In this abstract we present dynamic imaging of the eyes and the optic nerves in humans using golden angle radial MRI. Continuous 15 s radial scans with azimuthal profile steps of 111.246 degrees are acquired under various eye motion states. Qualitative analyses of the images reveal features of basic eye and nerve mechanics. Image-based characterization of eye mechanics can improve understanding eye physiology and disease.
INTRODUCTION
The eyes and optic nerves make up the primary sensory segments of the human anterior visual pathway, yet have been exceptionally challenging to image directly. In-vivo MRI of the eyes and optic nerves has largely been limited to the stationary eye with ocular fixation to stabilize the nerve1,2, since motion is a major confound rather than the point of interest. However, imaging dynamic eye and nerve motion can be potentially valuable in understanding basic eye mechanics as well as pathologies of the visual system. Aberrations in these dynamics can serve as early indicators of disease3,4 and combined with traditional radiological markers give a complete picture of visual system sensory function. In this abstract, we present the use of Golden angle (GA) radial MRI5 for imaging the moving eye and optic nerve at 3 Tesla. GA radial MRI has recently been employed for several dynamic imaging applications, including imaging of speech6, joints7 and the abdomen8 but has never been applied to dynamically survey the orbit in humans.Acquisition: All experiments were performed on a Philips Achieva 3 Tesla human imaging system (Philips Healthcare) with a 2-channel body transmit / 8 channel head receive coil. Two healthy subjects were scanned after signed informed consent. One 8 mm axial, coronal or sagittal slice was planned based on T2 weighted scout images, such that the complete extent of the nerve was captured in the axial and sagittal slices along with the eyes. Data were acquired with a GA radial imaging sequence in which the radial spokes were stepped by 111.246 degrees without readout alternation. Data were acquired continuously for ~15 seconds with a TR/TE of 5.7/1.4 ms, flip angle of 20 degrees, in-plane FOV of 200 mm and a readout resolution of 1 mm. To probe eye and optic nerve dynamics, the subjects were instructed to perform various eye movements during the scan. These included 1. Resting 2. Both eyes blinking 3. Sweeping eyes from left to right and back. 4. Sweeping eyes up and down 5. Fixating on a cross on a screen.
Reconstruction: Complex axial image volumes were reconstructed offline in Python (Anaconda version 3.4.2, TX) using 128 profiles per reconstructed slice (0.73 s temporal span) and a window step of 10 radial spokes (0.057 ms per frame). GA image reconstruction involved correction for k-space shifts and sampling density prior to gridding, 2D FFT and roll-off compensation9. Channel combination was performed in the image domain using the method of Walsh et al.10 to yield final dynamic images.
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