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.

Intravoxel incoherent motion (IVIM) and non-Gaussian diffusion MRI of the lactating breast

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Breastfeeding significantly affects IVIM and non-Gaussian diffusion parameters.

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The distribution of ADC₀ values becomes more heterogeneous after breastfeeding.

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Care needs to be taken in interpreting DWI data in lactating breasts.

Purpose

To investigate the effect of breastfeeding on IVIM and non-Gaussian diffusion MRI in the breast.

Materials and methods

An IRB approved prospective study enrolled seventeen volunteers (12 in lactation and 5 with post-weaning, range 31–43 years; mean 35.4 years). IVIM (f_IVIM and D*) and non-Gaussian diffusion (ADC₀ and K) parameters using 16 b values, plus synthetic apparent diffusion coefficients (sADCs) from 2 key b values (b = 200 and 1500 s/mm²) were calculated using regions of interest. ADC₀ maps of the whole breast were generated and their contrast patterns were evaluated by two independent readers using retroareolar and segmental semi-quantitative scores. To compare the diffusion and IVIM parameters, Wilcoxon signed rank tests were used between pre- and post-breastfeeding and Mann-Whitney tests were used between post-weaning and pre- or post-breastfeeding.

Results

ADC₀ and sADC values significantly decreased post-breastfeeding (1.90 vs. 1.72 × 10⁻³ mm²/s, P < 0.001 and 1.39 vs. 1.25 × 10⁻³ mm²/s, P < 0.001) while K values significantly increased (0.33 vs. 0.44, P < 0.05). f_IVIM values significantly increased after breastfeeding (1.97 vs. 2.97%, P < 0.01). No significant difference was found in D* values. There was significant heterogeneity in ADC₀ maps post-breastfeeding, both in retroareolar and segmental scores (P < 0.0001 and =0.0001).

Conclusion

IVIM and non-Gaussian diffusion parameters significantly changed between pre- and post-breastfeeding status, and care needs to be taken in interpreting diffusion-weighted imaging (DWI) data in lactating breasts.

Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI

Objective

To differentiate prostate cancer lesions in transition zone by diffusion-weighted-MRI (DW-MRI).

Methods

Data from a total of 63 patients who underwent preoperative DWI (b of 0–1000 s/mm²) were prospectively collected and processed by a monoexponential (DWI) model and compared with a biexponential (IVIM) model for quantitation of apparent diffusion coefficients (ADCs), perfusion fraction f, diffusivity D and pseudo-diffusivity D*. Histogram analyses were performed by outlining entire-tumor regions of interest (ROIs). These parameters (separately and combined in a logistic regression model) were used to differentiate lesions depending on histopathological analysis of Magnetic Resonance/transrectal Ultrasound (MR/TRUS) fusion-guided biopsy. The diagnostic ability of differentiate the PCa from BHP in TZ was analyzed by ROC regression. Histogram analysis of quantitative parameters and Gleason score were assessed with Spearman correlation.

Results

Thirty (30 foci) cases of PCa in PZ and 33 (36 foci) cases of BPH were confirmed by pathology. Mean ADC, median ADC, 10th percentile ADC, 90th percentile ADC, kurtosis and skewness of ADC and mean D values, median D and 90th percentile D differed significantly between PCa and BHP in TZ. The highest classification accuracy was achieved by the mean ADC (0.841) and mean D (0.809). A logistic regression model based on mean ADC and mean D led to an AUC of 0.873, however, the difference is not significant. There were 7 Gleason 6 areas, 9 Gleason 7 areas, 8 Gleason 8 areas, 5 Gleason 9 areas and 2 Gleason 10 areas detected from the 31 prostate cancer areas, the mean Gleason value was(7.5 ± 1.2). The mean ADC and mean D had correlation with Gleason score(r = −0.522 and r = −0.407 respectively, P < 0.05).

Conclusion

The diagnosis efficiency of IVIM parameters was not superior to ADC in the diagnosis of PCa in TZ. Moreover, the combination of mean ADC and mean D did not perform better than the parameters alone significantly; It is feasible to stratify the pathological grade of prostate cancer by mean ADC.

Hepatocellular carcinoma: IVIM diffusion quantification for prediction of tumor necrosis compared to enhancement ratios

Purpose

To correlate intra voxel incoherent motion (IVIM) diffusion parameters of liver parenchyma and hepatocellular carcinoma (HCC) with degree of liver/tumor enhancement and necrosis; and to assess the diagnostic performance of diffusion parameters vs. enhancement ratios (ER) for prediction of complete tumor necrosis.

Patients and methods

In this IRB approved HIPAA compliant study, we included 46 patients with HCC who underwent IVIM diffusion-weighted (DW) MRI in addition to routine sequences at 3.0 T. True diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (PF) and apparent diffusion coefficient (ADC) were quantified in tumors and liver parenchyma. Tumor ER were calculated using contrast-enhanced imaging, and degree of tumor necrosis was assessed using post-contrast image subtraction. IVIM parameters and ER were compared between HCC and background liver and between necrotic and viable tumor components. ROC analysis for prediction of complete tumor necrosis was performed.

Results

79 HCCs were assessed (mean size 2.5 cm). D, PF and ADC were significantly higher in HCC vs. liver (p < 0.0001). There were weak significant negative/positive correlations between D/PF and ER, and significant correlations between D/PF/ADC and tumor necrosis (for D, r 0.452, p < 0.001). Among diffusion parameters, D had the highest area under the curve (AUC 0.811) for predicting complete tumor necrosis. ER outperformed diffusion parameters for prediction of complete tumor necrosis (AUC > 0.95, p < 0.002).

Conclusion

D has a reasonable diagnostic performance for predicting complete tumor necrosis, however lower than that of contrast-enhanced imaging.