Functional mapping of the human visual cortex with intravoxel incoherent motion MRI

Clinical Interpretation of Intravoxel Incoherent Motion Perfusion Imaging in the Brain

Intravoxel incoherent motion (IVIM) perfusion imaging extracts information on blood motion in biological tissue from diffusion-weighted MR images. The method is attractive from a clinical stand point, because it measures in essence local quantitative perfusion, without intravenous contrast injection. Currently, the clinical interpretation of IVIM perfusion maps focuses on the IVIM perfusion fraction maps, but improvements in image quality of the IVIM pseudo-diffusion maps, using advanced postprocessing tools involving artificial intelligence, could lead to an increased interest in this parameters, as it could provide additional local perfusion information in the clinical setting, not otherwise available with other perfusion techniques.

Registration and quantification network (RQnet) for IVIM-DKI analysis in MRI

PURPOSE

A deep learning method is proposed for aligning diffusion weighted images (DWIs) and estimating intravoxel incoherent motion-diffusion kurtosis imaging parameters simultaneously.

METHODS

We propose an unsupervised deep learning method that performs 2 tasks: registration and quantification for intravoxel incoherent motion-diffusion kurtosis imaging analysis. A common registration method in diffusion MRI is based on minimizing dissimilarity between various DWIs, which may result in registration errors due to different contrasts in different DWIs. We designed a novel unsupervised deep learning method for both accurate registration and quantification of various diffusion parameters. In order to generate motion-simulated training data and test data, 17 volunteers were scanned without moving their heads, and 4 volunteers moved their heads during the scan in a 3 Tesla MRI. In order to investigate the applicability of the proposed method to other organs, kidney images were also obtained. We compared the registration accuracy of the proposed method, statistical parametric mapping, and a deep learning method with a normalized cross-correlation loss. In the quantification part of the proposed method, a deep learning method that considered the diffusion gradient direction was used.

RESULTS

Simulations and experimental results showed that the proposed method accurately performed registration and quantification for intravoxel incoherent motion-diffusion kurtosis imaging analysis. The registration accuracy of the proposed method was high for all b values. Furthermore, quantification performance was analyzed through simulations and in vivo experiments, where the proposed method showed the best performance among the compared methods.

CONCLUSION

The proposed method aligns the DWIs and accurately quantifies the intravoxel incoherent motion-diffusion kurtosis imaging parameters.

Simulation of intravoxel incoherent perfusion signal using a realistic capillary network of a mouse brain

PURPOSE

To simulate the intravoxel incoherent perfusion magnetic resonance magnitude signal from the motion of blood particles in three realistic vascular network graphs from a mouse brain.

METHODS

In three networks generated from the cortex of a mouse scanned by two-photon laser microscopy, blood flow in each vessel was simulated using Poiseuille's law. The trajectories, flow speeds and phases acquired by a fixed number of simulated blood particles during a Stejskal-Tanner bipolar pulse gradient scheme were computed. The resulting magnitude signal was obtained by integrating all phases and the pseudo-diffusion coefficient D* was estimated by fitting an exponential signal decay. To better understand the anatomical source of the intravoxel incoherent motion (IVIM) perfusion signal, the above was repeated restricting the simulation to various types of vessel.

RESULTS

The characteristics of the three microvascular networks were respectively vessel lengths (mean ± std. dev.) 67.2 ± 53.6 μm, 59.8 ± 46.2 μm and 64.5 ± 50.9 μm, diameters 6.0 ± 3.5 μm, 5.7 ± 3.6 μm and 6.1 ± 3.7 μm and simulated blood velocity 0.9 ± 1.7 μm/ms, 1.4 ± 2.5 μm/ms and 0.7 ± 2.1 μm/ms. Exponential fitting of the simulated signal decay as a function of b-value resulted in the following D*-values [10-3 mm2 /s]: 31.7, 40.4 and 33.4. The signal decay for low b-values was the largest in the larger vessels, but the smaller vessels and the capillaries accounted for more of the total volume of the networks.

CONCLUSION

This simulation improves the theoretical understanding of the IVIM perfusion estimation method by directly linking the MR IVIM perfusion signal to an ultra-high resolution measurement of the microvascular network and a realistic blood flow simulation.

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.

Impact of b-Value Sampling Scheme on Brain IVIM Parameter Estimation in Healthy Subjects

Purpose:

Intravoxel incoherent motion (IVIM) analysis has attracted the interest of the clinical community due to its close relationship with microperfusion. Nevertheless, there is no clear reference protocol for its implementation; one of the questions being which b-value distribution to use. This study aimed to stress the importance of the sampling scheme and to show that an optimized b-value distribution decreases the variance associated with IVIM parameters in the brain with respect to a regular distribution in healthy volunteers.

Methods:

Ten volunteers were included in this study; images were acquired on a 1.5T MR scanner. Two distributions of 16 b-values were used: one considered ‘regular’ due to its close association with that used in other studies, and the other considered ‘optimized’ according to previous studies. IVIM parameters were adjusted according to the bi-exponential model, using two-step method. Analysis was undertaken in ROI defined using in the Automated Anatomical Labeling atlas, and parameters distributions were compared in a total of 832 ROI.

Results:

Maps with fewer speckles were obtained with the ‘optimized’ distribution. Coefficients of variation did not change significantly for the estimation of the diffusion coefficient D but decreased by approximately 39% for the pseudo-diffusion coefficient estimation and by 21% for the perfusion fraction. Distributions of adjusted parameters were found significantly different in 50% of the cases for the perfusion fraction, in 80% of the cases for the pseudo-diffusion coefficient and 17% of the cases for the diffusion coefficient. Observations across brain areas show that the range of average values for IVIM parameters is smaller in the ‘optimized’ case.

Conclusion:

Using an optimized distribution, data are sampled in a way that the IVIM signal decay is better described and less variance is obtained in the fitted parameters. The increased precision gained could help to detect small variations in IVIM parameters.

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.

Collateral blood flow measurement with intravoxel incoherent motion perfusion imaging in hyperacute brain stroke

Objective

To determine if intravoxel incoherent motion (IVIM) magnetic resonance perfusion can measure the quality of the collateral blood flow in the penumbra in hyperacute stroke.

Methods

A 6 b values IVIM MRI sequence was acquired in stroke patients with large vessel occlusion imaged <16 hours of last seen well. IVIM perfusion measures were evaluated in regions of interest drawn in the infarct core (D < 600 mm2/s), in the corresponding region in the contralateral hemisphere, and in the dynamic susceptibility contrast penumbra. In patients with a penumbra >15 mL, images were reviewed for the presence of a penumbra perfusion lesion on the IVIM f map, which was correlated with infarct size metrics. Statistical significance was tested using Student t test, Mann-Whitney U test, and Fisher exact test.

Results

A total of 34 patients were included. In the stroke core, IVIM f was significantly lower (4.6 ± 3.3%) compared to the healthy contralateral region (6.3 ± 2.2%, p < 0.001). In the 25 patients with a penumbra >15 mL, 9 patients had an IVIM penumbra perfusion lesion (56 ± 76 mL), and 16 did not. Patients with an IVIM penumbra perfusion lesion had a larger infarct core (82 ± 84 mL) at baseline, a larger infarct growth (68 ± 40 mL), and a larger final infarct size (126 ± 81 mL) on follow-up images compared to the patients without (resp. 20 ± 17 mL, p < 0.05; 13 ± 19 mL, p < 0.01; 29 ± 24 mL, p < 0.05). All IVIM penumbra perfusion lesions progressed to infarction despite thrombectomy treatment.

Conclusions

IVIM is a promising tool to assess the quality of the collateral blood flow in hyperacute stroke. IVIM penumbra perfusion lesion may be a marker of nonsalvageable tissue despite treatment with thrombectomy, suggesting that the IVIM penumbra perfusion lesion might be counted to the stroke core, together with the DWI lesion.

Isotropically weighted intravoxel incoherent motion brain imaging at 7T

Perfusion magnetic resonance imaging (MRI) is a promising non-invasive technique providing insights regarding the brain's microvascular architecture in vivo. The scalar perfusion metrics can be used for quantitative diagnostics of various brain abnormalities, in particular, in the stroke cases and tumours. However, conventional MRI-based perfusion approaches such as dynamic contrast-enhanced perfusion imaging or arterial spin labelling have a few weaknesses, for instance, contrast agent deposition, low signal-to-noise ratio, limited temporal and spatial resolution, and specific absorption rate constraints. As an alternative, the intravoxel incoherent motion (IVIM) approach exploits an extension of diffusion MRI in order to estimate perfusion parameters in the human brain. Application of IVIM imaging at ultra-high field MRI might employ the advantage of a higher signal-to-noise ratio, and thereby the use of higher spatial and temporal resolutions. In the present work, we demonstrate an application of recently developed isotropic diffusion weighted sequences to the evaluation of IVIM parameters at an ultra-high 7T field. The used sequence exhibits high immunity to image degrading factors and allows one to acquire the data in a fast and efficient way. Utilising the bi-exponential fitting model of the signal attenuation, we performed an extensive analysis of the IVIM scalar metrics obtained by a isotropic diffusion weighted sequence in vivo and compared results with a conventional pulsed gradient sequence at 7T. In order to evaluate a possible metric bias originating from blood flows, we additionally used a truncated b-value protocol (b-values from 100 to 200 s/mm² with the step 20 s/mm²) accompanied to the full range (b-values from 0 to 200 s/mm²). The IVIM scalar metrics have been assessed and analysed together with a large and middle vessel density atlas of the human brain. We found that the diffusion coefficients and perfusion fractions of the voxels consisting of large and middle vessels have higher values in contrast to other tissues. Additionally, we did not find a strong dependence of the IVIM metrics on the density values of the vessel atlas. Perspectives and limitations of the developed isotropic diffusion weighted perfusion are presented and discussed.

Bayesian intravoxel incoherent motion parameter mapping in the human heart

BACKGROUND

Intravoxel incoherent motion (IVIM) imaging of diffusion and perfusion in the heart suffers from high parameter estimation error. The purpose of this work is to improve cardiac IVIM parameter mapping using Bayesian inference.

METHODS

A second-order motion-compensated diffusion weighted spin-echo sequence with navigator-based slice tracking was implemented to collect cardiac IVIM data in early systole in eight healthy subjects on a clinical 1.5 T CMR system. IVIM data were encoded along six gradient optimized directions with b-values of 0-300 s/mm2. Subjects were scanned twice in two scan sessions one week apart to assess intra-subject reproducibility. Bayesian shrinkage prior (BSP) inference was implemented to determine IVIM parameters (diffusion D, perfusion fraction F and pseudo-diffusion D*). Results were compared to least-squares (LSQ) parameter estimation. Signal-to-noise ratio (SNR) requirements for a given fitting error were assessed for the two methods using simulated data. Reproducibility analysis of parameter estimation in-vivo using BSP and LSQ was performed.

RESULTS

BSP resulted in reduced SNR requirements when compared to LSQ in simulations. In-vivo, BSP analysis yielded IVIM parameter maps with smaller intra-myocardial variability and higher estimation certainty relative to LSQ. Mean IVIM parameter estimates in eight healthy subjects were (LSQ/BSP): 1.63 ± 0.28/1.51 ± 0.14·10-3 mm2/s for D, 13.13 ± 19.81/13.11 ± 5.95% for F and 201.45 ± 313.23/13.11 ± 14.53·10-3 mm2/s for D ∗. Parameter variation across all volunteers and measurements was lower with BSP compared to LSQ (coefficient of variation BSP vs. LSQ: 9% vs. 17% for D, 45% vs. 151% for F and 111% vs. 155% for D ∗). In addition, reproducibility of the IVIM parameter estimates was higher with BSP compared to LSQ (Bland-Altman coefficients of repeatability BSP vs. LSQ: 0.21 vs. 0.26·10-3 mm2/s for D, 5.55 vs. 6.91% for F and 15.06 vs. 422.80·10-3 mm2/s for D*).

CONCLUSION

Robust free-breathing cardiac IVIM data acquisition in early systole is possible with the proposed method. BSP analysis yields improved IVIM parameter maps relative to conventional LSQ fitting with fewer outliers, improved estimation certainty and higher reproducibility. IVIM parameter mapping holds promise for myocardial perfusion measurements without the need for contrast agents.

Intravoxel incoherent motion MRI as a means to measure in vivo perfusion: A review of the evidence

The idea that in vivo intravoxel incoherent motion magnetic resonance signal is influenced by blood motion in the microvasculature is exciting, because it suggests that local and quantitative perfusion information can be obtained in a simple and elegant way from a few diffusion-weighted images, without contrast injection. When the method was proposed in the late 1980s some doubts appeared as to its feasibility, and, probably because the signal to noise and image quality at the time was not sufficient, no obvious experimental evidence could be produced to alleviate them. Helped by the tremendous improvements seen in the last three decades in MR hardware, pulse design, and post-processing capabilities, an increasing number of encouraging reports on the value of intravoxel incoherent motion perfusion imaging have emerged. The aim of this article is to review the current published evidence on the feasibility of in vivo perfusion imaging with intravoxel incoherent motion MRI.

Diffusional kurtosis imaging (DKI) incorporation into an intravoxel incoherent motion (IVIM) MR model to measure cerebral hypoperfusion induced by hyperventilation challenge in healthy subjects

OBJECTIVES

The objectives were to investigate the diffusional kurtosis imaging (DKI) incorporation into the intravoxel incoherent motion (IVIM) model for measurements of cerebral hypoperfusion in healthy subjects.

MATERIALS AND METHODS

Eight healthy subjects underwent a hyperventilation challenge with a 4-min diffusion weighted imaging protocol, using 8 b values chosen with the Cramer-Rao Lower Bound optimization approach. Four regions of interest in gray matter (GM) were analyzed with the DKI-IVIM model and the bi-exponential IVIM model, for normoventilation and hyperventilation conditions.

RESULTS

A significant reduction in the perfusion fraction (f) and in the product fD* of the perfusion fraction with the pseudodiffusion coefficient (D*) was found with the DKI-IVIM model, during the hyperventilation challenge. In the cerebellum GM, the percentage changes were f: -43.7 ± 40.1, p = 0.011 and fD*: -50.6 ± 32.1, p = 0.011; in thalamus GM, f: -47.7 ± 34.7, p = 0.012 and fD*: -47.2 ± 48.7, p = 0.040. In comparison, using the bi-exponential IVIM model, only a significant decrease in the parameter fD* was observed for the same regions of interest. In frontal-GM and posterior-GM, the reduction in f and fD* did not reach statistical significance, either with DKI-IVIM or the bi-exponential IVIM model.

CONCLUSION

When compared to the bi-exponential IVIM model, the DKI-IVIM model displays a higher sensitivity to detect changes in perfusion induced by the hyperventilation condition.

Advanced diffusion MRI and biomarkers in the central nervous system: a new approach

The introduction of diffusion-weighted sequences has revolutionized the detection and characterization of central nervous system (CNS) disease. Nevertheless, the assessment of diffusion studies of the CNS is often limited to qualitative estimation. Moreover, the pathophysiological complexity of the different entities that affect the CNS cannot always be correctly explained through classical models. The development of new models for the analysis of diffusion sequences provides numerous parameters that enable a quantitative approach to both diagnosis and prognosis as well as to monitoring the response to treatment; these parameters can be considered potential biomarkers of health and disease. In this update, we review the physical bases underlying diffusion studies and diffusion tensor imaging, advanced models for their analysis (intravoxel coherent motion and kurtosis), and the biological significance of the parameters derived.

Intravoxel Incoherent Motion Diffusion Weighted Magnetic Resonance Imaging for Differentiation Between Nasopharyngeal Carcinoma and Lymphoma at the Primary Site

OBJECTIVE

The aim of the study was to investigate the utility of intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (DWI) for differentiating nasopharyngeal carcinoma (NPC) from lymphoma.

METHODS

Intravoxel incoherent motion-based parameters including the apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), and fD* (the product of D* and f) were retrospectively compared between 102 patients (82 with NPC, 20 with lymphoma) who received pretreatment IVIM DWI.

RESULTS

Compared with lymphoma, NPC exhibited higher ADC, D, D*, fD* values (P < 0.001) and f value (P = 0.047). The optimal cutoff values (area under the curve, sensitivity, and specificity, respectively) for distinguishing the 2 tumors were as follows: ADC value of 0.761 × 10 mm/s (0.781, 93.90%, 55.00%); D, 0.66 × 10 mm/s (0.802, 54.88%, 100.00%); D*, 7.89 × 10 mm/s (0.898, 82.93%, 85.00%); f, 0.29 (0.644, 41.46%, 95.00%); and fD*, 1.99 × 10 mm/s (0.960, 85.37%, 100.00%).

CONCLUSIONS

Nasopharyngeal carcinoma exhibits different IVIM-based imaging features from lymphoma. Intravoxel incoherent motion DWI is useful for differentiating lymphoma from NPC.

Stroke assessment with intravoxel incoherent motion diffusion-weighted MRI

Intravoxel incoherent motion (IVIM) diffusion-weighted MRI can simultaneously measure diffusion and perfusion characteristics in a non-invasive way. This study aimed to determine the potential utility of IVIM in characterizing brain diffusion and perfusion properties for clinical stroke. The multi-b-value diffusion-weighted images of 101 patients diagnosed with acute/subacute ischemic stroke were retrospectively evaluated. The diffusion coefficient D, representing the water apparent diffusivity, was obtained by fitting the diffusion data with increasing high b-values to a simple mono-exponential model. The IVIM-derived perfusion parameters, pseudodiffusion coefficient D*, vascular volume fraction f and blood flow-related parameter fD*, were calculated with the bi-exponential model. Additionally, the apparent diffusion coefficient (ADC) was fitted according to the mono-exponential model using all b-values. The diffusion parameters for the ischemic lesion and normal contralateral region were measured in each patient. Statistical analysis was performed using the paired Student t-test and Pearson correlation test. Diffusion data in both the ischemic lesion and normal contralateral region followed the IVIM bi-exponential behavior, and the IVIM model showed better goodness of fit than the mono-exponential model with lower Akaike information criterion values. The paired Student t-test revealed significant differences for all diffusion parameters (all P < 0.001) except D* (P = 0.218) between ischemic and normal areas. For all patients in both ischemic and normal regions, ADC was significantly positively correlated with D (both r = 1, both P < 0.001) and f (r = 0.541, P < 0.001; r = 0.262, P = 0.008); significant correlation was also found between ADC and fD* in the ischemic region (r = 0.254, P = 0.010). For all pixels within the region of interest from a representative subject in both ischemic and normal regions, ADC was significantly positively correlated with D (both r = 1, both P < 0.001), f (r = 0.823, P < 0.001; r = 0.652, P < 0.001) and fD* (r = 0.294, P < 0.001; r = 0.340, P < 0.001). These findings may have clinical implications for the use of IVIM imaging in the assessment and management of acute/subacute stroke patients. Copyright © 2016 John Wiley & Sons, Ltd.