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Scan-rescan repeatability of axonal imaging metrics using high-gradient diffusion MRI and statistical implications for study design.

ABSTRACT: Axon diameter mapping using diffusion MRI in the living human brain has attracted growing interests with the increasing availability of high gradient strength MRI systems. A systematic assessment of the consistency of axon diameter estimates within and between individuals is needed to gain a comprehensive understanding of how such methods extend to quantifying differences in axon diameter index between groups and facilitate the design of neurobiological studies using such measures. We examined the scan-rescan repeatability of axon diameter index estimation based on the spherical mean technique (SMT) approach using diffusion MRI data acquired with gradient strengths up to 300 mT/m on a 3T Connectom system in 7 healthy volunteers. We performed statistical power analyses using data acquired with the same protocol in a larger cohort consisting of 15 healthy adults to investigate the implications for study design. Results revealed a high degree of repeatability in voxel-wise restricted volume fraction estimates and tract-wise estimates of axon diameter index derived from high-gradient diffusion MRI data. On the region of interest (ROI) level, across white matter tracts in the whole brain, the Pearson's correlation coefficient of the axon diameter index estimated between scan and rescan experiments was r = 0.72 with an absolute deviation of 0.18 μm. For an anticipated 10% effect size in studies of axon diameter index, most white matter regions required a sample size of less than 15 people to observe a measurable difference between groups using an ROI-based approach. To facilitate the use of high-gradient strength diffusion MRI data for neuroscientific studies of axonal microstructure, the comprehensive multi-gradient strength, multi-diffusion time data used in this work will be made publicly available, in support of open science and increasing the accessibility of such data to the greater scientific community.

SUBMITTER: Fan Q

PROVIDER: S-EPMC8646020 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeTo determine the influence of preprocessing on the repeatability and redundancy of radiomics features extracted using a popular open-source radiomics software package in a scan-rescan glioblastoma MRI study.Materials and methodsIn this study, a secondary analysis of T2-weighted fluid-attenuated inversion recovery (FLAIR) and T1-weighted postcontrast images from 48 patients (mean age, 56 years [range, 22-77 years]) diagnosed with glioblastoma were included from two prospective studies (ClinicalTrials.gov NCT00662506 [2009-2011] and NCT00756106 [2008-2011]). All patients underwent two baseline scans 2-6 days apart using identical imaging protocols on 3-T MRI systems. No treatment occurred between scan and rescan, and tumors were essentially unchanged visually. Radiomic features were extracted by using PyRadiomics (https://pyradiomics.readthedocs.io/) under varying conditions, including normalization strategies and intensity quantization. Subsequently, intraclass correlation coefficients were determined between feature values of the scan and rescan.ResultsShape features showed a higher repeatability than intensity (adjusted P < .001) and texture features (adjusted P < .001) for both T2-weighted FLAIR and T1-weighted postcontrast images. Normalization improved the overlap between the region of interest intensity histograms of scan and rescan (adjusted P < .001 for both T2-weighted FLAIR and T1-weighted postcontrast images), except in scans where brain extraction fails. As such, normalization significantly improves the repeatability of intensity features from T2-weighted FLAIR scans (adjusted P = .003 [z score normalization] and adjusted P = .002 [histogram matching]). The use of a relative intensity binning strategy as opposed to default absolute intensity binning reduces correlation between gray-level co-occurrence matrix features after normalization.ConclusionBoth normalization and intensity quantization have an effect on the level of repeatability and redundancy of features, emphasizing the importance of both accurate reporting of methodology in radiomics articles and understanding the limitations of choices made in pipeline design. Supplemental material is available for this article. © RSNA, 2020See also the commentary by Tiwari and Verma in this issue.

Project description:BackgroundPrevious quantitative synthetic MRI of the brain has been solely performed in 2D.PurposeTo evaluate the feasibility of the recently developed sequence 3D-QALAS for brain cortical thickness and volumetric analysis.Study typeReproducibility/repeatability study.SubjectsTwenty-one healthy volunteers (35.6 ± 13.8 years).Field strength/sequence3D T1 -weighted fast spoiled gradient recalled echo (FSPGR) sequence was performed once, and 3D-QALAS sequence was performed twice with a 3T scanner.AssessmentFreeSurfer and FIRST were used to measure cortical thickness and volume of subcortical structures, respectively. Agreement with FSPGR and scan-rescan repeatability were evaluated for 3D-QALAS.Statistical testsPercent relative difference and intraclass correlation coefficient (ICC) were used to assess reproducibility and scan-rescan repeatability of the 3D-QALAS sequence-derived measurements.ResultsPercent relative difference compared with FSPGR in cortical thickness of the whole cortex was 3.1%, and 89% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.65, and 74% of the structures showed substantial to almost perfect agreement. For volumes of subcortical structures, the median percent relative differences were lower than 10% across all subcortical structures, except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement. For the scan-rescan test, percent relative difference in cortical thickness of the whole cortex was 2.3%, and 97% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.73, and 80% showed substantial to almost perfect agreement. For volumes of subcortical structures, relative differences were less than 10% across all subcortical structures except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement.Data conclusion3D-QALAS could be reliably used for measuring cortical thickness and subcortical volumes in most brain regions.Level of evidence3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1834-1842.

Project description:Cerebral white matter exhibits age-related degenerative changes during the course of normal aging, including decreases in axon density and alterations in axonal structure. Noninvasive approaches to measure these microstructural alterations throughout the lifespan would be invaluable for understanding the substrate and regional variability of age-related white matter degeneration. Recent advances in diffusion magnetic resonance imaging (MRI) have leveraged high gradient strengths to increase sensitivity toward axonal size and density in the living human brain. Here, we examined the relationship between age and indices of axon diameter and packing density using high-gradient strength diffusion MRI in 36 healthy adults (aged 22-72) in well-defined central white matter tracts in the brain. A recently validated method for inferring the effective axonal compartment size and packing density from diffusion MRI measurements acquired with 300 mT/m maximum gradient strength was applied to the in vivo human brain to obtain indices of axon diameter and density in the corpus callosum, its sub-regions, and adjacent anterior and posterior fibers in the forceps minor and forceps major. The relationships between the axonal metrics, corpus callosum area and regional gray matter volume were also explored. Results revealed a significant increase in axon diameter index with advancing age in the whole corpus callosum. Similar analyses in sub-regions of the corpus callosum showed that age-related alterations in axon diameter index and axon density were most pronounced in the genu of the corpus callosum and relatively absent in the splenium, in keeping with findings from previous histological studies. The significance of these correlations was mirrored in the forceps minor and forceps major, consistent with previously reported decreases in FA in the forceps minor but not in the forceps major with age. Alterations in the axonal imaging metrics paralleled decreases in corpus callosum area and regional gray matter volume with age. Among older adults, results from cognitive testing suggested an association between larger effective compartment size in the corpus callosum, particularly within the genu of the corpus callosum, and lower scores on the Montreal Cognitive Assessment, largely driven by deficits in short-term memory. The current study suggests that high-gradient diffusion MRI may be sensitive to the axonal substrate of age-related white matter degeneration reflected in traditional DTI metrics and provides further evidence for regionally selective alterations in white matter microstructure with advancing age.

Project description:BACKGROUND:MRI of hyperpolarized 129 Xenon (HP 129 Xe) is increasingly utilized for investigating pulmonary function. The solubility of HP 129 Xe in lung tissue, blood plasma (Barrier), and red blood cells (RBC), with unique chemical shifts, enables spectroscopic imaging of potential imaging biomarkers of gas exchange and microstructural pulmonary physiology. PURPOSE:To quantify global average and regional repeatability of Barrier:gas, RBC:gas, and RBC:Barrier ratios derived from dissolved-phase 129 Xe imaging and their dependence on intervisit changes in lung inflation volume. STUDY TYPE:Prospective. POPULATION:Fourteen healthy volunteers. One subject was unable to complete the study resulting in 13 subjects for analysis (eight female, five male, ages 24-69, 53.8 ± 13.9). FIELD STRENGTH:1.5T. ASSESSMENT:Subjects were imaged using a 3D radial 1-point Dixon method to separate Barrier and RBC component signals, at two different timepoints, with ~1 month between visits. RBC:Gas, Barrier:Gas, and RBC:Barrier measures were compared across time and with pulmonary function tests (PFTs). STATISTICAL TESTS:Repeatablilty was quantified using Bland-Altman plots, coefficient of repeatability, coefficient of variation (CV), and intraclass correlation coefficients (ICCs). Dependence of imaging measures on PFTs and lung volume was evaluated using Spearman and Pearson correlation coefficients, respectively. Statistical significance was determined by F-test for intraclass correlations, and t-test for Spearman correlations and regression. RESULTS:Mean RBC:Gas, Barrier:Gas, and RBC:Barrier had CVs of 19.2%, 20.0%, and 11.5%, respectively, and had significant ICCs, equal to 0.78, 0.79, and 0.92, respectively. Intervisit differences in RBC:Barrier were significantly correlated with intervisit differences in DLCO (r = 0.93, P = 0.007). Significant correlations with intervisit lung volume differences and intervisit differences in mean RBC:Gas (r = -0.73, P = 0.005) and Barrier:Gas (r = -0.69, P = 0.009) were found. DATA CONCLUSION:Three commonly used 129 Xe MRI-based measures of gas-exchange show good repeatability, particularly the Barrier:RBC ratio, which did not depend on lung inflation volume and was strongly associated with intervisit changes in DLCO . LEVEL OF EVIDENCE:1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1182-1190.

Project description:Monitoring time dependence with diffusion MRI yields observables sensitive to compartment sizes (restricted diffusion) and membrane permeability (water exchange). However, restricted diffusion and exchange have opposite effects on the diffusion-weighted signal, which can lead to errors in parameter estimates. In this work, we propose a signal representation that incorporates the effects of both restricted diffusion and exchange up to second order in b-value and is compatible with gradient waveforms of arbitrary shape. The representation features mappings from a gradient waveform to two scalars that separately control the sensitivity to restriction and exchange. We demonstrate that these scalars span a two-dimensional space that can be used to choose waveforms that selectively probe restricted diffusion or exchange, eliminating the correlation between the two phenomena. We found that waveforms with specific but unconventional shapes provide an advantage over conventional pulsed and oscillating gradient acquisitions. We also show that parametrization of waveforms into a two-dimensional space can be used to understand protocols from other approaches that probe restricted diffusion and exchange. For example, we found that the variation of mixing time in filter-exchange imaging corresponds to variation of our exchange-weighting scalar at a fixed value of the restriction-weighting scalar. The proposed signal representation was evaluated using Monte Carlo simulations in identical parallel cylinders with hexagonal and random packing as well as parallel cylinders with gamma-distributed radii. Results showed that the approach is sensitive to sizes in the interval 4-12 μm and exchange rates in the simulated range of 0 to 20 s-1 , but also that there is a sensitivity to the extracellular geometry. The presented theory constitutes a simple and intuitive description of how restricted diffusion and exchange influence the signal as well as a guide to protocol design capable of separating the two effects.

Project description:Development of diffusion MRI (dMRI) denoising approaches has experienced considerable growth over the last years. As noise can inherently reduce accuracy and precision in measurements, its effects have been well characterised both in terms of uncertainty increase in dMRI-derived features and in terms of biases caused by the noise floor, the smallest measurable signal given the noise level. However, gaps in our knowledge still exist in objectively characterising dMRI denoising approaches in terms of both of these effects and assessing their efficacy. In this work, we reconsider what a denoising method should and should not do and we accordingly define criteria to characterise the performance. We propose a comprehensive set of evaluations, including i) benefits in improving signal quality and reducing noise variance, ii) gains in reducing biases and the noise floor and improving, iii) preservation of spatial resolution, iv) agreement of denoised data against a gold standard, v) gains in downstream parameter estimation (precision and accuracy), vi) efficacy in enabling noise-prone applications, such as ultra-high-resolution imaging. We further provide newly acquired complex datasets (magnitude and phase) with multiple repeats that sample different SNR regimes to highlight performance differences under different scenarios. Without loss of generality, we subsequently apply a number of exemplar patch-based denoising algorithms to these datasets, including Non-Local Means, Marchenko-Pastur PCA (MPPCA) in the magnitude and complex domain and NORDIC, and compare them with respect to the above criteria and against a gold standard complex average of multiple repeats. We demonstrate that all tested denoising approaches reduce noise-related variance, but not always biases from the elevated noise floor. They all induce a spatial resolution penalty, but its extent can vary depending on the method and the implementation. Some denoising approaches agree with the gold standard more than others and we demonstrate challenges in even defining such a standard. Overall, we show that dMRI denoising performed in the complex domain is advantageous to magnitude domain denoising with respect to all the above criteria.

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Scan-rescan repeatability of axonal imaging metrics using high-gradient diffusion MRI and statistical implications for study design.

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