Dataset Information

Mapping Pulsatile Optic Nerve Head Deformation Using OCT.

ABSTRACT:

Objective

To develop a noninvasive technique to quantitatively assess the pulsatile deformation due to cardiac contractions of the optic nerve head (ONH).

Design

Evaluation of a diagnostic test or technology.

Participants

Healthy subjects with no history of refractive surgery, divided into 2 cohorts on the basis of their axial length (AL).

Methods

We present a noninvasive technique to quantitatively assess the pulsatile deformation of the ONH tissue by combining high-frequency OCT imaging and widely available image processing algorithms. We performed a thorough validation of the approach, numerically and experimentally, evaluating the sensitivity of the method to artificially induced deformation and its robustness to different noise levels. We performed deformation measurements in cohorts of healthy (n = 9) and myopic (n = 5) subjects in different physiological strain conditions by calculating the amplitude of tissue displacement in both the primary position and abduction. The head rotation was measured using a goniometer. During imaging in abduction, the head was rotated 40° ± 3°, and subjects were instructed to direct their gaze toward the OCT visual target.

Main outcome measures

Pulsatile tissue displacement maps.

Results

The robustness of the method was assessed using artificial deformations and increasing noise levels. The results show acceptable absolute errors before the noise simulations grossly exaggerate image degradation. For the group of subjects with AL of < 25 mm (n = 9), the median pulsatile displacement of the ONH was 7.8 ± 1.3 μm in the primary position and 8.9 ± 1.2 μm in abduction. The Wilcoxon test showed a significant difference (P ≤ 0.005) between the 2 paired measures. Reproducibility was tested in 2 different sessions in 5 different subjects with the same intraocular pressure, and an intraclass correlation coefficient of 0.99 was obtained (P < 0.005).

Conclusions

The computational pipeline demonstrated good reproducibility and had the capacity to accurately map the pulsatile deformation of the optic nerve. In a clinical setting, we detected physiological changes in normal subjects supporting its translation potential as a novel biomarker for the diagnosis and progression of optic nerve diseases.

SUBMITTER: Solano MM

PROVIDER: S-EPMC9754981 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Mapping Pulsatile Optic Nerve Head Deformation Using OCT.

Solano Marissé Masís MM Richer Emmanuelle E Cheriet Farida F Lesk Mark R MR Costantino Santiago S

Ophthalmology science 20220806 4

<h4>Objective</h4>To develop a noninvasive technique to quantitatively assess the pulsatile deformation due to cardiac contractions of the optic nerve head (ONH).<h4>Design</h4>Evaluation of a diagnostic test or technology.<h4>Participants</h4>Healthy subjects with no history of refractive surgery, divided into 2 cohorts on the basis of their axial length (AL).<h4>Methods</h4>We present a noninvasive technique to quantitatively assess the pulsatile deformation of the ONH tissue by combining high ...[more]

PMID: 36531582

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Project description:We developed a method to measure the 3-dimensional (3D) strain field in the optic nerve head (ONH) in vivo between two intraocular pressures (IOP). Radial optical coherence tomography (OCT) scans were taken of the ONH of 5 eyes from 5 glaucoma patients before and after IOP-lowering surgery and from 5 eyes from 3 glaucoma suspect patients before and after raising IOP by wearing tight-fitting swimming goggles. Scans taken at higher and lower IOP were compared using a custom digital volume correlation (DVC) algorithm to calculate strains in the anterior lamina cribrosa (ALC), retina, and choroid. Changes in anterior lamina depth (ALD) relative to Bruch's membrane were also analyzed. Average displacement error was estimated to be subpixel and strain errors were smaller than 0.37%. Suturelysis decreased IOP by 9-20 mmHg and decreased compressive anterior-posterior strain Ezz in the ALC by 0.76% (p=0.002,n=5). Goggle-wearing increased IOP by 3-4 mmHg and produced compressive Ezz in the ALC (-0.32%,p=0.001,n=5). Greater IOP decrease was associated with greater ALD change (p=0.047,n=10) and greater strains in the ALC (Ezz:p=0.002,n=10). A deepening of ALD was associated with lower IOP and greater ALC strains (p⩽0.045,n=10). A DVC-based method to measure strains from OCT images caused by IOP changes as small as 2.3 mmHg provides preliminary evidence that ALD is shallower and ALC strains are less compressive at higher IOP and that ALD change is associated with ALC strains. STATEMENT OF SIGNIFICANCE: Glaucoma causes vision loss through progressive damage of the retinal ganglion axons at the lamina cribrosa, a connective tissue structure in the optic nerve head that supports the axons as they pass through the eye wall. It is hypothesized that strains caused by intraocular pressure (IOP) may initiate this damage, but few studies have measured the strain response to pressure of the optic nerve head in patients. We present a method to measure the 3D displacement and strain field in the optic nerve head caused by IOP alteration in glaucoma patients using clinically available images. We used this method to measure strain within the optic nerve head from IOP changes caused by glaucoma surgery and wearing tight-fitting swimming goggles.

Project description:PurposeTo develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.DesignEvaluation of a diagnostic technology.ParticipantsA total of 9765 visual field (VF) SD OCT pairs collected from 1194 participants with and without GVFD (1909 eyes).MethodsDeep learning models were trained to use SD OCT retinal nerve fiber layer (RNFL) thickness maps, RNFL en face images, and confocal scanning laser ophthalmoscopy (CSLO) images to identify eyes with GVFD and predict quantitative VF mean deviation (MD), pattern standard deviation (PSD), and mean VF sectoral pattern deviation (PD) from SD OCT data.Main outcome measuresDeep learning models were compared with mean RNFL thickness for identifying GVFD using area under the curve (AUC), sensitivity, and specificity. For predicting MD, PSD, and mean sectoral PD, models were evaluated using R2 and mean absolute error (MAE).ResultsIn the independent test dataset, the deep learning models based on RNFL en face images achieved an AUC of 0.88 for identifying eyes with GVFD and 0.82 for detecting mild GVFD significantly (P < 0.001) better than using mean RNFL thickness measurements (AUC = 0.82 and 0.73, respectively). Deep learning models outperformed standard RNFL thickness measurements in predicting all quantitative VF metrics. In predicting MD, deep learning models based on RNFL en face images achieved an R2 of 0.70 and MAE of 2.5 decibels (dB) compared with 0.45 and 3.7 dB for RNFL thickness measurements. In predicting mean VF sectoral PD, deep learning models achieved high accuracy in the inferior nasal (R2 = 0.60) and superior nasal (R2 = 0.67) sectors, moderate accuracy in inferior (R2 = 0.26) and superior (R2 = 0.35) sectors, and lower accuracy in the central (R2 = 0.15) and temporal (R2 = 0.12) sectors.ConclusionsDeep learning models had high accuracy in identifying eyes with GFVD and predicting the severity of functional loss from SD OCT images. Accurately predicting the severity of GFVD from SD OCT imaging can help clinicians more effectively individualize the frequency of VF testing to the individual patient.

Dataset Information

Mapping Pulsatile Optic Nerve Head Deformation Using OCT.

Objective

Design

Participants

Methods

Main outcome measures

Results

Conclusions

Publications

Mapping Pulsatile Optic Nerve Head Deformation Using OCT.

Similar Datasets

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