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Oeschger JM, Tabelow K, Mohammadi S. Investigating apparent differences between standard DKI and axisymmetric DKI and its consequences for biophysical parameter estimates. Magn Reson Med 2024; 92:69-81. [PMID: 38308141 DOI: 10.1002/mrm.30034] [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: 06/07/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 02/04/2024]
Abstract
PURPOSE The purpose of the study is to identify differences between axisymmetric diffusion kurtosis imaging (DKI) and standard DKI, their consequences for biophysical parameter estimates, and the protocol choice influence on parameter estimation. METHODS Noise-free and noisy, synthetic diffusion MRI human brain data is simulated using standard DKI for a standard and the fast "199" acquisition protocol. First the noise-free "baseline" difference between both DKI models is estimated and the influence of fiber complexity is investigated. Noisy data is used to establish the signal-to-noise ratio at which the baseline difference exceeds noise variability. The influence of protocol choices and denoising is investigated. The five axisymmetric DKI tensor metrics (AxTM), the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor are used to compare both DKI models. Additionally, the baseline difference is also estimated for the five parameters of the WMTI-Watson model. RESULTS The parallel and perpendicular kurtosis and all of the WMTI-Watson parameters had large baseline differences. Using a Westin or FA mask reduced the number of voxels with large baseline difference, that is, by selecting voxels with less complex fibers. For the noisy data, precision was worsened by the fast "199" protocol but adaptive denoising can help counteract these effects. CONCLUSION For the diffusivities and mean of the kurtosis tensor, axisymmetric DKI with a standard protocol delivers similar results as standard DKI. Fiber complexity is one main driver of the baseline differences. Using the "199" protocol worsens precision in noisy data but adaptive denoising mitigates these effects.
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Affiliation(s)
- Jan Malte Oeschger
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karsten Tabelow
- Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany
| | - Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Sachsen, Germany
- Max Planck Research Group MR Physics, Max Planck Institute for Human Development, Berlin, Germany
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2
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Farrher E, Grinberg F, Khechiashvili T, Neuner I, Konrad K, Shah NJ. Spatiotemporal Patterns of White Matter Maturation after Pre-Adolescence: A Diffusion Kurtosis Imaging Study. Brain Sci 2024; 14:495. [PMID: 38790472 PMCID: PMC11119177 DOI: 10.3390/brainsci14050495] [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: 04/11/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Diffusion tensor imaging (DTI) enables the assessment of changes in brain tissue microstructure during maturation and ageing. In general, patterns of cerebral maturation and decline render non-monotonic lifespan trajectories of DTI metrics with age, and, importantly, the rate of microstructural changes is heterochronous for various white matter fibres. Recent studies have demonstrated that diffusion kurtosis imaging (DKI) metrics are more sensitive to microstructural changes during ageing compared to those of DTI. In a previous work, we demonstrated that the Cohen's d of mean diffusional kurtosis (dMK) represents a useful biomarker for quantifying maturation heterochronicity. However, some inferences on the maturation grades of different fibre types, such as association, projection, and commissural, were of a preliminary nature due to the insufficient number of fibres considered. Hence, the purpose of this follow-up work was to further explore the heterochronicity of microstructural maturation between pre-adolescence and middle adulthood based on DTI and DKI metrics. Using the effect size of the between-group parametric changes and Cohen's d, we observed that all commissural fibres achieved the highest level of maturity, followed by the majority of projection fibres, while the majority of association fibres were the least matured. We also demonstrated that dMK strongly correlates with the maxima or minima of the lifespan curves of DTI metrics. Furthermore, our results provide substantial evidence for the existence of spatial gradients in the timing of white matter maturation. In conclusion, our data suggest that DKI provides useful biomarkers for the investigation of maturation spatial heterogeneity and heterochronicity.
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Affiliation(s)
- Ezequiel Farrher
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
| | - Farida Grinberg
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany
| | - Tamara Khechiashvili
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
| | - Irene Neuner
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
| | - Kerstin Konrad
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry and Psychotherapy, RWTH Aachen University, 52074 Aachen, Germany
- Institute of Neuroscience and Medicine 3, INM-3, Forschungszentrum Jülich, 52425 Jülich, Germany
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, 52425 Jülich, Germany
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3
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Malte Oeschger J, Tabelow K, Mohammadi S. Axisymmetric diffusion kurtosis imaging with Rician bias correction: A simulation study. Magn Reson Med 2023; 89:787-799. [PMID: 36198046 DOI: 10.1002/mrm.29474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 09/01/2022] [Accepted: 09/04/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE To compare the estimation accuracy of axisymmetric diffusion kurtosis imaging (DKI) and standard DKI in combination with Rician bias correction (RBC). METHODS Axisymmetric DKI is more robust against noise-induced variation in the measured signal than standard DKI because of its reduced parameter space. However, its susceptibility to Rician noise bias at low signal-to-noise ratios (SNR) is unknown. Here, we investigate two main questions: first, does RBC improve estimation accuracy of axisymmetric DKI?; second, is estimation accuracy of axisymmetric DKI increased compared to standard DKI? Estimation accuracy was investigated on the five axisymmetric DKI tensor metrics (AxTM): the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor, using a noise simulation study based on synthetic data of tissues with varying fiber alignment and in-vivo data focusing on white matter. RESULTS RBC mainly increased accuracy for the parallel AxTM in tissues with highly to moderately aligned fibers. For the perpendicular AxTM, axisymmetric DKI without RBC performed slightly better than with RBC. However, the combination of axisymmetric DKI with RBC was the overall best performing algorithm across all five AxTM in white matter and axisymmetric DKI itself substantially improved accuracy in axisymmetric tissues with low fiber alignment. CONCLUSION Combining axisymmetric DKI with RBC facilitates accurate DKI parameter estimation at unprecedented low SNRs ( ≈ 15 $$ \approx 15 $$ ) in white matter, possibly making it a valuable tool for neuroscience and clinical research studies where scan time is a limited resource. The tools used here are available in the open-source ACID toolbox for SPM.
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Affiliation(s)
- Jan Malte Oeschger
- Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karsten Tabelow
- Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany
| | - Siawoosh Mohammadi
- Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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4
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deSouza NM, van der Lugt A, Deroose CM, Alberich-Bayarri A, Bidaut L, Fournier L, Costaridou L, Oprea-Lager DE, Kotter E, Smits M, Mayerhoefer ME, Boellaard R, Caroli A, de Geus-Oei LF, Kunz WG, Oei EH, Lecouvet F, Franca M, Loewe C, Lopci E, Caramella C, Persson A, Golay X, Dewey M, O'Connor JPB, deGraaf P, Gatidis S, Zahlmann G. Standardised lesion segmentation for imaging biomarker quantitation: a consensus recommendation from ESR and EORTC. Insights Imaging 2022; 13:159. [PMID: 36194301 PMCID: PMC9532485 DOI: 10.1186/s13244-022-01287-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/01/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. METHODS A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. RESULTS/CONCLUSIONS Items with ≥ 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with ≤ 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified.
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Affiliation(s)
- Nandita M deSouza
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK.
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Christophe M Deroose
- Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium.,Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | | | - Luc Bidaut
- College of Science, University of Lincoln, Lincoln, Lincoln, LN6 7TS, UK
| | - Laure Fournier
- INSERM, Radiology Department, AP-HP, Hopital Europeen Georges Pompidou, Université de Paris, PARCC, 75015, Paris, France
| | - Lena Costaridou
- School of Medicine, University of Patras, University Campus, Rio, 26 500, Patras, Greece
| | - Daniela E Oprea-Lager
- Department of Radiology and Nuclear Medicine, Amsterdam, UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Elmar Kotter
- Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Marius E Mayerhoefer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.,Memorial Sloan Kettering Cancer Centre, New York, NY, USA
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam, UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anna Caroli
- Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy
| | - Lioe-Fee de Geus-Oei
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Biomedical Photonic Imaging Group, University of Twente, Enschede, The Netherlands
| | - Wolfgang G Kunz
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Edwin H Oei
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Frederic Lecouvet
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), 10 Avenue Hippocrate, 1200, Brussels, Belgium
| | - Manuela Franca
- Department of Radiology, Centro Hospitalar Universitário do Porto, Instituto de Ciências Biomédicas de Abel Salazar, University of Porto, Porto, Portugal
| | - Christian Loewe
- Division of Cardiovascular and Interventional Radiology, Department for Bioimaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Egesta Lopci
- Nuclear Medicine, IRCCS - Humanitas Research Hospital, via Manzoni 56, Rozzano, MI, Italy
| | - Caroline Caramella
- Radiology Department, Hôpital Marie Lannelongue, Institut d'Oncologie Thoracique, Université Paris-Saclay, Le Plessis-Robinson, France
| | - Anders Persson
- Department of Radiology, and Department of Health, Medicine and Caring Sciences, Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Xavier Golay
- Queen Square Institute of Neurology, University College London, London, UK
| | - Marc Dewey
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - James P B O'Connor
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK
| | - Pim deGraaf
- Department of Radiology and Nuclear Medicine, Amsterdam, UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sergios Gatidis
- Department of Radiology, University of Tubingen, Tübingen, Germany
| | - Gudrun Zahlmann
- Radiological Society of North America (RSNA), Oak Brook, IL, USA
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5
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Clark IA, Mohammadi S, Callaghan MF, Maguire EA. Conduction velocity along a key white matter tract is associated with autobiographical memory recall ability. eLife 2022; 11:79303. [PMID: 36166372 PMCID: PMC9514844 DOI: 10.7554/elife.79303] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 09/12/2022] [Indexed: 12/01/2022] Open
Abstract
Conduction velocity is the speed at which electrical signals travel along axons and is a crucial determinant of neural communication. Inferences about conduction velocity can now be made in vivo in humans using a measure called the magnetic resonance (MR) g-ratio. This is the ratio of the inner axon diameter relative to that of the axon plus the myelin sheath that encases it. Here, in the first application to cognition, we found that variations in MR g-ratio, and by inference conduction velocity, of the parahippocampal cingulum bundle were associated with autobiographical memory recall ability in 217 healthy adults. This tract connects the hippocampus with a range of other brain areas. We further observed that the association seemed to be with inner axon diameter rather than myelin content. The extent to which neurites were coherently organised within the parahippocampal cingulum bundle was also linked with autobiographical memory recall ability. Moreover, these findings were specific to autobiographical memory recall and were not apparent for laboratory-based memory tests. Our results offer a new perspective on individual differences in autobiographical memory recall ability, highlighting the possible influence of specific white matter microstructure features on conduction velocity when recalling detailed memories of real-life past experiences.
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Affiliation(s)
- Ian A Clark
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Siawoosh Mohammadi
- Institute of Systems Neuroscience, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
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6
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Kornaropoulos EN, Winzeck S, Rumetshofer T, Wikstrom A, Knutsson L, Correia MM, Sundgren PC, Nilsson M. Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus. Front Neurol 2022; 13:837385. [PMID: 35557624 PMCID: PMC9087851 DOI: 10.3389/fneur.2022.837385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.
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Affiliation(s)
- Evgenios N. Kornaropoulos
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
| | - Stefan Winzeck
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
- BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom
| | | | - Anna Wikstrom
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Marta M. Correia
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Pia C. Sundgren
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
- Lund University BioImaging Center, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
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7
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Guo L, Lyu J, Zhang Z, Shi J, Feng Q, Feng Y, Gao M, Zhang X. A Joint Framework for Denoising and Estimating Diffusion Kurtosis Tensors Using Multiple Prior Information. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:308-319. [PMID: 34520348 DOI: 10.1109/tmi.2021.3112515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Diffusion kurtosis imaging (DKI) has been shown to be valuable in a wide range of neuroscientific and clinical applications. However, reliable estimation of DKI tensors is often compromised by noise, especially for the kurtosis tensor (KT). Here, we propose a joint denoising and estimating framework that integrates multiple sources of prior information, including nonlocal structural self-similarity (NSS), local spatial smoothness (LSS), physical relevance (PR) of the DKI model, and noise characteristics of magnitude diffusion MRI (dMRI) images for improved estimation of DKI tensors. The local and nonlocal spatial smoothing constraints are complementary to each other, making the proposed framework highly effective in reducing the noise fluctuations on DKI tensors, especially KT. As an additional refinement, we propose to impose a physically relevant constraint within our joint denoising and estimation framework. We further adopt the first-moment noise-corrected fitting model (M1NCM) to remove the noncentral χ -distribution noise bias. The effectiveness of integrating multiple sources of priors into the joint framework is verified by comparing the proposed M1NCM-NSS-LSS-PR method with various versions of M1NCM-based estimators and two state-of-the-art methods. Results show that the proposed method outperformed the compared methods in simulations and in-vivo dMRI datasets of both spatially stationary and nonstationary noise distributions. The in-vivo experiments also show that the proposed M1NCM-NSS-LSS-PR method was robust to the number of diffusion directions.
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8
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Xu P, Guo L, Feng Y, Zhang X. [A diffusion-weighted image denoising algorithm using HOSVD combined with Rician noise corrected model]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2021; 41:1400-1408. [PMID: 34658356 DOI: 10.12122/j.issn.1673-4254.2021.09.16] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To propose a novel diffusion-weighted (DW) image denoising algorithm based on HOSVD to improve the signal-to-noise ratio (SNR) of DW images and the accuracy of subsequent quantization parameters. METHODS This HOSVDbased denoising method incorporated the sparse constraint and noise-correction model. The signal expectations with Rician noise were integrated into the traditional HOSVD denoising framework for direct denoising of the DW images with Rician noise. HOSVD denoising was performed directly on each local DW image block to avoid the stripe artifacts. We compared the proposed method with 4 image denoising algorithms (LR + Edge, GL-HOSVD, BM3D and NLM) to verify the effect of the proposed method. RESULTS The experimental results showed that the proposed method effectively reduced the noise of DW images while preserving the image details and edge structure information. The proposed algorithm was significantly better than LR +Edge, BM3D and NLM in terms of quantitative metrics of PSNR, SSIM and FA-RMSE and in visual evaluation of denoising images and FA images. GL-HOSVD obtained good denoising results but introduced stripe artifacts at a high noise level during the denoising process. In contrast, the proposed method achieved good denoising results without causing stripe artifacts. CONCLUSION This HOSVD-based denoising method allows direct processing of DW images with Rician noise without introducing artifacts and can provide accurate quantitative parameters for diagnostic purposes.
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Affiliation(s)
- P Xu
- School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing//Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology//Center for Brain Science and Brain-Inspired Intelligence of Guangdong-Hong Kong-Macao Greater Bay Area, Southern Medical University, Guangzhou 510515, China
| | - L Guo
- School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing//Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology//Center for Brain Science and Brain-Inspired Intelligence of Guangdong-Hong Kong-Macao Greater Bay Area, Southern Medical University, Guangzhou 510515, China
| | - Y Feng
- School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing//Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology//Center for Brain Science and Brain-Inspired Intelligence of Guangdong-Hong Kong-Macao Greater Bay Area, Southern Medical University, Guangzhou 510515, China
| | - X Zhang
- School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing//Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology//Center for Brain Science and Brain-Inspired Intelligence of Guangdong-Hong Kong-Macao Greater Bay Area, Southern Medical University, Guangzhou 510515, China
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9
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Emmenegger TM, David G, Ashtarayeh M, Fritz FJ, Ellerbrock I, Helms G, Balteau E, Freund P, Mohammadi S. The Influence of Radio-Frequency Transmit Field Inhomogeneities on the Accuracy of G-ratio Weighted Imaging. Front Neurosci 2021; 15:674719. [PMID: 34290579 PMCID: PMC8287210 DOI: 10.3389/fnins.2021.674719] [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: 03/01/2021] [Accepted: 06/01/2021] [Indexed: 11/13/2022] Open
Abstract
G-ratio weighted imaging is a non-invasive, in-vivo MRI-based technique that aims at estimating an aggregated measure of relative myelination of axons across the entire brain white matter. The MR g-ratio and its constituents (axonal and myelin volume fraction) are more specific to the tissue microstructure than conventional MRI metrics targeting either the myelin or axonal compartment. To calculate the MR g-ratio, an MRI-based myelin-mapping technique is combined with an axon-sensitive MR technique (such as diffusion MRI). Correction for radio-frequency transmit (B1+) field inhomogeneities is crucial for myelin mapping techniques such as magnetization transfer saturation. Here we assessed the effect of B1+ correction on g-ratio weighted imaging. To this end, the B1+ field was measured and the B1+ corrected MR g-ratio was used as the reference in a Bland-Altman analysis. We found a substantial bias (≈-89%) and error (≈37%) relative to the dynamic range of g-ratio values in the white matter if the B1+ correction was not applied. Moreover, we tested the efficiency of a data-driven B1+ correction approach that was applied retrospectively without additional reference measurements. We found that it reduced the bias and error in the MR g-ratio by a factor of three. The data-driven correction is readily available in the open-source hMRI toolbox (www.hmri.info) which is embedded in the statistical parameter mapping (SPM) framework.
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Affiliation(s)
- Tim M Emmenegger
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gergely David
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Mohammad Ashtarayeh
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Francisco J Fritz
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Isabel Ellerbrock
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Gunther Helms
- Medical Radiation Physics, Clinical Sciences Lund (IKVL), Lund University, Lund, Sweden
| | | | - Patrick Freund
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
| | - Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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10
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Zhang Z, Vernekar D, Qian W, Kim M. Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord. BMC Med Imaging 2021; 21:16. [PMID: 33516178 PMCID: PMC7847150 DOI: 10.1186/s12880-021-00549-9] [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: 05/14/2020] [Accepted: 01/18/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND To investigate the effect of using a Rician nonlocal means (NLM) filter on quantification of diffusion tensor (DT)- and diffusion kurtosis (DK)-derived metrics in various anatomical regions of the human brain and the spinal cord, when combined with a constrained linear least squares (CLLS) approach. METHODS Prospective brain data from 9 healthy subjects and retrospective spinal cord data from 5 healthy subjects from a 3 T MRI scanner were included in the study. Prior to tensor estimation, registered diffusion weighted images were denoised by an optimized blockwise NLM filter with CLLS. Mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) and fractional anisotropy (FA), were determined in anatomical structures of the brain and the spinal cord. DTI and DKI metrics, signal-to-noise ratio (SNR) and Chi-square values were quantified in distinct anatomical regions for all subjects, with and without Rician denoising. RESULTS The averaged SNR significantly increased with Rician denoising by a factor of 2 while the averaged Chi-square values significantly decreased up to 61% in the brain and up to 43% in the spinal cord after Rician NLM filtering. In the brain, the mean MK varied from 0.70 (putamen) to 1.27 (internal capsule) while AK and RK varied from 0.58 (corpus callosum) to 0.92 (cingulum) and from 0.70 (putamen) to 1.98 (corpus callosum), respectively. In the spinal cord, FA varied from 0.78 in lateral column to 0.81 in dorsal column while MD varied from 0.91 × 10-3 mm2/s (lateral) to 0.93 × 10-3 mm2/s (dorsal). RD varied from 0.34 × 10-3 mm2/s (dorsal) to 0.38 × 10-3 mm2/s (lateral) and AD varied from 1.96 × 10-3 mm2/s (lateral) to 2.11 × 10-3 mm2/s (dorsal). CONCLUSIONS Our results show a Rician denoising NLM filter incorporated with CLLS significantly increases SNR and reduces estimation errors of DT- and KT-derived metrics, providing the reliable metrics estimation with adequate SNR levels.
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Affiliation(s)
- Zhongping Zhang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China.,Philips Healthcare, Shanghai, China
| | - Dhanashree Vernekar
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China
| | - Wenshu Qian
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China.,Laboratory of Clinical Investigation, National Institute on Aging, NIH, Baltimore, USA
| | - Mina Kim
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China. .,Department of Neuroinflammation, Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, London, UK.
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11
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Jiang M, Zhao C, Yan R, Li J, Song W, Peng R, Han Q, Jiang X. Continuous Inking Affects the Biological and Biochemical Responses of Cuttlefish Sepia pharaonis. Front Physiol 2019; 10:1429. [PMID: 31849695 PMCID: PMC6896823 DOI: 10.3389/fphys.2019.01429] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 11/04/2019] [Indexed: 11/13/2022] Open
Abstract
Several marine mollusks, including cephalopods (cuttlefish, squid, and octopus) and gastropods (e.g., sea hares), can release a colored ink secretion when chased by predators or stimulated. Ink release is part of a defensive response, but the threshold for the biochemical responses caused by stimulation is unknown. The present study aimed to reveal antipredator responses of cuttlefish, such as escaping via inking and/or jetting, and to investigate its biological and biochemical responses to continuous ink release. Results showed that the behavioral responses to continuous ink release mainly manifested as blazing body pattern changes. Cuttlefish escaped from predators covered by jetting/inking and warned the potential threats by displaying a unique body pattern. Moreover, persistent inking in the presence of an overt stimulus caused uncontrollable ink release from the ink duct/anal canal (loss of control). This study first verified the characteristics of the cuttlefish ink solution, prepared a standard curve of ink solution concentrations, and fitted the relationship function between the release frequency and the released ink weight. Biological statistics indicated that cuttlefish has the ability to continuously release ink (releasing ∼90% of the ink from the ink sac) and that the individuals adapted well during the recovery period. However, re-releasing ink would result in "overexploitation" and high mortality. Hexokinase (HK), pyruvate kinase (PK), and superoxide dismutase (SOD) activities, as well as malondialdehyde (MDA) concentration increased or remained stable in different tissues after releasing ink. The expression of heat shock protein 90 and arginine kinase (AK) were upregulated by stimuli in all tissues. Biochemical changes indicated that continuous inking not only consumed considerable energy but also damaged the tissues. In summary, cuttlefish released almost 90% of their ink for active defense against predators, and it took ∼30 days for the ink sac to be refilled, but "overexploitation" resulted in serious physiological damage. These findings will be helpful to further study the defense and ink release mechanisms and to consider animal health and welfare when using cephalopods as experimental animals and for aquaculture practices.
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Affiliation(s)
| | | | | | | | | | | | | | - Xiamin Jiang
- School of Marine Sciences, Ningbo University, Ningbo, China
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12
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Olson DV, Nencka AS, Arpinar VE, Muftuler LT. Analysis of errors in diffusion kurtosis imaging caused by slice crosstalk in simultaneous multi-slice imaging. NMR IN BIOMEDICINE 2019; 32:e4162. [PMID: 31385637 DOI: 10.1002/nbm.4162] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 06/07/2019] [Accepted: 07/03/2019] [Indexed: 06/10/2023]
Abstract
Simultaneous multi-slice (SMS) imaging techniques accelerate diffusion MRI data acquisition. However, slice separation is imperfect and results in residual signal leakage between the simultaneously excited slices. The resulting consistent bias may adversely affect diffusion model parameter estimation. Although this bias is usually small and might not affect the simplified diffusion tensor model significantly, higher order diffusion models such as kurtosis are likely to be more susceptible to such effects. In this work, two SMS reconstruction techniques and an alternative acquisition approach were tested to quantify the effects of slice crosstalk on diffusion kurtosis parameters. In reconstruction, two popular slice separation algorithms, slice GRAPPA and split-slice GRAPPA, are evaluated to determine the effect of slice leakage on diffusion kurtosis metrics. For the alternative acquisition, the slice pairings were varied across diffusion weighted images such that the signal leakage does not come from the same overlapped slice for all diffusion encodings. Simulation results demonstrated the potential benefits of randomizing the slice pairings. However, various experimental factors confounded the advantages of slice pair randomization. In volunteer experiments, region-of-interest analyses found high metric errors with each of the SMS acquisitions and reconstructions in the brain white matter.
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Affiliation(s)
- Daniel V Olson
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Magnetic Resonance Imaging, GE Healthcare, Waukesha, Wisconsin, USA
| | - Andrew S Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Center for Imaging Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Volkan E Arpinar
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Center for Imaging Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - L Tugan Muftuler
- Center for Imaging Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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13
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Maximov II, Alnæs D, Westlye LT. Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank. Hum Brain Mapp 2019; 40:4146-4162. [PMID: 31173439 PMCID: PMC6865652 DOI: 10.1002/hbm.24691] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 05/14/2019] [Accepted: 05/27/2019] [Indexed: 12/30/2022] Open
Abstract
Increasing interest in the structural and functional organisation of the human brain encourages the acquisition of big data sets comprising multiple neuroimaging modalities, often accompanied by additional information obtained from health records, cognitive tests, biomarkers and genotypes. Diffusion weighted magnetic resonance imaging data enables a range of promising imaging phenotypes probing structural connections as well as macroanatomical and microstructural properties of the brain. The reliability and biological sensitivity and specificity of diffusion data depend on processing pipeline. A state-of-the-art framework for data processing facilitates cross-study harmonisation and reduces pipeline-related variability. Using diffusion magnetic resonance imaging (MRI) data from 218 individuals in the UK Biobank, we evaluate the effects of different processing steps that have been suggested to reduce imaging artefacts and improve reliability of diffusion metrics. In lack of a ground truth, we compared diffusion metric sensitivity to age between pipelines. By comparing distributions and age sensitivity of the resulting diffusion metrics based on different approaches (diffusion tensor imaging, diffusion kurtosis imaging and white matter tract integrity), we evaluate a general pipeline comprising seven postprocessing blocks: noise correction; Gibbs ringing correction; evaluation of field distortions; susceptibility, eddy-current and motion-induced distortion corrections; bias field correction; spatial smoothing and final diffusion metric estimations. Based on this evaluation, we suggest an optimised processing pipeline for diffusion weighted MRI data.
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Affiliation(s)
- Ivan I. Maximov
- Department of PsychologyUniversity of OsloOsloNorway
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dag Alnæs
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOsloNorway
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
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14
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Olson DV, Arpinar VE, Muftuler LT. Optimization of q-space sampling for mean apparent propagator MRI metrics using a genetic algorithm. Neuroimage 2019; 199:237-244. [PMID: 31163267 DOI: 10.1016/j.neuroimage.2019.05.078] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/28/2019] [Accepted: 05/29/2019] [Indexed: 11/17/2022] Open
Abstract
Mean Apparent Propagator (MAP) MRI is a recently introduced technique to estimate the diffusion probability density function (PDF) robustly. Using the estimated PDF, MAP MRI then calculates zero-displacement and non-Gaussianity metrics, which might better characterize tissue microstructure compared to diffusion tensor imaging or diffusion kurtosis imaging. However, intensive q-space sampling required for MAP MRI limits its widespread adoption. A reduced q-space sampling scheme that maintains the accuracy of the derived metrics would make it more practical. A heuristic approach for acquiring MAP MRI with fewer q-space samples has been introduced earlier with scan duration of less than 10 minutes. However, the sampling scheme was not optimized systematically to preserve the accuracy of the model metrics. In this work, a genetic algorithm is implemented to determine optimal q-space subsampling schemes for MAP MRI that will keep total scan time under 10 min. Results show that the metrics derived from the optimized schemes more closely match those computed from the full set, especially in dense fiber tracts such as the corpus callosum.
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Affiliation(s)
- Daniel V Olson
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA; Magnetic Resonance Imaging, GE Healthcare, Waukesha, WI, USA.
| | - Volkan E Arpinar
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA; Center for Imaging Research, Medical College of Wisconsin, Milwaukee, WI, USA
| | - L Tugan Muftuler
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA; Center for Imaging Research, Medical College of Wisconsin, Milwaukee, WI, USA
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15
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Isotropically weighted intravoxel incoherent motion brain imaging at 7T. Magn Reson Imaging 2018; 57:124-132. [PMID: 30472300 DOI: 10.1016/j.mri.2018.11.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 10/30/2018] [Accepted: 11/17/2018] [Indexed: 12/13/2022]
Abstract
Perfusion magnetic resonance imaging (MRI) is a promising non-invasive technique providing insights regarding the brain's microvascular architecture in vivo. The scalar perfusion metrics can be used for quantitative diagnostics of various brain abnormalities, in particular, in the stroke cases and tumours. However, conventional MRI-based perfusion approaches such as dynamic contrast-enhanced perfusion imaging or arterial spin labelling have a few weaknesses, for instance, contrast agent deposition, low signal-to-noise ratio, limited temporal and spatial resolution, and specific absorption rate constraints. As an alternative, the intravoxel incoherent motion (IVIM) approach exploits an extension of diffusion MRI in order to estimate perfusion parameters in the human brain. Application of IVIM imaging at ultra-high field MRI might employ the advantage of a higher signal-to-noise ratio, and thereby the use of higher spatial and temporal resolutions. In the present work, we demonstrate an application of recently developed isotropic diffusion weighted sequences to the evaluation of IVIM parameters at an ultra-high 7T field. The used sequence exhibits high immunity to image degrading factors and allows one to acquire the data in a fast and efficient way. Utilising the bi-exponential fitting model of the signal attenuation, we performed an extensive analysis of the IVIM scalar metrics obtained by a isotropic diffusion weighted sequence in vivo and compared results with a conventional pulsed gradient sequence at 7T. In order to evaluate a possible metric bias originating from blood flows, we additionally used a truncated b-value protocol (b-values from 100 to 200 s/mm2 with the step 20 s/mm2) accompanied to the full range (b-values from 0 to 200 s/mm2). The IVIM scalar metrics have been assessed and analysed together with a large and middle vessel density atlas of the human brain. We found that the diffusion coefficients and perfusion fractions of the voxels consisting of large and middle vessels have higher values in contrast to other tissues. Additionally, we did not find a strong dependence of the IVIM metrics on the density values of the vessel atlas. Perspectives and limitations of the developed isotropic diffusion weighted perfusion are presented and discussed.
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16
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Olson DV, Arpinar VE, Muftuler LT. Assessing diffusion kurtosis tensor estimation methods using a digital brain phantom derived from human connectome project data. Magn Reson Imaging 2018; 48:122-128. [PMID: 29305126 DOI: 10.1016/j.mri.2017.12.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Accepted: 12/29/2017] [Indexed: 11/19/2022]
Abstract
PURPOSE Diffusion kurtosis imaging (DKI) has gained popularity in recent years as an advanced diffusion-weighted MRI technique. This work aims to quantitatively compare the performance and accuracy of four DKI processing algorithms. For this purpose, a digital DKI brain phantom is developed. METHODS Data from the Human Connectome Project database were used to generate a DKI digital phantom. In a Monte Carlo Rician noise simulation, four DKI processing algorithms were compared based on their mean squared error, squared bias, and variance. RESULTS Algorithm performance was region-dependent and differed for each diffusion metric and noise level. Crossover between variance and squared bias error occurred between signal-to-noise ratios of 30 and 40. CONCLUSION Through the framework presented here, DKI algorithms can be quantitatively compared via a ground truth data set. Error maps are critical as algorithm performance varies spatially. Bias-plus-variance decomposition provides a more complete picture than MSE alone. In combination with refinements in acquisition in future studies, the accuracy and efficiency of DKI will continue to improve promoting clinical adoption.
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Affiliation(s)
- Daniel V Olson
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA.
| | - Volkan E Arpinar
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - L Tugan Muftuler
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
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17
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Le Bihan D. What can we see with IVIM MRI? Neuroimage 2017; 187:56-67. [PMID: 29277647 DOI: 10.1016/j.neuroimage.2017.12.062] [Citation(s) in RCA: 221] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2017] [Revised: 11/28/2017] [Accepted: 12/19/2017] [Indexed: 12/18/2022] Open
Abstract
Intravoxel Incoherent Motion (IVIM) refers to translational movements which within a given voxel and during the measurement time present a distribution of speeds in orientation and/or amplitude. The IVIM concept has been used to estimate perfusion in tissues as blood flow in randomly oriented capillaries mimics a pseudo-diffusion process. IVIM-based perfusion MRI, which does not require contrast agents, has gained momentum recently, especially in the field oncology. In this introductory review the basic concepts, models, technical requirements and limitations inherent to IVIM-based perfusion MRI are outlined, as well as new, non-perfusion applications of IVIM MRI, such as virtual MR Elastography.
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Affiliation(s)
- Denis Le Bihan
- NeuroSpin, Frédéric Joliot Institute, Bât 145, CEA-Saclay Center, Gif-sur-Yvette, 91191 France.
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18
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Edwards LJ, Pine KJ, Ellerbrock I, Weiskopf N, Mohammadi S. NODDI-DTI: Estimating Neurite Orientation and Dispersion Parameters from a Diffusion Tensor in Healthy White Matter. Front Neurosci 2017; 11:720. [PMID: 29326546 PMCID: PMC5742359 DOI: 10.3389/fnins.2017.00720] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 12/11/2017] [Indexed: 11/25/2022] Open
Abstract
The NODDI-DTI signal model is a modification of the NODDI signal model that formally allows interpretation of standard single-shell DTI data in terms of biophysical parameters in healthy human white matter (WM). The NODDI-DTI signal model contains no CSF compartment, restricting application to voxels without CSF partial-volume contamination. This modification allowed derivation of analytical relations between parameters representing axon density and dispersion, and DTI invariants (MD and FA) from the NODDI-DTI signal model. These relations formally allow extraction of biophysical parameters from DTI data. NODDI-DTI parameters were estimated by applying the proposed analytical relations to DTI parameters estimated from the first shell of data, and compared to parameters estimated by fitting the NODDI-DTI model to both shells of data (reference dataset) in the WM of 14 in vivo diffusion datasets recorded with two different protocols, and in simulated data. The first two datasets were also fit to the NODDI-DTI model using only the first shell (as for DTI) of data. NODDI-DTI parameters estimated from DTI, and NODDI-DTI parameters estimated by fitting the model to the first shell of data gave similar errors compared to two-shell NODDI-DTI estimates. The simulations showed the NODDI-DTI method to be more noise-robust than the two-shell fitting procedure. The NODDI-DTI method gave unphysical parameter estimates in a small percentage of voxels, reflecting voxelwise DTI estimation error or NODDI-DTI model invalidity. In the course of evaluating the NODDI-DTI model, it was found that diffusional kurtosis strongly biased DTI-based MD values, and so, making assumptions based on healthy WM, a novel heuristic correction requiring only DTI data was derived and used to mitigate this bias. Since validations were only performed on healthy WM, application to grey matter or pathological WM would require further validation. Our results demonstrate NODDI-DTI to be a promising model and technique to interpret restricted datasets acquired for DTI analysis in healthy white matter with greater biophysical specificity, though its limitations must be borne in mind.
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Affiliation(s)
- Luke J Edwards
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Kerrin J Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Isabel Ellerbrock
- Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Siawoosh Mohammadi
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom.,Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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19
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Wang F, Jin D, Hua XL, Zhao ZZ, Wu LM, Chen WB, Wu GY, Chen XX, Chen HG. Investigation of diffusion kurtosis imaging for discriminating tumors from inflammatory lesions after treatment for bladder cancer. J Magn Reson Imaging 2017; 48:259-265. [PMID: 29232021 DOI: 10.1002/jmri.25924] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Accepted: 11/28/2017] [Indexed: 11/08/2022] Open
Affiliation(s)
- Fang Wang
- Department of Radiology; Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University; Shanghai China
| | - Di Jin
- Department of Urology; Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University; Shanghai China
| | - Xiao-Lan Hua
- Department of Radiology; Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University; Shanghai China
| | - Zi-Zhou Zhao
- Department of Radiology; Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University; Shanghai China
| | - Lian-Ming Wu
- Department of Radiology; Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University; Shanghai China
| | | | - Guang-Yu Wu
- Department of Radiology; Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University; Shanghai China
| | - Xiao-Xi Chen
- Department of Radiology; Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University; Shanghai China
| | - Hai-Ge Chen
- Department of Urology; Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University; Shanghai China
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20
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Ellerbrock I, Mohammadi S. Four in vivo g-ratio-weighted imaging methods: Comparability and repeatability at the group level. Hum Brain Mapp 2017; 39:24-41. [PMID: 29091341 DOI: 10.1002/hbm.23858] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 10/11/2017] [Accepted: 10/16/2017] [Indexed: 12/18/2022] Open
Abstract
A recent method, denoted in vivo g-ratio-weighted imaging, has related the microscopic g-ratio, only accessible by ex vivo histology, to noninvasive MRI markers for the fiber volume fraction (FVF) and myelin volume fraction (MVF). Different MRI markers have been proposed for g-ratio weighted imaging, leaving open the question which combination of imaging markers is optimal. To address this question, the repeatability and comparability of four g-ratio methods based on different combinations of, respectively, two imaging markers for FVF (tract-fiber density, TFD, and neurite orientation dispersion and density imaging, NODDI) and two imaging markers for MVF (magnetization transfer saturation rate, MT, and, from proton density maps, macromolecular tissue volume, MTV) were tested in a scan-rescan experiment in two groups. Moreover, it was tested how the repeatability and comparability were affected by two key processing steps, namely the masking of unreliable voxels (e.g., due to partial volume effects) at the group level and the calibration value used to link MRI markers to MVF (and FVF). Our data showed that repeatability and comparability depend largely on the marker for the FVF (NODDI outperformed TFD), and that they were improved by masking. Overall, the g-ratio method based on NODDI and MT showed the highest repeatability (90%) and lowest variability between groups (3.5%). Finally, our results indicate that the calibration procedure is crucial, for example, calibration to a lower g-ratio value (g = 0.6) than the commonly used one (g = 0.7) can change not only repeatability and comparability but also the reported dependency on the FVF imaging marker. Hum Brain Mapp 39:24-41, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Isabel Ellerbrock
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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21
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Grinberg F, Maximov II, Farrher E, Shah NJ. Microstructure-informed slow diffusion tractography in humans enhances visualisation of fibre pathways. Magn Reson Imaging 2017; 45:7-17. [PMID: 28870514 DOI: 10.1016/j.mri.2017.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 08/15/2017] [Accepted: 08/30/2017] [Indexed: 11/26/2022]
Abstract
Conventional fibre tractography methods based on diffusion tensor imaging exploit diffusion anisotropy and directionality in the range of low diffusion weightings (b-values). High b-value Biexponential Diffusion Tensor Analysis reported previously has demonstrated that fractional anisotropy of the slow diffusion component is essentially higher than that of conventional diffusion tensor imaging whereas popular compartment models associate this slow diffusion component with axonal water fraction. One of the primary aims of this study is to elucidate the feasibility and potential benefits of "microstructure-informed" whole-brain slow-diffusion fibre tracking (SDIFT) in humans. In vivo diffusion-weighted images in humans were acquired in the extended range of diffusion weightings≤6000smm-2 at 3T. Fast and slow diffusion tensors were reconstructed using the bi-exponential tensor decomposition, and a detailed statistical analysis of the relevant whole-brain tensor metrics was performed. We visualised three-dimensional fibre tracts in in vivo human brains using deterministic streamlining via the major eigenvector of the slow diffusion tensor. In particular, we demonstrated that slow-diffusion fibre tracking provided considerably higher fibre counts of long association fibres and allowed one to reconstruct more short association fibres than conventional diffusion tensor imaging. SDIFT is suggested to be useful as a complimentary method capable to enhance reliability and visualisation of the evaluated fibre pathways. It is especially informative in precortical areas where the uncertainty of the mono-exponential tensor evaluation becomes too high due to decreased anisotropy of low b-value diffusion in these areas. Benefits can be expected in assessment of the residual axonal integrity in tissues affected by various pathological conditions, in surgical planning, and in evaluation of cortical connectivity, in particular, between Brodmann's areas.
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Affiliation(s)
- Farida Grinberg
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany,; Department of Neurology, Faculty of Medicine, RWTH Aachen University, JARA, Aachen, Germany.
| | - Ivan I Maximov
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany
| | - Ezequiel Farrher
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany
| | - N Jon Shah
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany,; Department of Neurology, Faculty of Medicine, RWTH Aachen University, JARA, Aachen, Germany
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22
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Maximov II, Tonoyan AS, Pronin IN. Differentiation of glioma malignancy grade using diffusion MRI. Phys Med 2017; 40:24-32. [PMID: 28712716 DOI: 10.1016/j.ejmp.2017.07.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Revised: 06/26/2017] [Accepted: 07/04/2017] [Indexed: 12/31/2022] Open
Abstract
Modern diffusion MR protocols allow one to acquire the multi-shell diffusion data with high diffusion weightings in a clinically feasible time. In the present work we assessed three diffusion approaches based on diffusion and kurtosis tensor imaging (DTI, DKI), and neurite orientation dispersion and density imaging (NODDI) as possible biomarkers for human brain glioma grade differentiation based on the one diffusion protocol. We used three diffusion weightings (so called b-values) equal to 0, 1000, and 2500s/mm2 with 60 non-coplanar diffusion directions in the case of non-zero b-values. The patient groups of the glioma grades II, III, and IV consist of 8 subjects per group. We found that DKI, and NODDI scalar metrics can be effectively used as glioma grade biomarkers with a significant difference (p<0.05) for grading between low- and high-grade gliomas, in particular, for glioma II versus glioma III grades, and glioma III versus glioma IV grades. The use of mean/axial kurtosis and intra-axonal fraction/orientation dispersion index metrics allowed us to obtain the most feasible and reliable differentiation criteria. For example, in the case of glioma grades II, III, and IV the mean kurtosis is equal to 0.31, 0.51, and 0.90, and the orientation dispersion index is equal to 0.14, 0.30, and 0.59, respectively. The limitations and perspectives of the biophysical diffusion models based on intra-/extra-axonal compartmentalisation for glioma differentiation are discussed.
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Affiliation(s)
- Ivan I Maximov
- Experimental Physics III, TU Dortmund University, 44221, Germany.
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Vellmer S, Edelhoff D, Suter D, Maximov II. Anisotropic diffusion phantoms based on microcapillaries. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2017; 279:1-10. [PMID: 28410460 DOI: 10.1016/j.jmr.2017.04.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Revised: 03/30/2017] [Accepted: 04/02/2017] [Indexed: 06/07/2023]
Abstract
Diffusion MRI is an efficient and widely used technique for the investigation of tissue structure and organisation in vivo. Multiple phenomenological and biophysical diffusion models are intensively exploited for the analysis of the diffusion experiments. However, the verification of the applied diffusion models remains challenging. In order to provide a "gold standard" and to assess the accuracy of the derived parameters and the limitations of the diffusion models, anisotropic diffusion phantoms with well known architecture are demanded. In the present work we built four anisotropic diffusion phantoms consisting of hollow microcapillaries with very small inner diameters of 5, 10 and 20μm and outer diameters of 90 and 150μm. For testing the suitability of these phantoms, we performed diffusion measurements on all of them and evaluated the resulting data with a set of popular diffusion models, such as diffusion tensor and diffusion kurtosis imaging, a two compartment model with intra- and extra-capillary water spaces using bi-exponential fitting, and time-dependent diffusion coefficients in Mitra's limit. The perspectives and limitations of these diffusion phantoms are presented and discussed.
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Affiliation(s)
| | - Daniel Edelhoff
- Experimental Physics III, TU Dortmund University, Dortmund, Germany
| | - Dieter Suter
- Experimental Physics III, TU Dortmund University, Dortmund, Germany
| | - Ivan I Maximov
- Experimental Physics III, TU Dortmund University, Dortmund, Germany.
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Vellmer S, Tonoyan AS, Suter D, Pronin IN, Maximov II. Validation of DWI pre-processing procedures for reliable differentiation between human brain gliomas. Z Med Phys 2017; 28:14-24. [PMID: 28532604 DOI: 10.1016/j.zemedi.2017.04.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 02/21/2017] [Accepted: 04/20/2017] [Indexed: 01/06/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) is a powerful tool in clinical applications, in particular, in oncology screening. dMRI demonstrated its benefit and efficiency in the localisation and detection of different types of human brain tumours. Clinical dMRI data suffer from multiple artefacts such as motion and eddy-current distortions, contamination by noise, outliers etc. In order to increase the image quality of the derived diffusion scalar metrics and the accuracy of the subsequent data analysis, various pre-processing approaches are actively developed and used. In the present work we assess the effect of different pre-processing procedures such as a noise correction, different smoothing algorithms and spatial interpolation of raw diffusion data, with respect to the accuracy of brain glioma differentiation. As a set of sensitive biomarkers of the glioma malignancy grades we chose the derived scalar metrics from diffusion and kurtosis tensor imaging as well as the neurite orientation dispersion and density imaging (NODDI) biophysical model. Our results show that the application of noise correction, anisotropic diffusion filtering, and cubic-order spline interpolation resulted in the highest sensitivity and specificity for glioma malignancy grading. Thus, these pre-processing steps are recommended for the statistical analysis in brain tumour studies.
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Affiliation(s)
- Sebastian Vellmer
- Experimental Physics III, TU Dortmund University, Dortmund, Germany.
| | | | - Dieter Suter
- Experimental Physics III, TU Dortmund University, Dortmund, Germany
| | | | - Ivan I Maximov
- Experimental Physics III, TU Dortmund University, Dortmund, Germany.
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25
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Vellmer S, Stirnberg R, Edelhoff D, Suter D, Stöcker T, Maximov II. Comparative analysis of isotropic diffusion weighted imaging sequences. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2017; 275:137-147. [PMID: 28073068 DOI: 10.1016/j.jmr.2016.12.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 12/21/2016] [Accepted: 12/22/2016] [Indexed: 06/06/2023]
Abstract
Visualisation of living tissue structure and function is a challenging problem of modern imaging techniques. Diffusion MRI allows one to probe in vivo structures on a micrometer scale. However, conventional diffusion measurements are time-consuming procedures, because they require several measurements with different gradient directions. Considerable time savings are therefore possible by measurement schemes that generate an isotropic diffusion weighting in a single shot. Multiple approaches for generating isotropic diffusion weighting are known and have become very popular as useful tools in clinical research. Thus, there is a strong need for a comprehensive comparison of different isotropic weighting approaches. In the present work we introduce two new sequences based on simple (co)sine modulations and compare their performance to established q-space magic-angle spinning sequences and conventional DTI, using a diffusion phantom assembled from microcapillaries and in vivo experiments at 7T. The advantages and disadvantages of all compared schemes are demonstrated and discussed.
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Affiliation(s)
- Sebastian Vellmer
- Experimental Physics III, TU Dortmund University, Dortmund, Germany.
| | | | - Daniel Edelhoff
- Experimental Physics III, TU Dortmund University, Dortmund, Germany
| | - Dieter Suter
- Experimental Physics III, TU Dortmund University, Dortmund, Germany
| | - Tony Stöcker
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Physics and Astronomy, University of Bonn, Bonn, Germany
| | - Ivan I Maximov
- Experimental Physics III, TU Dortmund University, Dortmund, Germany.
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26
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Grinberg F, Maximov II, Farrher E, Neuner I, Amort L, Thönneßen H, Oberwelland E, Konrad K, Shah NJ. Diffusion kurtosis metrics as biomarkers of microstructural development: A comparative study of a group of children and a group of adults. Neuroimage 2017; 144:12-22. [DOI: 10.1016/j.neuroimage.2016.08.033] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2015] [Revised: 07/21/2016] [Accepted: 08/17/2016] [Indexed: 01/08/2023] Open
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27
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A finite-element reciprocity solution for EEG forward modeling with realistic individual head models. Neuroimage 2014; 103:542-551. [DOI: 10.1016/j.neuroimage.2014.08.056] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Revised: 08/27/2014] [Accepted: 08/30/2014] [Indexed: 11/21/2022] Open
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Hui ES, Russell Glenn G, Helpern JA, Jensen JH. Kurtosis analysis of neural diffusion organization. Neuroimage 2014; 106:391-403. [PMID: 25463453 DOI: 10.1016/j.neuroimage.2014.11.015] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2014] [Revised: 11/06/2014] [Accepted: 11/08/2014] [Indexed: 12/24/2022] Open
Abstract
A computational framework is presented for relating the kurtosis tensor for water diffusion in brain to tissue models of brain microstructure. The tissue models are assumed to be comprised of non-exchanging compartments that may be associated with various microstructural spaces separated by cell membranes. Within each compartment the water diffusion is regarded as Gaussian, although the diffusion for the full system would typically be non-Gaussian. The model parameters are determined so as to minimize the Frobenius norm of the difference between the measured kurtosis tensor and the model kurtosis tensor. This framework, referred to as kurtosis analysis of neural diffusion organization (KANDO), may be used to help provide a biophysical interpretation to the information provided by the kurtosis tensor. In addition, KANDO combined with diffusional kurtosis imaging can furnish a practical approach for developing candidate biomarkers for neuropathologies that involve alterations in tissue microstructure. KANDO is illustrated for simple tissue models of white and gray matter using data obtained from healthy human subjects.
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Affiliation(s)
- Edward S Hui
- Department of Diagnostic Radiology, The University of Hong Kong, Pokfulam, Hong Kong.
| | - G Russell Glenn
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Neurosciences, Medical University of South Carolina, Charleston, SC, USA.
| | - Joseph A Helpern
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Neurosciences, Medical University of South Carolina, Charleston, SC, USA; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
| | - Jens H Jensen
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
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29
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Local estimation of the noise level in MRI using structural adaptation. Med Image Anal 2014; 20:76-86. [PMID: 25465845 DOI: 10.1016/j.media.2014.10.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Revised: 10/27/2014] [Accepted: 10/27/2014] [Indexed: 01/01/2023]
Abstract
We present a method for local estimation of the signal-dependent noise level in magnetic resonance images. The procedure uses a multi-scale approach to adaptively infer on local neighborhoods with similar data distribution. It exploits a maximum-likelihood estimator for the local noise level. The validity of the method was evaluated on repeated diffusion data of a phantom and simulated data using T1-data corrupted with artificial noise. Simulation results were compared with a recently proposed estimate. The method was also applied to a high-resolution diffusion dataset to obtain improved diffusion model estimation results and to demonstrate its usefulness in methods for enhancing diffusion data.
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30
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Ziegler E, Rouillard M, André E, Coolen T, Stender J, Balteau E, Phillips C, Garraux G. Mapping track density changes in nigrostriatal and extranigral pathways in Parkinson's disease. Neuroimage 2014; 99:498-508. [PMID: 24956065 PMCID: PMC4121087 DOI: 10.1016/j.neuroimage.2014.06.033] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Revised: 06/05/2014] [Accepted: 06/13/2014] [Indexed: 12/13/2022] Open
Abstract
In Parkinson's disease (PD) the demonstration of neuropathological disturbances in nigrostriatal and extranigral brain pathways using magnetic resonance imaging remains a challenge. Here, we applied a novel diffusion-weighted imaging approach-track density imaging (TDI). Twenty-seven non-demented Parkinson's patients (mean disease duration: 5 years, mean score on the Hoehn & Yahr scale=1.5) were compared with 26 elderly controls matched for age, sex, and education level. Track density images were created by sampling each subject's spatially normalized fiber tracks in 1mm isotropic intervals and counting the fibers that passed through each voxel. Whole-brain voxel-based analysis was performed and significance was assessed with permutation testing. Statistically significant increases in track density were found in the Parkinson's patients, relative to controls. Clusters were distributed in disease-relevant areas including motor, cognitive, and limbic networks. From the lower medulla to the diencephalon and striatum, clusters encompassed the known location of the locus coeruleus and pedunculopontine nucleus in the pons, and from the substantia nigra up to medial aspects of the posterior putamen, bilaterally. The results identified in brainstem and nigrostriatal pathways show a large overlap with the known distribution of neuropathological changes in non-demented PD patients. Our results also support an early involvement of limbic and cognitive networks in Parkinson's disease.
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Affiliation(s)
- Erik Ziegler
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - Maud Rouillard
- MoVeRe Group, Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - Elodie André
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - Tim Coolen
- MoVeRe Group, Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - Johan Stender
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - Evelyne Balteau
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - Christophe Phillips
- Cyclotron Research Centre, University of Liège, Liège, Belgium; Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium.
| | - Gaëtan Garraux
- MoVeRe Group, Cyclotron Research Centre, University of Liège, Liège, Belgium; Department of Neurology, University of Liège, Liège, Belgium
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