Technical advancements and protocol optimization of diffusion-weighted imaging (DWI) in liver

Single-shot echo-planar imaging (EPI) is well established as the method of choice for clinical, diffusion-weighted imaging with MRI because of its low sensitivity to the motion-induced phase errors that occur during diffusion sensitization of the MR signal. However, the method is prone to artifacts due to susceptibility changes at tissue interfaces and has a limited spatial resolution. The introduction of parallel imaging techniques, such as GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisitions), has reduced these problems, but there are still significant limitations, particularly at higher field strengths, such as 3 Tesla (T), which are increasingly being used for routine clinical imaging. This study describes how the combination of readout-segmented EPI and parallel imaging can be used to address these issues by generating high-resolution, diffusion-weighted images at 1.5T and 3T with a significant reduction in susceptibility artifact compared with the single-shot case. The technique uses data from a 2D navigator acquisition to perform a nonlinear phase correction and to control the real-time reacquisition of unusable data that cannot be corrected. Measurements on healthy volunteers demonstrate that this approach provides a robust correction for motion-induced phase artifact and allows scan times that are suitable for routine clinical application. (c) 2009 Wiley-Liss, Inc.

Magnetic resonance (MR) imaging plays an increasingly important role in the evaluation of patients with liver disease because of its high contrast resolution, lack of ionizing radiation, and the possibility of performing functional imaging sequences. With advances in hardware and coil systems, diffusion-weighted (DW) MR imaging can now be applied to liver imaging with improved image quality. DW MR imaging enables qualitative and quantitative assessment of tissue diffusivity (apparent diffusion coefficient) without the use of gadolinium chelates, which makes it a highly attractive technique, particularly in patients with severe renal dysfunction at risk for nephrogenic systemic fibrosis. In this review, acquisition parameters, postprocessing, and quantification methods applied to liver DW MR imaging will be discussed. The current clinical uses of DW MR imaging (liver lesion detection and characterization, compared and combined with conventional sequences) and the emerging applications of DW MR imaging (tumor treatment response and diagnosis of liver fibrosis and cirrhosis) will be reviewed. Also, limitations, mainly image quality and reproducibility of diffusion parameters, and future directions of liver DW MR imaging will be discussed.

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.