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Gan L, Wang L, Liu H, Wang G. Based on neural network cascade abnormal texture information dissemination of classification of patients with schizophrenia and depression. Brain Res 2024; 1830:148819. [PMID: 38403037 DOI: 10.1016/j.brainres.2024.148819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 02/11/2024] [Accepted: 02/20/2024] [Indexed: 02/27/2024]
Abstract
This study used MRI brain image segmentation to identify novel magnetic resonance imaging (MRI) biomarkers to distinguish patients with schizophrenia (SCZ), major depressive disorder (MD), and healthy control (HC). Brain texture measurements, including entropy and contrast, were calculated to capture variability in adjacent MRI voxel intensity. These measures are then applied to group classification in deep learning techniques and combined with hierarchical correlations to locate results. Texture feature maps were extracted from segmented brain MRI scans of 141 patients with schizophrenia (SCZ), 103 patients with major depressive disorder (MD) and 238 healthy controls (HC). Gray scale coassociation matrix (GLCM) is a monomer matrix calculated in a voxel cube. Deep learning methods were evaluated to determine the application capability of texture feature mapping in binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method is implemented by repeated nesting and cross-validation for feature selection. Regions that show the highest correlation (positive or negative). In this study, the authors successfully classified SCZ, MD and HC. This suggests that texture analysis can be used as an effective feature extraction method to distinguish different disease states. Compared with other methods, texture analysis can capture richer image information and improve classification accuracy in some cases. The classification accuracy of SCZ and HC, MD and HC, SCZ and MD reached 84.6%, 86.4% and 76.21%, respectively. Among them, SCZ and HC are the most significant features with high sensitivity and specificity.
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Affiliation(s)
- Linfeng Gan
- School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China
| | - Linfeng Wang
- School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China
| | - Hu Liu
- Peking University Health Science Center, Institute of Medical Technology, Beijing 100069, China.
| | - Gang Wang
- School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China
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2
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Fitzgerald B, Bari S, Vike N, Lee TA, Lycke RJ, Auger JD, Leverenz LJ, Nauman E, Goñi J, Talavage TM. Longitudinal changes in resting state fMRI brain self-similarity of asymptomatic high school American football athletes. Sci Rep 2024; 14:1747. [PMID: 38243048 PMCID: PMC10799081 DOI: 10.1038/s41598-024-51688-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 01/08/2024] [Indexed: 01/21/2024] Open
Abstract
American football has become the focus of numerous studies highlighting a growing concern that cumulative exposure to repetitive, sports-related head acceleration events (HAEs) may have negative consequences for brain health, even in the absence of a diagnosed concussion. In this longitudinal study, brain functional connectivity was analyzed in a cohort of high school American football athletes over a single play season and compared against participants in non-collision high school sports. Football athletes underwent four resting-state functional magnetic resonance imaging sessions: once before (pre-season), twice during (in-season), and once 34-80 days after the contact activities play season ended (post-season). For each imaging session, functional connectomes (FCs) were computed for each athlete and compared across sessions using a metric reflecting the (self) similarity between two FCs. HAEs were monitored during all practices and games throughout the season using head-mounted sensors. Relative to the pre-season scan session, football athletes exhibited decreased FC self-similarity at the later in-season session, with apparent recovery of self-similarity by the time of the post-season session. In addition, both within and post-season self-similarity was correlated with cumulative exposure to head acceleration events. These results suggest that repetitive exposure to HAEs produces alterations in functional brain connectivity and highlight the necessity of collision-free recovery periods for football athletes.
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Affiliation(s)
- Bradley Fitzgerald
- Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA.
| | - Sumra Bari
- Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA
| | - Nicole Vike
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN, USA
| | - Taylor A Lee
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
| | - Roy J Lycke
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Joshua D Auger
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
| | - Larry J Leverenz
- Department of Health and Kinesiology, Purdue University, West Lafayette, IN, USA
| | - Eric Nauman
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN, USA
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Joaquín Goñi
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
| | - Thomas M Talavage
- Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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Singh D, Monga A, de Moura HL, Zhang X, Zibetti MVW, Regatte RR. Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review. Bioengineering (Basel) 2023; 10:1012. [PMID: 37760114 PMCID: PMC10525988 DOI: 10.3390/bioengineering10091012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches. This Systematic Literature Review (SLR) comprehensively analyzes deep MRI reconstruction models, emphasizing the key elements of recently proposed methods and highlighting their strengths and weaknesses. This SLR involves searching and selecting relevant studies from various databases, including Web of Science and Scopus, followed by a rigorous screening and data extraction process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It focuses on various techniques, such as residual learning, image representation using encoders and decoders, data-consistency layers, unrolled networks, learned activations, attention modules, plug-and-play priors, diffusion models, and Bayesian methods. This SLR also discusses the use of loss functions and training with adversarial networks to enhance deep MRI reconstruction methods. Moreover, we explore various MRI reconstruction applications, including non-Cartesian reconstruction, super-resolution, dynamic MRI, joint learning of reconstruction with coil sensitivity and sampling, quantitative mapping, and MR fingerprinting. This paper also addresses research questions, provides insights for future directions, and emphasizes robust generalization and artifact handling. Therefore, this SLR serves as a valuable resource for advancing fast MRI, guiding research and development efforts of MRI reconstruction for better image quality and faster data acquisition.
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Affiliation(s)
- Dilbag Singh
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA; (A.M.); (H.L.d.M.); (X.Z.); (M.V.W.Z.)
| | | | | | | | | | - Ravinder R. Regatte
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA; (A.M.); (H.L.d.M.); (X.Z.); (M.V.W.Z.)
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Krishnamurthy LC, Glassman C, Han JH, Song SE, Denmon C, Weatherill M, Rodriguez AD, Crosson BA, Krishnamurthy V. ASL MRI informs blood flow to chronic stroke lesions in patients with aphasia. Front Physiol 2023; 14:1240992. [PMID: 37546533 PMCID: PMC10397521 DOI: 10.3389/fphys.2023.1240992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 07/11/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction: Response to post-stroke aphasia language rehabilitation is difficult to anticipate, mainly because few predictors can help identify optimal, individualized treatment options. Imaging techniques, such as Voxel-based Lesion Symptom Mapping have been useful in linking specific brain areas to language behavior; however, further development is required to optimize the use of structural and physiological information in guiding individualized treatment for persons with aphasia (PWA). In this study, we will determine if cerebral blood flow (CBF) mapped in patients with chronic strokes can be further used to understand stroke-related factors and behavior. Methods: We collected perfusion MRI data using pseudo-Continuous Arterial Spin Labeling (pCASL) using a single post-labeling delay of 2,200 ms in 14 chronic PWA, along with high-resolution structural MRI to compute maps of tissue damage using Tissue Integrity Gradation via T2w T1w Ratio (TIGR). To quantify the CBF in chronic stroke lesions, we tested at what point spatial smoothing should be applied in the ASL analysis pipeline. We then related CBF to tissue damage, time since stroke, age, sex, and their respective cross-terms to further understand the variability in lesion CBF. Finally, we assessed the feasibility of computing multivariate brain-behavior maps using CBF and compared them to brain-behavior maps extracted with TIGR MRI. Results: We found that the CBF in chronic stroke lesions is significantly reduced compared to its homologue grey and white matter regions. However, a reliable CBF signal (although smaller than expected) was detected to reveal a negative relationship between CBF and increasing tissue damage. Further, the relationship between the lesion CBF and age, sex, time since stroke, and tissue damage and cross-terms suggested an aging-by-disease interaction. This relationship was strongest when smoothing was applied in the template space. Finally, we show that whole-brain CBF relates to domain-general visuospatial functioning in PWA. The CBF-based brain-behavior maps provide unique and complementary information to structural (lesion-based) brain-behavior maps. Discussion: Therefore, CBF can be detected in chronic stroke lesions using a standard pCASL MRI acquisition and is informative at the whole-brain level in identifying stroke rehabilitation targets in PWAs due to its relationship with demographic factors, stroke-related factors, and behavior.
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Affiliation(s)
- Lisa C. Krishnamurthy
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
- Joint GSU, Georgia Tech, and Emory Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, United States
- Department of Physics and Astronomy, Georgia State University, Atlanta, GA, United States
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States
| | - Clara Glassman
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
| | - Joo H. Han
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
| | - Serena E. Song
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
| | - Chanse Denmon
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
| | - Maryanne Weatherill
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
| | - Amy D. Rodriguez
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
- Department of Neurology, Emory University, Atlanta, GA, United States
| | - Bruce A. Crosson
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
- Department of Neurology, Emory University, Atlanta, GA, United States
| | - Venkatagiri Krishnamurthy
- Department of Neurology, Emory University, Atlanta, GA, United States
- Division of Geriatrics and Gerontology, Department of Medicine, Emory University, Atlanta, GA, United States
- Department of Veterans Affairs (VA) Health Care System, Decatur, GA, United States
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Shahid SS, Grecco GG, Atwood BK, Wu YC. Perturbed neurochemical and microstructural organization in a mouse model of prenatal opioid exposure: a multi-modal magnetic resonance study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.23.529659. [PMID: 36865153 PMCID: PMC9980104 DOI: 10.1101/2023.02.23.529659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Methadone-based treatment for pregnant women with opioid use disorder is quite prevalent in the clinical environment. A number of clinical and animal model-based studies have reported cognitive deficits in infants prenatally exposed to methadone-based opioid treatments. However, the long-term impact of prenatal opioid exposure (POE) on pathophysiological mechanisms that govern neurodevelopmental impairment is not well understood. Using a translationally relevant mouse model of prenatal methadone exposure (PME), the aim of this study is to investigate the role of cerebral biochemistry and its possible association with regional microstructural organization in PME offspring. To understand these effects, 8- week-old male offspring with PME (n=7) and prenatal saline exposure (PSE) (n=7) were scanned in vivo on 9.4 Tesla small animal scanner. Single voxel proton magnetic resonance spectroscopy ( 1 H-MRS) was performed in the right dorsal striatum (RDS) region using a short echo time (TE) Stimulated Echo Acquisition Method (STEAM) sequence. Neurometabolite spectra from the RDS was first corrected for tissue T1 relaxation and then absolute quantification was performed using the unsuppressed water spectra. High-resolution in vivo diffusion MRI (dMRI) for region of interest (ROI) based microstructural quantification was also performed using a multi-shell dMRI sequence. Cerebral microstructure was characterized using diffusion tensor imaging (DTI) and Bingham-neurite orientation dispersion and density imaging (Bingham-NODDI). MRS results in the RDS showed significant decrease in N-acetyl aspartate (NAA), taurine (tau), glutathione (GSH), total creatine (tCr) and glutamate (Glu) concentration levels in PME, compared to PSE group. In the same RDS region, mean orientation dispersion index (ODI) and intracellular volume fraction (VF IC ) demonstrated positive associations with tCr in PME group. ODI also exhibited significant positive association with Glu levels in PME offspring. Significant reduction in major neurotransmitter metabolites and energy metabolism along with strong association between the neurometabolites and perturbed regional microstructural complexity suggest a possible impaired neuroadaptation trajectory in PME offspring which could be persistent even into late adolescence and early adulthood.
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Korda AI, Andreou C, Avram M, Handels H, Martinetz T, Borgwardt S. Chaos analysis of the brain topology in first-episode psychosis and clinical high risk patients. Front Psychiatry 2022; 13:965128. [PMID: 36311536 PMCID: PMC9606602 DOI: 10.3389/fpsyt.2022.965128] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 09/16/2022] [Indexed: 11/17/2022] Open
Abstract
Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis for the identification of brain topology differences in people with psychosis. Structural MRI were acquired from 77 FEP, 73 CHR and 44 healthy controls (HC). Chaos analysis of the gray matter distribution was performed: First, the distances of each voxel from the center of mass in the gray matter image was calculated. Next, the distances multiplied by the voxel intensity were represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts thus how the gray matter topology changes. Between-group differences were identified by (a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and (b) matching the lambda series with the Morlet wavelet, which resulted in statistically significant differences in the scalograms of FEP against CHR and HC. The proposed framework using spatial-series extraction enhances the between-group differences of FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.
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Affiliation(s)
- Alexandra I. Korda
- Translational Psychiatry, Department of Psychiatry and Psycotherapy, University of Lübeck, Lübeck, Germany
| | - Christina Andreou
- Translational Psychiatry, Department of Psychiatry and Psycotherapy, University of Lübeck, Lübeck, Germany
| | - Mihai Avram
- Translational Psychiatry, Department of Psychiatry and Psycotherapy, University of Lübeck, Lübeck, Germany
| | - Heinz Handels
- Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
| | - Thomas Martinetz
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
| | - Stefan Borgwardt
- Translational Psychiatry, Department of Psychiatry and Psycotherapy, University of Lübeck, Lübeck, Germany
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Grecco GG, Shahid SS, Atwood BK, Wu YC. Alterations of brain microstructures in a mouse model of prenatal opioid exposure detected by diffusion MRI. Sci Rep 2022; 12:17085. [PMID: 36224335 PMCID: PMC9556691 DOI: 10.1038/s41598-022-21416-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 09/27/2022] [Indexed: 01/04/2023] Open
Abstract
Growing opioid use among pregnant women is fueling a crisis of infants born with prenatal opioid exposure. A large body of research has been devoted to studying the management of opioid withdrawal during the neonatal period in these infants, but less substantive work has explored the long-term impact of prenatal opioid exposure on neurodevelopment. Using a translationally relevant mouse model of prenatal methadone exposure (PME), the aim of the study is to investigate the cerebral microstructural differences between the mice with PME and prenatal saline exposure (PSE). The brains of eight-week-old male offspring with either PME (n = 15) or PSE (n = 15) were imaged using high resolution in-vivo diffusion magnetic resonance imaging on a 9.4 Tesla small animal scanner. Brain microstructure was characterized using diffusion tensor imaging (DTI) and Bingham neurite orientation dispersion and density imaging (Bingham-NODDI). Voxel-based analysis (VBA) was performed using the calculated microstructural parametric maps. The VBA showed significant (p < 0.05) bilateral alterations in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), orientation dispersion index (ODI) and dispersion anisotropy index (DAI) across several cortical and subcortical regions, compared to PSE. Particularly, in PME offspring, FA, MD and AD were significantly higher in the hippocampus, dorsal amygdala, thalamus, septal nuclei, dorsal striatum and nucleus accumbens. These DTI-based results suggest widespread bilateral microstructural alterations across cortical and subcortical regions in PME offspring. Consistent with the observations in DTI, Bingham-NODDI derived ODI exhibited significant reduction in PME offspring within the hippocampus, dorsal striatum and cortex. NODDI-based results further suggest reduction in dendritic arborization in PME offspring across multiple cortical and subcortical regions. To our best knowledge, this is the first study of prenatal opioid exposure to examine microstructural organization in vivo. Our findings demonstrate perturbed microstructural complexity in cortical and subcortical regions persisting into early adulthood which could interfere with critical neurodevelopmental processes in individuals with prenatal opioid exposure.
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Affiliation(s)
- Gregory G. Grecco
- grid.257413.60000 0001 2287 3919Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, IN 46202 USA ,grid.257413.60000 0001 2287 3919Indiana University School of Medicine, Medical Scientist Training Program, Indianapolis, IN 46202 USA
| | - Syed Salman Shahid
- grid.257413.60000 0001 2287 3919Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 West 16th Street, Suite 4100, Indianapolis, IN 46202 USA
| | - Brady K. Atwood
- grid.257413.60000 0001 2287 3919Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, IN 46202 USA ,grid.257413.60000 0001 2287 3919Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 West 16th Street, Suite 4100, Indianapolis, IN, 46202, USA. .,Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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Cheng H, Vinci-Booher S, Wang J, Caron B, Wen Q, Newman S, Pestilli F. Denoising diffusion weighted imaging data using convolutional neural networks. PLoS One 2022; 17:e0274396. [PMID: 36108272 PMCID: PMC9477507 DOI: 10.1371/journal.pone.0274396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/26/2022] [Indexed: 11/17/2022] Open
Abstract
Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural measurements. Here, we propose a simple denoising method that can be applied to any dataset, provided a low-noise, single-subject dataset is acquired using the same DWI sequence. The denoising method uses a one-dimensional convolutional neural network (1D-CNN) and deep learning to learn from a low-noise dataset, voxel-by-voxel. The trained model can then be applied to high-noise datasets from other subjects. We validated the 1D-CNN denoising method by first demonstrating that 1D-CNN denoising resulted in DWI images that were more similar to the noise-free ground truth than comparable denoising methods, e.g., MP-PCA, using simulated DWI data. Using the same DWI acquisition but reconstructed with two common reconstruction methods, i.e. SENSE1 and sum-of-square, to generate a pair of low-noise and high-noise datasets, we then demonstrated that 1D-CNN denoising of high-noise DWI data collected from human subjects showed promising results in three domains: DWI images, diffusion metrics, and tractography. In particular, the denoised images were very similar to a low-noise reference image of that subject, more than the similarity between repeated low-noise images (i.e. computational reproducibility). Finally, we demonstrated the use of the 1D-CNN method in two practical examples to reduce noise from parallel imaging and simultaneous multi-slice acquisition. We conclude that the 1D-CNN denoising method is a simple, effective denoising method for DWI images that overcomes some of the limitations of current state-of-the-art denoising methods, such as the need for a large number of training subjects and the need to account for the rectified noise floor.
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Affiliation(s)
- Hu Cheng
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States of America
- Program of Neuroscience, Indiana University, Bloomington, IN, United States of America
| | - Sophia Vinci-Booher
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States of America
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, United States of America
| | - Jian Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Bradley Caron
- Department of Psychology, Center for Perceptual Systems and Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, TX, United States of America
| | - Qiuting Wen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States of America
| | - Sharlene Newman
- Alabama Life Research Institute, The University of Alabama, Tuscaloosa, AL, United States of America
| | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems and Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, TX, United States of America
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9
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Cerebellar engagement in the attachment behavioral system. Sci Rep 2022; 12:13571. [PMID: 35945247 PMCID: PMC9363408 DOI: 10.1038/s41598-022-17722-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 07/29/2022] [Indexed: 11/08/2022] Open
Abstract
Brain structural bases of individual differences in attachment are not yet fully clarified. Given the evidence of relevant cerebellar contribution to cognitive, affective, and social functions, the present research was aimed at investigating potential associations between attachment dimensions (through the Attachment Style Questionnaire, ASQ) and cerebellar macro- and micro-structural measures (Volumetric and Diffusion Tensor Imaging data). In a sample of 79 healthy subjects, cerebellar and neocortical volumetric data were correlated with ASQ scores at the voxel level within specific Regions Of Interest. Also, correlations between ASQ scores and age, years of education, anxiety and depression levels were performed to control for the effects of sociodemographic and psychological variables on neuroimaging results. Positive associations between scores of the Preoccupation with Relationships (ASQ subscale associated to insecure/anxious attachment) and cortical volume were found in the cerebellum (right lobule VI and left Crus 2) and neocortex (right medial OrbitoFrontal Cortex, OFC) regions. Cerebellar contribution to the attachment behavioral system reflects the more general cerebellar engagement in the regulation of emotional and social behaviors. Cerebellar properties of timing, prediction, and learning well integrate with OFC processing, supporting the regulation of attachment experiences. Cerebellar areas might be rightfully included in the attachment behavioral system.
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10
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Haller OC, Aleksonis HA, Krishnamurthy LC, King TZ. White matter hyperintensities relate to executive dysfunction, apathy, but not disinhibition in long-term adult survivors of pediatric cerebellar tumor. Neuroimage Clin 2022; 33:102891. [PMID: 34922123 PMCID: PMC8686062 DOI: 10.1016/j.nicl.2021.102891] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 10/20/2021] [Accepted: 11/19/2021] [Indexed: 11/04/2022]
Abstract
Pediatric brain tumor survivors have more executive dysfunction than controls. White matter hyperintensities are positively associated with executive dysfunction. White matter hyperintensities are positively associated with apathy. Multivariate regression supports white matter hyperintensity associations. Survivors appear to drive white matter hyperintensities associations.
White matter hyperintensities (WMHs) have been related to executive dysfunction, apathy and disinhibition in a wide range of neurological populations. However, this relationship has not been examined in survivors of pediatric brain tumor. The goal of this study was to investigate how executive dysfunction, apathy, and disinhibition relate to WMHs in 31 long-term survivors of pediatric cerebellar brain tumor and 58 controls, using informant-report data from the Frontal Systems Behavior Scale. Total WMH volume was quantified using the Lesion Growth Algorithm. Further, periventricular, and subcortical volumes were identified based on proximity to custom ventricle masks generated in FSL. A ratio of WMH volume to whole brain volume was used to obtain normalized WMH volumes. Additionally, a multivariate regression analysis was performed. On average, informant-report scores were within normal limits and only executive dysfunction was significantly higher in survivors compared to controls (t(47.9) = -2.4, p=.023). Informants reported clinically significant levels of apathy in 32.3% of survivors. Informants also reported clinically significant executive dysfunction in 19.4 % of survivors and clinically significant disinhibition in, again, 19.4 % of survivors. Increased volume of WMHs was positively correlated with executive dysfunction (r = 0.33, p = 0.02) and apathy (r = 0.23, p = .04). Similarly, multivariate regression demonstrated correlations with executive dysfunction (p=.05, FDR corrected) and apathy (p=.05, FDR corrected). Exploratory analysis demonstrated an interaction wherein the relationship between total WMHs and executive dysfunction and apathy depends on whether the participant was a survivor. The current findings indicate that increased WMH volumes are associated with higher ratings of apathy and executive dysfunction, and that these results are likely unique to cerebellar brain tumor survivors. WMH burden may serve as a useful marker to identify survivors at risk of executive dysfunction or increased apathy.
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Affiliation(s)
- Olivia C Haller
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Holly A Aleksonis
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Lisa C Krishnamurthy
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA, Decatur, GA, USA; Department of Physics and Astronomy, Georgia State University, Atlanta, GA, USA
| | - Tricia Z King
- Department of Psychology, Georgia State University, Atlanta, GA, USA; Neuroscience Institute, Georgia State University, Atlanta, GA, USA.
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Fernández Patón M, Cerdá Alberich L, Sangüesa Nebot C, Martínez de Las Heras B, Veiga Canuto D, Cañete Nieto A, Martí-Bonmatí L. MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging. J Digit Imaging 2021; 34:1134-1145. [PMID: 34505958 DOI: 10.1007/s10278-021-00512-8] [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: 11/05/2020] [Revised: 07/24/2021] [Accepted: 08/17/2021] [Indexed: 12/25/2022] Open
Abstract
Several noise sources, such as the Johnson-Nyquist noise, affect MR images disturbing the visualization of structures and affecting the subsequent extraction of radiomic data. We evaluate the performance of 5 denoising filters (anisotropic diffusion filter (ADF), curvature flow filter (CFF), Gaussian filter (GF), non-local means filter (NLMF), and unbiased non-local means (UNLMF)), with 33 different settings, in T2-weighted MR images of phantoms (N = 112) and neuroblastoma patients (N = 25). Filters were discarded until the most optimal solutions were obtained according to 3 image quality metrics: peak signal-to-noise ratio (PSNR), edge-strength similarity-based image quality metric (ESSIM), and noise (standard deviation of the signal intensity of a region in the background area). The selected filters were ADFs and UNLMs. From them, 107 radiomics features preservation at 4 progressively added noise levels were studied. The ADF with a conductance of 1 and 2 iterations standardized the radiomic features, improving reproducibility and quality metrics.
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Affiliation(s)
- Matías Fernández Patón
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026, Valencia, Spain.
| | - Leonor Cerdá Alberich
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026, Valencia, Spain
| | - Cinta Sangüesa Nebot
- Área Clínica de Imagen Médica, Hospital Universitario Y Politécnico La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain
| | - Blanca Martínez de Las Heras
- Unidad de Oncohematología Pediátrica, Hospital Universitario Y Politécnico La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain
| | - Diana Veiga Canuto
- Área Clínica de Imagen Médica, Hospital Universitario Y Politécnico La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain
| | - Adela Cañete Nieto
- Unidad de Oncohematología Pediátrica, Hospital Universitario Y Politécnico La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain
| | - Luis Martí-Bonmatí
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026, Valencia, Spain.,Área Clínica de Imagen Médica, Hospital Universitario Y Politécnico La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain
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12
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Krishnamurthy LC, Krishnamurthy V, Rodriguez AD, McGregor KM, Glassman CN, Champion GS, Rocha N, Harnish SM, Belagaje SR, Kundu S, Crosson BA. Not All Lesioned Tissue Is Equal: Identifying Pericavitational Areas in Chronic Stroke With Tissue Integrity Gradation via T2w T1w Ratio. Front Neurosci 2021; 15:665707. [PMID: 34421509 PMCID: PMC8378269 DOI: 10.3389/fnins.2021.665707] [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: 02/08/2021] [Accepted: 07/05/2021] [Indexed: 11/14/2022] Open
Abstract
Stroke-related tissue damage within lesioned brain areas is topologically non-uniform and has underlying tissue composition changes that may have important implications for rehabilitation. However, we know of no uniformly accepted, objective non-invasive methodology to identify pericavitational areas within the chronic stroke lesion. To fill this gap, we propose a novel magnetic resonance imaging (MRI) methodology to objectively quantify the lesion core and surrounding pericavitational perimeter, which we call tissue integrity gradation via T2w T1w ratio (TIGR). TIGR uses standard T1-weighted (T1w) and T2-weighted (T2w) anatomical images routinely collected in the clinical setting. TIGR maps are analyzed with relation to subject-specific gray matter and cerebrospinal fluid thresholds and binned to create a false colormap of tissue damage within the stroke lesion, and these are further categorized into low-, medium-, and high-damage areas. We validate TIGR by showing that the cerebral blood flow within the lesion reduces with greater tissue damage (p = 0.005). We further show that a significant task activity can be detected in pericavitational areas and that medium-damage areas contain a significantly lower magnitude of hemodynamic response function than the adjacent damaged areas (p < 0.0001). We also demonstrate the feasibility of using TIGR maps to extract multivariate brain-behavior relationships (p < 0.05) and show general agreement in location compared to binary lesion, T1w-only, and T2w-only maps but that the extent of brain behavior maps may depend on signal sensitivity as denoted by the sparseness coefficient (p < 0.0001). Finally, we show the feasibility of quantifying TIGR in early and late subacute stroke phases, where higher-damage areas were smaller in size (p = 0.002) and that lesioned voxels transition from lower to higher damage with increasing time post-stroke (p = 0.004). We conclude that TIGR is able to (1) identify tissue damage gradient within the stroke lesion across different post-stroke timepoints and (2) more objectively delineate lesion core from pericavitational areas wherein such areas demonstrate reasonable and expected physiological and functional impairments. Importantly, because T1w and T2w scans are routinely collected in the clinic, TIGR maps can be readily incorporated in clinical settings without additional imaging costs or patient burden to facilitate decision processes related to rehabilitation planning.
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Affiliation(s)
- Lisa C. Krishnamurthy
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
- Department of Physics and Astronomy, Georgia State University, Atlanta, GA, United States
| | - Venkatagiri Krishnamurthy
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
- Division of Geriatrics and Gerontology, Department of Medicine, Emory University, Atlanta, GA, United States
- Department of Neurology, Emory University, Atlanta, GA, United States
| | - Amy D. Rodriguez
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
- Department of Neurology, Emory University, Atlanta, GA, United States
| | - Keith M. McGregor
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
- Department of Neurology, Emory University, Atlanta, GA, United States
| | - Clara N. Glassman
- Department of Nuclear and Radiological Engineering and Medical Physics, Georgia Institute of Technology, Atlanta, GA, United States
| | - Gabriell S. Champion
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
- Department of Psychology, Georgia State University, Atlanta, GA, United States
| | - Natalie Rocha
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
| | - Stacy M. Harnish
- Department of Speech and Hearing Science, The Ohio State University, Columbus, OH, United States
| | - Samir R. Belagaje
- Department of Neurology, Emory University, Atlanta, GA, United States
- Department of Rehabilitation Medicine, Emory University, Atlanta, GA, United States
| | - Suprateek Kundu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
| | - Bruce A. Crosson
- Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, GA, United States
- Department of Neurology, Emory University, Atlanta, GA, United States
- Department of Psychology, Georgia State University, Atlanta, GA, United States
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13
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Aleksonis HA, Krishnamurthy LC, King TZ. White matter hyperintensity volumes are related to processing speed in long-term survivors of childhood cerebellar tumors. J Neurooncol 2021; 154:63-72. [PMID: 34231115 DOI: 10.1007/s11060-021-03799-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 06/25/2021] [Indexed: 11/27/2022]
Abstract
PURPOSE Across several clinical populations, higher white matter hyperintensity (WMH) burden is consistently associated with decreases in cognitive performance, especially processing speed. Research of childhood cancer survivors has not utilized WMH quantification methodology to better understand the impact of WMH burden and its relationship with core cognitive skills. The present study aimed to quantify WMH volumes in a sample of long-term survivors of childhood cerebellar tumor and investigate the relationships with performance on a measure of oral processing speed. To further explore brain-behavior relationships, multivariate sparse canonical correlations was employed to identify WMH areas that predict processing speed performance. METHODS Thirty-five survivors and 56 healthy controls underwent neuroimaging and completed a measure of oral processing speed. The survivor group was further divided based on treatment (i.e., chemoradiation therapy (n = 20) vs. surgery only (n = 15)) to better understand the impact of treatment. RESULTS Survivors, and especially those treated with chemoradiation therapy, showed higher total WMH volumes and slower processing speed. Higher total WMH volumes were significantly associated with poorer processing speed (r = - 0.492, p = 0.003). Multivariate brain-behavior relationships revealed that periventricular WMHs were significantly associated with slower processing speed performance (p < 0.05). CONCLUSION Results exemplify that long-term survivors treated with and without chemoradiation therapy are at increased risk of developing higher WMH volumes compared to healthy peers. In addition, processing speed was robustly shown to be related to periventricular WMHs using an automated neuroimaging pipeline. This methodology to monitor WMH burden has the potential to be implemented efficiently with routine clinical neuroimaging of cancer survivors.
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Affiliation(s)
- Holly A Aleksonis
- Department of Psychology and the Neuroscience Institute, Georgia State University, Urban Life Building, 11th Floor, 140 Decatur St, Atlanta, GA, 30303, USA
| | - Lisa C Krishnamurthy
- Center for Visual and Neurocognitive Rehabilitation, Atlanta Veteran's Affairs Medical Center, Decatur, GA, USA
- Department of Physics and Astronomy, Georgia State University, Atlanta, GA, USA
| | - Tricia Z King
- Department of Psychology and the Neuroscience Institute, Georgia State University, Urban Life Building, 11th Floor, 140 Decatur St, Atlanta, GA, 30303, USA.
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14
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Kubicek J, Strycek M, Cerny M, Penhaker M, Prokop O, Vilimek D. Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System. SENSORS 2021; 21:s21124161. [PMID: 34204477 PMCID: PMC8233799 DOI: 10.3390/s21124161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/06/2021] [Accepted: 06/11/2021] [Indexed: 12/27/2022]
Abstract
In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.
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Affiliation(s)
- Jan Kubicek
- Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. listopadu 15, 70800 Ostrava Poruba, Czech Republic; (M.S.); (M.C.); (M.P.); (D.V.)
- Correspondence:
| | - Michal Strycek
- Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. listopadu 15, 70800 Ostrava Poruba, Czech Republic; (M.S.); (M.C.); (M.P.); (D.V.)
| | - Martin Cerny
- Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. listopadu 15, 70800 Ostrava Poruba, Czech Republic; (M.S.); (M.C.); (M.P.); (D.V.)
| | - Marek Penhaker
- Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. listopadu 15, 70800 Ostrava Poruba, Czech Republic; (M.S.); (M.C.); (M.P.); (D.V.)
| | - Ondrej Prokop
- MEDIN, a.s., Vlachovicka 619, 59231 Nove Mesto na Morave, Czech Republic;
| | - Dominik Vilimek
- Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. listopadu 15, 70800 Ostrava Poruba, Czech Republic; (M.S.); (M.C.); (M.P.); (D.V.)
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15
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Macro- and micro-structural cerebellar and cortical characteristics of cognitive empathy towards fictional characters in healthy individuals. Sci Rep 2021; 11:8804. [PMID: 33888760 PMCID: PMC8062506 DOI: 10.1038/s41598-021-87861-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 03/31/2021] [Indexed: 12/21/2022] Open
Abstract
Few investigations have analyzed the neuroanatomical substrate of empathic capacities in healthy subjects, and most of them have neglected the potential involvement of cerebellar structures. The main aim of the present study was to investigate the associations between bilateral cerebellar macro- and micro-structural measures and levels of cognitive and affective trait empathy (measured by Interpersonal Reactivity Index, IRI) in a sample of 70 healthy subjects of both sexes. We also estimated morphometric variations of cerebral Gray Matter structures, to ascertain whether the potential empathy-related peculiarities in cerebellar areas were accompanied by structural differences in other cerebral regions. At macro-structural level, the volumetric differences were analyzed by Voxel-Based Morphometry (VBM)- and Region of Interest (ROI)-based approaches, and at a micro-structural level, we analyzed Diffusion Tensor Imaging (DTI) data, focusing in particular on Mean Diffusivity and Fractional Anisotropy. Fantasy IRI-subscale was found to be positively associated with volumes in right cerebellar Crus 2 and pars triangularis of inferior frontal gyrus. The here described morphological variations of cerebellar Crus 2 and pars triangularis allow to extend the traditional cortico-centric view of cognitive empathy to the cerebellar regions and indicate that in empathizing with fictional characters the cerebellar and frontal areas are co-recruited.
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16
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Shahid SS, Kerskens CM, Burrows M, Witney AG. Elucidating the complex organization of neural micro-domains in the locust Schistocerca gregaria using dMRI. Sci Rep 2021; 11:3418. [PMID: 33564031 PMCID: PMC7873062 DOI: 10.1038/s41598-021-82187-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 01/13/2021] [Indexed: 01/30/2023] Open
Abstract
To understand brain function it is necessary to characterize both the underlying structural connectivity between neurons and the physiological integrity of these connections. Previous research exploring insect brain connectivity has typically used electron microscopy techniques, but this methodology cannot be applied to living animals and so cannot be used to understand dynamic physiological processes. The relatively large brain of the desert locust, Schistercera gregaria (Forksȧl) is ideal for exploring a novel methodology; micro diffusion magnetic resonance imaging (micro-dMRI) for the characterization of neuronal connectivity in an insect brain. The diffusion-weighted imaging (DWI) data were acquired on a preclinical system using a customised multi-shell diffusion MRI scheme optimized to image the locust brain. Endogenous imaging contrasts from the averaged DWIs and Diffusion Kurtosis Imaging (DKI) scheme were applied to classify various anatomical features and diffusion patterns in neuropils, respectively. The application of micro-dMRI modelling to the locust brain provides a novel means of identifying anatomical regions and inferring connectivity of large tracts in an insect brain. Furthermore, quantitative imaging indices derived from the kurtosis model that include fractional anisotropy (FA), mean diffusivity (MD) and kurtosis anisotropy (KA) can be extracted. These metrics could, in future, be used to quantify longitudinal structural changes in the nervous system of the locust brain that occur due to environmental stressors or ageing.
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Affiliation(s)
- Syed Salman Shahid
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Christian M Kerskens
- Trinity College Institute of Neuroscience, Trinity Centre for Biomedical Engineering, School of Medicine, Trinity College Dublin, Dublin 2, Ireland
| | - Malcolm Burrows
- Department of Zoology, University of Cambridge, Cambridge, UK
| | - Alice G Witney
- Department of Physiology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity Centre for Biomedical Engineering, Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.
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17
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Grélard F, Legland D, Fanuel M, Arnaud B, Foucat L, Rogniaux H. Esmraldi: efficient methods for the fusion of mass spectrometry and magnetic resonance images. BMC Bioinformatics 2021; 22:56. [PMID: 33557761 PMCID: PMC7869484 DOI: 10.1186/s12859-020-03954-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 12/30/2020] [Indexed: 11/29/2022] Open
Abstract
Background Mass spectrometry imaging (MSI) is a family of acquisition techniques producing images of the distribution of molecules in a sample, without any prior tagging of the molecules. This makes it a very interesting technique for exploratory research. However, the images are difficult to analyze because the enclosed data has high dimensionality, and their content does not necessarily reflect the shape of the object of interest. Conversely, magnetic resonance imaging (MRI) scans reflect the anatomy of the tissue. MRI also provides complementary information to MSI, such as the content and distribution of water. Results We propose a new workflow to merge the information from 2D MALDI–MSI and MRI images. Our workflow can be applied to large MSI datasets in a limited amount of time. Moreover, the workflow is fully automated and based on deterministic methods which ensures the reproducibility of the results. Our methods were evaluated and compared with state-of-the-art methods. Results show that the images are combined precisely and in a time-efficient manner. Conclusion Our workflow reveals molecules which co-localize with water in biological images. It can be applied on any MSI and MRI datasets which satisfy a few conditions: same regions of the shape enclosed in the images and similar intensity distributions.
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Affiliation(s)
- Florent Grélard
- UR BIA, INRAE, 44316, Nantes, France. .,BIBS Facility, INRAE, 44316, Nantes, France.
| | - David Legland
- UR BIA, INRAE, 44316, Nantes, France.,BIBS Facility, INRAE, 44316, Nantes, France
| | - Mathieu Fanuel
- UR BIA, INRAE, 44316, Nantes, France.,BIBS Facility, INRAE, 44316, Nantes, France
| | - Bastien Arnaud
- UR BIA, INRAE, 44316, Nantes, France.,BIBS Facility, INRAE, 44316, Nantes, France
| | - Loïc Foucat
- UR BIA, INRAE, 44316, Nantes, France.,BIBS Facility, INRAE, 44316, Nantes, France
| | - Hélène Rogniaux
- UR BIA, INRAE, 44316, Nantes, France.,BIBS Facility, INRAE, 44316, Nantes, France
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18
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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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19
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Afzali M, Pieciak T, Newman S, Garyfallidis E, Özarslan E, Cheng H, Jones DK. The sensitivity of diffusion MRI to microstructural properties and experimental factors. J Neurosci Methods 2021; 347:108951. [PMID: 33017644 PMCID: PMC7762827 DOI: 10.1016/j.jneumeth.2020.108951] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 08/27/2020] [Accepted: 09/15/2020] [Indexed: 12/13/2022]
Abstract
Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic.
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Affiliation(s)
- Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain.
| | - Sharlene Newman
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA.
| | - Eleftherios Garyfallidis
- Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47408, USA.
| | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
| | - Hu Cheng
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA.
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
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20
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Volumetric distribution of perivascular space in relation to mild cognitive impairment. Neurobiol Aging 2020; 99:28-43. [PMID: 33422892 DOI: 10.1016/j.neurobiolaging.2020.12.010] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 11/25/2020] [Accepted: 12/08/2020] [Indexed: 12/19/2022]
Abstract
Vascular contributions to early cognitive decline are increasingly recognized, prompting further investigation into the nature of related changes in perivascular spaces (PVS). Using magnetic resonance imaging, we show that, compared to a cognitively normal sample, individuals with early cognitive dysfunction have altered PVS presence and distribution, irrespective of Amyloid-β. Surprisingly, we noted lower PVS presence in the anterosuperior medial temporal lobe (asMTL) (1.29 times lower PVS volume fraction in cognitively impaired individuals, p < 0.0001), which was associated with entorhinal neurofibrillary tau tangle deposition (beta (standard error) = -0.98 (0.4); p = 0.014), one of the hallmarks of early Alzheimer's disease pathology. We also observed higher PVS volume fraction in centrum semi-ovale of the white matter, but only in female participants (1.47 times higher PVS volume fraction in cognitively impaired individuals, p = 0.0011). We also observed PVS changes in participants with history of hypertension (higher in the white matter and lower in the asMTL). Our results suggest that anatomically specific alteration of the PVS is an early neuroimaging feature of cognitive impairment in aging adults, which is differentially manifested in female.
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21
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Liu R, Zhang Y, Cheng S, Luo Z, Fan X. A Deep Framework Assembling Principled Modules for CS-MRI: Unrolling Perspective, Convergence Behaviors, and Practical Modeling. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:4150-4163. [PMID: 32746155 DOI: 10.1109/tmi.2020.3014193] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantly accelerates MR acquisition at a sampling rate much lower than the Nyquist criterion. A major challenge for CS-MRI lies in solving the severely ill-posed inverse problem to reconstruct aliasing-free MR images from the sparse k -space data. Conventional methods typically optimize an energy function, producing restoration of high quality, but their iterative numerical solvers unavoidably bring extremely large time consumption. Recent deep techniques provide fast restoration by either learning direct prediction to final reconstruction or plugging learned modules into the energy optimizer. Nevertheless, these data-driven predictors cannot guarantee the reconstruction following principled constraints underlying the domain knowledge so that the reliability of their reconstruction process is questionable. In this paper, we propose a deep framework assembling principled modules for CS-MRI that fuses learning strategy with the iterative solver of a conventional reconstruction energy. This framework embeds an optimal condition checking mechanism, fostering efficient and reliable reconstruction. We also apply the framework to three practical tasks, i.e., complex-valued data reconstruction, parallel imaging and reconstruction with Rician noise. Extensive experiments on both benchmark and manufacturer-testing images demonstrate that the proposed method reliably converges to the optimal solution more efficiently and accurately than the state-of-the-art in various scenarios.
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22
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Spalletta G, Iorio M, Vecchio D, Piras F, Ciullo V, Banaj N, Sensi SL, Gianni W, Assogna F, Caltagirone C, Piras F. Subclinical Cognitive and Neuropsychiatric Correlates and Hippocampal Volume Features of Brain White Matter Hyperintensity in Healthy People. J Pers Med 2020; 10:jpm10040172. [PMID: 33076372 PMCID: PMC7712953 DOI: 10.3390/jpm10040172] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 09/28/2020] [Accepted: 10/12/2020] [Indexed: 12/11/2022] Open
Abstract
White matter hyperintensities (WMH) are associated with brain aging and behavioral symptoms as a possible consequence of disrupted white matter pathways. In this study, we investigated, in a cohort of asymptomatic subjects aged 50 to 80, the relationship between WMH, hippocampal atrophy, and subtle, preclinical cognitive and neuropsychiatric phenomenology. Thirty healthy subjects with WMH (WMH+) and thirty individuals without (WMH−) underwent comprehensive neuropsychological and neuropsychiatric evaluations and 3 Tesla Magnetic Resonance Imaging scan. The presence, degree of severity, and distribution of WMH were evaluated with a semi-automated algorithm. Volumetric analysis of hippocampal structure was performed through voxel-based morphometry. A multivariable logistic regression analysis indicated that phenomenology of subclinical apathy and anxiety was associated with the presence of WMH. ROI-based analyses showed a volume reduction in the right hippocampus of WMH+. In healthy individuals, WMH are associated with significant preclinical neuropsychiatric phenomenology, as well as hippocampal atrophy, which are considered as risk factors to develop cognitive impairment and dementia.
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Affiliation(s)
- Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
- Division of Neuropsychiatry, Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA
- Correspondence: (G.S.); (F.P.); Tel.: +39-06-5150-1575; Fax: +39-06-5150-1575
| | - Mariangela Iorio
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
- Molecular Neurology Unit, Center of Advanced Studies and Technology (CAST), G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy;
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
- Department of Psychology, Sapienza University of Rome, Policlinico Umberto I, 00161 Rome, Italy
| | - Federica Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
| | - Valentina Ciullo
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
| | - Stefano L. Sensi
- Molecular Neurology Unit, Center of Advanced Studies and Technology (CAST), G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy;
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
- Institute for Mind Impairments and Neurological Disorders, University of California-Irvine, Irvine, CA 92697, USA
| | - Walter Gianni
- II Division of Internal Medicine and Geriatrics, Sapienza University of Rome, Policlinico Umberto I, 00161 Rome, Italy;
| | - Francesca Assogna
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
| | - Carlo Caltagirone
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
- Correspondence: (G.S.); (F.P.); Tel.: +39-06-5150-1575; Fax: +39-06-5150-1575
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23
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Poirier SE, Kwan BYM, Jurkiewicz MT, Samargandy L, Steven DA, Suller-Marti A, Lam Shin Cheung V, Khan AR, Romsa J, Prato FS, Burneo JG, Thiessen JD, Anazodo UC. 18F-FDG PET-guided diffusion tractography reveals white matter abnormalities around the epileptic focus in medically refractory epilepsy: implications for epilepsy surgical evaluation. Eur J Hybrid Imaging 2020; 4:10. [PMID: 34191151 PMCID: PMC8218143 DOI: 10.1186/s41824-020-00079-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 06/12/2020] [Indexed: 02/28/2023] Open
Abstract
BACKGROUND Hybrid PET/MRI can non-invasively improve localization and delineation of the epileptic focus (EF) prior to surgical resection in medically refractory epilepsy (MRE), especially when MRI is negative or equivocal. In this study, we developed a PET-guided diffusion tractography (PET/DTI) approach combining 18F-fluorodeoxyglucose PET (FDG-PET) and diffusion MRI to investigate white matter (WM) integrity in MRI-negative MRE patients and its potential impact on epilepsy surgical planning. METHODS FDG-PET and diffusion MRI of 14 MRI-negative or equivocal MRE patients were used to retrospectively pilot the PET/DTI approach. We used asymmetry index (AI) mapping of FDG-PET to detect the EF as brain areas showing the largest decrease in FDG uptake between hemispheres. Seed-based WM fiber tracking was performed on DTI images with a seed location in WM 3 mm from the EF. Fiber tractography was repeated in the contralateral brain region (opposite to EF), which served as a control for this study. WM fibers were quantified by calculating the fiber count, mean fractional anisotropy (FA), mean fiber length, and mean cross-section of each fiber bundle. WM integrity was assessed through fiber visualization and by normalizing ipsilateral fiber measurements to contralateral fiber measurements. The added value of PET/DTI in clinical decision-making was evaluated by a senior neurologist. RESULTS In over 60% of the patient cohort, AI mapping findings were concordant with clinical reports on seizure-onset localization and lateralization. Mean FA, fiber count, and mean fiber length were decreased in 14/14 (100%), 13/14 (93%), and 12/14 (86%) patients, respectively. PET/DTI improved diagnostic confidence in 10/14 (71%) patients and indicated that surgical candidacy be reassessed in 3/6 (50%) patients who had not undergone surgery. CONCLUSIONS We demonstrate here the utility of AI mapping in detecting the EF based on brain regions showing decreased FDG-PET activity and, when coupled with DTI, could be a powerful tool for detecting EF and assessing WM integrity in MRI-negative epilepsy. PET/DTI could be used to further enhance clinical decision-making in epilepsy surgery.
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Affiliation(s)
- Stefan E Poirier
- Lawson Imaging, Lawson Health Research Institute, 268 Grosvenor St., London, Ontario, N6A 4 V2, Canada. .,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
| | - Benjamin Y M Kwan
- Department of Diagnostic Radiology, Queen's University, Kingston, Ontario, Canada
| | - Michael T Jurkiewicz
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Lina Samargandy
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - David A Steven
- Epilepsy Program, Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Ana Suller-Marti
- Epilepsy Program, Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | | | - Ali R Khan
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Jonathan Romsa
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Frank S Prato
- Lawson Imaging, Lawson Health Research Institute, 268 Grosvenor St., London, Ontario, N6A 4 V2, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Jorge G Burneo
- Epilepsy Program, Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Jonathan D Thiessen
- Lawson Imaging, Lawson Health Research Institute, 268 Grosvenor St., London, Ontario, N6A 4 V2, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Udunna C Anazodo
- Lawson Imaging, Lawson Health Research Institute, 268 Grosvenor St., London, Ontario, N6A 4 V2, Canada. .,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
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24
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Yang J, Carl B, Nimsky C, Bopp MHA. The impact of position-orientation adaptive smoothing in diffusion weighted imaging-From diffusion metrics to fiber tractography. PLoS One 2020; 15:e0233474. [PMID: 32433682 PMCID: PMC7239461 DOI: 10.1371/journal.pone.0233474] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 05/05/2020] [Indexed: 11/22/2022] Open
Abstract
In contrast to commonly used approaches to improve data quality in diffusion weighted imaging, position-orientation adaptive smoothing (POAS) provides an edge-preserving post-processing approach. This study aims to investigate its potential and effects on image quality, diffusion metrics, and fiber tractography of the corticospinal tract in relation to non-post-processed and averaged data. 22 healthy volunteers were included in this study. For each volunteer five clinically applicable diffusion weighted imaging data sets were acquired and post-processed by standard averaging and POAS. POAS post-processing led to significantly higher signal-to-noise-ratios (p < 0.001), lower fractional anisotropy across the whole brain (p < 0.05) and reduced intra-subject variability of diffusion weighted imaging signal intensity and fractional anisotropy (p < 0.001, p = 0.006). Fiber tractography of the corticospinal tract resulted in significantly (p = 0.027, p = 0.014) larger tract volumes while fiber density was the lowest. Similarity across tractography results was highest for POAS post-processed data (p < 0.001). POAS post-processing enhances image quality, decreases the intra-subject variability of signal intensity and fractional anisotropy, increases fiber tract volume of the corticospinal tract, and leads to higher reproducibility of tractography results. Thus, POAS post-processing supports a reliable and more accurate fiber tractography of the corticospinal tract, being mandatory for the clinical use.
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Affiliation(s)
- Jia Yang
- Department of Neurosurgery, University of Marburg, Marburg, Germany
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Barbara Carl
- Department of Neurosurgery, University of Marburg, Marburg, Germany
- Department of Neurosurgery, Helios Dr. Horst Schmidt Kliniken, Wiesbaden, Germany
| | - Christopher Nimsky
- Department of Neurosurgery, University of Marburg, Marburg, Germany
- Marburg Center for Mind, Brain and Behavior (MCMBB), Marburg, Germany
| | - Miriam H. A. Bopp
- Department of Neurosurgery, University of Marburg, Marburg, Germany
- Marburg Center for Mind, Brain and Behavior (MCMBB), Marburg, Germany
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25
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Lin YC, Huang HM. Denoising of multi b-value diffusion-weighted MR images using deep image prior. ACTA ACUST UNITED AC 2020; 65:105003. [DOI: 10.1088/1361-6560/ab8105] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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26
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Denoise magnitude diffusion magnetic resonance images via variance-stabilizing transformation and optimal singular-value manipulation. Neuroimage 2020; 215:116852. [PMID: 32305566 PMCID: PMC7292796 DOI: 10.1016/j.neuroimage.2020.116852] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 04/07/2020] [Accepted: 04/10/2020] [Indexed: 12/12/2022] Open
Abstract
Although shown to have a great utility for a wide range of neuroscientific and clinical applications, diffusion-weighted magnetic resonance imaging (dMRI) faces a major challenge of low signal-to-noise ratio (SNR), especially when pushing the spatial resolution for improved delineation of brain's fine structure or increasing the diffusion weighting for increased angular contrast or both. Here, we introduce a comprehensive denoising framework for denoising magnitude dMRI. The framework synergistically combines the variance stabilizing transform (VST) with optimal singular value manipulation. The purpose of VST is to transform the Rician data to Gaussian-like data so that an asymptotically optimal singular value manipulation strategy tailored for Gaussian data can be used. The output of the framework is the estimated underlying diffusion signal for each voxel in the image domain. The usefulness of the proposed framework for denoising magnitude dMRI is demonstrated using both simulation and real-data experiments. Our results show that the proposed denoising framework can significantly improve SNR across the entire brain, leading to substantially enhanced performances for estimating diffusion tensor related indices and for resolving crossing fibers when compared to another competing method. More encouragingly, the proposed method when used to denoise a single average of 7 Tesla Human Connectome Project-style diffusion acquisition provided comparable performances relative to those achievable with ten averages for resolving multiple fiber populations across the brain. As such, the proposed denoising method is expected to have a great utility for high-quality, high-resolution whole-brain dMRI, desirable for many neuroscientific and clinical applications.
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27
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Li J, Choi S, Joshi AA, Wisnowski JL, Leahy RM. Temporal non-local means filtering for studies of intrinsic brain connectivity from individual resting fMRI. Med Image Anal 2020; 61:101635. [PMID: 32007699 DOI: 10.1016/j.media.2020.101635] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 10/17/2019] [Accepted: 01/04/2020] [Indexed: 11/20/2022]
Abstract
Characterizing functional brain connectivity using resting functional magnetic resonance imaging (fMRI) is challenging due to the relatively small Blood-Oxygen-Level Dependent contrast and low signal-to-noise ratio. Denoising using surface-based Laplace-Beltrami (LB) or volumetric Gaussian filtering tends to blur boundaries between different functional areas. To overcome this issue, a time-based Non-Local Means (tNLM) filtering method was previously developed to denoise fMRI data while preserving spatial structure. The kernel and parameters that define the tNLM filter need to be optimized for each application. Here we present a novel Global PDF-based tNLM filtering (GPDF) algorithm that uses a data-driven kernel function based on a Bayes factor to optimize filtering for spatial delineation of functional connectivity in resting fMRI data. We demonstrate its performance relative to Gaussian spatial filtering and the original tNLM filtering via simulations. We also compare the effects of GPDF filtering against LB filtering using individual in-vivo resting fMRI datasets. Our results show that LB filtering tends to blur signals across boundaries between adjacent functional regions. In contrast, GPDF filtering enables improved noise reduction without blurring adjacent functional regions. These results indicate that GPDF may be a useful preprocessing tool for analyses of brain connectivity and network topology in individual fMRI recordings.
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Affiliation(s)
- Jian Li
- Signal and Image Processing Institute, University of Southern California, Los Angeles 90089 USA.
| | - Soyoung Choi
- Neuroscience Graduate Program, University of Southern California, Los Angeles 90089 USA
| | - Anand A Joshi
- Signal and Image Processing Institute, University of Southern California, Los Angeles 90089 USA
| | - Jessica L Wisnowski
- Radiology, Children's Hospital Los Angeles, Los Angeles, CA 90027 USA; Fetal and Neonatal Institute, CHLA Division of Neonatology, Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033 USA
| | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern California, Los Angeles 90089 USA
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28
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Sta Cruz S, Dinov ID, Herting MM, González-Zacarías C, Kim H, Toga AW, Sepehrband F. Imputation Strategy for Reliable Regional MRI Morphological Measurements. Neuroinformatics 2020; 18:59-70. [PMID: 31054076 PMCID: PMC6829024 DOI: 10.1007/s12021-019-09426-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Regional morphological analysis represents a crucial step in most neuroimaging studies. Results from brain segmentation techniques are intrinsically prone to certain degrees of variability, mainly as results of suboptimal segmentation. To reduce this inherent variability, the errors are often identified through visual inspection and then corrected (semi)manually. Identification and correction of incorrect segmentation could be very expensive for large-scale studies. While identification of the incorrect results can be done relatively fast even with manual inspection, the correction step is extremely time-consuming, as it requires training staff to perform laborious manual corrections. Here we frame the correction phase of this problem as a missing data problem. Instead of manually adjusting the segmentation outputs, our computational approach aims to derive accurate morphological measures by machine learning imputation. Data imputation techniques may be used to replace missing or incorrect region average values with carefully chosen imputed values, all of which are computed based on other available multivariate information. We examined our approach of correcting segmentation outputs on a cohort of 970 subjects, which were undergone an extensive, time-consuming, manual post-segmentation correction. A random forest imputation technique recovered the gold standard results with a significant accuracy (r = 0.93, p < 0.0001; when 30% of the segmentations were considered incorrect in a non-random fashion). The random forest technique proved to be most effective for big data studies (N > 250).
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Affiliation(s)
- Shaina Sta Cruz
- Department of Communication Sciences and Disorders, California State University, Fullerton, CA, USA
- Public Health Graduate Program, University of California Merced, Merced, CA, USA
| | - Ivo D Dinov
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- Statistics Online Computational Resource, Department of Health Behavior and Biological, Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, USA
| | - Megan M Herting
- Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- Department of Pediatrics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Clio González-Zacarías
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - Hosung Kim
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Farshid Sepehrband
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
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29
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Pineda-Pardo JA, Obeso I, Guida P, Dileone M, Strange BA, Obeso JA, Oliviero A, Foffani G. Static magnetic field stimulation of the supplementary motor area modulates resting-state activity and motor behavior. Commun Biol 2019; 2:397. [PMID: 31701026 PMCID: PMC6823375 DOI: 10.1038/s42003-019-0643-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 09/10/2019] [Indexed: 12/12/2022] Open
Abstract
Focal application of a strong static magnetic field over the human scalp induces measurable local changes in brain function. Whether it also induces distant effects across the brain and how these local and distant effects collectively affect motor behavior remains unclear. Here we applied transcranial static magnetic field stimulation (tSMS) over the supplementary motor area (SMA) in healthy subjects. At a behavioral level, tSMS increased the time to initiate movement while decreasing errors in choice reaction-time tasks. At a functional level, tSMS increased SMA resting-state fMRI activity and bilateral functional connectivity between the SMA and both the paracentral lobule and the lateral frontotemporal cortex, including the inferior frontal gyrus. These results suggest that tSMS over the SMA can induce behavioral aftereffects associated with modulation of both local and distant functionally-connected cortical circuits involved in the control of speed-accuracy tradeoffs, thus offering a promising protocol for cognitive and clinical research.
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Affiliation(s)
- José A. Pineda-Pardo
- CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Universidad CEU-San Pablo, Madrid, Spain
| | - Ignacio Obeso
- CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Universidad CEU-San Pablo, Madrid, Spain
| | - Pasqualina Guida
- CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Universidad CEU-San Pablo, Madrid, Spain
| | - Michele Dileone
- CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Universidad CEU-San Pablo, Madrid, Spain
| | - Bryan A. Strange
- Laboratory for Clinical Neuroscience, CTB, Universidad Politecnica de Madrid, Madrid, Spain
- Department of Neuroimaging, Alzheimer’s Disease Research Centre, Reina Sofia-CIEN Foundation, Madrid, Spain
| | - José A. Obeso
- CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Universidad CEU-San Pablo, Madrid, Spain
- CIBERNED, Instituto de Salud Carlos III, Madrid, Spain
| | | | - Guglielmo Foffani
- CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Universidad CEU-San Pablo, Madrid, Spain
- Hospital Nacional de Parapléjicos, Toledo, Spain
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30
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Sepehrband F, Barisano G, Sheikh-Bahaei N, Cabeen RP, Choupan J, Law M, Toga AW. Image processing approaches to enhance perivascular space visibility and quantification using MRI. Sci Rep 2019; 9:12351. [PMID: 31451792 PMCID: PMC6710285 DOI: 10.1038/s41598-019-48910-x] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 08/15/2019] [Indexed: 02/03/2023] Open
Abstract
Imaging the perivascular spaces (PVS), also known as Virchow-Robin space, has significant clinical value, but there remains a need for neuroimaging techniques to improve mapping and quantification of the PVS. Current technique for PVS evaluation is a scoring system based on visual reading of visible PVS in regions of interest, and often limited to large caliber PVS. Enhancing the visibility of the PVS could support medical diagnosis and enable novel neuroscientific investigations. Increasing the MRI resolution is one approach to enhance the visibility of PVS but is limited by acquisition time and physical constraints. Alternatively, image processing approaches can be utilized to improve the contrast ratio between PVS and surrounding tissue. Here we combine T1- and T2-weighted images to enhance PVS contrast, intensifying the visibility of PVS. The Enhanced PVS Contrast (EPC) was achieved by combining T1- and T2-weighted images that were adaptively filtered to remove non-structured high-frequency spatial noise. EPC was evaluated on healthy young adults by presenting them to two expert readers and also through automated quantification. We found that EPC improves the conspicuity of the PVS and aid resolving a larger number of PVS. We also present a highly reliable automated PVS quantification approach, which was optimized using expert readings.
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Affiliation(s)
- Farshid Sepehrband
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Giuseppe Barisano
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Neuroscience graduate program, University of Southern California, Los Angeles, CA, USA
| | - Nasim Sheikh-Bahaei
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Radiology, Keck Hospital of USC, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ryan P Cabeen
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jeiran Choupan
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Meng Law
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Radiology, Alfred Health, Melbourne, Australia
| | - Arthur W Toga
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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31
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Chun J, Zhang H, Gach HM, Olberg S, Mazur T, Green O, Kim T, Kim H, Kim JS, Mutic S, Park JC. MRI super‐resolution reconstruction for MRI‐guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model. Med Phys 2019; 46:4148-4164. [DOI: 10.1002/mp.13717] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 06/14/2019] [Accepted: 07/07/2019] [Indexed: 11/06/2022] Open
Affiliation(s)
- Jaehee Chun
- Department of Radiation Oncology Washington University in St. Louis St Louis MO 63110 USA
- Department of Radiation Oncology, Yonsei Cancer Center Yonsei University College of Medicine Seoul South Korea
| | - Hao Zhang
- Department of Radiation Oncology Washington University in St. Louis St Louis MO 63110 USA
| | - H. Michael Gach
- Department of Radiation Oncology Washington University in St. Louis St Louis MO 63110 USA
- Departments of Radiology and Biomedical Engineering Washington University in St. Louis St Louis MO 63110 USA
| | - Sven Olberg
- Department of Radiation Oncology Washington University in St. Louis St Louis MO 63110 USA
- Department of Biomedical Engineering Washington University in St. Louis St Louis MO 63110 USA
| | - Thomas Mazur
- Department of Radiation Oncology Washington University in St. Louis St Louis MO 63110 USA
| | - Olga Green
- Department of Radiation Oncology Washington University in St. Louis St Louis MO 63110 USA
| | - Taeho Kim
- Department of Radiation Oncology Washington University in St. Louis St Louis MO 63110 USA
| | - Hyun Kim
- Department of Radiation Oncology Washington University in St. Louis St Louis MO 63110 USA
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center Yonsei University College of Medicine Seoul South Korea
| | - Sasa Mutic
- Department of Radiation Oncology Washington University in St. Louis St Louis MO 63110 USA
| | - Justin C. Park
- Department of Radiation Oncology Washington University in St. Louis St Louis MO 63110 USA
- Department of Biomedical Engineering Washington University in St. Louis St Louis MO 63110 USA
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32
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Cerebellar Structural Variations in Subjects with Different Hypnotizability. THE CEREBELLUM 2019; 18:109-118. [PMID: 30022466 DOI: 10.1007/s12311-018-0965-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Hypnotizability-the proneness to accept suggestions and behave accordingly-has a number of physiological and behavioral correlates (postural, visuomotor, and pain control) which suggest a possible involvement of cerebellar function and/or structure. The present study was aimed at investigating the association between cerebellar macro- or micro-structural variations (analyzed through a voxel-based morphometry and a diffusion tensor imaging approach) and hypnotic susceptibility. We also estimated morphometric variations of cerebral gray matter structures, to support current evidence of hypnotizability-related differences in some cerebral areas. High (highs, N = 12), and low (lows, N = 37) hypnotizable healthy participants (according to the Stanford Hypnotic Susceptibility Scale, form A) were submitted to a high field (3 T) magnetic resonance imaging protocol. In comparison to lows, highs showed smaller gray matter volumes in left cerebellar lobules IV/V and VI at uncorrected level, with the results in left lobule IV/V maintained also at corrected level. Highs showed also gray matter volumes smaller than lows in right inferior temporal gyrus, middle and superior orbitofrontal cortex, parahippocampal gyrus, and supramarginal parietal gyrus, as well as in left gyrus rectus, insula, and middle temporal cortex at uncorrected level. Results of right inferior temporal gyrus survived also at corrected level. Analyses on micro-structural data failed to reveal any significant association. The here found morphological variations allow to extend the traditional cortico-centric view of hypnotizability to the cerebellar regions, suggesting that cerebellar peculiarities may sustain hypnotizability-related differences in sensorimotor integration and emotional control.
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33
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Chen G, Wu Y, Shen D, Yap PT. Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space. Med Image Anal 2019; 53:79-94. [PMID: 30703580 PMCID: PMC6397790 DOI: 10.1016/j.media.2019.01.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 12/25/2018] [Accepted: 01/14/2019] [Indexed: 10/27/2022]
Abstract
Diffusion MRI affords valuable insights into white matter microstructures, but suffers from low signal-to-noise ratio (SNR), especially at high diffusion weighting (i.e., b-value). To avoid time-intensive repeated acquisition, post-processing algorithms are often used to reduce noise. Among existing methods, non-local means (NLM) has been shown to be particularly effective. However, most NLM algorithms for diffusion MRI focus on patch matching in the spatial domain (i.e., x-space) and disregard the fact that the data live in a combined 6D space covering both spatial domain and diffusion wavevector domain (i.e., q-space). This drawback leads to inaccurate patch matching in curved white matter structures and hence the inability to effectively use recurrent information for noise reduction. The goal of this paper is to overcome this limitation by extending NLM to the joint x-q space. Specifically, we define for each point in the x-q space a spherical patch from which we extract rotation-invariant features for patch matching. The ability to perform patch matching across q-samples allows patches from differentially orientated structures to be used for effective noise removal. Extensive experiments on synthetic, repeated-acquisition, and HCP data demonstrate that our method outperforms state-of-the-art methods, both qualitatively and quantitatively.
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Affiliation(s)
- Geng Chen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, USA.
| | - Yafeng Wu
- Data Processing Center, Northwestern Polytechnical University, Xi'an, China.
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea.
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, USA.
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34
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Chen W, You J, Pan B, Liang Z, Chen B. A sparse representation and dictionary learning based algorithm for image restoration in the presence of Rician noise. Neurocomputing 2018; 286:130-140. [PMID: 30214129 DOI: 10.1016/j.neucom.2018.01.066] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Rician noise removal for Magnetic Resonance Imaging (MRI) is very important because the MRI has been widely used in various clinical applications and the associated Rician noise deteriorates the image quality and causes errors in interpreting the images. Great efforts have recently been devoted to develop the corresponding noise-removal algorithms, particularly the development based on the newly-established Total Variation (TV) theorem. However, all the TV-based algorithms depend mainly on the gradient information and have been shown to produce the so called "blocky" artifact, which also deteriorates the image quality and causes image interpretation errors. In order to avoid producing the artifact, this paper presents a new de-noising model based on sparse representation and dictionary learning. The Split Bregman Iteration strategy is employed to implement the model. Furthermore, an appropriate dictionary is designed by the use of the Kernel Singular Value Decomposition method, resulting in a new Rician noise removal algorithm. Compared with other de-noising algorithms, the presented new algorithm can achieve superior performance, in terms of quantitative measures of the Structural Similarity Index and Peak Signal to Noise Ratio, by a series of experiments using different images in the presence of Rician noise.
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Affiliation(s)
- Wensheng Chen
- College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China
| | - Jie You
- College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China
| | - Binbin Pan
- College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China
| | - Zhengrong Liang
- Department of Radiology, State University of New York, Stony Brook, NY 11790, USA
| | - Bo Chen
- College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China
- Department of Radiology, State University of New York, Stony Brook, NY 11790, USA
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35
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Bouhrara M, Maring MC, Spencer RG. A simple and fast adaptive nonlocal multispectral filtering algorithm for efficient noise reduction in magnetic resonance imaging. Magn Reson Imaging 2018; 55:133-139. [PMID: 30149058 DOI: 10.1016/j.mri.2018.08.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/20/2018] [Accepted: 08/23/2018] [Indexed: 10/28/2022]
Abstract
PURPOSE We recently introduced a multispectral (MS) nonlocal (NL) filter based on maximum likelihood estimation (MLE) of voxel intensities, termed MS-NLML. While MS-NLML provides excellent noise reduction and improved image feature preservation as compared to other NL or MS filters, it requires considerable processing time, limiting its application in routine analyses. In this work, we introduced a fast, simple, and robust filter, termed nonlocal estimation of multispectral magnitudes (NESMA), for noise reduction in multispectral (MS) magnetic resonance imaging (MRI). METHODS Through extensive simulation and in-vivo analyses, we compared the performance of NESMA and MS-NLML in terms of noise reduction and processing efficiency. Further, we introduce two simple adaptive methods that permit spatial variation of similar voxels, R, used in the filtering. The first method is semi-adaptive and permits variation of R across the image by using a relative Euclidean distance (RED) similarity threshold. The second method is fully adaptive and filters the raw data with several RED similarity thresholds to spatially determine the optimal threshold value using an unbiased criterion. RESULTS NESMA shows very similar filtering performance as compared to MS-NLML, however, with much simple implementation and very fast processing time. Further, for both filters, the adaptive methods were shown to further reduce noise in comparison with the conventional non-adaptive method in which R is set to a constant value throughout the image. CONCLUSIONS NESMA is fast, robust, and straightforward to implement filter. These features render it suitable for routine clinical use and analysis of large MRI datasets.
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Affiliation(s)
- Mustapha Bouhrara
- National Institute on Aging, National Institute of Health, Baltimore, MD, USA.
| | - Michael C Maring
- National Institute on Aging, National Institute of Health, Baltimore, MD, USA
| | - Richard G Spencer
- National Institute on Aging, National Institute of Health, Baltimore, MD, USA
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36
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Li J, Choi S, Joshi AA, Wisnowski JL, Leahy RM. GLOBAL PDF-BASED TEMPORAL NON-LOCAL MEANS FILTERING REVEALS INDIVIDUAL DIFFERENCES IN BRAIN CONNECTIVITY. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:15-19. [PMID: 30713593 DOI: 10.1109/isbi.2018.8363513] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Characterizing functional brain connectivity using resting fMRI is challenging due to the relatively small BOLD signal contrast and low SNR. Gaussian filtering tends to undermine the individual differences detected by analysis of BOLD signal by smoothing signals across boundaries of different functional areas. Temporal non-local means (tNLM) filtering denoises fMRI data while preserving spatial structures but the kernel and parameters for tNLM filter need to be chosen carefully in order to achieve optimal results. Global PDF-based tNLM filtering (GPDF) is a new, data-dependent optimized kernel function for tNLM filtering which enables us to perform global filtering with improved noise reduction effects without blurring adjacent functional regions.
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Affiliation(s)
- Jian Li
- Signal and Image Processing Institute, University of Southern California, Los Angeles, 90089
| | - Soyoung Choi
- Neuroscience Graduate Program, University of Southern California, Los Angeles, 90089.,Signal and Image Processing Institute, University of Southern California, Los Angeles, 90089
| | - Anand A Joshi
- Signal and Image Processing Institute, University of Southern California, Los Angeles, 90089
| | - Jessica L Wisnowski
- Radiology and Pediatrics, Division of Neonatology, Children's Hospital Los Angeles
- Keck School of Medicine, University of Southern California, 90089
| | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern California, Los Angeles, 90089
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37
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Sjölund J, Eklund A, Özarslan E, Herberthson M, Bånkestad M, Knutsson H. Bayesian uncertainty quantification in linear models for diffusion MRI. Neuroimage 2018; 175:272-285. [PMID: 29604453 DOI: 10.1016/j.neuroimage.2018.03.059] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 02/16/2018] [Accepted: 03/25/2018] [Indexed: 01/22/2023] Open
Abstract
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification.
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Affiliation(s)
- Jens Sjölund
- Elekta Instrument AB, Kungstensgatan 18, Box 7593, SE-103 93, Stockholm, Sweden; Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Sweden.
| | - Anders Eklund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Sweden; Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Sweden
| | | | - Maria Bånkestad
- RISE SICS, Isafjordsgatan 22, Box 1263, SE-164 29, Kista, Sweden
| | - Hans Knutsson
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Sweden
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38
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Wegmann B, Eklund A, Villani M. Bayesian Rician Regression for Neuroimaging. Front Neurosci 2017; 11:586. [PMID: 29104529 PMCID: PMC5655010 DOI: 10.3389/fnins.2017.00586] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 10/04/2017] [Indexed: 11/13/2022] Open
Abstract
It is well-known that data from diffusion weighted imaging (DWI) follow the Rician distribution. The Rician distribution is also relevant for functional magnetic resonance imaging (fMRI) data obtained at high temporal or spatial resolution. We propose a general regression model for non-central χ (NC-χ) distributed data, with the heteroscedastic Rician regression model as a prominent special case. The model allows both parameters in the Rician distribution to be linked to explanatory variables, with the relevant variables chosen by Bayesian variable selection. A highly efficient Markov chain Monte Carlo (MCMC) algorithm is proposed to capture full model uncertainty by simulating from the joint posterior distribution of all model parameters and the binary variable selection indicators. Simulated regression data is used to demonstrate that the Rician model is able to detect the signal much more accurately than the traditionally used Gaussian model at low signal-to-noise ratios. Using a diffusion dataset from the Human Connectome Project, it is also shown that the commonly used approximate Gaussian noise model underestimates the mean diffusivity (MD) and the fractional anisotropy (FA) in the single-diffusion tensor model compared to the Rician model.
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Affiliation(s)
- Bertil Wegmann
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Anders Eklund
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden.,Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Mattias Villani
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden
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39
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Xu T, Feng Y, Wu Y, Zeng Q, Zhang J, He J, Zhuge Q. A Novel Richardson-Lucy Model with Dictionary Basis and Spatial Regularization for Isolating Isotropic Signals. PLoS One 2017; 12:e0168864. [PMID: 28081561 PMCID: PMC5233428 DOI: 10.1371/journal.pone.0168864] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 12/07/2016] [Indexed: 11/27/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging is a non-invasive imaging method that has been increasingly used in neuroscience imaging over the last decade. Partial volume effects (PVEs) exist in sampling signal for many physical and actual reasons, which lead to inaccurate fiber imaging. We overcome the influence of PVEs by separating isotropic signal from diffusion-weighted signal, which can provide more accurate estimation of fiber orientations. In this work, we use a novel response function (RF) and the correspondent fiber orientation distribution function (fODF) to construct different signal models, in which case the fODF is represented using dictionary basis function. We then put forward a new index Piso, which is a part of fODF to quantify white and gray matter. The classic Richardson-Lucy (RL) model is usually used in the field of digital image processing to solve the problem of spherical deconvolution caused by highly ill-posed least-squares algorithm. In this case, we propose an innovative model integrating RL model with spatial regularization to settle the suggested double-models, which improve noise resistance and accuracy of imaging. Experimental results of simulated and real data show that the proposal method, which we call iRL, can robustly reconstruct a more accurate fODF and the quantitative index Piso performs better than fractional anisotropy and general fractional anisotropy.
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Affiliation(s)
- Tiantian Xu
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Ye Wu
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Qingrun Zeng
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Jun Zhang
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Jianzhong He
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Qichuan Zhuge
- Zhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research, Wenzhou Medical University, Wenzhou, Zhejiang, China
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40
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Bouhrara M, Bonny JM, Ashinsky BG, Maring MC, Spencer RG. Noise Estimation and Reduction in Magnetic Resonance Imaging Using a New Multispectral Nonlocal Maximum-likelihood Filter. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:181-193. [PMID: 27552743 PMCID: PMC5958909 DOI: 10.1109/tmi.2016.2601243] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Denoising of magnetic resonance (MR) images enhances diagnostic accuracy, the quality of image manipulations such as registration and segmentation, and parameter estimation. The first objective of this paper is to introduce a new, high-performance, nonlocal filter for noise reduction in MR image sets consisting of progressively-weighted, that is, multispectral, images. This filter is a multispectral extension of the nonlocal maximum likelihood filter (NLML). Performance was evaluated on synthetic and in-vivo T2 - and T1 -weighted brain imaging data, and compared to the nonlocal-means (NLM) and its multispectral version, that is, MS-NLM, and the nonlocal maximum likelihood (NLML) filters. Visual inspection of filtered images and quantitative analyses showed that all filters provided substantial reduction of noise. Further, as expected, the use of multispectral information improves filtering quality. In addition, numerical and experimental analyses indicated that the new multispectral NLML filter, MS-NLML, demonstrated markedly less blurring and loss of image detail than seen with the other filters evaluated. In addition, since noise standard deviation (SD) is an important parameter for all of these nonlocal filters, a multispectral extension of the method of maximum likelihood estimation (MLE) of noise amplitude is presented and compared to both local and nonlocal MLE methods. Numerical and experimental analyses indicated the superior performance of this multispectral method for estimation of noise SD.
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41
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Yadav RB, Srivastava S, Srivastava R. A partial differential equation-based general framework adapted to Rayleigh's, Rician's and Gaussian's distributed noise for restoration and enhancement of magnetic resonance image. J Med Phys 2016; 41:254-265. [PMID: 28144118 PMCID: PMC5228049 DOI: 10.4103/0971-6203.195190] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
The proposed framework is obtained by casting the noise removal problem into a variational framework. This framework automatically identifies the various types of noise present in the magnetic resonance image and filters them by choosing an appropriate filter. This filter includes two terms: the first term is a data likelihood term and the second term is a prior function. The first term is obtained by minimizing the negative log likelihood of the corresponding probability density functions: Gaussian or Rayleigh or Rician. Further, due to the ill-posedness of the likelihood term, a prior function is needed. This paper examines three partial differential equation based priors which include total variation based prior, anisotropic diffusion based prior, and a complex diffusion (CD) based prior. A regularization parameter is used to balance the trade-off between data fidelity term and prior. The finite difference scheme is used for discretization of the proposed method. The performance analysis and comparative study of the proposed method with other standard methods is presented for brain web dataset at varying noise levels in terms of peak signal-to-noise ratio, mean square error, structure similarity index map, and correlation parameter. From the simulation results, it is observed that the proposed framework with CD based prior is performing better in comparison to other priors in consideration.
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Affiliation(s)
- Ram Bharos Yadav
- Department of Computer Science and Engineering, Indian Institute of Technology, BHU, Varanasi, Uttar Pradesh, India
| | - Subodh Srivastava
- Department of Computer Science and Engineering, Indian Institute of Technology, BHU, Varanasi, Uttar Pradesh, India
| | - Rajeev Srivastava
- Department of Computer Science and Engineering, Indian Institute of Technology, BHU, Varanasi, Uttar Pradesh, India
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42
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Liu X, Yuan Z, Guo Z, Xu D. A localized Richardson-Lucy algorithm for fiber orientation estimation in high angular resolution diffusion imaging. Med Phys 2016; 42:2524-39. [PMID: 25979045 DOI: 10.1118/1.4917082] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Diffusion tensor imaging is widely used for studying neural fiber trajectories in white matter and for quantifying changes in tissue using diffusion properties at each voxel in the brain. To better model the nature of crossing fibers within complex architectures, rather than using a simplified tensor model that assumes only a single fiber direction at each image voxel, a model mixing multiple diffusion tensors is used to profile diffusion signals from high angular resolution diffusion imaging (HARDI) data. Based on the HARDI signal and a multiple tensors model, spherical deconvolution methods have been developed to overcome the limitations of the diffusion tensor model when resolving crossing fibers. The Richardson-Lucy algorithm is a popular spherical deconvolution method used in previous work. However, it is based on a Gaussian distribution, while HARDI data are always very noisy, and the distribution of HARDI data follows a Rician distribution. This current work aims to present a novel solution to address these issues. METHODS By simultaneously considering both the Rician bias and neighbor correlation in HARDI data, the authors propose a localized Richardson-Lucy (LRL) algorithm to estimate fiber orientations for HARDI data. The proposed method can simultaneously reduce noise and correct the Rician bias. RESULTS Mean angular error (MAE) between the estimated Fiber orientation distribution (FOD) field and the reference FOD field was computed to examine whether the proposed LRL algorithm offered any advantage over the conventional RL algorithm at various levels of noise. Normalized mean squared error (NMSE) was also computed to measure the similarity between the true FOD field and the estimated FOD filed. For MAE comparisons, the proposed LRL approach obtained the best results in most of the cases at different levels of SNR and b-values. For NMSE comparisons, the proposed LRL approach obtained the best results in most of the cases at b-value = 3000 s/mm(2), which is the recommended schema for HARDI data acquisition. In addition, the FOD fields estimated by the proposed LRL approach in regions of fiber crossing regions using real data sets also showed similar fiber structures which agreed with common acknowledge in these regions. CONCLUSIONS The novel spherical deconvolution method for improved accuracy in investigating crossing fibers can simultaneously reduce noise and correct Rician bias. With the noise smoothed and bias corrected, this algorithm is especially suitable for estimation of fiber orientations in HARDI data. Experimental results using both synthetic and real imaging data demonstrated the success and effectiveness of the proposed LRL algorithm.
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Affiliation(s)
- Xiaozheng Liu
- Center for Cognition and Brain Disorders and Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou 310015, China
| | - Zhenming Yuan
- Department of Information Science and Engineering, Hangzhou Normal University, Hangzhou 310012, China
| | - Zhongwei Guo
- Tongde Hospital of Zhejiang Provence, Hangzhou 310012, China
| | - Dongrong Xu
- MRI Unit and Epidemiology Division, Department of Psychiatry, New York State Psychiatric Institute, NYSPI Unit 24, Columbia University, 1051 Riverside Drive, New York, New York 10032
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43
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Chen G, Zhang P, Wu Y, Shen D, Yap PT. Denoising Magnetic Resonance Images Using Collaborative Non-Local Means. Neurocomputing 2016; 177:215-227. [PMID: 26949289 PMCID: PMC4776654 DOI: 10.1016/j.neucom.2015.11.031] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Noise artifacts in magnetic resonance (MR) images increase the complexity of image processing workflows and decrease the reliability of inferences drawn from the images. It is thus often desirable to remove such artifacts beforehand for more robust and effective quantitative analysis. It is important to preserve the integrity of relevant image information while removing noise in MR images. A variety of approaches have been developed for this purpose, and the non-local means (NLM) filter has been shown to be able to achieve state-of-the-art denoising performance. For effective denoising, NLM relies heavily on the existence of repeating structural patterns, which however might not always be present within a single image. This is especially true when one considers the fact that the human brain is complex and contains a lot of unique structures. In this paper we propose to leverage the repeating structures from multiple images to collaboratively denoise an image. The underlying assumption is that it is more likely to find repeating structures from multiple scans than from a single scan. Specifically, to denoise a target image, multiple images, which may be acquired from different subjects, are spatially aligned to the target image, and an NLM-like block matching is performed on these aligned images with the target image as the reference. This will significantly increase the number of matching structures and thus boost the denoising performance. Experiments on both synthetic and real data show that the proposed approach, collaborative non-local means (CNLM), outperforms the classic NLM and yields results with markedly improved structural details.
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Affiliation(s)
- Geng Chen
- Data Processing Center, Northwestern Polytechnical University, Xi’an, China
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina at Chapel Hill, NC, U.S.A
| | - Pei Zhang
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina at Chapel Hill, NC, U.S.A
| | - Yafeng Wu
- Data Processing Center, Northwestern Polytechnical University, Xi’an, China
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina at Chapel Hill, NC, U.S.A
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina at Chapel Hill, NC, U.S.A
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Lui D, Modhafar A, Haider MA, Wong A. Monte Carlo-based noise compensation in coil intensity corrected endorectal MRI. BMC Med Imaging 2015; 15:43. [PMID: 26459631 PMCID: PMC4601140 DOI: 10.1186/s12880-015-0081-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 09/15/2015] [Indexed: 11/10/2022] Open
Abstract
Background Prostate cancer is one of the most common forms of cancer found in males making early diagnosis important. Magnetic resonance imaging (MRI) has been useful in visualizing and localizing tumor candidates and with the use of endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The coils introduce intensity inhomogeneities and the surface coil intensity correction built into MRI scanners is used to reduce these inhomogeneities. However, the correction typically performed at the MRI scanner level leads to noise amplification and noise level variations. Methods In this study, we introduce a new Monte Carlo-based noise compensation approach for coil intensity corrected endorectal MRI which allows for effective noise compensation and preservation of details within the prostate. The approach accounts for the ERC SNR profile via a spatially-adaptive noise model for correcting non-stationary noise variations. Such a method is useful particularly for improving the image quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available. Results SNR and contrast-to-noise ratio (CNR) analysis in patient experiments demonstrate an average improvement of 11.7 and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong performance when compared to existing approaches. Discussion Experimental results using both phantom and patient data showed that ACER provided strong performance in terms of SNR, CNR, edge preservation, subjective scoring when compared to a number of existing approaches. Conclusions A new noise compensation method was developed for the purpose of improving the quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level. We illustrate that promising noise compensation performance can be achieved for the proposed approach, which is particularly important for processing coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available.
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Affiliation(s)
- Dorothy Lui
- Department of Systems Design Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada.
| | - Amen Modhafar
- Department of Systems Design Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada.
| | - Masoom A Haider
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada.
| | - Alexander Wong
- Department of Systems Design Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada.
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45
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Liu RW, Shi L, Yu SCH, Wang D. A two-step optimization approach for nonlocal total variation-based Rician noise reduction in magnetic resonance images. Med Phys 2015; 42:5167-87. [DOI: 10.1118/1.4927793] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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46
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Borrelli P, Palma G, Tedeschi E, Cocozza S, Comerci M, Alfano B, Haacke EM, Salvatore M. Improving Signal-to-Noise Ratio in Susceptibility Weighted Imaging: A Novel Multicomponent Non-Local Approach. PLoS One 2015; 10:e0126835. [PMID: 26030293 PMCID: PMC4452483 DOI: 10.1371/journal.pone.0126835] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2014] [Accepted: 04/08/2015] [Indexed: 01/14/2023] Open
Abstract
In susceptibility-weighted imaging (SWI), the high resolution required to obtain a proper contrast generation leads to a reduced signal-to-noise ratio (SNR). The application of a denoising filter to produce images with higher SNR and still preserve small structures from excessive blurring is therefore extremely desirable. However, as the distributions of magnitude and phase noise may introduce biases during image restoration, the application of a denoising filter is non-trivial. Taking advantage of the potential multispectral nature of MR images, a multicomponent approach using a Non-Local Means (MNLM) denoising filter may perform better than a component-by-component image restoration method. Here we present a new MNLM-based method (Multicomponent-Imaginary-Real-SWI, hereafter MIR-SWI) to produce SWI images with high SNR and improved conspicuity. Both qualitative and quantitative comparisons of MIR-SWI with the original SWI scheme and previously proposed SWI restoring pipelines showed that MIR-SWI fared consistently better than the other approaches. Noise removal with MIR-SWI also provided improvement in contrast-to-noise ratio (CNR) and vessel conspicuity at higher factors of phase mask multiplications than the one suggested in the literature for SWI vessel imaging. We conclude that a proper handling of noise in the complex MR dataset may lead to improved image quality for SWI data.
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Affiliation(s)
- Pasquale Borrelli
- Advanced Biomedical Sciences Department, University of Napoli “Federico II”, Napoli, Italy
- IRCCS SDN, Naples, Italy
- * E-mail:
| | - Giuseppe Palma
- Institute of Biostructures and Bioimaging, National Research Council, Napoli, Italy
| | - Enrico Tedeschi
- Advanced Biomedical Sciences Department, University of Napoli “Federico II”, Napoli, Italy
| | - Sirio Cocozza
- Advanced Biomedical Sciences Department, University of Napoli “Federico II”, Napoli, Italy
| | - Marco Comerci
- Institute of Biostructures and Bioimaging, National Research Council, Napoli, Italy
| | - Bruno Alfano
- Institute of Biostructures and Bioimaging, National Research Council, Napoli, Italy
| | - E. Mark Haacke
- The MRI Institute for Biomedical Research, Detroit, MI, United States of America
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47
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Chen G, Zhang P, Wu Y, Shen D, Yap PT. COLLABORATIVE NON-LOCAL MEANS DENOISING OF MAGNETIC RESONANCE IMAGES. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2015; 2015:564-567. [PMID: 34306520 DOI: 10.1109/isbi.2015.7163936] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Noise artifacts in magnetic resonance (MR) images increase the complexity of image processing workflows and decrease the reliability of inferences drawn from the images. To reduce noise, the non-local means (NLM) filter has been shown to yield state-of-the-art denoising performance. However, NLM relies heavily on the existence of recurring structural patterns and this condition might not always be satisfied especially within a single image, where complex patterns might not recur. In this paper, we propose to leverage common structures from multiple images to collaboratively denoise an image. The assumption is that, although the human brain is structurally complex, common structures can be found with greater probability from multiple scans than from a single scan. More specifically, to denoise an image, multiple images from different individuals are spatially aligned to the image and NLM-like block matching is performed on these aligned images with the image as the reference. Experiments on synthetic and real data indicate that the proposed approach - collaborative non-local means (CNLM) - outperforms the classic NLM and yields results with markedly improved structural details.
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Affiliation(s)
- Geng Chen
- Data Processing Center, Northwestern Polytechnical University, Xi'an, China.,Department of Radiology and BRIC, UNC Chapel Hill, U.S.A
| | - Pei Zhang
- Department of Radiology and BRIC, UNC Chapel Hill, U.S.A
| | - Yafeng Wu
- Data Processing Center, Northwestern Polytechnical University, Xi'an, China
| | - Dinggang Shen
- Department of Radiology and BRIC, UNC Chapel Hill, U.S.A
| | - Pew-Thian Yap
- Department of Radiology and BRIC, UNC Chapel Hill, U.S.A
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48
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Lam F, Liu D, Song Z, Schuff N, Liang ZP. A fast algorithm for denoising magnitude diffusion-weighted images with rank and edge constraints. Magn Reson Med 2015; 75:433-40. [PMID: 25733066 DOI: 10.1002/mrm.25643] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2014] [Revised: 11/14/2014] [Accepted: 12/08/2014] [Indexed: 11/07/2022]
Abstract
PURPOSE To accelerate denoising of magnitude diffusion-weighted images subject to joint rank and edge constraints. METHODS We extend a previously proposed majorize-minimize method for statistical estimation that involves noncentral χ distributions to incorporate joint rank and edge constraints. A new algorithm is derived which decomposes the constrained noncentral χ denoising problem into a series of constrained Gaussian denoising problems each of which is then solved using an efficient alternating minimization scheme. RESULTS The performance of the proposed algorithm has been evaluated using both simulated and experimental data. Results from simulations based on ex vivo data show that the new algorithm achieves about a factor of 10 speed up over the original Quasi-Newton-based algorithm. This improvement in computational efficiency enabled denoising of large datasets containing many diffusion-encoding directions. The denoising performance of the new efficient algorithm is found to be comparable to or even better than that of the original slow algorithm. For an in vivo high-resolution Q-ball acquisition, comparison of fiber tracking results around hippocampus region before and after denoising will also be shown to demonstrate the denoising effects of the new algorithm. CONCLUSION The optimization problem associated with denoising noncentral χ distributed diffusion-weighted images subject to joint rank and edge constraints can be solved efficiently using a majorize-minimize-based algorithm.
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Affiliation(s)
- Fan Lam
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Ding Liu
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Zhuang Song
- Center for Vital Longevity, University of Texas at Dallas, Dallas, Texas, USA
| | - Norbert Schuff
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.,Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, California, USA
| | - Zhi-Pei Liang
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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Evaluation of non-local means based denoising filters for diffusion kurtosis imaging using a new phantom. PLoS One 2015; 10:e0116986. [PMID: 25643162 PMCID: PMC4313935 DOI: 10.1371/journal.pone.0116986] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2014] [Accepted: 12/17/2014] [Indexed: 11/19/2022] Open
Abstract
Image denoising has a profound impact on the precision of estimated parameters in diffusion kurtosis imaging (DKI). This work first proposes an approach to constructing a DKI phantom that can be used to evaluate the performance of denoising algorithms in regard to their abilities of improving the reliability of DKI parameter estimation. The phantom was constructed from a real DKI dataset of a human brain, and the pipeline used to construct the phantom consists of diffusion-weighted (DW) image filtering, diffusion and kurtosis tensor regularization, and DW image reconstruction. The phantom preserves the image structure while minimizing image noise, and thus can be used as ground truth in the evaluation. Second, we used the phantom to evaluate three representative algorithms of non-local means (NLM). Results showed that one scheme of vector-based NLM, which uses DWI data with redundant information acquired at different b-values, produced the most reliable estimation of DKI parameters in terms of Mean Square Error (MSE), Bias and standard deviation (Std). The result of the comparison based on the phantom was consistent with those based on real datasets.
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50
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A Non-Local Means Filtering Algorithm for Restoration of Rician Distributed MRI. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2015. [DOI: 10.1007/978-3-319-13731-5_1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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