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We introduce and evaluate a post-processing technique for fast denoising of diffusion-weighted MR images. By exploiting the intrinsic redundancy in diffusion MRI using universal properties of the eigenspectrum of random covariance matrices, we remove noise-only principal components, thereby enabling signal-to-noise ratio enhancements. This yields parameter maps of improved quality for visual, quantitative, and statistical interpretation. By studying statistics of residuals, we demonstrate that the technique suppresses local signal fluctuations that solely originate from thermal noise rather than from other sources such as anatomical detail. Furthermore, we achieve improved precision in the estimation of diffusion parameters and fiber orientations in the human brain without compromising the accuracy and spatial resolution. •Denoising enhances the image quality for improved visual, quantitative, and statistical interpretation.•Random matrix theory enables data-driven threshold for PCA denoising.•The Marchenko-Pastur distribution is a universal signature of noise.•The technique suppresses signal fluctuations that solely originate in thermal noise.•Precision of diffusion parameter estimators increases without lowering accuracy.

Estimating intra- and extra-axonal microstructure parameters, such as volume fractions and diffusivities, has been one of the major efforts in brain microstructure imaging with MRI. The Standard Model (SM) of diffusion in white matter has unified various modeling approaches based on impermeable narrow cylinders embedded in locally anisotropic extra-axonal space. However, estimating the SM parameters from a set of conventional diffusion MRI (dMRI) measurements is ill-conditioned. Multidimensional dMRI helps resolve the estimation degeneracies, but there remains a need for clinically feasible acquisitions that yield robust parameter maps. Here we find optimal multidimensional protocols by minimizing the mean-squared error of machine learning-based SM parameter estimates for two 3T scanners with corresponding gradient strengths of 40and80mT/m. We assess intra-scanner and inter-scanner repeatability for 15-minute optimal protocols by scanning 20 healthy volunteers twice on both scanners. The coefficients of variation all SM parameters except free water fraction are ≲10% voxelwise and 1−4% for their region-averaged values. As the achieved SM reproducibility outcomes are similar to those of conventional diffusion tensor imaging, our results enable robust in vivo mapping of white matter microstructure in neuroscience research and in the clinic.

The clinical translation of diffusion magnetic resonance imaging (dMRI)‐derived quantitative contrasts hinges on robust reproducibility, minimizing both same‐scanner and cross‐scanner variability. As multi‐site data sets, including multi‐shell dMRI, expand in scope, enhancing reproducibility across variable MRI systems and MRI protocols becomes crucial. This study evaluates the reproducibility of diffusion kurtosis imaging (DKI) metrics (beyond conventional diffusion tensor imaging (DTI)), at the voxel and region‐of‐interest (ROI) levels on magnitude and complex‐valued dMRI data, using denoising with and without harmonization. We compared same‐scanner, cross‐scanner, and cross‐protocol variability for a multi‐shell dMRI protocol (2‐mm isotropic resolution, b = 0, 1000, 2000 s/mm2) in 20 subjects. We first evaluated the effectiveness of Marchenko‐Pastur Principal Component Analysis (MPPCA) based denoising strategies for both magnitude and complex data to mitigate noise‐induced bias and variance, to improve dMRI parametric maps and reproducibility. Next, we examined the impact of denoising under different population analysis approaches, specifically comparing voxel‐wise versus region of interest (ROI)‐based methods. We also evaluated the role of denoising when harmonizing dMRI across scanners and protocols. The results indicate that DTI and DKI maps visually improve after MPPCA denoising, with noticeably fewer outliers in kurtosis maps. Denoising, either using magnitude or complex dMRI, enhances voxel‐wise reproducibility, with test–retest variability of kurtosis indices reduced from 15%–20% without denoising to 5%–10% after denoising. Complex dMRI denoising reduces the noise floor by up to 60%. Denoising not only reduced variability across scans and protocols, but also increased statistical power for low SNR voxel‐wise comparisons when comparing cross sectional groups. In conclusion, MPPCA denoising, either over magnitude or complex dMRI data, enhances the reproducibility and precision of higher‐order diffusion metrics across same‐scanner, cross‐scanner, and cross‐protocol assessments. The enhancement in data quality and precision facilitates the broader application and acceptance of these advanced imaging techniques in both clinical practice and large‐scale neuroimaging studies. MPPCA denoising enhances the reproducibility and precision of higher‐order diffusion metrics in dMRI, by reducing variability and noise across same‐scanner, cross‐scanner, and cross‐protocol assessments. This improvement supports broader clinical application and acceptance of advanced imaging techniques.

Myelin insulates neuronal axons and enables fast signal transmission, constituting a key component of brain development, aging and disease. Yet, myelin-specific imaging of macroscopic samples remains a challenge. Here, we exploit myelin’s nanostructural periodicity, and use small-angle X-ray scattering tensor tomography (SAXS-TT) to simultaneously quantify myelin levels, nanostructural integrity and axon orientations in nervous tissue. Proof-of-principle is demonstrated in whole mouse brain, mouse spinal cord and human white and gray matter samples. Outcomes are validated by 2D/3D histology and compared to MRI measurements sensitive to myelin and axon orientations. Specificity to nanostructure is exemplified by concomitantly imaging different myelin types with distinct periodicities. Finally, we illustrate the method’s sensitivity towards myelin-related diseases by quantifying myelin alterations in dysmyelinated mouse brain. This non-destructive, stain-free molecular imaging approach enables quantitative studies of myelination within and across samples during development, aging, disease and treatment, and is applicable to other ordered biomolecules or nanostructures. Small-angle X-ray scattering (SAXS) combines the high tissue penetration of X-rays with specificity to periodic nanostructures. The authors use SAXS tensor tomography (SAXS-TT) on intact mouse and human brain tissue samples, to quantify myelin levels and determine myelin integrity, myelinated axon orientation, and fibre tracts non-destructively.

At very low diffusion weighting the diffusion MRI signal is affected by intravoxel incoherent motion (IVIM) caused by dephasing of magnetization due to incoherent blood flow in capillaries or other sources of microcirculation. While IVIM measurements at low diffusion weightings have been frequently used to investigate perfusion in the body as well as in malignant tissue, the effect and origin of IVIM in normal brain tissue is not completely established. We investigated the IVIM effect on the brain diffusion MRI signal in a cohort of 137 radiologically-normal patients (62 male; mean age = 50.2 ± 17.8, range = 18 to 94). We compared the diffusion tensor parameters estimated from a mono-exponential fit at b = 0 and 1000 s/mm2 versus at b = 250 and 1000 s/mm2. The asymptotic fitting method allowed for quantitative assessment of the IVIM signal fraction f* in specific brain tissue and regions. Our results show a mean (median) percent difference in the mean diffusivity of about 4.5 (4.9)% in white matter (WM), about 7.8 (8.7)% in cortical gray matter (GM), and 4.3 (4.2)% in thalamus. Corresponding perfusion fraction f* was estimated to be 0.033 (0.032) in WM, 0.066 (0.065) in cortical GM, and 0.033 (0.030) in the thalamus. The effect of f* with respect to age was found to be significant in cortical GM (Pearson correlation ρ ​= ​0.35, p ​= ​3*10−5) and the thalamus (Pearson correlation ρ = 0.20, p = 0.022) with an average increase in f* of 5.17*10−4/year and 3.61*10−4/year, respectively. Significant correlations between f* and age were not observed for WM, and corollary analysis revealed no effect of gender on f*. Possible origins of the IVIM effect in normal brain tissue are discussed. •Study of the effect of IVIM on the diffusion brain signal in 137 radiologically normal subjects.•Comparison of DTI parameters derived from b = 0 and 1000 s/mm2 versus from b = 250 and1000 s/mm2.•MD changes, with corresponding IVIM signal fractions, are ≈4% in white matter and ≈8% in cortical gray matter.•IVIM signal fraction increases with age in cortical gray matter and thalamus.•Possible origins of the IVIM effect in normal brain are discussed.

We asked whether pharmacological stimulation of endogenous neural precursor cells (NPCs) may promote cognitive recovery and brain repair, focusing on the drug metformin, in parallel rodent and human studies of radiation injury. In the rodent cranial radiation model, we found that metformin enhanced the recovery of NPCs in the dentate gyrus, with sex-dependent effects on neurogenesis and cognition. A pilot double-blind, placebo-controlled crossover trial was conducted (ClinicalTrials.gov, NCT02040376 ) in survivors of pediatric brain tumors who had been treated with cranial radiation. Safety, feasibility, cognitive tests and MRI measures of white matter and the hippocampus were evaluated as endpoints. Twenty-four participants consented and were randomly assigned to complete 12-week cycles of metformin (A) and placebo (B) in either an AB or BA sequence with a 10-week washout period at crossover. Blood draws were conducted to monitor safety. Feasibility was assessed as recruitment rate, medication adherence and procedural adherence. Linear mixed modeling was used to examine cognitive and MRI outcomes as a function of cycle, sequence and treatment. We found no clinically relevant safety concerns and no serious adverse events associated with metformin. Sequence effects were observed for all cognitive outcomes in our linear mixed models. For the subset of participants with complete data in cycle 1, metformin was associated with better performance than placebo on tests of declarative and working memory. We present evidence that a clinical trial examining the effects of metformin on cognition and brain structure is feasible in long-term survivors of pediatric brain tumors and that metformin is safe to use and tolerable in this population. This pilot trial was not intended to test the efficacy of metformin for cognitive recovery and brain growth, but the preliminary results are encouraging and warrant further investigation in a large multicenter phase 3 trial. A pilot clinical trial evaluating metformin in patients with pediatric brain tumors shows that it is a safe approach resulting in improved cognitive function that is consistent with the recovery of adult hippocampal neurogenesis observed in mouse models.

This work evaluates the accuracy and precision of the Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline, developed to identify and minimize common sources of methodological variability including: thermal noise, Gibbs ringing artifacts, Rician bias, EPI and eddy current induced spatial distortions, and motion-related artifacts. Following this processing pipeline, iterative parameter estimation techniques were used to derive diffusion parameters of interest based on the diffusion tensor and kurtosis tensor. We evaluated accuracy using a software phantom based on 36 diffusion datasets from the Human Connectome project and tested the precision by analyzing data from 30 healthy volunteers scanned three times within one week. Preprocessing with both DESIGNER or a standard pipeline based on smoothing (instead of noise removal) improved parameter precision by up to a factor of 2 compared to preprocessing with motion correction alone. When evaluating accuracy, we report average decreases in bias (deviation from simulated parameters) over all included regions for fractional anisotropy, mean diffusivity, mean kurtosis, and axonal water fraction of 9.7%, 8.7%, 4.2%, and 7.6% using DESIGNER compared to the standard pipeline, demonstrating that preprocessing with DESIGNER improves accuracy compared to other processing methods. •Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline.•DESIGNER removes parameter bias caused by noise, artifacts, and partial volume.•DESIGNER maximizes accuracy of diffusion parameters without lowering resolution.•DESIGNER increases precision by a factor ∼2 compared to other pipelines.•DESIGNER works optimally on noisy clinical diffusion data.

Purpose Gait improvement following high-volume lumbar puncture (HVLP) and continuous lumbar drain (cLD) is widely used to predict shunt response in patients with suspected normal pressure hydrocephalus (NPH). Here, we investigate differences in MRI volumetric and traditional measures between HVLP/cLD responders and non-responders to identify imaging features that may help predict HVLP/cLD response. Methods Eighty-two patients with suspected NPH were studied retrospectively. Gait testing was performed before and immediately/24 h/72 h after HVLP/cLD. A positive response was defined as improvement in gait post-procedure. Thirty-six responders (26 men; mean age 79.3 ± 6.3) and 46 non-responders (25 men; mean age 77.2 ± 6.1) underwent pre-procedure brain MRI including a 3D T1-weighted sequence. Subcortical regional volumes were segmented using FreeSurfer. After normalizing for total intracranial volume, two-way type III ANCOVA test and chi-square test were used to characterize statistical group differences. Evans’ index, callosal angle (CA), and disproportionately enlarged subarachnoid space hydrocephalus were assessed. Multivariable logistic regression models were tested using Akaike information criterion to determine which combination of metrics most accurately predicts HVLP/cLD response. Results Responders and non-responders demonstrated no differences in total ventricular and white/gray matter volumes. CA (men only) and third and fourth ventricular volumes were smaller; and hippocampal volume was larger in responders ( p  < 0.05). Temporal horns volume correlated with degree of improvement in gait velocity in responders ( p  = 0.0006). The regression model was 76.8% accurate for HVLP/cLD response. Conclusion CA and third and fourth ventricular volumes and hippocampal volume may serve as potentially useful imaging features that may help predict spinal tap response and hence potentially shunt response.

MRI is a fundamental clinical tool used to diagnose pathology and understand the structure and function of healthy tissue. Variability inherent to the creation of an MRI image degrades its information content and clinical utility. Removal of noise and imaging artifacts are essential preprocessing steps prior to analysis and visualization of this data. Diffusion MRI (dMRI) and functional MRI (fMRI) are acquired using echo-planar-imaging pulse sequences, which allow for rapid data acquisition (up to 100ms per image slice) but come with a trade-off between spatial resolution and signal-to-noise ratio (SNR). At 3T field strength, a clinically feasible resolution (2mm isotropic voxels) comes with an SNR of ~20 db without diffusion-weighting and decreases exponentially with increasing diffusion-weighting. In addition to high noise levels, MRI data is also plagued by under-sampling artifacts such as aliasing and Gibbs-ringing, along with chemical shift artifacts, shape distortions, and ghosting caused by imperfect readouts, eddy currents and motion.In this thesis, we develop noise and artifact reduction methods for MRI data and evaluate their clinical impact, either by exploiting redundancy in MRI using Random Matrix Theory (RMT) or by removing noise with a novel modification to a convolutional neural network (CNN). Using RMT, the first major contribution applied the Marchenko-Pastur Principal Component Analysis (MPPCA) approach to fMRI data, where we demonstrate substantial gain in sensitivity for fMRI language mapping in brain tumor patients, enabling shortening scan time and gain in statistical power. We also evaluated the impact of RMT-based denoising on artifact correction and clinical translation by comparing intra-scan, cross-scan, and cross-protocol variability of higher-order diffusion MRI parameters by performing multi-shell diffusion MRI in 20 subjects. Here we found that increased precision across scanners and protocols increases statistical power, harmonizes across protocols and enables the clinical utility of quantitative higher-order diffusion MRI parameters. The second major contribution involves development of CNN denoising architectures to enhance anatomical MR images acquired using 3D Fast Gray Matter Acquisition T1 Inversion Recovery (FGATIR), where we evaluated the effects of Gaussian and Rician noise distributions present in MRI. Denoised FGATIR data provides the clinical potential to visualize specific thalamus, basal ganglia and brainstem structures targeted by functional neurosurgery for the first time in-vivo. We also demonstrate how CNNs can be used to reduce Gibbs artifacts in diffusion MRI, and find that CNN methods are able to mitigate artifacts in diffusion weighted images and diffusion parameter maps, and that using a complex image channel also allows the CNN to reduce artifacts in partial Fourier acquisitions.

The clinical translation of diffusion MRI (dMRI)-derived quantitative contrasts hinges on robust reproducibility, minimizing both same-scanner and cross-scanner variability. This study evaluates the reproducibility of higher-order diffusion metrics (beyond conventional diffusion tensor imaging), at the voxel and region-of-interest levels on magnitude and complex-valued dMRI data, using denoising with and without harmonization. We compared same-scanner, cross-scanner, and cross-protocol variability for a multi-shell dMRI protocol in 20 subjects. We first evaluated the effectiveness of denoising strategies for both magnitude and complex data to mitigate noise-induced bias and variance, to improve dMRI parametric maps and reproducibility. We examined the impact of denoising under different analysis approaches, comparing voxel-wise and region of interest (ROI)-based methods. We also evaluated the role of denoising when harmonizing dMRI across scanners and protocols. DTI and DKI maps visually improve after MPPCA denoising, with noticeably fewer outliers in kurtosis maps. Denoising enhances voxel-wise reproducibility, with test-retest variability of kurtosis indices reduced from 15-20% to 5-10% after denoising. Complex dMRI denoising reduces the noise floor by up to 60%. In ROI-analyses, denoising also increased statistical power, with reduction in sample size requirements by up to 40% for detecting differences across populations. Combining denoising with linear-RISH harmonization, in voxel-wise assessments, improved intra-scanner intraclass correlation coefficients for FA from moderate to excellent repeatability over harmonization alone. The enhancement in data quality and precision due to denoising facilitates the broader application and acceptance of these advanced imaging techniques in both clinical practice and large-scale neuroimaging studies.