Characterizing non-gaussian, high b-value diffusion in liver fibrosis: Stretched exponential and diffusional kurtosis modeling

Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice

Technologic advances enable performance of diffusion-weighted imaging (DWI) at ultrahigh b-values, where standard monoexponential model analysis may not apply. Rather, non-Gaussian water diffusion properties emerge, which in cellular tissues are, in part, influenced by the intracellular environment that is not well evaluated by conventional DWI. The novel technique, diffusion kurtosis imaging (DKI), enables characterization of non-Gaussian water diffusion behavior. More advanced mathematical curve fitting of the signal intensity decay curve using the DKI model provides an additional parameter Kapp that presumably reflects heterogeneity and irregularity of cellular microstructure, as well as the amount of interfaces within cellular tissues. Although largely applied for neural applications over the past decade, a small number of studies have recently explored DKI outside the brain. The most investigated organ is the prostate, with preliminary studies suggesting improved tumor detection and grading using DKI. Although still largely in the research phase, DKI is being explored in wider clinical settings. When assessing extracranial applications of DKI, careful attention to details with which body radiologists may currently be unfamiliar is important to ensure reliable results. Accordingly, a robust understanding of DKI is necessary for radiologists to better understand the meaning of DKI-derived metrics in the context of different tumors and how these metrics vary between tumor types and in response to treatment. In this review, we outline DKI principles, propose biostructural basis for observations, provide a comparison with standard monoexponential fitting and the apparent diffusion coefficient, report on extracranial clinical investigations to date, and recommend technical considerations for implementation in body imaging.

Diffusion Magnetic Resonance Imaging: What Water Tells Us about Biological Tissues

Since its introduction in the mid-1980s, diffusion magnetic resonance imaging (MRI), which measures the random motion of water molecules in tissues, revealing their microarchitecture, has become a pillar of modern neuroimaging. Its main clinical domain has been the diagnosis of acute brain stroke and neurogical disorders, but it is also used in the body for the detection and management of cancer lesions. It can also produce stunning maps of white matter tracks in the brain, with the potential to aid in the understanding of some psychiatric disorders. However, in order to exploit fully the potential of this method, a deeper understanding of the mechanisms that govern the diffusion of water in tissues is needed.

Diffusion kurtosis imaging to assess response to treatment in hypervascular hepatocellular carcinoma

OBJECTIVE

The objective of our study was to compare diffusion kurtosis imaging (DKI) with conventional diffusion-weighted imaging (DWI) for assessing the response to treatment in hypervascular hepatocellular carcinoma (HCC).

SUBJECTS AND METHODS

Sixty-two consecutive patients with treated or untreated hypervascular HCC underwent MRI of the liver including DKI (b values of 0, 100, 500, 1000, 1500, and 2000 s/mm(2)). The mean kurtosis (MK) and apparent diffusion coefficient (ADC) values of the hepatic parenchyma and of the HCCs were computed. The detectability of viable HCC based on MK and ADC values was compared. We also assessed the correlation between Child-Pugh grades and MK or ADC values.

RESULTS

For a total of 112 HCC nodules (viable, n = 63; nonviable, n = 49), the MK value was significantly higher for the viable group (mean ± SD, 0.81 ± 0.11) than for the non-viable group (0.57 ± 0.11) (p < 0.001). The mean ADC value was significantly lower for the viable group (1.44 ± 0.42 × 10(-3) mm(2)/s) than for the nonviable group (1.94 ± 0.52 × 10(-3) mm(2)/s) (p < 0.001). The sensitivity, specificity, and AUC of the ROC curve for the assessment of HCC viability were greater (p < 0.001) using MK (85.7%, 98.0%, and 0.95, respectively; cutoff value = 0.710) than using ADC (79.6%, 68.3%, and 0.77, respectively; cutoff value = 1.535 × 10(-3) mm(2)/s). Although the ADC of hepatic parenchyma was lower in patients with Child-Pugh grade B or C disease than in those with grade A disease (p = 0.02), no significant difference in MK (p = 0.45) was found among the Child-Pugh grades.

CONCLUSION

DKI can be a new option for the assessment of posttherapeutic response in HCC.

Characterization of breast tumors using diffusion kurtosis imaging (DKI)

Aim

The aim of this study was to investigate and evaluate the role of magnetic resonance (MR) diffusion kurtosis imaging (DKI) in characterizing breast lesions.

Materials and Methods

One hundred and twenty-four lesions in 103 patients (mean age: 57±14 years) were evaluated by MR DKI performed with 7 b-values of 0, 250, 500, 750, 1,000, 1,500, 2,000 s/mm² and dynamic contrast-enhanced (DCE) MR imaging. Breast lesions were histologically characterized and DKI related parameters—mean diffusivity (MD) and mean kurtosis (MK)—were measured. The MD and MK in normal fibroglandular breast tissue, benign and malignant lesions were compared by One-way analysis of variance (ANOVA) with Tukey's multiple comparison test. Receiver operating characteristic (ROC) analysis was performed to assess the sensitivity and specificity of MD and MK in the diagnosis of breast lesions.

Results

The benign lesions (n = 42) and malignant lesions (n = 82) had mean diameters of 11.4±3.4 mm and 35.8±20.1 mm, respectively. The MK for malignant lesions (0.88±0.17) was significantly higher than that for benign lesions (0.47±0.14) (P<0.001), and, in contrast, MD for benign lesions (1.97±0.35 (10⁻³ mm²/s)) was higher than that for malignant lesions (1.20±0.31 (10⁻³ mm²/s)) (P<0.001). At a cutoff MD/MK 1.58 (10⁻³ mm²/s)/0.69, sensitivity and specificity of MD/MK for the diagnosis of malignant were 79.3%/84.2% and 92.9%/92.9%, respectively. The area under the curve (AUC) is 0.86/0.92 for MD/MK.

Conclusions

DKI could provide valuable information on the diffusion properties related to tumor microenvironment and increase diagnostic confidence of breast tumors.