We currently obtain pre- and post-contrast enhanced whole brain 3D-real inversion recovery images for the evaluation of endolymphatic hydrops. We noticed that the space between the pial sheath surrounding the cortical veins and the cortical venous wall is enhanced and this enhancement seems to connect to the meningeal lymphatics along superior sagittal sinus. This new anatomical concept regarding the outflow from the glymphatic system might be important for the future research in neuroscience.

Purpose: Recently, the use of 3D real inversion recovery (3D-real IR) imaging has been proposed for the evaluation of endolymphatic hydrops (EH). This method shows similar contrast between the endolymphatic and perilymphatic spaces and surrounding bone compared with the hybrid of reversed image of positive endolymph signal and native image of perilymph signal multiplied with heavily T₂-weighted MR cisternography (HYDROPS-Mi2) image. We measured the volume of the endolymphatic space using 3D-real IR and HYDROPS-Mi2 images, and compared the measurements obtained with both techniques.

Methods: HYDROPS-Mi2 and 3D-real IR images were obtained for 30 ears from 15 patients with clinical suspicion of EH; imaging was performed 4 h after intravenous administration of a single dose of gadolinium-based contrast agent. We measured the volume of the endolymphatic space in the cochlea and vestibule by manually drawing the regions of interest. The correlation between endolymphatic volume determined from HYDROPS-Mi2 images and 3D-real IR images was calculated.

Results: There was a strong positive linear correlation between the cochlear and vestibular endolymphatic volume determined from HYDROPS-Mi2 and 3D-real IR images. The Spearman’s rank correlation coefficient (ρ) between the measurements obtained with both images was 0.805 (P < 0.001) for the cochlea and 0.826 (P < 0.001) for the vestibule.

Conclusion: The endolymphatic volume measured using 3D-real IR images strongly correlated with that measured using HYDROPS-Mi2 images. Thus, 3D-real IR imaging might be a suitable method for the measurement of endolymphatic volume.

Purpose: To improve the quality of images obtained via dynamic contrast enhanced MRI (DCE-MRI), which contain motion artifacts and blurring using a deep learning approach.

Materials and Methods: A multi-channel convolutional neural network-based method is proposed for reducing the motion artifacts and blurring caused by respiratory motion in images obtained via DCE-MRI of the liver. The training datasets for the neural network included images with and without respiration-induced motion artifacts or blurring, and the distortions were generated by simulating the phase error in k-space. Patient studies were conducted using a multi-phase T₁-weighted spoiled gradient echo sequence for the liver, which contained breath-hold failures occurring during data acquisition. The trained network was applied to the acquired images to analyze the filtering performance, and the intensities and contrast ratios before and after denoising were compared via Bland–Altman plots.

Results: The proposed network was found to be significantly reducing the magnitude of the artifacts and blurring induced by respiratory motion, and the contrast ratios of the images after processing via the network were consistent with those of the unprocessed images.

Conclusion: A deep learning-based method for removing motion artifacts in images obtained via DCE-MRI of the liver was demonstrated and validated.

Purpose: To develop a fast 3D MRI simulator for arbitrary k-space sampling using a graphical processing unit (GPU) and demonstrate its performance by comparing simulation and experimental results in a real MRI system.

Materials and Methods: A fast 3D MRI simulator using a GeForce GTX 1080 GPU (NVIDIA Corporation, Santa Clara, CA, USA) was developed using C++ and the CUDA 8.0 platform (NVIDIA Corporation). The unique advantage of this simulator was that it could use the same pulse sequence as used in the experiment. The performance of the MRI simulator was measured using two GTX 1080 GPUs and 3D Cones sequences. The MRI simulation results for 3D non-Cartesian sampling trajectories like 3D Cones sequences using a numerical 3D phantom were compared with the experimental results obtained with a real MRI system and a real 3D phantom.

Results: The performance of the MRI simulator was about 3800–4900 gigaflops for 128- to 4-shot 3D Cones sequences with 256³ voxels, which was about 60% of the performance of the previous MRI simulator optimized for Cartesian sampling calculated for a Cartesian sampling gradient-echo sequence with 256³ voxels. The effects of the static magnetic field inhomogeneity, radio-frequency field inhomogeneity, gradient field nonlinearity, and fast repetition times on the MR images were reproduced in the simulated images as observed in the experimental images.

Conclusion: The 3D MRI simulator developed for arbitrary k-space sampling optimized using GPUs is a powerful tool for the development and evaluation of advanced imaging sequences including both Cartesian and non-Cartesian k-space sampling.

In the 1980’s some of the earliest studies of arterial spin labeling (ASL) MRI have demonstrated its ability to generate MR angiography (MRA) images. Thanks to many technical improvements, ASL has been successfully moving its position from the realm of research into the clinical area, albeit more known as perfusion imaging than as MRA. For MRA imaging, other techniques such as time-of-flight, phase contrast MRA and contrast-enhanced (CE) MRA are more popular choices for clinical applications. In the last decade, however, ASL-MRA has been experiencing a remarkable revival, especially because of its non-invasive nature, i.e. the fact that it does not rely on the use of contrast agent. Very importantly, there are additional benefits of using ASL for MRA. For example, its higher flexibility to achieve both high spatial and temporal resolution than CE dynamic MRA, and the capability of vessel specific visualization, in which the vascular tree arising from a selected artery can be exclusively visualized. In this article, the implementation and recent developments of ASL-based MRA are discussed; not only focusing on the basic sequences based upon pulsed ASL or pseudo-continuous ASL, but also including more recent labeling approaches, such as vessel-selective labeling, velocity-selective ASL, vessel-encoded ASL and time-encoded ASL. Although these ASL techniques have been already utilized in perfusion imaging and their usefulness has been suggested by many studies, some additional considerations should be made when employing them for MRA, since there is something more than the difference of the spatial resolution of the readout sequence. Moreover, extensive discussion is included on what readout sequence to use, especially by highlighting how to achieve high spatial resolution while keeping scan-time reasonable such that the ASL-MRA sequence can easily be included into a clinical examination.