Shear Wave Optical Coherence Elastography Imaging by Deep Learning DOI
Xingyu Zhou,

Shenju Zhu,

Kexin Shen

и другие.

Journal of Biophotonics, Год журнала: 2025, Номер unknown

Опубликована: Апрель 10, 2025

ABSTRACT Quantifying ocular tissue mechanical properties is pivotal for elucidating eye disease etiology and progression. Optical coherence elastography (OCE), leveraging high‐resolution optical tomography, promises stiffness assessment. Traditional OCE relies on data processing of the time‐of‐flight method encounters challenges like low repeatability. Our study presents an optimized workflow integrating with deep learning to predict biomechanical properties. The concentration prediction network (CPN), a 3D convolutional neural network, predicts sample's concentrations calculates Young's modulus based relationship between agar from testing. CPN showed high accuracy, mean absolute error 0.028 ± 0.036 training 0.024 testing phantoms. In situ porcine corneas various intraocular pressures was measured, yielding corneal distribution via method. This approach enhances efficiency underscores potential clinical applications in ophthalmology.

Язык: Английский

Air-pulse optical coherence elastography: how excitation angle affects mechanical wave propagation DOI Creative Commons
Pengfei Song, Chengjin Song, Yubao Zhang

и другие.

Biomedical Optics Express, Год журнала: 2025, Номер 16(4), С. 1371 - 1371

Опубликована: Март 4, 2025

We evaluate the effect of excitation angles on observation and characterization surface wave propagations used to derive tissue’s mechanical properties in optical coherence tomography (OCT)-based elastography (OCE). Air-pulse stimulation was performed at center sample with ranging from oblique (e.g., 70° or 45°) perpendicular (0°). OCT scanning conducted radially record en face 360°, features (amplitude, attenuation, group phase velocities) were calculated spatiotemporal wavenumber-frequency domains. measurements isotropic, homogeneous samples (1–1.6% agar phantoms), anisotropic (chicken breast), complex boundaries, coupling media, stress conditions ( ex vivo porcine cornea, intraocular pressure (IOP): 5–20 mmHg). Our findings indicate that velocities are less affected by compared displacement features, demonstrating robustness using waves for elasticity estimations. Agar chicken breast showed all these metrics (particularly relatively consistent when smaller than 45°. However, significant disparities observed cornea across different (even between 15° 0°), particularly high IOP levels 20 provide valuable insights enhancing accuracy biomechanical assessments air-pulse-based other dynamic OCE approaches. This facilitates refinement clinical translation technique could ultimately improve diagnostic therapeutic applications various biomedical fields.

Язык: Английский

Процитировано

0
Shear Wave Optical Coherence Elastography Imaging by Deep Learning DOI
Xingyu Zhou,

Shenju Zhu,

Kexin Shen

и другие.

Journal of Biophotonics, Год журнала: 2025, Номер unknown

Опубликована: Апрель 10, 2025

ABSTRACT Quantifying ocular tissue mechanical properties is pivotal for elucidating eye disease etiology and progression. Optical coherence elastography (OCE), leveraging high‐resolution optical tomography, promises stiffness assessment. Traditional OCE relies on data processing of the time‐of‐flight method encounters challenges like low repeatability. Our study presents an optimized workflow integrating with deep learning to predict biomechanical properties. The concentration prediction network (CPN), a 3D convolutional neural network, predicts sample's concentrations calculates Young's modulus based relationship between agar from testing. CPN showed high accuracy, mean absolute error 0.028 ± 0.036 training 0.024 testing phantoms. In situ porcine corneas various intraocular pressures was measured, yielding corneal distribution via method. This approach enhances efficiency underscores potential clinical applications in ophthalmology.

Язык: Английский

Процитировано

0