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Cai LY, Tanase C, Anderson AW, Ramadass K, Rheault F, Lee CA, Patel NJ, Jones S, LeStourgeon LM, Mahon A, Pruthi S, Gwal K, Ozturk A, Kang H, Glaser N, Ghetti S, Jaser SS, Jordan LC, Landman BA. Multimodal neuroimaging in pediatric type 1 diabetes: a pilot multisite feasibility study of acquisition quality, motion, and variability. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12032:120323U. [PMID: 36303580 PMCID: PMC9604061 DOI: 10.1117/12.2611553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Type 1 diabetes (T1D) affects over 200,000 children and is associated with an increased risk of cognitive dysfunction. Prior imaging studies suggest the neurological changes underlying this risk are multifactorial, including macrostructural, microstructural, and inflammatory changes. However, these studies have yet to be integrated, limiting investigation into how these phenomena interact. To better understand these complex mechanisms of brain injury, a well-powered, prospective, multisite, and multimodal neuroimaging study is needed. We take the first step in accomplishing this with a preliminary characterization of multisite, multimodal MRI quality, motion, and variability in pediatric T1D. We acquire structural T1 weighted (T1w) MRI, diffusion tensor MRI (DTI), functional MRI (fMRI), and magnetic resonance spectroscopy (MRS) of 5-7 participants from each of two sites. First, we assess the contrast-to-noise ratio of the T1w MRI and find no differences between sites. Second, we characterize intervolume motion in DTI and fMRI and find it to be on the subvoxel level. Third, we investigate variability in regional gray matter volumes and local gyrification indices, bundle-wise DTI microstructural measures, and N-acetylaspartate to creatine ratios. We find the T1-based measures to be comparable between sites before harmonization and the DTI and MRS-based measures to be comparable after. We find a 5-15% coefficient of variation for most measures, suggesting ~150-200 participants per group on average are needed to detect a 5% difference across these modalities at 0.9 power. We conclude that multisite, multimodal neuroimaging of pediatric T1D is feasible with low motion artifact after harmonization of DTI and MRS.
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Affiliation(s)
- Leon Y. Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Costin Tanase
- Department of Psychiatry, University of California, Davis, Davis, CA, USA
| | - Adam W. Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA,Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Francois Rheault
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Chelsea A. Lee
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Niral J. Patel
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sky Jones
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Alix Mahon
- Department of Psychology, University of California, Davis, Davis, CA, USA
| | - Sumit Pruthi
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kriti Gwal
- Department of Radiology, UC Davis Health, UC Davis School of Medicine, Sacramento, CA, USA
| | - Arzu Ozturk
- Department of Radiology, UC Davis Health, UC Davis School of Medicine, Sacramento, CA, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nicole Glaser
- Department of Pediatrics, UC Davis Health, UC Davis School of Medicine, Sacramento, CA, USA
| | - Simona Ghetti
- Department of Psychology, University of California, Davis, Davis, CA, USA
| | - Sarah S. Jaser
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lori C. Jordan
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A. Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA,Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA,Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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2
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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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Nath V, Schilling KG, Remedios S, Bayrak RG, Gao Y, Blaber JA, Huo Y, Landman BA, Anderson AW. LEARNING 3D WHITE MATTER MICROSTRUCTURE FROM 2D HISTOLOGY. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2019; 2019:186-190. [PMID: 32211122 PMCID: PMC7092618 DOI: 10.1109/isbi.2019.8759388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Histological analysis is typically the gold standard for validating measures of tissue microstructure derived from magnetic resonance imaging (MRI) contrasts. However, most histological investigations are inherently 2-dimensional (2D), due to increased field-of-view, higher in-plane resolutions, ease of acquisition, decreased costs, and a large number of available contrasts compared to 3-dimensional (3D) analysis. Because of this, it would be of great interest to be able to learn the 3D tissue microstructure from 2D histology. In this study, we use diffusion MRI (dMRI) of a squirrel monkey brain and corresponding myelin stained sections in combination with a convolution neural network to learn the relationship between the 3D diffusion estimated axonal fiber orientation distributions and the 2D myelin stain. We find that we are able to estimate the 3D fiber distribution with moderate to high angular agreement with the ground truth (median angular correlation coefficients of 0.48 across the unseen slices). This network could be used to validate dMRI neuronal structural measurements in 3D, even if only 2D histology is available for validation. Generalization is possible to transfer this network to human stained sections to infer the 3D fiber distribution at resolutions currently unachievable with dMRI, which would allow diffusion fiber tractography at unprecedented resolutions. We envision the use of similar networks to learn other 3D microstructural measures from an array of potential common 2D histology contrasts.
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Affiliation(s)
- Vishwesh Nath
- Department of Computer Science, Vanderbilt University, Nashville, TN
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN
| | - Samuel Remedios
- Department of Computer Science, Vanderbilt University, Nashville, TN
| | - Roza G Bayrak
- Department of Computer Science, Vanderbilt University, Nashville, TN
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN
| | - Justin A Blaber
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN
| | - Yuankai Huo
- Department of Computer Science, Vanderbilt University, Nashville, TN
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN
- Department of Computer Science, Vanderbilt University, Nashville, TN
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN
| | - A W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN
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Huo Y, Terry JG, Wang J, Nath V, Bermudez C, Bao S, Parvathaneni P, Carr JJ, Landman BA. Coronary Calcium Detection using 3D Attention Identical Dual Deep Network Based on Weakly Supervised Learning. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2019; 10949:1094917. [PMID: 31762534 PMCID: PMC6874228 DOI: 10.1117/12.2512541] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Coronary artery calcium (CAC) is biomarker of advanced subclinical coronary artery disease and predicts myocardial infarction and death prior to age 60 years. The slice-wise manual delineation has been regarded as the gold standard of coronary calcium detection. However, manual efforts are time and resource consuming and even impracticable to be applied on large-scale cohorts. In this paper, we propose the attention identical dual network (AID-Net) to perform CAC detection using scan-rescan longitudinal non-contrast CT scans with weakly supervised attention by only using per scan level labels. To leverage the performance, 3D attention mechanisms were integrated into the AID-Net to provide complementary information for classification tasks. Moreover, the 3D Gradient-weighted Class Activation Mapping (Grad-CAM) was also proposed at the testing stage to interpret the behaviors of the deep neural network. 5075 non-contrast chest CT scans were used as training, validation and testing datasets. Baseline performance was assessed on the same cohort. From the results, the proposed AID-Net achieved the superior performance on classification accuracy (0.9272) and AUC (0.9627).
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Affiliation(s)
- Yuankai Huo
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA
| | - James G Terry
- Departments of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Jiachen Wang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA
| | - Vishwesh Nath
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA
| | - Camilo Bermudez
- Department of Biomedical Engineering, Vanderbilt University, Nashville, USA
| | - Shunxing Bao
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA
| | - Prasanna Parvathaneni
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA
| | - J Jeffery Carr
- Departments of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, USA
- Department of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, USA
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA
- Departments of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, USA
- Institute of Imaging Science, Vanderbilt University, Nashville, USA
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