Jianhua (Jay) Wang, M.D., Ph.D., M.S.
Research Subject
Ophthalmic imaging and its clinical applications
Focus
Ocular imaging, image processing, optical imaging, functional imaging, anterior and posterior imaging, human and animal imaging, and the image marker in the eye as a window to the brain.
Roles
Summary
Dr. Wang’s research involves optics engineers to develop many prototypes of spectral-domain optical coherence tomography (OCT) devices, including ultra-high-resolution OCT, ultra-long scan depth OCT, dual-channel OCT, magnetomotive OCT, and CMOS camera-based ultra-high-speed OCT. In recent years, Dr. Wang focuses on vascular imaging of the eye and developed the methods and hardware to image microvasculature, microstructure, and microcirculation in the retina and ocular surface. Working with a group of clinicians, he recently focuses on microvasculature and microcirculation in the retina as a window of the cerebral vasculature in aging, dementia, and multiple sclerosis.
Research Overview
Novel approaches for quantitative analysis of microvasculature, microstructure, and microcirculation. Recently, Dr. Wang contributed significantly to image microvasculature on the ocular surface and retina. A system called functional slit-lamp biomicroscope (FSLB) was developed. This novel system enables easy imaging of the conjunctival microvascular network and small vessel blood flow velocity, which was used to study microvascular response to contact lens wear and changes in dry eye. Worked with experts in neuro-ophthalmology, we developed automatic segmentation of retinal microvascular network obtained using Retinal Function Imager (RFI) and optical coherence tomography angiography (OCTA) for studying retinal microvascular changes in multiple sclerosis, AD, diabetics, and cerebral small vessel diseases. In addition, we developed ultra-high resolution OCT for imaging the retina and adapted Orion segmentation software which can segment 7 retinal sub-layers. We visualized focal thickness alterations of intraretinal layers. For the first time, we developed the measurement of retinal tissue perfusion (RTP), volumetric vessel density (VVD), and retinal capillary perfusion (RCP). These measurements have been applied in our research on dementia, multiple sclerosis, and aging.
Retinal microvascular biomarkers for monitoring vascular contribution to dementia. Easily accessible biomarkers to predict and monitor vascular contribution to cognitive impairment and dementia (VCID) is imperative because accumulating evidence from epidemiology and pathology implicates that microvascular dysfunction is an important contributing factor to age-related dementia. The retinal vessel is regarded as the proxy of small vessels. Retinal and brain vessels share similar anatomic and physiologic features. Retinal microstructural alterations, such as thinning of the retinal nerve fiber layer and ganglion cell layer, are potential biomarkers of neurodegeneration. In addition, the trending decline of retinal blood flow volume and microvascular network density in cognitive normal controls compared to patients with mild cognitive impairment (MCI), then to Alzheimer’s dementia suggests the presence of vasculopathies in these patients. Dr. Wang’s lab focuses on refining retinal microvascular biomarkers for monitoring vascular contribution VCID. We are studying the ocular changes during normal aging and their relations to cognitive function in patients with cognitive impairment.
Retinal tissue perfusion (RTP): Macular blood flow and tissue volume. (A) Retinal blood flow velocity (BFV) was imaged using Retinal Function Imager (RFI). The field of view of 20 degrees was used, and the BFV of the secondary and tertiary branches of the retinal vessels was measured. The arterioles are marked in red and overlaid with the measured blood flow velocities (mm/s). The venules and their respective velocities are marked in pink. A negative value indicates blood flow is moving away from the heart. In this case, the arteriolar flow moved toward the fovea. A positive value indicates blood flow is moving toward the heart. In this case, the vessels are venules. Vessel diameters were calculated at the location where the vessels crossed a circle (f2.5 mm) centered on the fovea and the vessel diameters were used to calculate the blood flow in the arterioles (green dots) and venules (yellow dots). The macular blood flow volume of the arterioles was the sum of the blood flow volumes in each arteriole crossing the circle. Similarly, the macular blood flow volume of the venules was the sum of the blood flow volumes in each venule crossing the circle. (B) The intraretinal layers were imaged using UHR-OCT and segmented using Orion software. (C) The thickness of the inner retina including RNFL, GCIPL, INL, and OPL in a disc (f2.5 mm). RTP was calculated as the blood flow volume divided by tissue volume (cited from Lin et al. Age-Related Alterations in Retinal Tissue Perfusion and Volumetric Vessel Density. IOVS 2019;60:686).
RNFL: retinal nerve fiber layer;
GCIPL: ganglion cell-inner plexiform layer;
INL: inner nuclear layer;
OPL: outer plexiform layer.
Volumetric vessel density (VVD): Tissue volumes and vessel densities of the intraretinal layers. (A) Intraretinal layers were imaged using ultra-high resolution OCT (UHR-OCT) and segmented using the Orion software. (B) The superficial vascular plexus (SVP) of a scan of 3x3 mm was imaged using optical coherence tomography angiography (OCTA) with the analyzed area of a 2.5-mm disc (red circle). (C) The thickness map of the RNFL+GCIPL in a circular area (f 2.5 mm). The VVD of the SVP (VVDs) was the vessel density of the SVP (analyzed as fractal dimension Dbox) divided by the tissue volume of the RNFL and GCIPL in the disc (f 2.5 mm). (D) Retinal vascular network (RVN). (E) The thickness map of the inner retina includes RNFL, GCIPL, INL, and OPL. The VVDr was the vessel density of the RVN (analyzed as fractal dimension Dbox) divided by the tissue volume of the inner retina. (F) Deep vascular plexus (DVP). (G) The thickness map of the INL and OPL in a disc (f 2.5 mm). The VVDd was the vessel density of the DVP (analyzed as fractal dimension Dbox) divided by the tissue volume of the INL and OPL (cited from Lin et al. Age-related alterations in retinal tissue perfusion and volumetric vessel density. IOVS 2019;60:686).
RNFL: retinal nerve fiber layer;
GCIPL: ganglion cell-inner plexiform layer;
INL: inner nuclear layer;
OPL: outer plexiform layer.
