A robust deconvolution method to disentangle multiple water pools in diffusion MRI

A framework for optimizing the acquisition protocol multishell diffusion-weighted imaging for multimodel assessment

Complementary aspects of tissue microstructure can be studied with diffusion-weighted imaging (DWI). However, there is no consensus on how to design a diffusion acquisition protocol for multiple models within a clinically feasible time. The purpose of this study is to provide a flexible framework that is able to optimize the shell acquisition protocol given a set of DWI models. Eleven healthy subjects underwent an extensive DWI acquisition protocol, including 15 candidate shells, ranging from 10 to 3500 s/mm2. The proposed framework aims to determine the optimized acquisition scheme (OAS) with a data-driven procedure minimizing the squared error of model-estimated parameters. We tested the proposed method over five heterogeneous DWI models exploiting both low and high b-values (i.e., diffusion tensor imaging [DTI], free water, intra-voxel incoherent motion [IVIM], diffusion kurtosis imaging [DKI], and neurite orientation dispersion and density imaging [NODDI]). A voxel-level and region of interest (ROI)-level analysis was conducted over the white matter and in 48 fiber bundles, respectively. Results showed that acquiring data for the five abovementioned models via OAS requires 14 min, compared with 35 min for the joint recommended acquisition protocol. The parameters derived from the reference acquisition scheme and the OAS are comparable in terms of estimated values, noise, and tissue contrast. Furthermore, the power analysis showed that the OAS retains the potential sensitivity to group-level differences in the parameters of interest, with the exception of the free water model. Overall, there is a linear correspondence (R2 = 0.91) between OAS and reference-derived parameters. In conclusion, the proposed framework optimizes the shell acquisition scheme for a given set of DWI models (i.e., DTI, free water, IVIM, DKI, and NODDI), combining low and high b-values while saving acquisition time.

The dependence of cerebral interstitial fluid on diffusion-sensitizing directions: A multi-b-value diffusion MRI study in a memory clinic sample

Three-component intravoxel incoherent motion (3C-IVIM) imaging with spectral analysis provides a proxy for interstitial fluid (ISF) (e.g., in perivascular spaces (PVS), granting a potential marker for altered cerebral clearance. When 3C-IVIM images are acquired with three orthogonal diffusion-sensitizing directions, these are often averaged into the Trace image. This may result in loss of valuable direction-specific information, particularly in PVS-rich regions (basal ganglia (BG) and centrum semiovale (CSO)). This study assessed the dependence of individual diffusion-sensitizing directions to the ISF fraction in PVS-rich regions. Additionally, we explored the value of diffusion direction-specific information on ISF characteristics in distinguishing thirty-one patients with cognitive impairment (CI) (Alzheimer's disease (n = 15) or Mild Cognitive Impairment (n = 16)) from thirty cognitively healthy elderly controls (CON). Multi-b-value diffusion-weighted images were acquired in three orthogonal directions (L-R (left-right), A-P (anterior-posterior) and S-I (superior-inferior)) at 3 T. Voxel-based spectral analysis using non-negative least squares was conducted to independently analyze the L-R, A-P, S-I, and Trace images. 3C-IVIM measures were first compared between diffusion-sensitizing directions and the Trace within the BG using repeated measures ANOVA. Subsequently, the 3C-IVIM measures were compared per direction between the CI and CSO group in the BG and CSO with multivariable linear regression. Our results show that the ISF fraction significantly differs between all diffusion-sensitizing directions and Trace in the BG, with the highest ISF fraction detected using S-I. Solely using S-I, a higher ISF fraction was identified in CI compared to CON in the BG (p = .020) and CSO (p = .046). Thereby, this study found that the measured ISF fraction depends on the acquired diffusion-sensitizing direction, where S-I is most sensitive to detect ISF and differences between CI and CON. The Trace approach is not always sensitive enough to ISF characteristics. Solely acquiring S-I may offer an alternative to reduce scanning time.

Three-compartment spectral diffusion analysis for breast cancer magnetic resonance imaging

RATIONALE AND OBJECTIVES

To examine the diagnostic performance of a three-compartment diffusion model with the fixed cut-off diffusion coefficient (D) using magnetic resonance spectral diffusion analysis for differentiating between invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS) and compare the conventional apparent D (ADC), and mean kurtosis (MK), with the tissue D (DIVIM), perfusion D (D*IVIM), and perfusion fraction (fIVIM) calculated by conventional intravoxel incoherent motion.

PATIENTS AND METHODS

This retrospective study included women who underwent breast MRI with eight b-value diffusion-weighted imaging between February 2019 and March 2022. Spectral diffusion analysis was performed; very-slow, cellular, and perfusion compartments were defined using cut-off Ds of 0.1 × 10-3 and 3.0 × 10-3 mm2/s (static water D). The mean D (Ds, Dc, Dp, respectively) and fraction F (Fs, Fc, Fp, respectively) for each compartment were calculated. ADC and MK values were also calculated; receiver operating characteristic analyses were performed.

RESULTS

Histologically confirmed 132 ICD and 62 DCIS (age range 31-87 [53 ± 11] years) were evaluated. The areas under the curve (AUCs) for ADC, MK, DIVIM, D*IVIM, fIVIM, Ds, Dc, Dp, Fs, Fc, and Fp were 0.77, 0.72, 0.77, 0.51, 0.67, 0.54, 0.78, 0.51, 0.57, 0.54, and 0.57, respectively. The AUCs for the model combining very-slow and cellular compartments and the model combining the three compartments were 0.81 each, slightly and significantly higher than for ADC, DIVIM, and Dc (P = 0.09-0.14); and MK (P < 0.05), respectively.

CONCLUSION

Three-compartment model analysis using the diffusion spectrum accurately differentiated IDC from DCIS; however, it was not superior to ADC and DIVIM. The diagnostic performance of MK was lower than that of the three-compartment model.

Novel developments in non-contrast enhanced MRI of the perivascular clearance system: What are the possibilities for Alzheimer's disease research?

The cerebral waste clearance system (i.e, glymphatic or intramural periarterial drainage) works through a network of perivascular spaces (PVS). Dysfunction of this system likely contributes to aggregation of Amyloid-β and subsequent toxic plaques in Alzheimer's disease (AD). A promising, non-invasive technique to study this system is MRI, though applications in dementia are still scarce. This review focusses on recent non-contrast enhanced (non-CE) MRI techniques which determine and visualise physiological aspects of the clearance system at multiple levels, i.e., cerebrospinal fluid flow, PVS-flow and interstitial fluid movement. Furthermore, various MRI studies focussing on aspects of the clearance system which are relevant to AD are discussed, such as studies on ageing, sleep alterations, and cognitive decline. Additionally, the complementary function of non-CE to CE methods is elaborated upon. We conclude that non-CE studies have great potential to determine which parts of the waste clearance system are affected by AD and in which stages of cognitive impairment dysfunction of this system occurs, which could allow future clinical trials to target these specific mechanisms.

Diffusion MRI harmonization enables joint-analysis of multicentre data of patients with cerebral small vessel disease

OBJECTIVES

Acquisition-related differences in diffusion magnetic resonance imaging (dMRI) hamper pooling of multicentre data to achieve large sample sizes. A promising solution is to harmonize the raw diffusion signal using rotation invariant spherical harmonic (RISH) features, but this has not been tested in elderly subjects. Here we aimed to establish if RISH harmonization effectively removes acquisition-related differences in multicentre dMRI of elderly subjects with cerebral small vessel disease (SVD), while preserving sensitivity to disease effects.

METHODS

Five cohorts of patients with SVD (N = 397) and elderly controls (N = 175) with 3 Tesla MRI on different systems were included. First, to establish effectiveness of harmonization, the RISH method was trained with data of 13 to 15 age and sex-matched controls from each site. Fractional anisotropy (FA) and mean diffusivity (MD) were compared in matched controls between sites using tract-based spatial statistics (TBSS) and voxel-wise analysis, before and after harmonization. Second, to assess sensitivity to disease effects, we examined whether the contrast (effect sizes of FA, MD and peak width of skeletonized MD - PSMD) between patients and controls within each site remained unaffected by harmonization. Finally, we evaluated the association between white matter hyperintensity (WMH) burden, FA, MD and PSMD using linear regression analyses both within individual cohorts as well as with pooled scans from multiple sites, before and after harmonization.

RESULTS

Before harmonization, significant differences in FA and MD were observed between matched controls of different sites (p < 0.05). After harmonization these site-differences were removed. Within each site, RISH harmonization did not alter the effect sizes of FA, MD and PSMD between patients and controls (relative change in Cohen's d = 4 %) nor the strength of association with WMH volume (relative change in R2 = 2.8 %). After harmonization, patient data of all sites could be aggregated in a single analysis to infer the association between WMH volume and FA (R2 = 0.62), MD (R2 = 0.64), and PSMD (R2 = 0.60).

CONCLUSIONS

We showed that RISH harmonization effectively removes acquisition-related differences in dMRI of elderly subjects while preserving sensitivity to SVD-related effects. This study provides proof of concept for future multicentre SVD studies with pooled datasets.

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

Task-driven assessment of experimental designs in diffusion MRI: A computational framework

This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly measures quantitative task performance. Traditional computational experimental design (CED) methods may be ill-suited to experimental tasks, such as clinical classification, where outcome does not depend on parameter-estimation accuracy or precision alone. Current assessment metrics evaluate experiments’ ability to faithfully recover microstructural parameters rather than their task performance. The method we propose addresses this shortcoming. For a given MRI experimental design (protocol, parameter-estimation method, model, etc.), experiments are simulated start-to-finish and task performance is computed from receiver operating characteristic (ROC) curves and associated summary metrics (e.g. area under the curve (AUC)). Two experiments were performed: first, a validation of the pipeline’s task performance predictions against clinical results, comparing in-silico predictions to real-world ROC/AUC; and second, a demonstration of the pipeline’s advantages over traditional CED approaches, using two simulated clinical classification tasks. Comparison with clinical datasets validates our method’s predictions of (a) the qualitative form of ROC curves, (b) the relative task performance of different experimental designs, and (c) the absolute performance (AUC) of each experimental design. Furthermore, we show that our method outperforms traditional task-agnostic assessment methods, enabling improved, more useful experimental design. Our pipeline produces accurate, quantitative predictions of real-world task performance. Compared to current approaches, such task-driven assessment is more likely to identify experimental designs that perform well in practice. Our method is not limited to diffusion MRI; the pipeline generalises to any task-based quantitative MRI application, and provides the foundation for developing future task-driven end-to end CED frameworks.

Accurate free-water estimation in white matter from fast diffusion MRI acquisitions using the spherical means technique

PURPOSE

To accurately estimate the partial volume fraction of free water in the white matter from diffusion MRI acquisitions not demanding strong sensitizing gradients and/or large collections of different b-values. Data sets considered comprise ∼ 32-64 gradients near b = 1000 s / mm 2 plus ∼ 6 gradients near b = 500 s / mm 2 .

THEORY AND METHODS

The spherical means of each diffusion MRI set with the same b-value are computed. These means are related to the inherent diffusion parameters within the voxel (free- and cellular-water fractions; cellular-water diffusivity), which are solved by constrained nonlinear least squares regression.

RESULTS

The proposed method outperforms those based on mixtures of two Gaussians for the kind of data sets considered. W.r.t. the accuracy, the former does not introduce significant biases in the scenarios of interest, while the latter can reach a bias of 5%-7% if fiber crossings are present. W.r.t. the precision, a variance near 10 % , compared to 15%, can be attained for usual configurations.

CONCLUSION

It is possible to compute reliable estimates of the free-water fraction inside the white matter by complementing typical DTI acquisitions with few gradients at a lowb-value. It can be done voxel-by-voxel, without imposing spatial regularity constraints.

Associations of increased interstitial fluid with vascular and neurodegenerative abnormalities in a memory clinic sample

The vascular and neurodegenerative processes related to clinical dementia cause cell loss which induces, amongst others, an increase in interstitial fluid (ISF). We assessed microvascular, parenchymal integrity, and a proxy of ISF volume alterations with intravoxel incoherent motion imaging in 21 healthy controls and 53 memory clinic patients - mainly affected by neurodegeneration (mild cognitive impairment, Alzheimer's disease dementia), vascular pathology (vascular cognitive impairment), and presumed to be without significant pathology (subjective cognitive decline). The microstructural components were quantified with spectral analysis using a non-negative least squares method. Linear regression was employed to investigate associations of these components with hippocampal and white matter hyperintensity (WMH) volumes. In the normal appearing white matter, a large f_int (a proxy of ISF volume) was associated with a large WMH volume and low hippocampal volume. Likewise, a large f_int value was associated with a lower hippocampal volume in the hippocampi. Large ISF volume (f_int) was shown to be a prominent factor associated with both WMHs and neurodegenerative abnormalities in memory clinic patients and is argued to play a potential role in impaired glymphatic functioning.

Continuous diffusion spectrum computation for diffusion-weighted magnetic resonance imaging of the kidney tubule system

Background

The use of rigid multi-exponential models (with a priori predefined numbers of components) is common practice for diffusion-weighted MRI (DWI) analysis of the kidney. This approach may not accurately reflect renal microstructure, as the data are forced to conform to the a priori assumptions of simplified models. This work examines the feasibility of less constrained, data-driven non-negative least squares (NNLS) continuum modelling for DWI of the kidney tubule system in simulations that include emulations of pathophysiological conditions.

Methods

Non-linear least squares (LS) fitting was used as reference for the simulations. For performance assessment, a threshold of 5% or 10% for the mean absolute percentage error (MAPE) of NNLS and LS results was used. As ground truth, a tri-exponential model using defined volume fractions and diffusion coefficients for each renal compartment (tubule system: D_tubules , f_tubules ; renal tissue: D_tissue , f_tissue ; renal blood: D_blood , f_blood ;) was applied. The impact of: (I) signal-to-noise ratio (SNR) =40-1,000, (II) number of b-values (n=10-50), (III) diffusion weighting (b-range_small =0-800 up to b-range_large =0-2,180 s/mm²), and (IV) fixation of the diffusion coefficients D_tissue and D_blood was examined. NNLS was evaluated for baseline and pathophysiological conditions, namely increased tubular volume fraction (ITV) and renal fibrosis (10%: grade I, mild) and 30% (grade II, moderate).

Results

NNLS showed the same high degree of reliability as the non-linear LS. MAPE of the tubular volume fraction (f_tubules ) decreased with increasing SNR. Increasing the number of b-values was beneficial for f_tubules precision. Using the b-range_large led to a decrease in MAPE _ftubules compared to b-range_small. The use of a medium b-value range of b=0-1,380 s/mm² improved f_tubules precision, and further b_max increases beyond this range yielded diminishing improvements. Fixing D_blood and D_tissue significantly reduced MAPE _ftubules and provided near perfect distinction between baseline and ITV conditions. Without constraining the number of renal compartments in advance, NNLS was able to detect the (fourth) fibrotic compartment, to differentiate it from the other three diffusion components, and to distinguish between 10% vs. 30% fibrosis.

Conclusions

This work demonstrates the feasibility of NNLS modelling for DWI of the kidney tubule system and shows its potential for examining diffusion compartments associated with renal pathophysiology including ITV fraction and different degrees of fibrosis.

Untangling the diffusion signal using the phasor transform

Separating the decay signal from diffusion-weighted scans into two or more components can be challenging. The phasor technique is well established in the field of optical microscopy for visualization and separation of fluorescent dyes with different lifetimes. The use of the phasor technique for separation of diffusion-weighted decay signals was recently proposed. In this study, we investigate the added value of this technique for fitting decay models and visualization of decay rates. Phasor visualization was performed in five glioblastoma patients. Using simulations, the influence of incorrect diffusivity values and of the number of b-values on fitting a three-component model with fixed diffusivities (dubbed "unmixing") was investigated for both a phasor-based fit and a linear least squares (LLS) fit. Phasor-based intravoxel incoherent motion (IVIM) fitting was compared with nonlinear least squares (NLLS) and segmented fitting (SF) methods in terms of accuracy and precision. The distributions of the parameter estimates of simulated data were compared with those obtained in a healthy volunteer. In the phasor visualizations of two glioblastoma patients, a cluster of points was observed that was not seen in healthy volunteers. The identified cluster roughly corresponded to the enhanced edge region of the tumor of two glioblastoma patients visible on fluid-attenuated inversion recovery (FLAIR) images. For fitting decay models the usefulness of the phasor transform is less pronounced, but the additional knowledge gained from the geometrical configuration of phasor space can aid fitting routines. This has led to slightly improved fitting results for the IVIM model: phasor-based fitting yielded parameter maps with higher precision than the NLLS and SF methods for parameters f and D (interquartile range [IQR] for f: NLLS 27, SF 12, phasor 5.7%; IQR for D: NLLS 0.28, SF 0.18, phasor 0.10 μm² /s). For unmixing, LLS fitting slightly but consistently outperformed phasor-based fitting in all of the tested scenarios.

Pattern recognition analysis of directional intravoxel incoherent motion MRI in ischemic rodent brains

This study aimed to demonstrate a reliable automatic segmentation method for independently separating reduced diffusion and decreased perfusion areas in ischemic stroke brains using constrained nonnegative matrix factorization (cNMF) pattern recognition in directional intravoxel incoherent motion MRI (IVIM-MRI). First, the feasibility of cNMF-based segmentation of IVIM signals was investigated in both simulations and in vivo experiments. The cNMF analysis was independently performed for S₀ -normalized and scaled (by the difference between the maximum and minimum) IVIM signals, respectively. Segmentations of reduced diffusion (from S₀ -normalized IVIM signals) and decreased perfusion (from scaled IVIM signals) areas were performed using the corresponding cNMF pattern weight maps. Second, Monte Carlo simulations were performed for directional IVIM signals to investigate the relationship between the degree of vessel alignment and the direction of the diffusion gradient. Third, directional IVIM-MRI experiments (x, y and z diffusion-gradient directions, 20 b values at 7 T) were performed for normal (n = 4), sacrificed (n = 1, no flow) and ischemic stroke models (n = 4, locally reduced flow). The results showed that automatic segmentation of the hypoperfused lesion using cNMF analysis was more accurate than segmentation using the conventional double-exponential fitting. Consistent with the simulation, the double-exponential pattern of the IVIM signals was particularly strong in white matter and ventricle regions when the directional flows were aligned with the applied diffusion-gradient directions. cNMF analysis of directional IVIM signals allowed robust automated segmentation of white matter, ventricle, vascular and hypoperfused regions in the ischemic brain. In conclusion, directional IVIM signals were simultaneously sensitive to diffusion and aligned flow and were particularly useful for automatically segmenting ischemic lesions via cNMF-based pattern recognition.

Multicomponent diffusion analysis reveals microstructural alterations in spinal cord of a mouse model of amyotrophic lateral sclerosis ex vivo

The microstructure changes associated with degeneration of spinal axons in amyotrophic lateral sclerosis (ALS) may be reflected in altered water diffusion properties, potentially detectable with diffusion-weighted (DW) MRI. Prior work revealed the classical mono-exponential model fails to precisely depict decay in DW signal at high b-values. In this study, we aim to investigate signal decay behaviors at ultra-high b-values for non-invasive assessment of spinal cord alterations in the transgenic SOD1^G93A mouse model of ALS. A multiexponential diffusion analysis using regularized non-negative least squares (rNNLS) algorithm was applied to a series of thirty DW MR images with b-values ranging from 0 to 858,022 s/mm² on ex vivo spinal cords of transgenic SOD1^G93A and age-matched control mice. We compared the distributions of measured diffusion coefficient fractions between the groups. The measured diffusion weighted signals in log-scale showed non-linear decay behaviors with increased b-values. Faster signal decays were observed with diffusion gradients applied parallel to the long axis of the spinal cord compared to when oriented in the transverse direction. Multiexponential analysis at the lumbar level in the spinal cord identified ten subintervals. A significant decrease of diffusion coefficient fractions was found in the ranges of [1.63×10⁻⁸,3.70×10⁻⁶] mm²/s (P = 0.0002) and of [6.01×10⁻⁶,4.20×10⁻⁵] mm²/s (P = 0.0388) in SOD1^G93A mice. Anisotropic diffusion signals persisted at ultra-high b-value DWIs of the mouse spinal cord and multiexponential diffusion analysis offers the potential to evaluate microstructural alterations of ALS-affected spinal cord non-invasively.

Spectral Diffusion Analysis of Intravoxel Incoherent Motion MRI in Cerebral Small Vessel Disease

Background

Cerebral intravoxel incoherent motion (IVIM) imaging assumes two components. However, more compartments are likely present in pathologic tissue. We hypothesized that spectral analysis using a nonnegative least‐squares (NNLS) approach can detect an additional, intermediate diffusion component, distinct from the parenchymal and microvascular components, in lesion‐prone regions.

Purpose

To investigate the presence of this intermediate diffusion component and its relation with cerebral small vessel disease (cSVD)‐related lesions.

Study Type

Prospective cross‐sectional study.

Population

Patients with cSVD (n = 69, median age 69.8) and controls (n = 39, median age 68.9).

Field Strength/Sequence

Whole‐brain inversion recovery IVIM acquisition at 3.0T.

Assessment

Enlarged perivascular spaces (PVS) were rated by three raters. White matter hyperintensities (WMH) were identified on a fluid attenuated inversion recovery (FLAIR) image using a semiautomated algorithm.

Statistical Tests

Relations between IVIM measures and cSVD‐related lesions were studied using the Spearman's rank order correlation.

Results

NNLS yielded diffusion spectra from which the intermediate volume fraction f_int was apparent between parenchymal diffusion and microvasular pseudodiffusion. WMH volume and the extent of MRI‐visible enlarged PVS in the basal ganglia (BG) and centrum semiovale (CSO) were correlated with f_int in the WMHs, BG, and CSO, respectively. f_int was 4.2 ± 1.7%, 7.0 ± 4.1% and 13.6 ± 7.7% in BG and 3.9 ± 1.3%, 4.4 ± 1.4% and 4.5 ± 1.2% in CSO for the groups with low, moderate, and high number of enlarged PVS, respectively, and increased with the extent of enlarged PVS (BG: r = 0.49, P < 0.01; CSO: r = 0.23, P = 0.02). f_int in the WMHs was 27.1 ± 13.1%, and increased with the WMH volume (r = 0.57, P < 0.01).

Data Conclusion

We revealed the presence of an intermediate diffusion component in lesion‐prone regions of cSVD and demonstrated its relation with enlarged PVS and WMHs. In tissue with these lesions, tissue degeneration or perivascular edema can lead to more freely diffusing interstitial fluid contributing to f_int.

Level of Evidence: 2

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2020;51:1170–1180.

On the sensitivity of the diffusion MRI signal to brain activity in response to a motor cortex paradigm

Diffusion functional magnetic resonance imaging (dfMRI) is a promising technique to map functional activations by acquiring diffusion‐weighed spin‐echo images. In previous studies, dfMRI showed higher spatial accuracy at activation mapping compared to classic functional MRI approaches. However, it remains unclear whether dfMRI measures result from changes in the intracellular/extracellular environment, perfusion, and/or T₂ values. We designed an acquisition/quantification scheme to disentangle such effects in the motor cortex during a finger‐tapping paradigm. dfMRI was acquired at specific diffusion weightings to selectively suppress perfusion and free‐water diffusion, then time series of the apparent diffusion coefficient (ADC‐fMRI) and of intravoxel incoherent motion (IVIM) effects were derived. ADC‐fMRI provided ADC estimates sensitive to changes in perfusion and free‐water volume, but not to T₂/T₂* values. With IVIM modeling, we isolated the perfusion contribution to ADC, while suppressing T₂ effects. Compared to conventional gradient‐echo blood oxygenation level‐dependent fMRI, activation maps obtained with dfMRI and ADC‐fMRI had smaller clusters, and the spatial overlap between the three techniques was below 50%. Increases of perfusion fractions were observed during task in both dfMRI and ADC‐fMRI activations. Perfusion effects were more prominent with ADC‐fMRI than with dfMRI but were significant in less than 25% of activation regions. IVIM modeling suggests that the sensitivity to task of dfMRI derives from a decrease of intracellular/extracellular diffusion and an increase of the pseudo‐diffusion signal fraction, leading to different, more confined spatial activation patterns compared to classic functional MRI.