Proton dose calculation on cone-beam computed tomography using unsupervised 3D deep learning networks
Casper Dueholm Vestergaard,
U.V. Elstrøm,
L.P. Muren
и другие.
Physics and Imaging in Radiation Oncology,
Год журнала:
2024,
Номер
32, С. 100658 - 100658
Опубликована: Окт. 1, 2024
Язык: Английский
A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer
Physics and Imaging in Radiation Oncology,
Год журнала:
2025,
Номер
33, С. 100731 - 100731
Опубликована: Янв. 1, 2025
Язык: Английский
Quantitative use of cone-beam computed tomography in proton therapy: challenges and opportunities
Physics in Medicine and Biology,
Год журнала:
2025,
Номер
70(9), С. 09TR01 - 09TR01
Опубликована: Апрель 24, 2025
Abstract
The
fundamental
goal
in
radiation
therapy
(RT)
is
to
simultaneously
maximize
tumor
cell
killing
and
healthy
tissue
sparing.
Reducing
uncertainty
margins
improves
normal
sparing,
but
generally
requires
advanced
techniques.
Adaptive
RT
(ART)
a
compelling
technique
that
leverages
daily
imaging
anatomical
information
support
reduced
optimize
plan
quality
for
each
treatment
fraction.
An
especially
exciting
avenue
ART
proton
(PT),
which
aims
combine
re-optimization
with
the
unique
advantages
provided
by
protons,
including
integral
dose
near-zero
deposition
distal
target
along
beam
direction.
A
core
component
onboard
image
guidance,
currently
two
options
are
available
on
systems,
cone-beam
computed
tomography
(CBCT)
CT-on-rail
(CToR)
imaging.
While
CBCT
suffers
from
poorer
compared
CToR
imaging,
platforms
can
be
more
easily
integrated
PT
systems
thus
may
streamlined
adaptive
(APT).
In
this
review,
we
present
current
status
of
application
evaluation
adaptation,
progress,
challenges
future
directions.
Язык: Английский
A treatment-site-specific evaluation of commercial synthetic computed tomography solutions for proton therapy
Physics and Imaging in Radiation Oncology,
Год журнала:
2024,
Номер
31, С. 100639 - 100639
Опубликована: Июль 1, 2024
Despite
the
superior
dose
conformity
of
proton
therapy,
distribution
is
sensitive
to
daily
anatomical
changes,
which
can
affect
treatment
accuracy.
This
study
evaluated
recalculation
accuracy
two
synthetic
computed
tomography
(sCT)
generation
algorithms
in
a
commercial
planning
system.
Язык: Английский
НЕЙРОМЕРЕЖЕВИЙ ПІДХІД СЕГМЕНТАЦІЇ СІЛЬСЬКОГОСПОДАРСЬКИХ УГІДЬ НА СУПУТНИКОВИХ ЗОБРАЖЕННЯХ
O. Honcharov,
Hnatushenko Vik.,
O. Shevtsova
и другие.
System technologies,
Год журнала:
2024,
Номер
4(153), С. 87 - 101
Опубликована: Май 1, 2024
Precision
mapping
and
monitoring
of
agricultural
lands
using
satellite
imagery
have
become
crucial
for
optimizing
practices.
This
research
focuses
on
ex-ploring
the
effectiveness
deep
learning
models,
particularly
U-Net
modifications,
semantic
segmentation
in
images.
Recent
Studies
Publications
Analysis.
advancements
convolutional
neural
networks
(CNNs)
shown
promising
results
various
tasks,
including
medical
imaging,
flood
mapping,
environmental
monitoring.
such
as
"Residual
wave
vision
dual
polarization
Sentinel-1
SAR
imagery"
"Deep
learning-based
hybrid
feature
selection
seg-mentation
crops
weeds"
underline
adaptability
architectures
to
di-verse
data
characteristics,
motivating
their
application
land
segmenta-tion.
Research
Objective.
The
primary
aim
this
study
is
assess
applicability
efficiency
modified
accurately
segmenting
from
It
seeks
identify
optimal
model
modifications
that
enhance
accuracy
while
maintaining
computational
efficiency,
contributing
more
ef-fective
Main
Body
Research.
Utilizing
images
Copernicus
HUB
archive,
work
experiments
with
incorporating
residual
blocks,
normalization
methods,
regularization
techniques.
compares
perform-ance
these
models
lands,
highlighting
impact
archi-tectural
enhancements
improving
precision
generalization
capabilities.
Conclusions.
concludes
specific
archi-tecture
significantly
Implementing
batch
normalization,
dropout
proved
effective
overcoming
overfitting,
suggesting
a
direction
future
geospatial
processing
agriculture.
Further
investigation
into
hyperparameter
tuning,
data-set
expansion,
ensemble
methods
recommended
refine
models'
predictive
performance.
Язык: Английский
MARes-Net: multi-scale attention residual network for jaw cyst image segmentation
Frontiers in Bioengineering and Biotechnology,
Год журнала:
2024,
Номер
12
Опубликована: Авг. 5, 2024
Jaw
cyst
is
a
fluid-containing
cystic
lesion
that
can
occur
in
any
part
of
the
jaw
and
cause
facial
swelling,
dental
lesions,
fractures,
other
associated
issues.
Due
to
diversity
complexity
images,
existing
deep-learning
methods
still
have
challenges
segmentation.
To
this
end,
we
propose
MARes-Net,
an
innovative
multi-scale
attentional
residual
network
architecture.
Firstly,
connection
used
optimize
encoder-decoder
process,
which
effectively
solves
gradient
disappearance
problem
improves
training
efficiency
optimization
ability.
Secondly,
scale-aware
feature
extraction
module
(SFEM)
significantly
enhances
network's
perceptual
abilities
by
extending
its
receptive
field
across
various
scales,
spaces,
channel
dimensions.
Thirdly,
compression
excitation
(MCEM)
compresses
excites
map,
combines
it
with
contextual
information
obtain
better
model
performance
capabilities.
Furthermore,
introduction
attention
gate
marks
significant
advancement
refining
map
output.
Finally,
rigorous
experimentation
conducted
on
original
dataset
provided
Quzhou
People's
Hospital
verify
validity
MARes-Net
The
experimental
data
showed
precision,
recall,
IoU
F1-score
reached
93.84%,
93.70%,
86.17%,
93.21%,
respectively.
Compared
models,
our
shows
unparalleled
capabilities
accurately
delineating
localizing
anatomical
structures
image
Язык: Английский