Free-breathing liver perfusion imaging using 3-dimensional through-time spiral generalized autocalibrating partially parallel acquisition acceleration

Abstract

Objectives: The goal of this study was to develop free-breathing high-spatiotemporal resolution dynamic contrast-enhanced liver magnetic resonance imaging using non-Cartesian parallel imaging acceleration, and quantitative liver perfusion mapping.

Materials and methods: This study was approved by the local institutional review board and written informed consent was obtained from all participants. Ten healthy subjects and 5 patients were scanned on a Siemens 3-T Skyra scanner. A stack-of-spirals trajectory was undersampled in-plane with a reduction factor of 6 and reconstructed using 3-dimensional (3D) through-time non-Cartesian generalized autocalibrating partially parallel acquisition. High-resolution 3D images were acquired with a true temporal resolution of 1.6 to 1.9 seconds while the subjects were breathing freely. A dual-input single-compartment model was used to retrieve liver perfusion parameters from dynamic contrast-enhanced magnetic resonance imaging data, which were coregistered using an algorithm designed to reduce the effects of dynamic contrast changes on registration. Image quality evaluation was performed on spiral images and conventional images from 5 healthy subjects.

Results: Images with a spatial resolution of 1.9 × 1.9 × 3 mm3 were obtained with whole-liver coverage. With an imaging speed of better than 2 s/vol, free-breathing scans were achieved and dynamic changes in enhancement were captured. The overall image quality of free-breathing spiral images was slightly lower than that of conventional long breath-hold Cartesian images, but it provided clinically acceptable or better image quality. The free-breathing 3D images were registered with almost no residual motion in liver tissue. After the registration, quantitative whole-liver 3D perfusion maps were obtained and the perfusion parameters are all in good agreement with the literature.

Conclusions: This high-spatiotemporal resolution free-breathing 3D liver imaging technique allows voxelwise quantification of liver perfusion.

Figure 1

Measurement of liver motion in craniocaudal direction. (a) A simple edge-detection method was used to automatically find the top of liver (white bar) at different contrast enhancement levels. (b) Motion waveform from the measurement. The solid diamonds represent the selected references/time frames from the same respiratory level.

Figure 2

Representative undersampled (a-c) and through-time GRAPPA reconstructed (d-f) spiral images acquired at arterial phase (a&d), portal phase (b&e) and equilibrium phase (c&f) from a healthy volunteer after contrast injection.

Figure 3

Representative reference, unregistered and registered images in both axial (a) and reformatted coronal (b) views from a healthy subject. The subtraction images before and after image registration are also shown in the last two columns. Robust image registration with different contrast enhancement is demonstrated with representative images at both arterial phase and portal phase.

Figure 4

(a) T₁-weighted image showing a liver pixel close to a small vessel. (b) Concentration-time curves in the aorta, portal vein and the selected single-voxel liver tissue shown in (a). (c) Measured and fitted data of liver tissue using a dual-input single-compartment model. After image registration, the concentration-time curve is much smoother and the residual error from the fitting is lower. Substantial changes in arterial fraction, distribution volume and mean transit time were also observed before and after registration.

Figure 5

Representative pre-contrast and post-contrast images acquired at arterial phase, portal phase and equilibrium phase of a subject with metastatic breast cancer. (a) Clinical standard images acquired with long breath-hold of ~18 sec/volume. (b) Free-breathing images acquired with 1.9 sec/volume.

Figure 6

Liver perfusion maps for the subject with metastatic breast cancer. (a) Representative concentration-time curves of both lesion and normal surrounding tissue as shown in the T₁-weighted image (b). Corresponding liver perfusion maps of (c) arterial fraction, (d) distribution volume, and (e) mean transit time.

Figure 7

Liver perfusion images for the subject with sclerosing hemangioma. (a) Clinical standard images acquired with long breath-hold of ~18 sec/volume. (b) Free-breathing images acquired with 1.9 sec/volume. (c) Liver perfusion maps.

Figure 8

Representative images from a healthy subject for image quality analysis: (a) 18-sec breath-hold VIBE (BH-VIBE); (b) 18-sec free-breathing VIBE (FB-VIBE); (c) 1.9-sec free-breathing spiral (FB-SPIRAL). The scores from both readers for motion artifact, image blurring, liver edge sharpness, clarity of vessels, and overall image quality are shown as inset.