Intravoxel incoherent motion MRI in the brain: Impact of the fitting model on perfusion fraction and lesion differentiability

Noninvasive Magnetic Resonance Imaging Measures of Glymphatic System Activity

The comprehension of the glymphatic system, a postulated mechanism responsible for the removal of interstitial solutes within the central nervous system (CNS), has witnessed substantial progress recently. While direct measurement techniques involving fluorescence and contrast agent tracers have demonstrated success in animal studies, their application in humans is invasive and presents challenges. Hence, exploring alternative noninvasive approaches that enable glymphatic research in humans is imperative. This review primarily focuses on several noninvasive magnetic resonance imaging (MRI) techniques, encompassing perivascular space (PVS) imaging, diffusion tensor image analysis along the PVS, arterial spin labeling, chemical exchange saturation transfer, and intravoxel incoherent motion. These methodologies provide valuable insights into the dynamics of interstitial fluid, water permeability across the blood-brain barrier, and cerebrospinal fluid flow within the cerebral parenchyma. Furthermore, the review elucidates the underlying concept and clinical applications of these noninvasive MRI techniques, highlighting their strengths and limitations. It addresses concerns about the relationship between glymphatic system activity and pathological alterations, emphasizing the necessity for further studies to establish correlations between noninvasive MRI measurements and pathological findings. Additionally, the challenges associated with conducting multisite studies, such as variability in MRI systems and acquisition parameters, are addressed, with a suggestion for the use of harmonization methods, such as the combined association test (COMBAT), to enhance standardization and statistical power. Current research gaps and future directions in noninvasive MRI techniques for assessing the glymphatic system are discussed, emphasizing the need for larger sample sizes, harmonization studies, and combined approaches. In conclusion, this review provides invaluable insights into the application of noninvasive MRI methods for monitoring glymphatic system activity in the CNS. It highlights their potential in advancing our understanding of the glymphatic system, facilitating clinical applications, and paving the way for future research endeavors in this field. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 5.

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.

Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas

BACKGROUND

The consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery.

METHODS

Forty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student's t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs.

RESULTS

DDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03).

CONCLUSION

Histogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma.

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.

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.

Hemodynamic Imaging in Cerebral Diffuse Glioma—Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions

Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques

Objective

Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments.

Methods

Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics).

Results

High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies.

Conclusion

Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.

Noncontrast Pediatric Brain Perfusion: Arterial Spin Labeling and Intravoxel Incoherent Motion

Noncontrast magnetic resonance imaging techniques for measuring brain perfusion include arterial spin labeling (ASL) and intravoxel incoherent motion (IVIM). These techniques provide noninvasive and repeatable assessment of cerebral blood flow or cerebral blood volume without the need for intravenous contrast. This article discusses the technical aspects of ASL and IVIM with a focus on normal physiologic variations, technical parameters, and artifacts. Multiple pediatric clinical applications are presented, including tumors, stroke, vasculopathy, vascular malformations, epilepsy, migraine, trauma, and inflammation.

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.

Signal to noise and b-value analysis for optimal intra-voxel incoherent motion imaging in the brain

Intravoxel incoherent motion (IVIM) is a method that can provide quantitative information about perfusion in the human body, in vivo, and without contrast agent. Unfortunately, the IVIM perfusion parameter maps are known to be relatively noisy in the brain, in particular for the pseudo-diffusion coefficient, which might hinder its potential broader use in clinical applications. Therefore, we studied the conditions to produce optimal IVIM perfusion images in the brain. IVIM imaging was performed on a 3-Tesla clinical system in four healthy volunteers, with 16 b values 0, 10, 20, 40, 80, 110, 140, 170, 200, 300, 400, 500, 600, 700, 800, 900 s/mm², repeated 20 times. We analyzed the noise characteristics of the trace images as a function of b-value, and the homogeneity of the IVIM parameter maps across number of averages and sub-sets of the acquired b values. We found two peaks of noise of the trace images as function of b value, one due to thermal noise at high b-value, and one due to physiological noise at low b-value. The selection of b value distribution was found to have higher impact on the homogeneity of the IVIM parameter maps than the number of averages. Based on evaluations, we suggest an optimal b value acquisition scheme for a 12 min scan as 0 (7), 20 (4), 140 (19), 300 (9), 500 (19), 700 (1), 800 (4), 900 (1) s/mm².

Short-Term Repeatability of in Vivo Cardiac Intravoxel Incoherent Motion Tensor Imaging in Healthy Human Volunteers

BACKGROUND

Intravoxel incoherent motion (IVIM) tensor imaging is a promising technique for diagnosis and monitoring of cardiovascular diseases. Knowledge about measurement repeatability, however, remains limited.

PURPOSE

To evaluate short-term repeatability of IVIM tensor imaging in normal in vivo human hearts.

STUDY TYPE

Prospective.

POPULATION

Ten healthy subjects without history of heart diseases.

FIELD STRENGTH/SEQUENCE

Balanced steady-state free-precession cine sequence and single-shot spin-echo echo planar IVIM tensor imaging sequence (9 b-values, 0-400 seconds/mm² and six diffusion-encoding directions) at 3.0 T.

ASSESSMENT

Subjects were scanned twice with an interval of 15 minutes, leaving the scanner between studies. The signal-to-noise ratio (SNR) was evaluated in anterior, lateral, septal, and inferior segments of the left ventricle wall. Fractional anisotropy (FA), mean diffusivity (MD), mean fraction (MF), and helix angle (HA) in the four segments were independently measured by five radiologists.

STATISTICAL TESTS

IVIM tensor indexes were compared between observers using a one-way analysis of variance or between scans using a paired t-test (normal data) or a Wilcoxon rank-sum test (non-normal data). Interobserver agreement and test-retest repeatability were assessed using the intraclass correlation coefficient (ICC), within-subject coefficient of variation (WCV), and Bland-Altman limits of agreements.

RESULTS

SNR of inferior segment was significantly lower than the other three segments, and inferior segment was therefore excluded from repeatability analysis. Interobserver repeatability was excellent for all IVIM tensor indexes (ICC: 0.886-0.972; WCV: 0.62%-4.22%). Test-retest repeatability was excellent for MD of the self-diffusion tensor (D) and MF of the perfusion fraction tensor (f_p ) (ICC: 0.803-0.888; WCV: 1.42%-9.51%) and moderate for FA and MD of the pseudo-diffusion tensor (D^* ) (ICC: 0.487-0.532; WCV: 6.98%-10.89%). FA of D and f_p and HA of D presented good test-retest repeatability (ICC: 0.732-0.788; WCV: 3.28%-8.71%).

DATA CONCLUSION

The D and f_p indexes exhibited satisfactory repeatability, but further efforts were needed to improve repeatability of D^* indexes.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

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.

Operator dependency of arterial input function in dynamic contrast-enhanced MRI

Objective

To investigate the effect of inter-operator variability in arterial input function (AIF) definition on kinetic parameter estimates (KPEs) from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas.

Methods

The study included 118 DCE series from 23 patients. AIFs were measured by three domain experts (DEs), and a population AIF (pop-AIF) was constructed from the measured AIFs. The DE-AIFs, pop-AIF and AUC-normalized DE-AIFs were used for pharmacokinetic analysis with the extended Tofts model. AIF-dependence of KPEs was assessed by intraclass correlation coefficient (ICC) analysis, and the impact on relative longitudinal change in K^trans was assessed by Fleiss’ kappa (κ).

Results

There was a moderate to substantial agreement (ICC 0.51–0.76) between KPEs when using DE-AIFs, while AUC-normalized AIFs yielded ICC 0.77–0.95 for K^trans, k_ep and v_e and ICC 0.70 for v_p. Inclusion of the pop-AIF did not reduce agreement. Agreement in relative longitudinal change in K^trans was moderate (κ = 0.591) using DE-AIFs, while AUC-normalized AIFs gave substantial (κ = 0.809) agreement.

Discussion

AUC-normalized AIFs can reduce the variation in kinetic parameter results originating from operator input. The pop-AIF presented in this work may be applied in absence of a satisfactory measurement.

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.

Bayesian inference using hierarchical and spatial priors for intravoxel incoherent motion MR imaging in the brain: Analysis of cancer and acute stroke

The intravoxel incoherent motion (IVIM) model allows to map diffusion (D) and perfusion-related parameters (F and D*). Parameter estimation is, however, error-prone due to the non-linearity of the signal model, the limited signal-to-noise ratio (SNR) and the small volume fraction of perfusion in the in-vivo brain. In the present work, the performance of Bayesian inference was examined in the presence of brain pathologies characterized by hypo- and hyperperfusion. In particular, a hierarchical and a spatial prior were combined. Performance was compared relative to conventional segmented least squares regression, hierarchical prior only (non-segmented and segmented data likelihoods) and a deep learning approach. Realistic numerical brain IVIM simulations were conducted to assess errors relative to ground truth. In-vivo, data of 11 central nervous system cancer patients and 9 patients with acute stroke were acquired. The proposed method yielded reduced error in simulations for both the cancer and acute stroke scenarios compared to other methods across the whole investigated SNR range. The contrast-to-noise ratio of the proposed method was better or on par compared to the other techniques in-vivo. The proposed Bayesian approach hence improves IVIM parameter estimation in brain cancer and acute stroke.

Measuring Perfusion: Intravoxel Incoherent Motion MR Imaging

The signal acquired in vivo using a diffusion-weighted MR imaging (DWI) sequence is influenced by blood motion in the tissue. This means that perfusion information from a DWI sequence can be obtained in addition to thermal diffusion, if the appropriate sequence parameters and postprocessing methods are applied. This is commonly regrouped under the denomination intravoxel incoherent motion (IVIM) perfusion MR imaging. Of relevance, the perfusion information acquired with IVIM is essentially local, quantitative and acquired without intravenous injection of contrast media. The aim of this work is to review the IVIM method and its clinical applications.

Preliminary Study of Subclinical Brain Alterations in Patients With Asymptomatic Carotid Vulnerable Plaques Using Intravoxel Incoherent Motion Imaging by Voxelwise Comparison: A Study of Whole-Brain Imaging Measures

Objective

To preliminarily explore subclinical brain alterations in an asymptomatic carotid vulnerable plaque group based on intravoxel incoherent motion (IVIM) imaging through voxelwise comparison in the whole brain.

Materials and Methods

Forty-nine elderly participants underwent multi-b-value DWI, of whom 24 participants with asymptomatic carotid vulnerable plaques and <50% stenosis served as the test group, while the rest served as the healthy control group. After fitting the double-exponential model, slow ADC (Ds) and the fraction of fast ADC (f) values of the whole brain were obtained, which then were compared in a voxelwise manner by two-sample t-test. Multiple comparisons were corrected by the family-wise error (FWE) method with a corrected threshold of P < 0.05. Pearson correlations between IVIM parameters in altered brain regions and blood pressure, glucose, lipid, and homocysteine were calculated.

Results

For the test group, the Z-normalized Ds values were significantly higher in the left median cingulate and paracingulate gyrus (DCG.L), posterior cingulate gyrus (PCG. L), and left precuneus gyrus (PCUN.L) (cluster size = 156) and in the left middle frontal gyrus (MFG.L), orbital middle frontal gyrus (ORBmid.L), and superior frontal gyrus (SFG.L) (cluster size = 165); the Z-normalized Ds values were significantly lower in the right middle temporal gyrus (MTG.R) and inferior temporal gyrus (ITG.R) (cluster size = 116); and the Z-normalized f-values were significantly lower in the MTG.R and ITG.R (cluster size = 85) (p < 0.05, FWE correction). LDL-C was negatively correlated with the Z-normalized Ds values in the DCG.L, PCG.L, and PCUN.L (r = 0.601, p = 0.002). LDL-C was positively correlated with the Z-normalized f-value in the MTG.R and ITG.R (r = 0.405, p = 0.05). Systolic blood pressure was positively correlated with the Z-normalized Ds values in the MFG.L, ORBmid.L, and SFG.L (r = 0.433, p = 0.035).

Conclusion

This study was the first to detect subclinical brain alterations in asymptomatic carotid vulnerable plaque group through IVIM using whole-brain voxelwise comparisons, which were partially correlated with blood pressure and lipids. Thus, IVIM might be utilized as a noninvasive biomarker of microvascular and microstructural brain changes in the asymptomatic carotid vulnerable plaque group.

Accurate intravoxel incoherent motion parameter estimation using Bayesian fitting and reduced number of low b-values

PURPOSE

Intravoxel incoherent motion (IVIM) magnetic resonance imaging is a potential noninvasive technique for the diagnosis of brain tumors. However, perfusion-related parameter mapping is a persistent problem. The purpose of this paper is to investigate the IVIM parameter mapping of brain tumors using Bayesian fitting and low b-values.

METHODS

Bayesian shrinkage prior (BSP) fitting method and different low b-value distributions were used to estimate IVIM parameters (diffusion D, pseudo-diffusion D*, and perfusion fraction F). The results were compared to those obtained by least squares (LSQ) on both simulated and in vivo brain data. Relative error (RE) and reproducibility were used to evaluate the results. The differences of IVIM parameters between brain tumor and normal regions were compared and used to assess the performance of Bayesian fitting in the IVIM application of brain tumor.

RESULTS

In tumor regions, the value of D* tended to be decreased when the number of low b-values was insufficient, especially with LSQ. BSP required less low b-values than LSQ for the correct estimation of perfusion parameters of brain tumors. The IVIM parameter maps of brain tumors yielded by BSP had smaller variability, lower RE, and higher reproducibility with respect to those obtained by LSQ. Obvious differences were observed between tumor and normal regions in parameters D (P < 0.05) and F (P < 0.001), especially F. BSP generated fewer outliers than LSQ, and distinguished better tumors from normal regions in parameter F.

CONCLUSIONS

Intravoxel incoherent motion parameters clearly allow brain tumors to be differentiated from normal regions. Bayesian fitting yields robust IVIM parameter mapping with fewer outliers and requires less low b-values than LSQ for the parameter estimation.

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.

Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas

Background

Dynamic-susceptibility contrast and diffusion-weighted imaging are promising techniques in diagnosing glioma grade.

Purpose

To compare the inter-observer reproducibility of multiple dynamic-susceptibility contrast and diffusion-weighted imaging parameters and to assess their potential in differentiating low- and high-grade gliomas.

Material and Methods

Thirty patients (16 men; mean age = 40.6 years) with low-grade (n = 13) and high-grade (n = 17) gliomas and known pathology, scanned with dynamic-susceptibility contrast and diffusion-weighted imaging were included retrospectively between March 2006 and March 2014. Three observers used three different methods to define the regions of interest: (i) circles at maximum perfusion and minimum apparent diffusion coefficient; (ii) freeform 2D encompassing the tumor at largest cross-section only; (iii) freeform 3D on all cross-sections. The dynamic-susceptibility contrast curve was analyzed voxelwise: maximum contrast enhancement; time-to-peak; wash-in rate; wash-out rate; and relative cerebral blood volume. The mean was calculated for all regions of interest. For 2D and 3D methods, histogram analysis yielded additional statistics: the minimum and maximum 5% and 10% pixel values of the tumor (min5%, min10%, max5%, max10%). Intraclass correlations coefficients (ICC) were calculated between observers. Low- and high-grade tumors were compared with independent t-tests or Mann–Whitney tests.

Results

ICCs were highest for 3D freeform (ICC = 0.836–0.986) followed by 2D freeform (ICC = 0.854–0.974) and circular regions of interest (0.141–0.641). High ICC and significant discrimination between low- and high-grade gliomas was found for the following optimized parameters: apparent diffusion coefficient (P < 0.001; ICC = 0.641; mean; circle); time-to-peak (P = 0.015; ICC = 0.986; mean; 3D); wash-in rate (P = 0.004; ICC = 0.826; min10%; 3D); wash-out rate (P < 0.001; ICC = 0.860; min10%; 2D); and relative cerebral blood volume (P ≤ 0.001; ICC = 0.961; mean; 3D).

Conclusion

Dynamic-susceptibility contrast perfusion parameters relative cerebral blood volume and time-to-peak yielded high inter-observer reproducibility and significant glioma grade differentiation for the means of 2D and 3D freeform regions of interest. Choosing a freeform 2D method optimizes observer agreement and differentiation in clinical practice, while a freeform 3D method provides no additional benefit.

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.

Correlation Between Intravoxel Incoherent Motion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameters in Rectal Cancer

RATIONALE AND OBJECTIVES

This study aimed to determine the correlation between intravoxel incoherent motion (IVIM) and multiphase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters in patients with rectal cancer.

MATERIALS AND METHODS

Ninety-seven patients with rectal cancer were included in this study. All pelvic MRI examinations were performed in a 3.0 T MR unit, including diffusion-weighted imaging with 16 b values, DCE-MRI with two different flip angles (5° and 10°, respectively), and T1-fast field echo sequences as the reference. The IVIM perfusion-related parameters (f, perfusion fraction; D*, pseudo-diffusion coefficient; f·D*, the multiplication of the two parameters) were calculated by biexponential analysis. Quantitative DCE-MRI parameters were transfer constant (K^trans) between blood plasma and extravascular extracellular space), K_ep (rate between extravascular extracellular space and blood plasma), V_e (extravascular volume fraction), V_p (plasma volume fraction), and area under the gadolinium concentration curve. Interobserver agreements were evaluated using the intraclass correlation coefficient and Bland-Altman analysis. A p value <0.05 indicated a statistically significant difference.

RESULTS

The study included 75 males and 22 females with a median age of 58.8 years (range, 26-85years). Interobserver reproducibility for IVIM perfusion-related parameters and DCE-MRI quantitative parameters was good to excellent (intraclass correlation coefficient = 0.8618-0.9181, intraclass correlation coefficient = 0.7826-0.9088, respectively). Moderate correlations were found between f·D* and K^trans (r = 0.533; p < 0.001), and relatively weak correlations between D* and K^trans (r = 0.389; p < 0.001), D* and V_p (r = 0.442; p < 0.001), f·D* and V_p (r = 0.466; p < 0.001), and f and V_p (r = -0.234; p = 0.021).

CONCLUSION

IVIM perfusion-related parameters, especially f·D*, demonstrated moderate correlations with DCE-MRI quantitative parameters in rectal cancer.

Estimation of diffusion, perfusion and fractional volumes using a multi-compartment relaxation-compensated intravoxel incoherent motion (IVIM) signal model

Compartmental diffusion MRI models that account for intravoxel incoherent motion (IVIM) of blood perfusion allow for estimation of the fractional volume of the microvascular compartment. Conventional IVIM models are known to be biased by not accounting for partial volume effects caused by free water and cerebrospinal fluid (CSF), or for tissue-dependent relaxation effects. In this work, a three-compartment model (tissue, free water and blood) that includes relaxation terms is introduced. To estimate the model parameters, in vivo human data were collected with multiple echo times (TE), inversion times (TI) and b-values, which allowed a direct relaxation estimate alongside estimation of perfusion, diffusion and fractional volume parameters. Compared to conventional two-compartment models (with and without relaxation compensation), the three-compartment model showed less effects of CSF contamination. The proposed model yielded significantly different volume fractions of blood and tissue compared to the non-relaxation-compensated model, as well as to the conventional two-compartment model, suggesting that previously reported parameter ranges, using models that do not account for relaxation, should be reconsidered.

A new analysis approach for T2 relaxometry myelin water quantification: Orthogonal Matching Pursuit

Purpose

In vivo myelin quantification can provide valuable noninvasive information on neuronal maturation and development, as well as insights into neurological disorders. Multiexponential analysis of multiecho T₂ relaxation is a powerful and widely applied method for the quantification of the myelin water fraction (MWF). In recent literature, the MWF is most commonly estimated using a regularized nonnegative least squares algorithm.

Methods

The orthogonal matching pursuit algorithm is proposed as an alternative method for the estimation of the MWF. The orthogonal matching pursuit is a greedy sparse reconstruction algorithm with a low computation complexity. For validation, both methods are compared to a ground truth using numerical simulations and a phantom model using comparable computation times. The numerical simulations were used to measure the theoretical errors, as well as the effects of varying the SNR, strength of the regularization, and resolution of the basis set. Additionally, a phantom model was used to estimate the performance of the 2 methods while including errors occurring due to the MR measurement. Lastly, 4 healthy subjects were scanned to evaluate the in vivo performance.

Results

The results in simulations and phantoms demonstrate that the MWFs determined with the orthogonal matching pursuit are 1.7 times more accurate as compared to the nonnegative least squares, with a comparable precision. The remaining bias of the MWF is shown to be related to the regularization of the nonnegative least squares algorithm and the Rician noise present in magnitude MR images.

Conclusion

The orthogonal matching pursuit algorithm provides a more accurate alternative for T₂ relaxometry myelin water quantification.

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

The diffusion-weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood pseudo-diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcoming generally make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal-to-noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data-driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro-compartments, which were consistent with hindered diffusion, free water and pseudo-diffusion. Taking free water and pseudo-diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co-existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels.

Intravoxel incoherent motion MRI in neurological and cerebrovascular diseases

Intravoxel Incoherent Motion (IVIM) is a recently rediscovered noninvasive magnetic resonance imaging (MRI) method based on diffusion-weighted imaging. It enables the separation of the intravoxel signal into diffusion due to Brownian motion and perfusion-related contributions and provides important information on microperfusion in the tissue and therefore it is a promising tool for applications in neurological and neurovascular diseases. This review focuses on the basic principles and outputs of IVIM and details it major applications in the brain, such as stroke, tumor, and cerebral small vessel disease. A bi-exponential model that considers two different compartments, namely capillaries, and medium-sized vessels, has been frequently used for the description of the IVIM signal and may be important in those clinical applications cited before. Moreover, the combination of IVIM and arterial spin labeling MRI enables the estimation of water permeability across the blood-brain barrier (BBB), suggesting a potential imaging biomarker for disrupted-BBB diseases.

Impaired Cerebral Perfusion in Multiple Sclerosis: Relevance of Endothelial Factors

Magnetic resonance imaging techniques measuring in vivo brain perfusion and integrity of the blood-brain barrier have developed rapidly in the past decade, resulting in a wide range of available methods. This review first discusses their principles, possible pitfalls, and potential for quantification and outlines clinical application in neurological disorders. Then, we focus on the endothelial cells of the blood-brain barrier, pointing out their contribution in regulating vascular tone by production of vasoactive substances. Finally, the role of these substances in brain hypoperfusion in multiple sclerosis is discussed.