1
|
Tax CMW, Bastiani M, Veraart J, Garyfallidis E, Okan Irfanoglu M. What's new and what's next in diffusion MRI preprocessing. Neuroimage 2022; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.
Collapse
Affiliation(s)
- Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, The Netherlands; Cardiff University Brain Research Imaging Centre, School of Physics and Astronomy, Cardiff University, UK.
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Jelle Veraart
- Center for Biomedical Imaging, New York University Grossman School of Medicine, NY, USA
| | | | - M Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| |
Collapse
|
2
|
Romero-Garcia R, Suckling J, Owen M, Assem M, Sinha R, Coelho P, Woodberry E, Price SJ, Burke A, Santarius T, Erez Y, Hart MG. Memory recovery in relation to default mode network impairment and neurite density during brain tumor treatment. J Neurosurg 2021; 136:358-368. [PMID: 34359041 DOI: 10.3171/2021.1.jns203959] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 01/25/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The aim of this study was to test brain tumor interactions with brain networks, thereby identifying protective features and risk factors for memory recovery after resection. METHODS Seventeen patients with diffuse nonenhancing glioma (ages 22-56 years) underwent longitudinal MRI before and after surgery, and during a 12-month recovery period (47 MRI scans in total after exclusion). After each scanning session, a battery of memory tests was performed using a tablet-based screening tool, including free verbal memory, overall verbal memory, episodic memory, orientation, forward digit span, and backward digit span. Using structural MRI and neurite orientation dispersion and density imaging (NODDI) derived from diffusion-weighted images, the authors estimated lesion overlap and neurite density, respectively, with brain networks derived from normative data in healthy participants (somatomotor, dorsal attention, ventral attention, frontoparietal, and default mode network [DMN]). Linear mixed-effect models (LMMs) that regressed out the effect of age, gender, tumor grade, type of treatment, total lesion volume, and total neurite density were used to test the potential longitudinal associations between imaging markers and memory recovery. RESULTS Memory recovery was not significantly associated with either the tumor location based on traditional lobe classification or the type of treatment received by patients (i.e., surgery alone or surgery with adjuvant chemoradiotherapy). Nonlocal effects of tumors were evident on neurite density, which was reduced not only within the tumor but also beyond the tumor boundary. In contrast, high preoperative neurite density outside the tumor but within the DMN was associated with better memory recovery (LMM, p value after false discovery rate correction [Pfdr] < 10-3). Furthermore, postoperative and follow-up neurite density within the DMN and frontoparietal network were also associated with memory recovery (LMM, Pfdr = 0.014 and Pfdr = 0.001, respectively). Preoperative tumor and postoperative lesion overlap with the DMN showed a significant negative association with memory recovery (LMM, Pfdr = 0.002 and Pfdr < 10-4, respectively). CONCLUSIONS Imaging biomarkers of cognitive recovery and decline can be identified using NODDI and resting-state networks. Brain tumors and their corresponding treatment affecting brain networks that are fundamental for memory functioning such as the DMN can have a major impact on patients' memory recovery.
Collapse
Affiliation(s)
| | - John Suckling
- 1Department of Psychiatry, University of Cambridge.,2Behavioural and Clinical Neuroscience Institute, University of Cambridge.,3Cambridge and Peterborough NHS Foundation Trust, Cambridge
| | - Mallory Owen
- 1Department of Psychiatry, University of Cambridge
| | - Moataz Assem
- 4MRC Cognition and Brain Sciences Unit, University of Cambridge
| | | | | | - Emma Woodberry
- 7Department of Neuropsychology, Cambridge University Hospitals NHS Foundation Trust, Cambridge
| | - Stephen J Price
- 5Department of Neurosurgery, Addenbrooke's Hospital, Cambridge
| | - Amos Burke
- 8Department of Paediatric Haematology, Oncology, and Palliative Care, Addenbrooke's Hospital, Cambridge; and
| | - Thomas Santarius
- 5Department of Neurosurgery, Addenbrooke's Hospital, Cambridge.,9Department of Physiology, Development, and Neuroscience, University of Cambridge, Cambridgeshire, United Kingdom
| | - Yaara Erez
- 4MRC Cognition and Brain Sciences Unit, University of Cambridge
| | - Michael G Hart
- 5Department of Neurosurgery, Addenbrooke's Hospital, Cambridge
| |
Collapse
|
3
|
Pogosbekian EL, Pronin IN, Zakharova NE, Batalov AI, Turkin AM, Konakova TA, Maximov II. Feasibility of generalised diffusion kurtosis imaging approach for brain glioma grading. Neuroradiology 2021; 63:1241-1251. [PMID: 33410948 PMCID: PMC8295088 DOI: 10.1007/s00234-020-02613-7] [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: 09/18/2020] [Accepted: 11/23/2020] [Indexed: 01/02/2023]
Abstract
Purpose An accurate differentiation of brain glioma grade constitutes an important clinical issue. Powerful non-invasive approach based on diffusion MRI has already demonstrated its feasibility in glioma grade stratification. However, the conventional diffusion tensor (DTI) and kurtosis imaging (DKI) demonstrated moderate sensitivity and performance in glioma grading. In the present work, we apply generalised DKI (gDKI) approach in order to assess its diagnostic accuracy and potential application in glioma grading. Methods Diffusion scalar metrics were obtained from 50 patients with different glioma grades confirmed by histological tests following biopsy or surgery. All patients were divided into two groups with low- and high-grade gliomas as grade II versus grades III and IV, respectively. For a comparison, trained radiologists segmented the brain tissue into three regions with solid tumour, oedema, and normal appearing white matter. For each region, we estimated the conventional and gDKI metrics including DTI maps. Results We found high correlations between DKI and gDKI metrics in high-grade glioma. Further, gDKI metrics enabled introduction of a complementary measure for glioma differentiation based on correlations between the conventional and generalised approaches. Both conventional and generalised DKI metrics showed quantitative maps of tumour heterogeneity and oedema behaviour. gDKI approach demonstrated largely similar sensitivity and specificity in low-high glioma differentiation as in the case of conventional DKI method. Conclusion The generalised diffusion kurtosis imaging enables differentiation of low- and high-grade gliomas at the same level as the conventional DKI. Additionally, gDKI exhibited higher sensitivity to tumour heterogeneity and tissue contrast between tumour and healthy tissue and, thus, may contribute as a complementary source of information on tumour differentiation.
Collapse
Affiliation(s)
- E L Pogosbekian
- Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation.,General and Clinical Neurophysiology Lab, Institute of Higher Nervous Activity and Neurophysiology of RAS, Moscow, Russian Federation
| | - I N Pronin
- Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation
| | - N E Zakharova
- Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation
| | - A I Batalov
- Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation
| | - A M Turkin
- Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation
| | - T A Konakova
- Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation
| | - I I Maximov
- Department of Psychology, University of Oslo, Oslo, Norway. .,Department of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway. .,Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Kremneva EI, Legostaeva LA, Morozova SN, Sergeev DV, Sinitsyn DO, Iazeva EG, Suslin AS, Suponeva NA, Krotenkova MV, Piradov MA, Maximov II. Feasibility of Non-Gaussian Diffusion Metrics in Chronic Disorders of Consciousness. Brain Sci 2019; 9:brainsci9050123. [PMID: 31137909 PMCID: PMC6562474 DOI: 10.3390/brainsci9050123] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 05/23/2019] [Accepted: 05/23/2019] [Indexed: 01/06/2023] Open
Abstract
Diagnostic accuracy of different chronic disorders of consciousness (DOC) can be affected by the false negative errors in up to 40% cases. In the present study, we aimed to investigate the feasibility of a non-Gaussian diffusion approach in chronic DOC and to estimate a sensitivity of diffusion kurtosis imaging (DKI) metrics for the differentiation of vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS) from a healthy brain state. We acquired diffusion MRI data from 18 patients in chronic DOC (11 VS/UWS, 7 MCS) and 14 healthy controls. A quantitative comparison of the diffusion metrics for grey (GM) and white (WM) matter between the controls and patient group showed a significant (p < 0.05) difference in supratentorial WM and GM for all evaluated diffusion metrics, as well as for brainstem, corpus callosum, and thalamus. An intra-subject VS/UWS and MCS group comparison showed only kurtosis metrics and fractional anisotropy differences using tract-based spatial statistics, owing mainly to macrostructural differences on most severely lesioned hemispheres. As a result, we demonstrated an ability of DKI metrics to localise and detect changes in both WM and GM and showed their capability in order to distinguish patients with a different level of consciousness.
Collapse
Affiliation(s)
- Elena I Kremneva
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | | | - Sofya N Morozova
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Dmitry V Sergeev
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Dmitry O Sinitsyn
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Elizaveta G Iazeva
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Aleksandr S Suslin
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Natalia A Suponeva
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Marina V Krotenkova
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Michael A Piradov
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Ivan I Maximov
- Department of Psychology, University of Oslo, Forskningsveien 3A, 0373 Oslo, Norway.
- Norwegian Centre for Mental Disorders Research (NORMENT), Norway and Institute of Clinical Medicine, University of Oslo, Oslo Universitetssykehus Bygg 48 Ullevål, 0317 Oslo, Norway.
| |
Collapse
|
6
|
Threshold Isocontouring on High b-Value Diffusion-Weighted Images in Magnetic Resonance Mammography. J Comput Assist Tomogr 2019; 43:434-442. [PMID: 31082949 DOI: 10.1097/rct.0000000000000868] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Motivated by the similar appearance of malignant breast lesions in high b-value diffusion-weighted imaging (DWI) and positron emission tomography, the purpose of this work was to evaluate the applicability of a threshold isocontouring approach commonly used in positron emission tomography to analyze DWI data acquired from female human breasts with minimal interobserver variability. METHODS Twenty-three female participants (59.4 ± 10.0 years) with 23 lesions initially classified as suggestive of cancers in x-ray mammography screening were subsequently imaged on a 1.5-T magnetic resonance imaging scanner. Diffusion-weighted imaging was performed prior to biopsy with b values of 0, 100, 750, and 1500 s/mm. Isocontouring with different threshold levels was performed on the highest b-value image to determine the voxels used for subsequent evaluation of diffusion metrics. The coefficient of variation was computed by specifying 4 different regions of interest drawn around the lesion. Additionally, a receiver operating statistical analysis was performed. RESULTS Using a relative threshold level greater than or equal to 0.85 almost completely suppresses the intra-individual and inter-individual variability. Among 4 studied diffusion metrics, the diffusion coefficients from the intravoxel incoherent motion model returned the highest area under curve value of 0.9. The optimal cut-off diffusivity was found to be 0.85 μm/ms with a sensitivity of 87.5% and specificity of 90.9%. CONCLUSION Threshold isocontouring on high b-value maps is a viable approach to reliably evaluate DWI data of suspicious focal lesions in magnetic resonance mammography.
Collapse
|
7
|
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.
Collapse
Affiliation(s)
- Ivan I Maximov
- Experimental Physics III, TU Dortmund University, 44221, Germany.
| | | | | |
Collapse
|