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The variability of MR axon radii estimates in the human white matter

Veraart, Jelle; Raven, Erika P; Edwards, Luke J; Weiskopf, Nikolaus; Jones, Derek K
The noninvasive quantification of axonal morphology is an exciting avenue for gaining understanding of the function and structure of the central nervous system. Accurate non-invasive mapping of micron-sized axon radii using commonly applied neuroimaging techniques, that is, diffusion-weighted MRI, has been bolstered by recent hardware developments, specifically MR gradient design. Here the whole brain characterization of the effective MR axon radius is presented and the inter- and intra-scanner test-retest repeatability and reproducibility are evaluated to promote the further development of the effective MR axon radius as a neuroimaging biomarker. A coefficient-of-variability of approximately 10% in the voxelwise estimation of the effective MR radius is observed in the test-retest analysis, but it is shown that the performance can be improved fourfold using a customized along-tract analysis.
PMID: 33576105
ISSN: 1097-0193
CID: 4780142

Improved Task-based Functional MRI Language Mapping in Patients with Brain Tumors through Marchenko-Pastur Principal Component Analysis Denoising

Ades-Aron, Benjamin; Lemberskiy, Gregory; Veraart, Jelle; Golfinos, John; Fieremans, Els; Novikov, Dmitry S; Shepherd, Timothy
Background Functional MRI improves preoperative planning in patients with brain tumors, but task-correlated signal intensity changes are only 2%-3% above baseline. This makes accurate functional mapping challenging. Marchenko-Pastur principal component analysis (MP-PCA) provides a novel strategy to separate functional MRI signal from noise without requiring user input or prior data representation. Purpose To determine whether MP-PCA denoising improves activation magnitude for task-based functional MRI language mapping in patients with brain tumors. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant study, MP-PCA performance was first evaluated by using simulated functional MRI data with a known ground truth. Right-handed, left-language-dominant patients with brain tumors who successfully performed verb generation, sentence completion, and finger tapping functional MRI tasks were retrospectively identified between January 2017 and August 2018. On the group level, for each task, histograms of z scores for original and MP-PCA denoised data were extracted from relevant regions and contralateral homologs were seeded by a neuroradiologist blinded to functional MRI findings. Z scores were compared with paired two-sided t tests, and distributions were compared with effect size measurements and the Kolmogorov-Smirnov test. The number of voxels with a z score greater than 3 was used to measure task sensitivity relative to task duration. Results Twenty-three patients (mean age ± standard deviation, 43 years ± 18; 13 women) were evaluated. MP-PCA denoising led to a higher median z score of task-based functional MRI voxel activation in left hemisphere cortical regions for verb generation (from 3.8 ± 1.0 to 4.5 ± 1.4; P < .001), sentence completion (from 3.7 ± 1.0 to 4.3 ± 1.4; P < .001), and finger tapping (from 6.9 ± 2.4 to 7.9 ± 2.9; P < .001). Median z scores did not improve in contralateral homolog regions for verb generation (from -2.7 ± 0.54 to -2.5 ± 0.40; P = .90), sentence completion (from -2.3 ± 0.21 to -2.4 ± 0.37; P = .39), or finger tapping (from -2.3 ± 1.20 to -2.7 ± 1.40; P = .07). Individual functional MRI task durations could be truncated by at least 40% after MP-PCA without degradation of clinically relevant correlations between functional cortex and functional MRI tasks. Conclusion Denoising with Marchenko-Pastur principal component analysis led to higher task correlations in relevant cortical regions during functional MRI language mapping in patients with brain tumors. © RSNA, 2020 Online supplemental material is available for this article.
PMID: 33289611
ISSN: 1527-1315
CID: 4708782

Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: algorithms and results

Ning, Lipeng; Bonet-Carne, Elisenda; Grussu, Francesco; Sepehrband, Farshid; Kaden, Enrico; Veraart, Jelle; Blumberg, Stefano B; Khoo, Can Son; Palombo, Marco; Kokkinos, Iasonas; Alexander, Daniel C; Coll-Font, Jaume; Scherrer, Benoit; Warfield, Simon K; Karayumak, Suheyla Cetin; Rathi, Yogesh; Koppers, Simon; Weninger, Leon; Ebert, Julia; Merhof, Dorit; Moyer, Daniel; Pietsch, Maximilian; Christiaens, Daan; Gomes Teixeira, Rui Azeredo; Tournier, Jacques-Donald; Schilling, Kurt G; Huo, Yuankai; Nath, Vishwesh; Hansen, Colin; Blaber, Justin; Landman, Bennett A; Zhylka, Andrey; Pluim, Josien; Parker, Greg; Rudrapatna, Umesh; Evans, John; Charron, Cyril; Jones, Derek K; Tax, Chantal M W
Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging (dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the ability to aggregate dMRI data and derived measures. Computational algorithms that harmonize the data and minimize such variability are critical to reliably combine datasets acquired from different scanners and/or protocols, thus improving the statistical power and sensitivity of multi-site studies. Different computational approaches have been proposed to harmonize diffusion MRI data or remove scanner-specific differences. To date, these methods have mostly been developed for or evaluated on single b-value diffusion MRI data. In this work, we present the evaluation results of 19 algorithms that are developed to harmonize the cross-scanner and cross-protocol variability of multi-shell diffusion MRI using a benchmark database. The proposed algorithms rely on various signal representation approaches and computational tools, such as rotational invariant spherical harmonics, deep neural networks and hybrid biophysical and statistical approaches. The benchmark database consists of data acquired from the same subjects on two scanners with different maximum gradient strength (80 and 300 mT/m) and with two protocols. We evaluated the performance of these algorithms for mapping multi-shell diffusion MRI data across scanners and across protocols using several state-of-the-art imaging measures. The results show that data harmonization algorithms can reduce the cross-scanner and cross-protocol variabilities to a similar level as scan-rescan variability using the same scanner and protocol. In particular, the LinearRISH algorithm based on adaptive linear mapping of rotational invariant spherical harmonics features yields the lowest variability for our data in predicting the fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK) and the rotationally invariant spherical harmonic (RISH) features. But other algorithms, such as DIAMOND, SHResNet, DIQT, CMResNet show further improvement in harmonizing the return-to-origin probability (RTOP). The performance of different approaches provides useful guidelines on data harmonization in future multi-site studies.
PMID: 32673745
ISSN: 1095-9572
CID: 4528402

The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis

Abdalla, Gehad; Dixon, Luke; Sanverdi, Eser; Machado, Pedro M; Kwong, Joey S W; Panovska-Griffiths, Jasmina; Rojas-Garcia, Antonio; Yoneoka, Daisuke; Veraart, Jelle; Van Cauter, Sofie; Abdel-Khalek, Ahmed M; Settein, Magdy; Yousry, Tarek; Bisdas, Sotirios
PURPOSE/OBJECTIVE:We aim to illustrate the diagnostic performance of diffusional kurtosis imaging (DKI) in the diagnosis of gliomas. METHODS:A review protocol was developed according to the (PRISMA-P) checklist, registered in the international prospective register of systematic reviews (PROSPERO) and published. A literature search in 4 databases was performed using the keywords 'glioma' and 'diffusional kurtosis'. After applying a robust inclusion/exclusion criteria, included articles were independently evaluated according to the QUADAS-2 tool and data extraction was done. Reported sensitivities and specificities were used to construct 2 × 2 tables and paired forest plots using the Review Manager (RevMan®) software. A random-effect model was pursued using the hierarchical summary receiver operator characteristics. RESULTS: = 73.8%). CONCLUSION/CONCLUSIONS:DKI shows good diagnostic accuracy in the differentiation of high- and low-grade gliomas further supporting its potential role in clinical practice. Further exploration of DKI in differentiating IDH status and in characterising non-glioma CNS tumours is however needed.
PMID: 32367349
ISSN: 1432-1920
CID: 4430012

Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising

Grussu, Francesco; Battiston, Marco; Veraart, Jelle; Schneider, Torben; Cohen-Adad, Julien; Shepherd, Timothy M; Alexander, Daniel C; Fieremans, Els; Novikov, Dmitry S; Gandini Wheeler-Kingshott, Claudia A M
Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.
PMID: 32360689
ISSN: 1095-9572
CID: 4429722

Noninvasive quantification of axon radii using diffusion MRI

Veraart, Jelle; Nunes, Daniel; Rudrapatna, Umesh; Fieremans, Els; Jones, Derek K; Novikov, Dmitry S; Shemesh, Noam
Axon caliber plays a crucial role in determining conduction velocity and, consequently, in the timing and synchronization of neural activation. Noninvasive measurement of axon radii could have significant impact on the understanding of healthy and diseased neural processes. Until now, accurate axon radius mapping has eluded in vivo neuroimaging, mainly due to a lack of sensitivity of the MRI signal to micron-sized axons. Here, we show how - when confounding factors such as extra-axonal water and axonal orientation dispersion are eliminated - heavily diffusion-weighted MRI signals become sensitive to axon radii. However, diffusion MRI is only capable of estimating a single metric, the effective radius, representing the entire axon radius distribution within a voxel that emphasizes the larger axons. Our findings, both in rodents and humans, enable noninvasive mapping of critical information on axon radii, as well as resolve the long-standing debate on whether axon radii can be quantified.
PMCID:7015669
PMID: 32048987
ISSN: 2050-084x
CID: 4304432

Multi -parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising

Grussu, Francesco; Battiston, Marco; Veraart, Jelle; Schneider, Torben; Cohen-Adad, Julien; Shepherd, Timothy M.; Alexander, Daniel C.; Fieremans, Els; Novikov, Dmitry S.; Wheeler-Kingshott, Claudia A. M. Gandini
ISI:000542370300008
ISSN: 1053-8119
CID: 4525782

Chapter 11: Model-based Analysis of Advanced Diffusion Data

Veraart, J; Lemberskiy, G; Baete, S; Novikov, D S; Fieremans, E
The diagnosis of various disorders is hindered by the lack of an imaging technique that reveals the architecture of living tissue at the fine resolution of the associated pathological processes. Indeed, even the most powerful imaging techniques such as MRI can only resolve or visualize biological tissue down to the scale of a cubic millimetre. However, MRI may be able to reveal what happens on a much finer scale, as it is sensitive to the random thermal motion of water molecules and, more importantly, their interactions with surrounding cells constituting the microstructure of the tissue. The gap between being sensitive and specific is bridged by the development of a tissue model that decomposes the MRI signal into components that probe relevant features of the underlying microstructure, typically affected by pathology. Hence, biophysical modelling is potentially a diagnostic tool that allows scientists to identify problems that arise in the unexplored depths of our organs, driving forward treatment and understanding of disease progression. In this chapter, we will introduce the main concepts of multiparametric modelling, lay out a general framework of multi-compartmental models, and discuss limitations and challenges.
Copyright
EMBASE:633348060
ISSN: 2044-253x
CID: 4666312

On the need for bundle-specific microstructure kernels in diffusion MRI

Christiaens, Daan; Veraart, Jelle; Cordero-Grande, Lucilio; Price, Anthony N; Hutter, Jana; Hajnal, Joseph V; Tournier, J-Donald
Probing microstructure with diffusion magnetic resonance imaging (dMRI) on a scale orders of magnitude below the imaging resolution relies on biophysical modelling of the signal response in the tissue. The vast majority of these biophysical models of diffusion in white matter assume that the measured dMRI signal is the sum of the signals emanating from each of the constituent compartments, each of which exhibits a distinct behaviour in the b-value and/or orientation domain. Many of these models further assume that the dMRI behaviour of the oriented compartments (e.g. the intra-axonal space) is identical between distinct fibre populations, at least at the level of a single voxel. This implicitly assumes that any potential biological differences between fibre populations are negligible, at least as far as is measurable using dMRI. Here, we validate this assumption by means of a voxel-wise, model-free signal decomposition that, under the assumption above and in the absence of noise, is shown to be rank-1. We evaluate the effect size of signal components beyond this rank-1 representation and use permutation testing to assess their significance. We conclude that in the healthy adult brain, the dMRI signal is adequately represented by a rank-1 model, implying that biologically more realistic, but mathematically more complex fascicle-specific microstructure models do not capture statistically significant or anatomically meaningful structure, even in extended high-b diffusion MRI scans.
PMID: 31843710
ISSN: 1095-9572
CID: 4242312

A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol

Adhikari, Bhim M; Jahanshad, Neda; Shukla, Dinesh; Turner, Jessica; Grotegerd, Dominik; Dannlowski, Udo; Kugel, Harald; Engelen, Jennifer; Dietsche, Bruno; Krug, Axel; Kircher, Tilo; Fieremans, Els; Veraart, Jelle; Novikov, Dmitry S; Boedhoe, Premika S W; van der Werf, Ysbrand D; van den Heuvel, Odile A; Ipser, Jonathan; Uhlmann, Anne; Stein, Dan J; Dickie, Erin; Voineskos, Aristotle N; Malhotra, Anil K; Pizzagalli, Fabrizio; Calhoun, Vince D; Waller, Lea; Veer, Ilja M; Walter, Hernik; Buchanan, Robert W; Glahn, David C; Hong, L Elliot; Thompson, Paul M; Kochunov, Peter
Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.
PMID: 30191514
ISSN: 1931-7565
CID: 3271572