Cramér-Rao bound for Intravoxel Incoherent Motion Diffusion Weighted Imaging fitting

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.

Simplified perfusion fraction from diffusion-weighted imaging in preoperative prediction of IDH1 mutation in WHO grade II-III gliomas: comparison with dynamic contrast-enhanced and intravoxel incoherent motion MRI

Background Effect of isocitr ate dehydrogenase 1 (IDH1) mutation in neovascularization might be linked with tissue perfusion in gliomas. At present, the need of injection of contrast agent and the increasing scanning time limit the application of perfusion techniques. We used a simplified intravoxel incoherent motion (IVIM)-derived perfusion fraction (SPF) calculated from diffusion-weighted imaging (DWI) using only three b-values to quantitatively assess IDH1-linked tissue perfusion changes in WHO grade II-III gliomas (LGGs). Additionally, by comparing accuracy with dynamic contrast-enhanced (DCE) and full IVIM MRI, we tried to find the optimal imaging markers to predict IDH1 mutation status. Patients and methods Thirty patients were prospectively examined using DCE and multi-b-value DWI. All parameters were compared between the IDH1 mutant and wild-type LGGs using the Mann-Whitney U test, including the DCE MRI-derived Ktrans, ve and vp, the conventional apparen t diffusion coefficient (ADC0,1000), IVIM-de rived perfusion fraction (f), diffusion coefficient (D) and pseudo-diffusion coefficient (D*), SPF. We evaluated the diagnostic performance by receive r operating characteristic (ROC) analysis. Results Significant differences were detected between WHO grade II-III gliomas for all perfusion and diffusion parameters (P < 0.05). When compared to IDH1 mutant LGGs, IDH1 wild-type LGGs exhibited significantly higher perfusion metrics (P < 0.05) and lower diffusion metrics (P < 0.05). Among all parameters, SPF showed a higher diagnostic performance (area under the curve 0.861), with 94.4% sensitivity and 75% specificity. Conclusions DWI, DCE and IVIM MRI may noninvasively help discriminate IDH1 mutation statuses in LGGs. Specifically, simplified DWI-derived SPF showed a superior diagnostic performance.

Topics on quantitative liver magnetic resonance imaging

Liver magnetic resonance imaging (MRI) is subject to continuous technical innovations through advances in hardware, sequence and novel contrast agent development. In order to utilize the abilities of liver MR to its full extent and perform high-quality efficient exams, it is mandatory to use the best imaging protocol, to minimize artifacts and to select the most adequate type of contrast agent. In this article, we review the routine clinical MR techniques applied currently and some latest developments of liver imaging techniques to help radiologists and technologists to better understand how to choose and optimize liver MRI protocols that can be used in clinical practice. This article covers topics on (I) fat signal suppression; (II) diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) analysis; (III) dynamic contrast-enhanced (DCE) MR imaging; (IV) liver fat quantification; (V) liver iron quantification; and (VI) scan speed acceleration.

Intravoxel incoherent motion derived liver perfusion/diffusion readouts can be reliable biomarker for the detection of viral hepatitis B induced liver fibrosis

BACKGROUND

Recent two studies reported that intravoxel incoherent motion (IVIM) analysis can separate healthy livers and viral hepatitis B (VHB) induced liver fibrosis. However, in these two studies the starting b value for bi-exponential decay analysis was b =10 and 15 s/mm2 respectively. The current study has two primary aims. The first is to further confirm the diagnostic value of IVIM in detecting liver fibrosis. The second is to test whether by sampling very low b value densely, then b =0 s/mm2 image could be included to improve IVIM's diagnostic performance.

METHODS

This was a prospective study with data acquired at the Third Xiangya Hospital of Central South University, Changsha, China. Healthy volunteers and patients suspected of VHB induced liver fibrosis with liver biopsy performed, as well as hepatocellular carcinoma patients scheduled for surgery, were recruited. All the hepatocellular carcinoma patients had liver fibrosis. After exclusions based on pre-defined criteria for image data quality, for IVIM analysis this study included 20 healthy volunteers; 4 chronic VHB patients with biopsy showing no liver fibrosis; 11 stage-1 liver fibrosis patients, 10 stage-2 liver fibrosis patients, 2 stage-3 liver fibrosis patients, and 5 stage-4 liver fibrosis patients. In the liver fibrosis patients, 1, 19, and 8 cases had inflammation grade-0, grade-1, and grade-2 respectively. The reference IVIM bi-exponential decay curve fitting analysis was segmented fitting performed with b =2 s/mm2 image as the starting point and a threshold-b of 60 s/mm2. This reference fitting method was compared with threshold-b of 40 s/mm2, full fitting, fitting starting from b =0, 5, and 10 s/mm2 respectively. The potential correlation between IVIM readouts and liver function was assessed for the liver fibrosis patients.

RESULTS

Based on the smaller coefficient of variation (CoV) for the volunteer group and the smaller patient/volunteer ratios [= (mean measurement for patient groups)/(mean measurement for healthy volunteers)], the comparison of fitting methods favored the reference approach starting from b =2 s/mm2 with a threshold-b of 60 s/mm2. The IVIM measures of four patients without liver fibrosis resembled those of healthy subjects. PF offered the best diagnostic value for separating healthy livers and fibrotic livers, and a threshold of PF =0.1406 separated all fibrotic livers and healthy livers with an exception of one hepatocellular carcinoma patient (fibrosis grade-2/inflammation grade-2). The correlation between fibrosis grading and inflammation grading was weakly positive; while compared with fibrotic livers with inflammation grade-1, fibrotic livers with inflammation grade-2 showed a trend of higher Dfast. A weak correlation is shown with lower PF and lower Dfast associated with lower total protein, lower albumin; higher alanine transaminase, higher aspartate transaminase; higher total bilirubin, and higher direct bilirubin.

CONCLUSIONS

Segmented-fitting with threshold-b =60 s/mm2 and starting from non-zero very low b value outperforms other methods. IVIM has high sensitivity in detecting liver fibrosis, and PF and Dfast have potential correlation with serum liver function biomarkers. IVIM measures and liver fibrosis grading are not in a linear relationship.

High performance of intravoxel incoherent motion diffusion MRI in detecting viral hepatitis-b induced liver fibrosis

BACKGROUND

Recently a small cohort study demonstrated that intravoxel incoherent motion (IVIM) diffusion MRI can detect early stage liver fibrosis. Using modified IVIM data acquisition parameters, the current study aims to confirm this finding.

METHODS

Twenty-six healthy volunteers, three patients of chronic viral hepatitis-b but without fibrosis and one mild liver steatosis subject, and 12 viral hepatitis-b patients with fibrosis (stage 1-2=7, stage 3-4=5) were included in this study. With a 1.5-T MR scanner and respiration-gating, IVIM diffusion imaging was acquired using a single-shot echo-planar sequence with a b-value series of 2, 0, 1, 15, 20, 30, 45, 50, 60, 80, 100, 200, 300, 600, 800 s/mm2. Signal measurement was performed on right liver parenchyma. The first three very low b-values were excluded to improve the curve fitting stability, and bi-exponential segmented fitting was performed using the 12 b-values of 15~800 s/mm2. Both threshold b-values of 60 s/mm2 and 200 s/mm2 were tested. With a 3-dimensional tool, Dslow (D), PF (f) and Dfast (D*) values were placed along the x-axis, y-axis, and z-axis, and a plane was defined to separate healthy volunteers from liver fibrosis patients.

RESULTS

Threshold b-value of 60 s/mm2 was preferred over 200 s/mm2 for separating healthy volunteers and liver fibrosis patients. The IVIM measures of the four patients without fibrosis resembled those of healthy volunteers. When threshold b-value =60 s/mm2 was applied, PF (PF <6.49%) could differentiate healthy livers and all fibrotic livers with 100% sensitivity and specificity. For the patients' measurement, PF and Dfast were highly correlated with a Pearson correlation coefficient r of 0.865 (P<0.001); while the correlations between slow diffusion compartment (Dslow) and fast diffusion compartment (Dfast or PF) were not statistically significant.

CONCLUSIONS

This study confirms previous report that IVIM diffusion MRI has high diagnostic performance in detecting viral hepatitis-b induced liver fibrosis.

A Simplified Model for Intravoxel Incoherent Motion Perfusion Imaging of the Brain

BACKGROUND AND PURPOSE

Despite a recent resurgence, intravoxel incoherent motion MRI faces practical challenges, including limited SNR and demanding acquisition and postprocessing requirements. A simplified approach using linear fitting of a subset of higher b-values has seen success in other organ systems. We sought to validate this method for evaluation of brain pathology by comparing perfusion measurements using simplified linear fitting to conventional biexponential fitting.

MATERIALS AND METHODS

Forty-nine patients with gliomas and 17 with acute strokes underwent 3T MRI, including DWI with 16 b-values (range, 0-900 s/mm²). Conventional intravoxel incoherent motion was performed using nonlinear fitting of the standard biexponential equation. Simplified intravoxel incoherent motion was performed using linear fitting of the log-normalized signal curves for subsets of b-values >200 s/mm². Comparisons between ROIs (tumors, strokes, contralateral brain) and between models (biexponential and simplified linear) were performed by using 2-way ANOVA. The root mean square error and coefficient of determination (R²) were computed for the simplified model, with biexponential fitting as the reference standard.

RESULTS

Perfusion maps using simplified linear fitting were qualitatively similar to conventional biexponential fitting. The perfusion fraction was elevated in high-grade (n = 33) compared to low-grade (n = 16) gliomas and was reduced in strokes compared to the contralateral brain (P < .001 for both main effects). Decreasing the number of b-values used for linear fitting resulted in reduced accuracy (higher root mean square error and lower R²) compared with full biexponential fitting.

CONCLUSIONS

Intravoxel incoherent motion perfusion imaging of common brain pathology can be performed by using simplified linear fitting, with preservation of clinically relevant perfusion information.

Statistical assessment of bi-exponential diffusion weighted imaging signal characteristics induced by intravoxel incoherent motion in malignant breast tumors

BACKGROUND

The purpose of this study is to statistically assess whether bi-exponential intravoxel incoherent motion (IVIM) model better characterizes diffusion weighted imaging (DWI) signal of malignant breast tumor than mono-exponential Gaussian diffusion model.

METHODS

3 T DWI data of 29 malignant breast tumors were retrospectively included. Linear least-square mono-exponential fitting and segmented least-square bi-exponential fitting were used for apparent diffusion coefficient (ADC) and IVIM parameter quantification, respectively. F-test and Akaike Information Criterion (AIC) were used to statistically assess the preference of mono-exponential and bi-exponential model using region-of-interests (ROI)-averaged and voxel-wise analysis.

RESULTS

For ROI-averaged analysis, 15 tumors were significantly better fitted by bi-exponential function and 14 tumors exhibited mono-exponential behavior. The calculated ADC, D (true diffusion coefficient) and f (pseudo-diffusion fraction) showed no significant differences between mono-exponential and bi-exponential preferable tumors. Voxel-wise analysis revealed that 27 tumors contained more voxels exhibiting mono-exponential DWI decay while only 2 tumors presented more bi-exponential decay voxels. ADC was consistently and significantly larger than D for both ROI-averaged and voxel-wise analysis.

CONCLUSIONS

Although the presence of IVIM effect in malignant breast tumors could be suggested, statistical assessment shows that bi-exponential fitting does not necessarily better represent the DWI signal decay in breast cancer under clinically typical acquisition protocol and signal-to-noise ratio (SNR). Our study indicates the importance to statistically examine the breast cancer DWI signal characteristics in practice.

Caveat of measuring perfusion indexes using intravoxel incoherent motion magnetic resonance imaging in the human brain

OBJECTIVES

To numerically and experimentally investigate the robustness of intravoxel incoherent motion (IVIM) magnetic resonance imaging in measuring perfusion indexes in the human brain.

METHODS

Eighteen healthy volunteers were imaged on a 3 T clinical system. Data of IVIM imaging (12 b-values ranging from 0 to 1000 s/mm(2), 12 repetitions) were fitted with a bi-exponential model to extract blood volume fraction (f) and pseudo-diffusion coefficient (D*). The robustness of measurement was assessed by bootstrapping. Dynamic susceptibility contrast (DSC) imaging and arterial spin-labelling (ASL) imaging were performed for cross-modal comparison. Numerical simulations were performed to assess the accuracy and precision of f and D* estimates at varied signal-to-noise ratio (SNRb1000).

RESULTS

Based on our experimental setting (SNRb1000 ~ 30), the average error/variability is ~5 %/25 % for f and ~100 %/30 % for D* in gray matter, and ~10 %/50 % for f and ~300 %/60 % for D* in white matter. Correlation was found between f and DSC-derived cerebral blood volume in gray matter (r = 0.29 - 0.48 across subjects, p < 10(-5)), but not in white matter. No correlation was found between f-D* product and ASL-derived cerebral blood flow.

CONCLUSIONS

f may provide noninvasive measurement of cerebral blood volume, particularly in gray matter. D* has limited robustness and should be interpreted with caution.

KEY POINTS

• A minimum SNR b1000 of 30 is recommended for reliable IVIM imaging. • f may provide noninvasive measurement of cerebral blood volume. • f correlates with CBV DSC in gray matter. • There is no correlation between fD* and CBF ASL . • D* has limited robustness and should be interpreted with caution.

Decreases in molecular diffusion, perfusion fraction and perfusion-related diffusion in fibrotic livers: a prospective clinical intravoxel incoherent motion MR imaging study

Purpose

This study was aimed to determine whether pure molecular-based diffusion coefficient (D) and perfusion-related diffusion parameters (perfusion fraction f, perfusion-related diffusion coefficient D*) differ in healthy livers and fibrotic livers through intra-voxel incoherent motion (IVIM) MR imaging.

Material and Methods

17 healthy volunteers and 34 patients with histopathologically confirmed liver fibrosis patients (stage 1 = 14, stage 2 = 8, stage 3& 4 = 12, METAVIR grading) were included. Liver MR imaging was performed at 1.5-T. IVIM diffusion weighted imaging sequence was based on standard single-shot DW spin echo-planar imaging, with ten b values of 10, 20, 40, 60, 80, 100, 150, 200, 400, 800 sec/mm² respectively. Pixel-wise realization and regions-of-interest based quantification of IVIM parameters were performed.

Results

D, f, and D* in healthy volunteer livers and patient livers were 1.096±0.155 vs 0.917±0.152 (10⁻³ mm²/s, p = 0.0015), 0.164±0.021 vs 0.123±0.029 (p<0.0001), and 13.085±2.943 vs 9.423±1.737 (10⁻³ mm²/s, p<0.0001) respectively, all significantly lower in fibrotic livers. As the fibrosis severity progressed, D, f, and D* values decreased, with a trend significant for f and D*.

Conclusion

Fibrotic liver is associated with lower pure molecular diffusion, lower perfusion volume fraction, and lower perfusion-related diffusion. The decrease of f and D* in the liver is significantly associated liver fibrosis severity.

Non-Gaussian analysis of diffusion weighted imaging in head and neck at 3T: a pilot study in patients with nasopharyngeal carcinoma

PURPOSE

To technically investigate the non-Gaussian diffusion of head and neck diffusion weighted imaging (DWI) at 3 Tesla and compare advanced non-Gaussian diffusion models, including diffusion kurtosis imaging (DKI), stretched-exponential model (SEM), intravoxel incoherent motion (IVIM) and statistical model in the patients with nasopharyngeal carcinoma (NPC).

MATERIALS AND METHODS

After ethics approval was granted, 16 patients with NPC were examined using DWI performed at 3T employing an extended b-value range from 0 to 1500 s/mm(2). DWI signals were fitted to the mono-exponential and non-Gaussian diffusion models on primary tumor, metastatic node, spinal cord and muscle. Non-Gaussian parameter maps were generated and compared to apparent diffusion coefficient (ADC) maps in NPC.

RESULTS

Diffusion in NPC exhibited non-Gaussian behavior at the extended b-value range. Non-Gaussian models achieved significantly better fitting of DWI signal than the mono-exponential model. Non-Gaussian diffusion coefficients were substantially different from mono-exponential ADC both in magnitude and histogram distribution.

CONCLUSION

Non-Gaussian diffusivity in head and neck tissues and NPC lesions could be assessed by using non-Gaussian diffusion models. Non-Gaussian DWI analysis may reveal additional tissue properties beyond ADC and holds potentials to be used as a complementary tool for NPC characterization.