Objective Quantification of Spontaneous Retinal Venous Pulsations Using a Novel Tablet-Based Ophthalmoscope

The Pseudotumor Cerebri Syndrome

Pseudotumor cerebri syndrome is a syndrome of increased cerebrospinal fluid pressure without ventriculomegaly, mass lesion, or meningeal abnormality. It is either primary (idiopathic intracranial hypertension, IIH) or secondary. A secondary cause is unlikely when adhering to the diagnostic criteria. Permanent visual loss occurs if undetected or untreated, and the associated headaches may be debilitating. Fulminant disease may result in blindness despite aggressive treatment. This study addresses the diagnosis and management of IIH including new insights into the pathobiology of IIH, updates in therapeutics and causes of overdiagnosis.

Synchronous functional magnetic resonance eye imaging, video ophthalmoscopy, and eye surface imaging reveal the human brain and eye pulsation mechanisms

The eye possesses a paravascular solute transport pathway that is driven by physiological pulsations, resembling the brain glymphatic pathway. We developed synchronous multimodal imaging tools aimed at measuring the driving pulsations of the human eye, using an eye-tracking functional eye camera (FEC) compatible with magnetic resonance imaging (MRI) for measuring eye surface pulsations. Special optics enabled integration of the FEC with MRI-compatible video ophthalmoscopy (MRcVO) for simultaneous retinal imaging along with functional eye MRI imaging (fMREye) of the BOLD (blood oxygen level dependent) contrast. Upon optimizing the fMREye parameters, we measured the power of the physiological (vasomotor, respiratory, and cardiac) eye and brain pulsations by fast Fourier transform (FFT) power analysis. The human eye pulsated in all three physiological pulse bands, most prominently in the respiratory band. The FFT power means of physiological pulsation for two adjacent slices was significantly higher than in one-slice scans (RESP1 vs. RESP2; df = 5, p = 0.045). FEC and MRcVO confirmed the respiratory pulsations at the eye surface and retina. We conclude that in addition to the known cardiovascular pulsation, the human eye also has respiratory and vasomotor pulsation mechanisms, which are now amenable to study using non-invasive multimodal imaging of eye fluidics.

Autonomous assessment of spontaneous retinal venous pulsations in fundus videos using a deep learning framework

The presence or absence of spontaneous retinal venous pulsations (SVP) provides clinically significant insight into the hemodynamic status of the optic nerve head. Reduced SVP amplitudes have been linked to increased intracranial pressure and glaucoma progression. Currently, monitoring for the presence or absence of SVPs is performed subjectively and is highly dependent on trained clinicians. In this study, we developed a novel end-to-end deep model, called U3D-Net, to objectively classify SVPs as present or absent based on retinal fundus videos. The U3D-Net architecture consists of two distinct modules: an optic disc localizer and a classifier. First, a fast attention recurrent residual U-Net model is applied as the optic disc localizer. Then, the localized optic discs are passed on to a deep convolutional network for SVP classification. We trained and tested various time-series classifiers including 3D Inception, 3D Dense-ResNet, 3D ResNet, Long-term Recurrent Convolutional Network, and ConvLSTM. The optic disc localizer achieved a dice score of 95% for locating the optic disc in 30 milliseconds. Amongst the different tested models, the 3D Inception model achieved an accuracy, sensitivity, and F1-Score of 84 ± 5%, 90 ± 8%, and 81 ± 6% respectively, outperforming the other tested models in classifying SVPs. To the best of our knowledge, this research is the first study that utilizes a deep neural network for an autonomous and objective classification of SVPs using retinal fundus videos.

Heart rate and age modulate retinal pulsatile patterns

Theoretical models of retinal hemodynamics showed the modulation of retinal pulsatile patterns (RPPs) by heart rate (HR), yet in-vivo validation and scientific merit of this biological process is lacking. Such evidence is critical for result interpretation, study design, and (patho-)physiological modeling of human biology spanning applications in various medical specialties. In retinal hemodynamic video-recordings, we characterize the morphology of RPPs and assess the impact of modulation by HR or other variables. Principal component analysis isolated two RPPs, i.e., spontaneous venous pulsation (SVP) and optic cup pulsation (OCP). Heart rate modulated SVP and OCP morphology (p_FDR < 0.05); age modulated SVP morphology (p_FDR < 0.05). In addition, age and HR demonstrated the effect on between-group differences. This knowledge greatly affects future study designs, analyses of between-group differences in RPPs, and biophysical models investigating relationships between RPPs, intracranial, intraocular pressures, and cardiovascular physiology.

Video-recordings of retinal pulsatile patterns in human eye are modulated by heart rate and age. This invivo evidence fundamentally impacts modeling of retinal function and clinical design in wide range of medical specialties.

At the Pulse of Time: Machine Vision in Retinal Videos

Spontaneous venous pulsations (SVP) are a common finding in healthy people. The absence of SVP is associated with rapid progression in glaucoma and increased intracranial pressure. Traditionally, SVP has been documented qualitatively by clinicians during biomicroscopy. Nowadays numerous imaging devices recording the fundus exist. Hence, video data for objectification of SVP is readily available. Still, these clinical datasets are afflicted with various quality issues and artifacts. In this machine vision based study, we explore methods to overcome challenges in identifying SVP in fundus videos of varying quality and provide a detailed protocol thereof. Hereby, we aim to lower the burden of access of implementing machine vision in clinical video datasets and quantification of SVP.

Blind Source Separation of Retinal Pulsatile Patterns in Optic Nerve Head Video-Recordings

Dynamic optical imaging of retinal hemodynamics is a rapidly evolving technique in vision and eye-disease research. Video-recording, which may be readily accessible and affordable, captures several distinct functional phenomena such as the spontaneous venous pulsations (SVP) of central vein or local arterial blood supply etc. These phenomena display specific dynamic patterns that have been detected using manual or semi-automated methods. We propose a pioneering concept in retina video-imaging using blind source separation (BSS) serving as an automated localizer of distinct areas with temporally synchronized hemodynamics. The feasibility of BSS techniques (such as spatial principal component analysis and spatial independent component analysis) and K-means based post-processing method were successfully tested on the monocular and binocular video-ophthalmoscopic (VO) recordings of optic nerve head (ONH) in healthy subjects. BSSs automatically detected three spatially distinct reproducible areas, i.e. SVP, optic cup pulsations (OCP) that included areas of larger vessels in the nasal part of ONH, and "other" pulsations (OP). The K-means post-processing reduced a spike noise from the patterns' dynamics while high linear dependence between the non-filtered and post-processed signals was preserved. Although the dynamics of all patterns were heart rate related, the morphology analysis demonstrated significant phase shifts between SVP and OCP, and between SVP and OP. In addition, we detected low frequency oscillations that may represent respiratory-induced effects in time-courses of the VO recordings.