fractional anisotropy mapping bears considerable potential for the future assessment of disease progression in normal-appearing white matter in both relapsing-remitting and progressive forms of multiple sclerosis as well as other white-matter-related brain diseases.
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Affiliation(s)
- Kasper Winther Andersen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650 Hvidovre, Denmark
| | - Samo Lasič
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650 Hvidovre, Denmark
- Random Walk Imaging, AB, 222 24 Lund, Sweden
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650 Hvidovre, Denmark
| | - Markus Nilsson
- Department of Radiology, Clinical Sciences, Lund, Lund University, 221 00 Lund, Sweden
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund University, 221 00 Lund, Sweden
| | - Finn Sellebjerg
- Danish Multiple Sclerosis Center, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
| | - Filip Szczepankiewicz
- Department of Medical Radiation Physics, Clinical Sciences, Lund, Lund University, 221 00 Lund, Sweden
| | - Hartwig Roman Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650 Hvidovre, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
- Department of Neurology, Copenhagen University Hospital Bispebjerg, 2400 Copenhagen NV, Denmark
| | - Morten Blinkenberg
- Danish Multiple Sclerosis Center, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650 Hvidovre, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2700 Kongens Lyngby, Denmark
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46
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Arbabi A, Kai J, Khan AR, Baron CA. Diffusion dispersion imaging: Mapping oscillating gradient spin-echo frequency dependence in the human brain. Magn Reson Med 2019; 83:2197-2208. [PMID: 31762110 DOI: 10.1002/mrm.28083] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 10/24/2019] [Accepted: 10/25/2019] [Indexed: 12/24/2022]
Abstract
PURPOSE Oscillating gradient spin-echo (OGSE) diffusion MRI provides information about the microstructure of biological tissues by means of the frequency dependence of the apparent diffusion coefficient (ADC). ADC dependence on OGSE frequency has been explored in numerous rodent studies, but applications in the human brain have been limited and have suffered from low contrast between different frequencies, long scan times, and a limited exploration of the nature of the ADC dependence on frequency. THEORY AND METHODS Multiple frequency OGSE acquisitions were acquired in healthy subjects at 7T to explore the power-law frequency dependence of ADC, the "diffusion dispersion." Furthermore, a method for optimizing the estimation of the ADC difference between different OGSE frequencies was developed, which enabled the design of a highly efficient protocol for mapping diffusion dispersion. RESULTS For the first time, evidence of a linear dependence of ADC on the square root of frequency in healthy human white matter was obtained. Using the optimized protocol, high-quality, full-brain maps of apparent diffusion dispersion rate were also demonstrated at an isotropic resolution of 2 mm in a scan time of 6 min. CONCLUSIONS This work sheds light on the nature of diffusion dispersion in the healthy human brain and introduces full-brain diffusion dispersion mapping at clinically relevant scan times. These advances may lead to new biomarkers of pathology or improved microstructural modeling.
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Affiliation(s)
- Aidin Arbabi
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada
| | - Jason Kai
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada
| | - Ali R Khan
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada
| | - Corey A Baron
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada
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47
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Nilsson M, Szczepankiewicz F, Brabec J, Taylor M, Westin CF, Golby A, van Westen D, Sundgren PC. Tensor-valued diffusion MRI in under 3 minutes: an initial survey of microscopic anisotropy and tissue heterogeneity in intracranial tumors. Magn Reson Med 2019; 83:608-620. [PMID: 31517401 PMCID: PMC6900060 DOI: 10.1002/mrm.27959] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 07/05/2019] [Accepted: 07/30/2019] [Indexed: 12/23/2022]
Abstract
PURPOSE To evaluate the feasibility of a 3-minutes protocol for assessment of the microscopic anisotropy and tissue heterogeneity based on tensor-valued diffusion MRI in a wide range of intracranial tumors. METHODS B-tensor encoding was performed in 42 patients with intracranial tumors (gliomas, meningiomas, adenomas, and metastases). Microscopic anisotropy and tissue heterogeneity were evaluated by estimating the anisotropic kurtosis (MKA ) and isotropic kurtosis (MKI ), respectively. An extensive imaging protocol was compared with a 3-minutes protocol. RESULTS The fast imaging protocol yielded parameters with characteristics in terms of bias and precision similar to the full protocol. Glioblastomas had lower microscopic anisotropy than meningiomas (MKA = 0.29 ± 0.06 vs. 0.45 ± 0.08, P = 0.003). Metastases had higher tissue heterogeneity (MKI = 0.57 ± 0.07) than both the glioblastomas (0.44 ± 0.06, P < 0.001) and meningiomas (0.46 ± 0.06, P = 0.03). CONCLUSION Evaluation of the microscopic anisotropy and tissue heterogeneity in intracranial tumor patients is feasible in clinically relevant times frames.
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Affiliation(s)
- Markus Nilsson
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | | | - Jan Brabec
- Department of Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Marie Taylor
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | | | - Alexandra Golby
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Danielle van Westen
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | - Pia C Sundgren
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden.,Lund University Bioimaging Center (LBIC), Lund University, Lund, Sweden
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