Frontiers in Aging Neuroscience,
Год журнала:
2022,
Номер
14
Опубликована: Март 3, 2022
Parkinson's
disease
(PD)
is
one
of
the
most
common
progressive
degenerative
diseases,
and
its
diagnosis
challenging
on
clinical
grounds.
Clinically,
effective
quantifiable
biomarkers
to
detect
PD
are
urgently
needed.
In
our
study,
we
analyzed
data
from
two
centers,
primary
set
was
used
train
model,
independent
external
validation
validate
model.
We
applied
amplitude
low-frequency
fluctuation
(ALFF)-based
radiomics
method
extract
features
(including
first-
high-order
features).
Subsequently,
Cancers,
Год журнала:
2019,
Номер
11(1), С. 111 - 111
Опубликована: Янв. 18, 2019
A
World
Health
Organization
(WHO)
Feb
2018
report
has
recently
shown
that
mortality
rate
due
to
brain
or
central
nervous
system
(CNS)
cancer
is
the
highest
in
Asian
continent.
It
of
critical
importance
be
detected
earlier
so
many
these
lives
can
saved.
Cancer
grading
an
important
aspect
for
targeted
therapy.
As
diagnosis
highly
invasive,
time
consuming
and
expensive,
there
immediate
requirement
develop
a
non-invasive,
cost-effective
efficient
tools
characterization
grade
estimation.
Brain
scans
using
magnetic
resonance
imaging
(MRI),
computed
tomography
(CT),
as
well
other
modalities,
are
fast
safer
methods
tumor
detection.
In
this
paper,
we
tried
summarize
pathophysiology
cancer,
modalities
automatic
computer
assisted
machine
deep
learning
paradigm.
Another
objective
paper
find
current
issues
existing
engineering
also
project
future
Further,
have
highlighted
relationship
between
disorders
like
stroke,
Alzheimer’s,
Parkinson’s,
Wilson’s
disease,
leukoriaosis,
neurological
context
Radiology,
Год журнала:
2021,
Номер
300(2), С. 260 - 278
Опубликована: Июнь 8, 2021
Parkinson
disease
is
characterized
by
dopaminergic
cell
loss
in
the
substantia
nigra
of
midbrain.
There
are
various
imaging
markers
for
disease.
Recent
advances
MRI
have
enabled
elucidation
underlying
pathophysiologic
changes
nigral
structure.
This
has
contributed
to
accurate
and
early
diagnosis
improved
progression
monitoring.
article
aims
review
recent
developments
other
parkinsonian
syndromes,
including
nigrosome
imaging,
neuromelanin
quantitative
iron
mapping,
diffusion-tensor
imaging.
In
particular,
this
examines
using
7-T
3-T
susceptibility-weighted
Finally,
discusses
volumetry
its
clinical
importance
related
symptom
manifestation.
will
improve
understanding
advancements
Published
under
a
CC
BY
4.0
license.
Computer Methods and Programs in Biomedicine,
Год журнала:
2020,
Номер
198, С. 105793 - 105793
Опубликована: Окт. 15, 2020
Background
and
objectives:
Qualitative
quantitative
analyses
of
Magnetic
Resonance
Imaging
(MRI)
scans
are
carried
out
to
study
understand
Parkinson's
Disease,
the
second
most
common
neurodegenerative
disorder
in
people
at
their
60's.
Some
based
on
application
voxel-based
morphometry
(VBM)
magnetic
resonance
images
determine
regions
interest,
within
gray
matter,
where
there
is
a
loss
nerve
cells
that
generate
dopamine.
This
dopamine
indicative
disease.
The
purpose
this
research
introduction
new
method
classify
3-D
an
individual,
as
assisting
tool
for
diagnosis
disease
by
using
largest
MRI
dataset
(Parkinson's
Progression
Markers
Initiative)
from
population
patients
with
control
individuals.
A
contribution
separate
studies
conducted
men
women
since
gender
plays
significant
role
Neurobiology,
which
demonstrated
fact
more
prone
than
are.
Methods:
Previous
classification,
VBM
detect
features
extracted
first-
second-order
statistics
methods.
Furthermore,
number
considerably
reduced
feature
selection
techniques.
Seven
classifiers
used
we
conducting
experiments
women.
Results:
best
detection
performance
achieved
99.01%
accuracy,
99.35%
sensitivity,
100%
specificity,
precision.
96.97%
96.15%
97.22%
During
classification
images,
corresponding
computational
complexity
few
selected.
Conclusions:
proposed
provides
high
disease,
While
previous
works
have
focused
analysis
striatum
region
brain
(the
nuclear
complex
basal
ganglia),
approach
over
whole
looking
decreases
tissue
thickness,
consequence
finding
other
interest
such
cortex.
