|
|
|
| The Test-Retest Assessment of Apparent Fiber Density Measurements Using Diffusion Weighted Imaging in English |
| Luo Yichao1,2, Yan Jingguo1,2, Chen Yuanyuan2,3, Fan Qiuyun1,2,3* |
1(School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China)
2(Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin 300072, China)
3(Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China) |
|
|
|
|
Abstract Diffusion weighted imaging has been widely used in quantitative study of human brain white matter. Apparent fiber density (AFD) is a quantitative metric based on the fiber orientation distribution (FOD), which can reflect the fiber density of white matter and has been applied in the research of healthy development and neurodegenerative diseases. However, the value of AFD measurement strongly depends on experimental set-up, and the test-retest repeatability of AFD under different experimental conditions has not been systematically assessed. In this paper, the test-retest repeatability of AFD was examined at different diffusion weighted values (b-values) using the scan-rescan data of 42 subjects from two separate datasets. One dataset (n=35) is from Human Connectome Project (HCP), the other (n=7) is form Connectome Diffusion Microstructure Dataset (CDMD). The AFD value of fixel level was estimated using fixel-based analysis (FBA), and tract ROIs were obtained using TractSeg to estimate the fiber bundle averaged AFD. Results showed that at the fixel level, as the b-value increased, the Pearson’s correlation coefficient (r) and intraclass correlation coefficient (ICC) increased from 0.796 9 and 0.898 5 to 0.882 8 and 0.941 4 respectively in the test-retest experiment with HCP, and the absolute deviation decreased from 0.124 1 to 0.107 3. Similar trend was also found in the dataset from CDMD. At the tract-average level, the test-retest repeatability can be achieved at all b-values in both datasets. In summary, this paper examined the repeatability of AFD estimation for b-values in the range of 1 000~5 000 s·mm-2, and our results might provide useful insights into the experimental design in future neurobiological investigations using AFD.
|
|
Received: 10 March 2023
|
|
|
|
Corresponding Authors:
*E-mail: fanqiuyun@tju.edu.cn
|
|
|
|
[1] Anderson AW. Measurement of fiber orientation distributions using high angular resolution diffusion imaging [J]. Magn Reson Med, 2005, 54(5): 1194-1206.
[2] Tournier JD, Calamante F, Gadian DG, et al. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution [J]. Neuroimage, 2004, 23(3): 1176-1185.
[3] Raffelt D, Tournier JD, Rose S, et al. Apparent Fibre Density: a novel measure for the analysis of diffusion-weighted magnetic resonance images [J]. NeuroImage, 2012, 59(4): 3976-3994.
[4] Raffelt DA, Smith RE, Ridgway GR, et al. Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres [J]. NeuroImage, 2015, 117: 40-55.
[5] Raffelt DA, Tournier JD, Smith RE, et al. Investigating white matter fibre density and morphology using fixel-based analysis [J]. NeuroImage, 2017, 144: 58-73.
[6] Choy SW, Bagarinao E, Watanabe H, et al. Changes in white matter fiber density and morphology across the adult lifespan: A cross-sectional fixel-based analysis [J]. Human Brain Mapping, 2020, 41(12): 3198-3211.
[7] Genc S, Seal ML, Dhollander T, et al. White matter alterations at pubertal onset [J]. NeuroImage, 2017, 156: 286-292.
[8] Genc S, Smith RE, Malpas CB, et al. Development of white matter fibre density and morphology over childhood: A longitudinal fixel-based analysis [J]. Neuroimage, 2018, 183: 666-676.
[9] Wu D, Lei J, Xie H, et al. Diffusion MRI revealed altered inter-hippocampal projections in the mouse brain after intrauterine inflammation [J]. Brain Imaging and Behavior, 2020, 14(2): 383-395.
[10] Lyon M, Welton T, Varda A, et al. Gender-specific structural abnormalities in major depressive disorder revealed by fixel-based analysis [J]. NeuroImage: Clinical, 2019, 21.
[11] Carandini T, Mancini M, Bogdan I, et al. Disruption of brainstem monoaminergic fibre tracts in multiple sclerosis as a putative mechanism for cognitive fatigue: a fixel-based analysis [J]. NeuroImage: Clinical, 2021, 30.
[12] Luo X, Wang S, Jiaerken Y, et al. Distinct fiber-specific white matter reductions pattern in early- and late-onset Alzheimer′s disease [J]. Aging, 2021, 13(9): 12410-12430.
[13] Bauer T, Ernst L, David B, et al. Fixel-based analysis links white matter characteristics, serostatus and clinical features in limbic encephalitis [J]. NeuroImage: Clinical, 2020, 27.
[14] Fekonja LS, Wang Z, Aydogan DB, et al. Detecting corticospinal tract impairment in tumor patients with fiber density and tensor-based metrics [J]. Frontiers in Oncology, 2021, 10.
[15] Dhollander T, Clemente A, Singh M, et al. Fixel-based analysis of diffusion mri: methods, applications, challenges and opportunities [J]. Neuroimage, 2021, 241: 118417.
[16] Genc S, Tax CMW, Raven EP, et al. Impact of b-value on estimates of apparent fibre density [J]. Human Brain Mapping, 2020, 41(10): 2583-2595.
[17] Mckinnon ET, Jensen JH, Glenn GR, et al. Dependence on b-value of the direction-averaged diffusion-weighted imaging signal in brain [J]. Magnetic Resonance Imaging, 2017, 36: 121-127.
[18] Veraart J, Fieremans E, Novikov DS. On the scaling behavior of water diffusion in human brain white matter [J]. Neuroimage, 2019, 185: 379-387.
[19] Moeller S, Yacoub E, Olman CA, et al. Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain FMRI [J]. Magnetic Resonance in Medicine, 2010, 63(5): 1144-1153.
[20] Setsompop K, Gagoski BA, Polimeni JR, et al. Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty [J]. Magnetic Resonance in Medicine, 2012, 67(5): 1210-1224.
[21] Sotiropoulos SN, Jbabdi S, Xu J, et al. Advances in diffusion MRI acquisition and processing in the Human Connectome Project [J]. NeuroImage, 2013, 80: 125-143.
[22] Tian Q, Fan Q, Witzel T, et al. Comprehensive diffusion MRI dataset for in vivo human brain microstructure mapping using 300 mT/m gradients[J]. Scientific Data, 2022, 9(1): 7.
[23] Fan Q, Nummenmaa A, Polimeni JR, et al. HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging [J]. NeuroImage, 2017, 150: 162-176.
[24] Fan Q, Nummenmaa A, Witzel T, et al. Investigating the capability to resolve complex white matter structures with high b-value diffusion magnetic resonance imaging on the MGH-USC Connectom scanner [J]. Brain connectivity, 2014, 4(9): 718-726.
[25] Fan Q, Polackal MN, Tian Q, et al. Scan-rescan repeatability of axonal imaging metrics using high-gradient diffusion MRI and statistical implications for study design [J]. Neuroimage, 2021, 240: 118323.
[26] Lee HH, Yaros K, Veraart J, et al. Along-axon diameter variation and axonal orientation dispersion revealed with 3D electron microscopy: implications for quantifying brain white matter microstructure with histology and diffusion MRI [J]. Brain Structure and Function, 2019, 224(4): 1469-1488.
[27] Schilling KG, Janve V, Gao Y, et al. Histological validation of diffusion MRI fiber orientation distributions and dispersion [J]. NeuroImage, 2018, 165: 200-221.
[28] Dhollander T, Raffelt D, Connelly A. Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image[C]//ISMRM Workshcp on Breaking the Barriers of Diffusion MRI. Lisbon: ISMRM,2016:5-5.
[29] Tournier JD, Smith R, Raffelt D, et al. MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation [J]. NeuroImage, 2019, 202.
[30] Tournier JD, Calamante F, Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution [J]. Neuroimage, 2007, 35(4): 1459-1472.
[31] Jeurissen B, Tournier JD, Dhollander T, et al. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data [J]. Neuroimage, 2014, 103: 411-426.
[32] Raffelt D, Tournier JD, Crozier S, et al. Reorientation of fiber orientation distributions using apodized point spread functions [J]. Magnetic Resonance in Medicine, 2012, 67(3): 844-855.
[33] Wasserthal J, Neher P, Maier-Hein KH. TractSeg - Fast and accurate white matter tract segmentation [J]. NeuroImage, 2018, 183: 239-253.
[34] Wasserthal J, Neher PF, Hirjak D, et al. Combined tract segmentation and orientation mapping for bundle-specific tractography [J]. Medical Image Analysis, 2019, 58: 101559.
[35] Duval T, Smith V, Stikov N, et al. Scan-rescan of axcaliber, macromolecular tissue volume, and g-ratio in the spinal cord [J]. Magnetic Resonance in Medicine, 2018, 79(5): 2759-2765.
[36] Marenco S, Rawlings R, Rohde GK, et al. Regional distribution of measurement error in diffusion tensor imaging [J]. Psychiatry Research: Neuroimaging, 2006, 147(1): 69-78.
[37] Zhou X, Sakaie KE, Debbins JP, et al. Scan-rescan repeatability and cross-scanner comparability of DTI metrics in healthy subjects in the SPRINT-MS multicenter trial [J]. Magnetic Resonance Imaging, 2018, 53: 105-111.
[38] Albi A, Pasternak O, Minati L, et al. Free water elimination improves test-retest reproducibility of diffusion tensor imaging indices in the brain: a longitudinal multisite study of healthy elderly subjects [J]. Human Brain Mapping, 2017, 38(1): 12-26.
[39] Uban KA, Horton MK, Jacobus J, et al. Biospecimens and the ABCD study: Rationale, methods of collection, measurement and early data [J]. Developmental Cognitive Neuroscience, 2018, 32: 97-106.
[40] Zucker RA, Gonzalez R, Feldstein Ewing SW, et al. Assessment of culture and environment in the Adolescent Brain and Cognitive Development Study: rationale, description of measures, and early data [J]. Developmental Cognitive Neuroscience, 2018, 32: 107-120.
[41] Huang SY, Witzel T, Keil B, et al. Connectome 2.0: developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome [J]. Neuroimage, 2021, 243: 118530. |
|
|
|
