Four-dimensional diffusion-weighted MR imaging (4D-DWI): a feasibility study.

Abstract

Diffusion-weighted Magnetic Resonance Imaging (DWI) has been shown to be a powerful tool for cancer detection with high tumor-to-tissue contrast. This study aims to investigate the feasibility of developing a four-dimensional DWI technique (4D-DWI) for imaging respiratory motion for radiation therapy applications. Image acquisition was performed by repeatedly imaging a volume of interest (VOI) using an interleaved multislice single-shot echo-planar imaging (EPI) 2D-DWI sequence in the axial plane. Each 2D-DWI image was acquired with an intermediately low b-value (b=500s/mm2 ) and with diffusion-encoding gradients in x, y, and z diffusion directions. Respiratory motion was simultaneously recorded using a respiratory bellow, and the synchronized respiratory signal was used to retrospectively sort the 2D images to generate 4D-DWI. Cine MRI using steady-state free precession was also acquired as a motion reference. As a preliminary feasibility study, this technique was implemented on a 4D digital human phantom (XCAT) with a simulated pancreas tumor. The respiratory motion of the phantom was controlled by regular sinusoidal motion profile. 4D-DWI tumor motion trajectories were extracted and compared with the input breathing curve. The mean absolute amplitude differences (D) were calculated in superior-inferior (SI) direction and anterior-posterior (AP) direction. The technique was then evaluated on two healthy volunteers. Finally, the effects of 4D-DWI on apparent diffusion coefficient (ADC) measurements were investigated for hypothetical heterogeneous tumors via simulations. Tumor trajectories extracted from XCAT 4D-DWI were consistent with the input signal: the average D value was 1.9mm (SI) and 0.4mm (AP). The average D value was 2.6mm (SI) and 1.7mm (AP) for the two healthy volunteers. A 4D-DWI technique has been developed and evaluated on digital phantom and human subjects. 4D-DWI can lead to more accurate respiratory motion measurement. This has a great potential to improve the visualization and delineation of cancer tumors for radiotherapy.