Background and Objectives:Multiple sclerosis (MS) is characterized by lesions and atrophy on conventional MRI, yet these often fail to explain disability. Diffusion MRI (dMRI) detects microstructural injury with diffusion tensor (DTI) and kurtosis imaging (DKI) offering sensitivity, and Standard Model Imaging (SMI) providing biologically interpretable parameters. We evaluated whether these clinically feasible dMRI metrics are associated with disability beyond volumetric and lesion measures, and whether effects arise from normal-appearing white matter (NAWM). Methods:This cross-sectional study included MS patients who underwent 3T MRI including T1- and T2-weighted and a ~7-minute multi-shell dMRI protocol. Brain volumes (gray matter, white matter, thalamus) and lesion load were derived using FreeSurfer and Icobrain. Diffusion metrics included radial diffusivity (RD) from DTI, radial kurtosis (RK) from DKI, intra-axonal water fraction (f) and fiber dispersion (p 2 ) from SMI. Clinical outcomes were the Expanded Disability Status Scale (EDSS), Multiple Sclerosis Functional Composite (MSFC), 9-Hole Peg Test (9HPT), Symbol Digit Modalities Test (SDMT), and disease duration. Voxelwise and tract-based regions of interest analyses were adjusted for sex and age at onset and repeated after excluding lesions. Results:Ninety-two patients (68 women; mean age 48, range 24-73; median disease duration 14 years; EDSS 3.0, range 0-8.5) were included. dMRI revealed widespread associations with all clinical measures that persisted after lesion exclusion, implicating NAWM. Functional outcomes were tract-specific: 9HPT correlated with corticospinal tract and optic radiations (RD ρ=0.45; RK ρ=-0.44; f ρ=-0.42); MSFC with brainstem and optic radiations (RD ρ=-0.52; RK ρ=0.40; f ρ=0.39). SDMT showed widespread correlation with diffusion and atrophy (white matter ρ=0.49; thalamus ρ=0.47). EDSS showed weaker diffusion highlighting commissural disorganization (forceps major/minor ρ ≈- 0.30 to -0.32) and was most strongly associated to infratentorial lesion load (ρ=0.42). Disease duration was dominated by gray-matter atrophy (ρ=-0.54) with commissural p 2 reductions (≈-0.45). Discussion:dMRI detects NAWM injury underlying functional impairment beyond atrophy and lesions. SMI adds specificity (f for axonal loss/demyelination; p 2 for inflammation). Structural measures capture the effect of cumulative burden in terms of disease duration and EDSS. Together, diffusion, volumetric, and lesion metrics offer complementary insights, supporting multimodal imaging for MS monitoring and stratification.

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.

Various diffusion MRI (dMRI) preprocessing pipelines are currently available to yield more accurate diffusion parameters. Here, we evaluated accuracy and robustness of the optimized Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline in a large clinical dMRI dataset and using ground-truth phantoms. DESIGNER, a preprocessing pipeline targeting various imaging artifacts in diffusion MRI data, has been modified to improve denoising and target Gibbs ringing for partial Fourier acquisitions. We compared the revised DESIGNER (Dv2) (including denoising, Gibbs removal, correction for motion, echo planar imaging (EPI) distortion, and eddy currents) against the original DESIGNER (Dv1) pipeline, minimal preprocessing (including correction for motion, EPI distortion, and eddy currents only), and no preprocessing on a large clinical dMRI dataset of 524 control subjects with ages between 25 and 75 years old. We evaluated the effect of specific processing steps on age correlations in white matter with diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics. We also evaluated the added effect of minimal Gaussian smoothing to deal with noise and to reduce outliers in parameter maps compared to DESIGNER-v2's noise removal method. Moreover, Dv2's updated noise and Gibbs removal methods were assessed using a ground truth dMRI phantom to evaluate accuracy. Results show age correlations of DTI and DKI metrics in white matter were affected by the preprocessing pipeline, causing systematic differences in absolute parameter values and loss or gain of statistical significance. Both in clinical dMRI and ground-truth phantoms, Dv2 pipeline resulted in the smallest number of outlier voxels and improved accuracy in DTI and DKI metrics as noise was reduced and Gibbs removal was improved. Thus, DESIGNER-v2 provides more accurate and robust DTI and DKI parameter maps by targeting common artifacts present in dMRI data acquired in clinical settings, as compared to no preprocessing or minimal preprocessing.

PyDesigner is a Python-based software package based on the original Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline (Dv1) for dMRI preprocessing and tensor estimation. This software is openly provided for non-commercial research and may not be used for clinical care. PyDesigner combines tools from FSL and MRtrix3 to perform denoising, Gibbs ringing correction, eddy current motion correction, brain masking, image smoothing, and Rician bias correction to optimize the estimation of multiple diffusion measures. It can be used across platforms on Windows, Mac, and Linux to accurately derive commonly used metrics from DKI, DTI, WMTI, FBI, and FBWM datasets as well as tractography ODFs and .fib files. It is also file-format agnostic, accepting inputs in the form of .nii, .nii.gz, .mif, and dicom format. User-friendly and easy to install, this software also outputs quality control metrics illustrating signal-to-noise ratio graphs, outlier voxels, and head motion to evaluate data integrity. Additionally, this dMRI processing pipeline supports multiple echo-time dataset processing and features pipeline customization, allowing the user to specify which processes are employed and which outputs are produced to meet a variety of user needs.

Diffusion magnetic resonance imaging offers unique in vivo sensitivity to tissue microstructure in brain white matter, which undergoes significant changes during development and is compromised in virtually every neurological disorder. Yet, the challenge is to develop biomarkers that are specific to micrometer-scale cellular features in a human MRI scan of a few minutes. Here we quantify the sensitivity and specificity of a multicompartment diffusion modeling framework to the density, orientation and integrity of axons. We demonstrate that using a machine learning based estimator, our biophysical model captures the morphological changes of axons in early development, acute ischemia and multiple sclerosis (total N=821). The methodology of microstructure mapping is widely applicable in clinical settings and in large imaging consortium data to study development, aging and pathology.

PyDesigner is a Python-based software package based on the original Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline (Dv1) for dMRI preprocessing and tensor estimation. This software is openly provided for non-commercial research and may not be used for clinical care. PyDesigner combines tools from FSL and MRtrix3 to perform denoising, Gibbs ringing correction, eddy current motion correction, brain masking, image smoothing, and Rician bias correction to optimize the estimation of multiple diffusion measures. It can be used across platforms on Windows, Mac, and Linux to accurately derive commonly used metrics from DKI, DTI, WMTI, FBI, and FBWM datasets as well as tractography ODFs and .fib files. It is also file-format agnostic, accepting inputs in the form of .nii, .nii.gz, .mif, and dicom format. User-friendly and easy to install, this software also outputs quality control metrics illustrating signal-to-noise ratio graphs, outlier voxels, and head motion to evaluate data integrity. Additionally, this dMRI processing pipeline supports multiple echo-time dataset processing and features pipeline customization, allowing the user to specify which processes are employed and which outputs are produced to meet a variety of user needs.

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 Complex dMRI denoising reduces the noise floor by up to 60 denoising also increased statistical power, with reduction in sample size requirements by up to 40 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.

Biophysical modeling of diffusion MRI (dMRI) offers the exciting potential of bridging the gap between the macroscopic MRI resolution and microscopic cellular features, effectively turning the MRI scanner into a noninvasive in vivo microscope. In brain white matter, the Standard Model (SM) interprets the dMRI signal in terms of axon dispersion, intra- and extra-axonal water fractions and diffusivities. However, for SM to be fully applicable and correctly interpreted, it needs to be carefully evaluated using histology. Here, we perform a comprehensive histological validation of the SM parameters, by characterizing WM microstructure in sham and injured rat brains using volume (3d) electron microscopy (EM) and ex vivo dMRI. Sensitivity is evaluated by how close each SM metric is to its histological counterpart, and specificity by how independent it is from other, non-corresponding histological features. This comparison reveals that SM is sensitive and specific to microscopic properties, clearing the way for the clinical adoption of in vivo dMRI derived SM parameters as biomarkers for neurological disorders.

Various diffusion MRI (dMRI) preprocessing pipelines are currently available to yield more accurate diffusion parameters. Here, we evaluated accuracy and robustness of the optimized Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline in a large clinical dMRI dataset and using ground truth phantoms. DESIGNER has been modified to improve denoising and target Gibbs ringing for partial Fourier acquisitions. We compared the revisited DESIGNER (Dv2) (including denoising, Gibbs removal, correction for motion, EPI distortion, and eddy currents) against the original DESIGNER (Dv1) pipeline, minimal preprocessing (including correction for motion, EPI distortion, and eddy currents only), and no preprocessing on a large clinical dMRI dataset of 524 control subjects with ages between 25 and 75 years old. We evaluated the effect of specific processing steps on age correlations in white matter with DTI and DKI metrics. We also evaluated the added effect of minimal Gaussian smoothing to deal with noise and to reduce outliers in parameter maps compared to DESIGNER (Dv2)'s noise removal method. Moreover, DESIGNER (Dv2)'s updated noise and Gibbs removal methods were assessed using ground truth dMRI phantom to evaluate accuracy. Results show age correlation in white matter with DTI and DKI metrics were affected by the preprocessing pipeline, causing systematic differences in absolute parameter values and loss or gain of statistical significance. Both in clinical dMRI and ground truth phantoms, DESIGNER (Dv2) pipeline resulted in the smallest number of outlier voxels and improved accuracy in DTI and DKI metrics as noise was reduced and Gibbs removal was improved. Thus, DESIGNER (Dv2) provides more accurate and robust DTI and DKI parameter maps as compared to no preprocessing or minimal preprocessing.

The basal ganglia, thalamus and brainstem are affected by movement disorders and contain key targets for functional neurosurgery. Targeting however is based on indirect coordinates originally derived from pneumoencephalograms! 3D Fast Gray Matter Acquisition T1 Inversion Recovery (FGATIR) can directly visualize potential targeted structures (e.g. dentatorubrothalamic tract), but is signal-starved in clinically-feasible acquisitions. We developed a convolutional neural network to improve FGATIR quality. Expert rater assessment suggested this CNN improved contrast resolution of individual structures and overall clinical image quality of 1-average data to the level of 4-averages. This could further enable investigations of functional neurosurgery for movement disorders.

Normal pressure hydrocephalus (NPH) is a treatable cause of dementia, urinary incontinence and gait abnormality which is important to diagnose early as the longer the delay in treatment, the worse the outcome after shunting. Gait improvement following high volume lumbar puncture (HVLP) and continuous lumbar drain (cLD) is widely used to predict shunt responsiveness in patients with suspected 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 shunt response.

Background Multiple system atrophy (MSA) is a fatal neurodegenerative disease characterized by the aggregation of alpha-synuclein in glia and neurons. Sirolimus (rapamycin) is an mTOR inhibitor that promotes alpha-synuclein autophagy and reduces its associated neurotoxicity in preclinical models. Objective To investigate the efficacy and safety of sirolimus in patients with MSA using a futility design. We also analyzed 1-year biomarker trajectories in the trial participants. Methods Randomized, double-blind, parallel group, placebo-controlled clinical trial at the New York University of patients with probable MSA randomly assigned (3:1) to sirolimus (2-6 mg daily) for 48 weeks or placebo. Primary endpoint was change in the Unified MSA Rating Scale (UMSARS) total score from baseline to 48 weeks. ( NCT03589976). Results The trial was stopped after a pre-planned interim analysis met futility criteria. Between August 15, 2018 and November 15, 2020, 54 participants were screened, and 47 enrolled and randomly assigned (35 sirolimus, 12 placebo). Of those randomized, 34 were included in the intention-to-treat analysis. There was no difference in change from baseline to week 48 between the sirolimus and placebo in UMSARS total score (mean difference, 2.66; 95% CI, -7.35-6.91; P = 0.648). There was no difference in UMSARS-1 and UMSARS-2 scores either. UMSARS scores changes were similar to those reported in natural history studies. Neuroimaging and blood biomarker results were similar in the sirolimus and placebo groups. Adverse events were more frequent with sirolimus. Analysis of 1-year biomarker trajectories in all participants showed that increases in blood neurofilament light chain (NfL) and reductions in whole brain volume correlated best with UMSARS progression. Conclusions Sirolimus for 48 weeks was futile to slow the progression of MSA and had no effect on biomarkers compared to placebo. One-year change in blood NfL and whole brain atrophy are promising biomarkers of disease progression for future clinical trials. (c) 2022 International Parkinson and Movement Disorder Society

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.

Diffusion MRI (dMRI) allows non‐invasive mapping of microstructural changes in the brain. In this study, we assess the relationship between amyloid and tau deposition and grey matter microstructure as detected with diffusion kurtosis imaging (DKI) [1,2]. We hypothesize that inflammation associated with amyloid deposits might result in more restricted diffusion and neuronal death associated with tau pathology might result in less restricted diffusion.

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. 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. 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. 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.

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.

ABSTRACTPyDesigner is an open-source and containerized Python software package, adapted from the DESIGNER pipeline, for diffusion weighted magnetic resonance imaging preprocessing and tensor estimation. PyDesigner combines tools from FSL and MRtrix3 to reduce the effects of signal noise and imaging artifacts on multiple diffusion measures that can be derived from the diffusion and kurtosis tensors. This publication describes the main features of PyDesigner and highlights its ease of use across platforms, while examining its accuracy and robustness in deriving commonly used diffusion and kurtosis metrics.

AbstractBackgroundTau PET offers the potential of in vivo imaging neurofibrillary tangles (NFT) in the brain to assess Alzheimer’s disease (AD) progression and help guide the development of disease‐modifying treatments. In this study, we evaluate visualization and quantification of NFT using simultaneous acquisition of MRI and PET using [18F]‐MK‐6240 [1]. To overcome limitations in PET spatial resolution resulting in partial volume effects (PVE), MRI‐guided PET reconstruction using an asymmetrical Bowsher prior [2,3] was tested.MethodCognitively normal volunteers from the NYU Center Sleep and Brain Health Center (n=18, 15 female, age=65.4±5.6) underwent examination on a 3‐T integrated PET‐MRI system (Biograph mMR (Siemens Healthcare, Erlangen, Germany). MRI and PET were performed simultaneously; MRI contrasts included MPRAGE (1‐mm isotropic) for anatomy and ultrashort echo‐time (UTE) (1.6‐mm isotropic) for attenuation correction. [18F]‐MK‐6240 was injected intravenously and PET data was reconstructed 70‐90 min post‐injection. Standard iterative reconstruction (OSEM) (2‐mm isotropic) was compared to asymmetrical Bowsher [2,3] reconstruction with MPRAGE prior regularization weight β=30 (1x1x2mm3). PET standard uptake value (SUV) maps were co‐registered to respective MPRAGE, and (cerebellum or pons normalized) SUVR values were extracted from FreeSurfer‐derived regions of interest (ROIs).ResultsExample SUVR maps of standard PET and MR‐guided PET (Fig.1) reveal the Bowsher reconstruction to have more localized and increased focal uptake in the amygdala and cortical regions including the entorhinal, posterior cingulate and precuneus, with regional SUVR‐increases of 39%, 53%, 32%, and 16% respectively. Over all subjects (Fig.2), Bowsher reconstruction results in 30% or 48% higher SUVR, and an 64% or 84%‐increase of dynamic SUVR‐range (Fig.3) when normalized versus cerebellum or pons, respectively. These trends are emphasized for entorhinal cortex (Fig.4).ConclusionsThese initial results demonstrate that MR‐guided PET reconstruction of tau data may improve NFT localization and quantification. Study limitations include small number of high uptake tau‐cases and lack of standard such as histology for comparison. Future work will compare against CSF tau‐markers, cognition and comparison to other PVE correction methods. References: [1] Betthauser et al, JNM 2018;60(1):93–99; [2] Bowsher et al, Proc. IEEE Nucl. Sci. Symp., 2004(4):2488–2492; [3] Vunckx & Nuyts, Proc. IEEE Nucl. Sci. Symp., 2010:3262–3266.

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.