Air-pulse optical coherence elastography: how excitation angle affects mechanical wave propagation
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
Shear Wave Optical Coherence Elastography Imaging by Deep Learning
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