Signal to noise and b-value analysis for optimal intra-voxel incoherent motion imaging in the brain.
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ABSTRACT:
SUBMITTER: Merisaari H
PROVIDER: S-EPMC8459980 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The intravoxel incoherent motion (IVIM) model for diffusion-weighted imaging (DWI) MRI data bears much promise as a tool for visualizing tumours and monitoring treatment response. To improve the currently poor precision of IVIM, several fit algorithms have been suggested. In this work, we compared the performance of two Bayesian IVIM fit algorithms and four other IVIM fit algorithms for pancreatic cancer imaging. DWI data were acquired in 14 pancreatic cancer patients during two MRI examinations. Three different measures of performance of the fitting algorithms were assessed: (i) uniqueness of fit parameters (Spearman's rho); (ii) precision (within-subject coefficient of variation, wCV); and (iii) contrast between tumour and normal-appearing pancreatic tissue. For the diffusivity D and perfusion fraction f, a Bayesian fit (IVIM-Bayesian-lin) offered the best trade-off between tumour contrast and precision. With the exception for IVIM-Bayesian-lin, all algorithms resulted in a very poor precision of the pseudo-diffusion coefficient D* with a wCV of more than 50%. The pseudo-diffusion coefficient D* of the Bayesian approaches were, however, significantly correlated with D and f. Therefore, the added value of fitting D* was considered limited in pancreatic cancer patients. The easier implemented least squares fit with fixed D* (IVIM-fixed) performed similar to IVIM-Bayesian-lin for f and D. In conclusion, the best performing IVIM fit algorithm was IVM-Bayesian-lin, but an easier to implement least squares fit with fixed D* performs similarly in pancreatic cancer patients.
| S-EPMC5884505 | biostudies-literature
Project description:In general, only one diffusion model would be applied to whole field-of-view voxels in the intravoxel incoherent motion-magnetic resonance imaging (IVIM-MRI) study. However, the choice of the applied diffusion model can significantly influence the estimated diffusion parameters. The quality of the diffusion analysis can influence the reliability of the perfusion analysis. This study proposed an optimal model mapping method to improve the reliability of the perfusion parameter estimation in the IVIM study. Six healthy volunteers (five males and one female; average age of 38.3 ± 7.5 years). Volunteers were examined using a 3.0 Tesla scanner. IVIM-MRI of the brain was applied at 17 b-values ranging from 0 to 2,500 s/mm2. The Gaussian model, the Kurtosis model, and the Gamma model were found to be optimal for the CSF, white matter (WM), and gray matter (GM), respectively. In the mean perfusion fraction (fp) analysis, the GM/WM ratios were 1.16 (Gaussian model), 1.80 (Kurtosis model), 1.94 (Gamma model), and 1.54 (Optimal model mapping); in the mean pseudo diffusion coefficient (D*) analysis, the GM/WM ratios were 1.18 (Gaussian model), 1.19 (Kurtosis model), 1.56 (Gamma model), and 1.24 (Optimal model mapping). With the optimal model mapping method, the estimated fp and D* were reliable compared with the conventional methods. In addition, the optimal model maps, the associated products of this method, may provide additional information for clinical diagnosis.
| S-EPMC7937866 | biostudies-literature
Project description:ObjectivesTo determine the potential of intravoxel incoherent motion (IVIM) MR imaging for staging of hepatic fibrosis (HF).MethodsWe searched PubMed and EMBASE from their inception to 31 July 2015 to select studies reporting IVIM MR imaging and HF staging. We defined F1-2 as non-advanced HF, F3-4 as advanced HF, F0 as normal liver, F1 as very early HF, and F2-4 as significant HF. Then we compared stage F0 with F1, F0-1 with F2-3, and F1-2 with F3-4 using IVIM-derived parameters (pseudo-diffusion coefficient D*, perfusion fraction f, and pure molecular diffusion parameter D). The effect estimate was expressed as a pooled weighted mean difference (WMD) with 95% confidence interval (CI), using the fixed-effects model.ResultsOverall, we included six papers (406 patients) in this study. Significant differences in D* were observed between F0 and F1, F0-1 and F2-3, and F1-2 and F3-4 (WMD 2.46, 95% CI 0.83-4.09, P = 0.006; WMD 13.10, 95% CI 9.53-16.67, P < 0.001; WMD 14.34, 95% CI 10.26-18.42, P < 0.001, respectively). Significant differences in f were also found between F0 and F1, F0-1 and F2-3, and F1-2 and F3-4 (WMD 1.62, 95% CI 0.06-3.18, P = 0.027; WMD 5.63, 95% CI 2.74-8.52, P < 0.001; WMD 3.30, 95% CI 2.10-4.50, P < 0.001, respectively). However, D showed no differences between F0 and F1, F0-1 and F2-3, and F1-2 and F3-4 (WMD 0.05, 95% CI -0.01─0.11, P = 0.105; WMD 0.04, 95% CI -0.01─0.10, P = 0.230; WMD 0.02, 95% CI -0.02─0.06, P = 0.378, respectively).ConclusionsIVIM MR imaging provides an effective method of staging HF and can distinguish early HF from normal liver, significant HF from normal liver or very early HF, and advanced HF from non-advanced HF.
| S-EPMC4731200 | biostudies-literature
Project description:The motion energy model is the standard account of motion detection in animals from beetles to humans. Despite this common basis, we show here that a difference in the early stages of visual processing between mammals and insects leads this model to make radically different behavioural predictions. In insects, early filtering is spatially lowpass, which makes the surprising prediction that motion detection can be impaired by "invisible" noise, i.e. noise at a spatial frequency that elicits no response when presented on its own as a signal. We confirm this prediction using the optomotor response of praying mantis Sphodromantis lineola. This does not occur in mammals, where spatially bandpass early filtering means that linear systems techniques, such as deriving channel sensitivity from masking functions, remain approximately valid. Counter-intuitive effects such as masking by invisible noise may occur in neural circuits wherever a nonlinearity is followed by a difference operation.
| S-EPMC5471215 | biostudies-literature
Project description:At very low diffusion weighting the diffusion MRI signal is affected by intravoxel incoherent motion (IVIM) caused by dephasing of magnetization due to incoherent blood flow in capillaries or other sources of microcirculation. While IVIM measurements at low diffusion weightings have been frequently used to investigate perfusion in the body as well as in malignant tissue, the effect and origin of IVIM in normal brain tissue is not completely established. We investigated the IVIM effect on the brain diffusion MRI signal in a cohort of 137 radiologically-normal patients (62 male; mean age = 50.2 ± 17.8, range = 18 to 94). We compared the diffusion tensor parameters estimated from a mono-exponential fit at b = 0 and 1000 s/mm2 versus at b = 250 and 1000 s/mm2. The asymptotic fitting method allowed for quantitative assessment of the IVIM signal fraction f* in specific brain tissue and regions. Our results show a mean (median) percent difference in the mean diffusivity of about 4.5 (4.9)% in white matter (WM), about 7.8 (8.7)% in cortical gray matter (GM), and 4.3 (4.2)% in thalamus. Corresponding perfusion fraction f* was estimated to be 0.033 (0.032) in WM, 0.066 (0.065) in cortical GM, and 0.033 (0.030) in the thalamus. The effect of f* with respect to age was found to be significant in cortical GM (Pearson correlation ρ = 0.35, p = 3*10-5) and the thalamus (Pearson correlation ρ = 0.20, p = 0.022) with an average increase in f* of 5.17*10-4/year and 3.61*10-4/year, respectively. Significant correlations between f* and age were not observed for WM, and corollary analysis revealed no effect of gender on f*. Possible origins of the IVIM effect in normal brain tissue are discussed.
| S-EPMC6886883 | biostudies-literature
Project description:PurposeFlow-compensated (FC) diffusion-weighted MRI (DWI) for intravoxel-incoherent motion (IVIM) modeling allows for a more detailed description of tissue microvasculature than conventional IVIM. The long acquisition time of current FC-IVIM protocols, however, has prohibited clinical application. Therefore, we developed an optimized abdominal FC-IVIM acquisition with a clinically feasible scan time.MethodsPrecision and accuracy of the FC-IVIM parameters were assessed by fitting the FC-IVIM model to signal decay curves, simulated for different acquisition schemes. Diffusion-weighted acquisitions were added subsequently to the protocol, where we chose the combination of b-value, diffusion time and gradient profile (FC or bipolar) that resulted in the largest improvement to its accuracy and precision. The resulting two optimized FC-IVIM protocols with 25 and 50 acquisitions (FC-IVIMopt25 and FC-IVIMopt50 ), together with a complementary acquisition consisting of 50 diffusion-weighting (FC-IVIMcomp ), were acquired in repeated abdominal free-breathing FC-IVIM imaging of seven healthy volunteers. Intersession and intrasession within-subject coefficient of variation of the FC-IVIM parameters were compared for the liver, spleen, and kidneys.ResultsSimulations showed that the performance of FC-IVIM improved in tissue with larger perfusion fraction and signal-to-noise ratio. The scan time of the FC-IVIMopt25 and FC-IVIMopt50 protocols were 8 and 16 min. The best in vivo performance was seen in FC-IVIMopt50 . The intersession within-subject coefficients of variation of FC-IVIMopt50 were 11.6%, 16.3%, 65.5%, and 36.0% for FC-IVIM model parameters diffusivity, perfusion fraction, characteristic time and blood flow velocity, respectively.ConclusionsWe have optimized the FC-IVIM protocol, allowing for clinically feasible scan times (8-16 min).
| S-EPMC6899942 | biostudies-literature
Project description:Measurement of microvascular perfusion with Intravoxel Incoherent Motion (IVIM) MRI is gaining interest. Yet, the physiological influences on the IVIM perfusion parameters ("pseudo-diffusion" coefficient D*, perfusion fraction f, and flow related parameter fD*) remain insufficiently characterized. In this article, we hypothesize that D* and fD*, which depend on blood speed, should vary during the cardiac cycle. We extended the IVIM model to include time dependence of D*?=?D*(t), and demonstrate in the healthy human brain that both parameters D* and fD* are significantly larger during systole than diastole, while the diffusion coefficient D and f do not vary significantly. The results non-invasively demonstrate the pulsatility of the brain's microvasculature.
| S-EPMC3758329 | biostudies-literature
Project description:The present study aimed to explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC). This study included 65 patients with malignant hepatic nodules (55 with HCC, 10 with ICC), and 17 control patients with normal livers. All patients underwent IVIM-DWI scans on a 3.0?T magnetic resonance imaging (MRI) scanner. The standard apparent diffusion coefficient (ADC), pure diffusion coefficient (Dslow), pseudo-diffusion coefficient (Dfast), and perfusion fraction (f) were obtained. Differences in the parameters among the groups were analysed using one-way ANOVA, with p?<?0.05 indicating statistical significance. Receiver operating characteristic (ROC) curve analysis was used to compare the efficacy of each parameter in differentiating HCC from ICC. ADC, Dslow, Dfast, f significantly differed among the three groups. ADC and Dslow were significantly lower in the HCC group than in the ICC group, while Dfast was significantly higher in the HCC group than in the ICC group; f did not significantly differ between the HCC and ICC groups. When the cut-off values of ADC, Dslow, and Dfast were 1.27 × 10-3 mm2/s, 0.81 × 10-3 mm2/s, and 26.04 × 10-3 mm2/s, respectively, their diagnostic sensitivities for differentiating HCC from ICC were 98.18%, 58.18%, and 94.55%, their diagnostic specificities were 50.00%, 80.00%, and 80.00%, and their areas under the ROC curve (AUCs) were 0.687, 0.721, and 0.896, respectively. Dfast displayed the largest AUC value. IVIM-DWI can be used to differentiate HCC from ICC.
| S-EPMC7206040 | biostudies-literature
Project description:ObjectiveTo evaluate the feasibility of a parameter-free intravoxel incoherent motion (IVIM) approach in cervical cancer, to assess the optimal b-value threshold, and to preliminarily examine differences in the derived perfusion and diffusion parameters for different histological cancer types.Materials and methodsAfter Institutional Review Board approval, 19 female patients (mean age, 54 years; age range, 37-78 years) gave consent and were enrolled in this prospective magnetic resonance imaging study. Clinical staging and biopsy results were obtained. Echo-planar diffusion weighted sequences at 13 b-values were acquired at 3 tesla field strength. Single-sliced region-of-interest IVIM analysis with adaptive b-value thresholds was applied to each tumor, yielding the optimal fit and the optimal parameters for pseudodiffusion (D*), perfusion fraction (Fp) and diffusion coefficient (D). Monoexponential apparent diffusion coefficient (ADC) was calculated for comparison with D.ResultsBiopsy revealed squamous cell carcinoma in 10 patients and adenocarcinoma in 9. The b-value threshold (median [interquartile range]) depended on the histological type and was 35 (22.5-50) s/mm2 in squamous cell carcinoma and 150 (100-150) s/mm2 in adenocarcinoma (p < 0.05). Comparing squamous cell vs. adenocarcinoma, D* (45.1 [25.1-60.4] × 10-3 mm2/s vs. 12.4 [10.5-21.2] × 10-3 mm2/s) and Fp (7.5% [7.0-9.0%] vs. 9.9% [9.0-11.4%]) differed significantly between the subtypes (p < 0.02), whereas D did not (0.89 [0.75-0.94] × 10-3 mm2/s vs. 0.90 [0.82-0.97] × 10-3 mm2/s, p = 0.27). The residuals did not differ (0.74 [0.60-0.92] vs. 0.94 [0.67-1.01], p = 0.32). The ADC systematically underestimated the magnitude of diffusion restriction compared to D (p < 0.001).ConclusionThe parameter-free IVIM approach is feasible in cervical cancer. The b-value threshold and perfusion-related parameters depend on the tumor histology type.
| S-EPMC5390620 | biostudies-literature
Project description:The variability inherently present in biophysical data is partly contributed by disparate sampling resolutions across instrumentations. This poses a potential problem for statistical inference using pooled data in open access repositories. Such repositories combine data collected from multiple research sites using variable sampling resolutions. One example is the Autism Brain Imaging Data Exchange repository containing thousands of imaging and demographic records from participants in the spectrum of autism and age-matched neurotypical controls. Further, statistical analyses of groups from different diagnoses and demographics may be challenging, owing to the disparate number of participants across different clinical subgroups. In this paper, we examine the noise signatures of head motion data extracted from resting state fMRI data harnessed under different sampling resolutions. We characterize the quality of the noise in the variability of the raw linear and angular speeds for different clinical phenotypes in relation to age-matched controls. Further, we use bootstrapping methods to ensure compatible group sizes for statistical comparison and report the ranges of physical involuntary head excursions of these groups. We conclude that different sampling rates do affect the quality of noise in the variability of head motion data and, consequently, the type of random process appropriate to characterize the time series data. Further, given a qualitative range of noise, from pink to brown noise, it is possible to characterize different clinical subtypes and distinguish them in relation to ranges of neurotypical controls. These results may be of relevance to the pre-processing stages of the pipeline of analyses of resting state fMRI data, whereby head motion enters the criteria to clean imaging data from motion artifacts.
| S-EPMC5844956 | biostudies-literature