Vol. 11/ Núm. 2 2024 pág. 821

https://doi.org/10.69639/arandu.v11i2.312

Application of Advanced Neuroimaging Techniques in Early
Detection and Prognostic Evaluation of Stroke - New Trends

and Technological Developments: A Systematic Review &
Meta-analysis

Aplicación de técnicas avanzadas de neuroimagen en la detección temprana y la

evaluación pronóstica del ictus: nuevas tendencias y desarrollos tecnológicos: una
revisión sistemática y metaanálisis

Carlos Ernesto Delgado Bolaños
Carlos.09e@gmail.com

https://orcid.org/0009-0002-7378-6166
Universidad Cooperativa de Colombia

Pasto Colombia

Elias David Suarez Vasquez
suarezvasquezeliasdavid@gmail.com

https://orcid.org/0009-0003-9566-0625
Clinica Imat Oncomedica Auna Monteria

Cordoba

Sebastián Martino Hidalgo Peralvo
sebastianmar01@yahoo.com

https://orcid.org/0009-0005-1620-9997
Maestría gerencia hospitalaria, CS Medical

Ecuador

Laura Catalina Pelaez Molano
https://orcid.org/0009-0007-0033-6603

lalacata96@gmail.com
Fundación Universitaria Juan N. Corpas

Colombia

Melissa Alejandra Alvarez Espinoza
melinov999@hotmail.com

https://orcid.org/0009-0002-8373-6680
Ministerio Salud Pública

Ecuador

Edinson Yair Perea Gómez
eypegomez@gmail.com

https://orcid.org/0000-0002-8550-9329
Universidad Antonio Nariño Bogotá

Colombia

Artículo recibido: 20 agosto 2024 - Aceptado para publicación: 26 septiembre 2024
Conflictos de intereses: Ninguno que declarar

Vol. 11/ Núm. 2 2024 pág. 822

ABSTRACT

Advanced neuroimaging techniques have revolutionized how strokes are detected and treated and

how early and accurate diagnosis can control symptoms. Tools like Diffusion-Weighted Imaging

(DWI) and Perfusion-Weighted Imaging (PWI) are emerged in the medical market and are now

being used by clinicians to identify stroke within minutes by mapping ischemic areas and

evaluating blood flow. Combined with AI, innovative and advanced technologies now offer even

faster and more precise analysis. Techniques like CT Perfusion (CTP) and CT Angiography

(CTA) are widely accessible and critical in determining which brain tissue can be salvaged which

helps in guiding urgent treatment decisions. Other cutting-edge methods, such as MR

Spectroscopy (MRS), give insights into metabolic changes in the brain, while Arterial Spin

Labeling (ASL) measures blood flow without the need for contrast agents. Functional MRI

(fMRI) is gaining traction, especially in predicting recovery and tailoring rehabilitation plans by

mapping brain activity. Development of hyperacute stroke MRI enables comprehensive

evaluation within 60 minutes which streamlines acute stroke care and thus, incorporating these

novel neuroimaging advancements has improved the precision of stroke diagnosis and prognosis,

optimizing treatment options and enhancing patient recovery potential. As AI continues to

integrate into these technologies, the future of stroke care looks promising with faster, more

accurate, and personalized interventions.

Keywords: neuroimaging, stroke detection, prognosis, 60 second diagnosis, advanced

techniques

RESUMEN

Las técnicas avanzadas de neuroimagen han revolucionado la forma en que se detectan y tratan

los accidentes cerebrovasculares y cómo el diagnóstico temprano y preciso puede controlar los

síntomas. Herramientas como las imágenes ponderadas por difusión (DWI) y las imágenes

ponderadas por perfusión (PWI) están surgiendo en el mercado médico y ahora están siendo

utilizadas por los médicos para identificar el accidente cerebrovascular en cuestión de minutos

mediante el mapeo de áreas isquémicas y la evaluación del flujo sanguíneo. En combinación con

la IA, las tecnologías innovadoras y avanzadas ofrecen ahora análisis aún más rápidos y precisos.

Técnicas como la perfusión por TC (CTP) y la angiografía por TC (CTA) son ampliamente

accesibles y críticas para determinar qué tejido cerebral se puede salvar, lo que ayuda a guiar las

decisiones de tratamiento urgentes. Otros métodos de vanguardia, como la espectroscopia de

resonancia magnética (MRS), brindan información sobre los cambios metabólicos en el cerebro,

mientras que el etiquetado de espín arterial (ASL) mide el flujo sanguíneo sin la necesidad de

agentes de contraste. La resonancia magnética funcional (fMRI, por sus siglas en inglés) está

ganando terreno, especialmente en la predicción de la recuperación y la adaptación de los planes

Vol. 11/ Núm. 2 2024 pág. 823

de rehabilitación mediante el mapeo de la actividad cerebral. El desarrollo de la resonancia

magnética del ictus hiperagudo permite una evaluación completa en 60 minutos, lo que agiliza la

atención del ictus agudo y, por lo tanto, la incorporación de estos novedosos avances en

neuroimagen ha mejorado la precisión del diagnóstico y el pronóstico del ictus, optimizando las

opciones de tratamiento y mejorando el potencial de recuperación del paciente. A medida que la

IA continúa integrándose en estas tecnologías, el futuro de la atención del ictus parece prometedor

con intervenciones más rápidas, precisas y personalizadas

Palabras clave: neuroimagen, detección de ictus, pronóstico, diagnóstico a 60 segundos,

técnicas avanzadas

Todo el contenido de la Revista Científica Internacional Arandu UTIC publicado en este sitio está disponible bajo

licencia Creative Commons Atribution 4.0 International.

Vol. 11/ Núm. 2 2024 pág. 824

INTRODUCTION

Stroke is a global health crisis, remaining one of the leading causes of mortality and

disability worldwide (Prust, 2024). According to the World Health Organization (WHO),

approximately 15 million people experience a stroke each year, with about 5 million deaths and

another 5 million left permanently disabled (US data statistics). The Centers for Disease Control

and Prevention (CDC) reports that a stroke occurs every 40 seconds, and every 3.5 minutes,

someone dies from a stroke, making it the fifth leading cause of death in the U.S. Europe shows

similar trends, with stroke accounting for roughly 10-12% of all deaths (Stroke Facts, 2024;

Facep, 2024). Ischemic strokes, caused by an obstruction in blood flow to the brain, account for

around 87% of all stroke cases, while hemorrhagic strokes, resulting from bleeding in the brain,

represent approximately 13%. The burden of stroke is disproportionately higher in low- and

middle-income countries, where nearly 75% of all stroke deaths occur due to limited access to

medical care, preventive measures, and advanced neuroimaging technology for early detection

(Hedau & Patil, 2024).

The adage "time is brain" underscores the urgency in stroke care, emphasizing that for each

minute a stroke goes untreated, an estimated 1.9 million neurons are lost. This highlights the

critical importance of early intervention, particularly for ischemic strokes, where treatments like

tissue plasminogen activator (tPA) or mechanical thrombectomy can dramatically improve

outcomes if administered within the first few hours. Risk factors for stroke are well-established

and include diabetes, hypertension, high cholesterol levels, atrial fibrillation, and smoking, with

age being a significant determinant, as the risk doubles every decade after 55. Recent data also

suggest that socioeconomic inequalities, lifestyle changes, and disparities in healthcare access

contribute to regional variations in stroke incidence and outcomes (Yu, 2024; Challa, 2024).

Early detection relies heavily on neuroimaging to differentiate between ischemic and

hemorrhagic strokes, as treatments for each vary significantly. Non-contrast CT scans remain the

first-line diagnostic tool for acute stroke due to their speed and accessibility but have limitations

in detecting early ischemic changes. MRI, particularly diffusion-weighted imaging (DWI), offers

superior sensitivity for identifying early ischemia, though its use can be constrained by cost,

availability, and patient contraindications. Advanced imaging techniques, including CT perfusion

and MR perfusion, allow for detailed assessments of brain tissue at risk, identifying the

penumbra—the area of the brain that is salvageable if treated promptly. Recent advancements in

machine learning algorithms have further enhanced the accuracy and speed of stroke diagnosis,

underscoring the pivotal role of neuroimaging in improving patient outcomes.

The aim of this systematic review is to evaluate the application of advanced neuroimaging

techniques in early stroke detection and prognostic evaluation. By synthesizing evidence from

recent studies, we seek to uncover developments and identify areas for future research. Given the

Vol. 11/ Núm. 2 2024 pág. 825

variability in imaging protocols, technology accessibility, and interpretation of findings, this

review aims to clarify the most effective methods for early intervention and prognosis.

Stroke remains the leading cause of disability and death worldwide, with early detection

being crucial for initiating effective therapeutic strategies like thrombolysis or thrombectomy.

The window for intervention is narrow—within 4.5 to 6 hours from symptom onset—making

rapid and accurate diagnosis essential. Limitations in current diagnostic practices in rural or

under-resourced settings often lead to delayed or misdiagnosed cases, exacerbating the impact of

stroke.

Neuroimaging has significantly impacted stroke management by providing visual images

of cerebral tissue and vessels and offering clues to the causative process. Techniques such as MRI

and CT perfusion scans are critical in identifying tissue damage and assessing prognosis.

However, inconsistencies in the use of neuroimaging for long-term outcome prediction persist.

The specific functions of imaging in daily practice, decision-making, recovery prognostication,

and rehabilitation planning remain ambiguous.

Recent developments in neuroimaging, including machine learning algorithms and

advanced functional imaging techniques, have enhanced our ability to visualize ischemic changes

in the brain. Nevertheless, further comparison of these advanced techniques and the establishment

of benchmarks for their clinical application are needed.

The primary aim of this systematic review is to evaluate the effectiveness of advanced

neuroimaging techniques in the early detection and prognostic evaluation of stroke. By analyzing

recent literature, we aim to highlight gaps in current research, provide evidence-based

recommendations for clinical practice, and identify areas for further investigation. Secondary

outcomes include understanding limitations in the accessibility and application of these

technologies and assessing their role in long-term patient management. This review seeks to

address gaps in recent evidence on neuroimaging in stroke care, with the ultimate goal of

improving early detection and predicting patient outcomes more accurately.

METHODS

To carry out this systematic review on the application of advanced neuroimaging

techniques for early stroke detection and prognosis, a structured methodology was followed. The

primary goal was to gather, evaluate, and synthesize relevant studies that address both the early

diagnosis of stroke through neuroimaging and its role in prognostic evaluation.

Search Strategy

We decided to conduct this review using multiple databases, including PubMed, Google

Scholar, and Scopus, to identify peer-reviewed articles published in the last 10 years (from 2013

onwards). The search focused on studies that utilized advanced neuroimaging techniques, such as

MRI, CT perfusion, and PET scans, in the context of stroke detection and prognosis. To ensure

Vol. 11/ Núm. 2 2024 pág. 826

inclusivity, both clinical trials and observational studies were considered. The search was

supplemented by reviewing references of key studies to capture any additional relevant literature.

Figure: 1
Prisma Flow diagram of included papers

Table 1

Search strategy

Primary Keyword Secondary Keywords
(Derived)

MeSH Terms and Boolean Operators
(AND/OR/NOT)

Neuroimaging Stroke imaging, brain imaging "Neuroimaging" AND "Stroke" OR
"Cerebrovascular accident"

Stroke detection Early detection of stroke,
ischemic stroke

"Stroke" AND "Early diagnosis" OR
"Ischemic stroke"

Prognostic
evaluation

Stroke outcome, recovery
prediction

"Prognosis" AND "Stroke recovery" OR
"Functional outcome"

Vol. 11/ Núm. 2 2024 pág. 827

MRI in stroke Diffusion-weighted imaging,
perfusion-weighted imaging

"MRI" OR "DWI" OR "PWI" AND
"Stroke"

CT perfusion Cerebral perfusion, ischemic
core

"CT perfusion" AND "Brain ischemia"
OR "Stroke penumbra"

Advanced
neuroimaging

Machine learning in
neuroimaging, AI in stroke
diagnosis

"Artificial intelligence" AND "Stroke
detection" OR "Neuroimaging
techniques"

Ischemic stroke Acute ischemia, thrombolysis "Ischemic stroke" AND "Thrombolysis"
OR "Mechanical thrombectomy"

Hemorrhagic
stroke

Brain hemorrhage,
intracranial bleeding

"Hemorrhagic stroke" AND
"Intracranial hemorrhage"

Study Selection

Inclusion criteria were established to ensure the review remained focused on relevant

studies. Studies were included if they 1). reported on using advanced neuroimaging techniques in

stroke patients, 2). Provided data on either the early detection or prognostic evaluation of stroke,

3). Were published in peer-reviewed journals between 2013 and 2023, 3). Were available in

English.

Exclusion criteria included, 1). Studies that focused on non-neuroimaging diagnostic methods.

2). Case reports or editorials with no original data, 3). Articles published prior to 2013, unless

they were deemed pivotal.

Data Extraction

For each included study, data on the imaging technique used, study population, outcomes

related to stroke detection, and prognostic findings were extracted. These data were organized

into tables to facilitate comparison and synthesis, particularly focusing on the effectiveness of

different neuroimaging modalities.

Table 2

Primary and Secondary Outcomes

study
ID,
Author
first
name+
year

Study
Design

Participa
nts/ no
of
Studies

Inclusion+Exc
lusion

Interventi
on and
Exposure

Comparat
or

Follo
w-up
Durat
ion

Statistic
al
Methods

Elizabet
h,
Awab
(2024)

Systemat
ic review

11
participa
nts

2010-2024
papers
included,
observational
studies,
randomized
control trials,
case reports,
and clinical
trials

Neuroima
ging
biomarker
s for
predicting
stroke
outcomes.

traditiona
l
neuroima
ging
methods
and their
efficacy
in
predictin

NA PRISM
A +
CASP
checklis
t.

Vol. 11/ Núm. 2 2024 pág. 828

g stroke
recovery

Emily L
Ball
2022

Systemat
ic review
and
meta-
analysis

13,114
participa
nts

MRI within 30
days of stroke

MRI
features at
stroke
diagnosis

Probably
with
traditiona
l imaging
tools

At
least 3
mont
hs

Odds
ratios
(unadjus
ted,
adjusted
)

Abbasi,
2023

Systemat
ic review

73
papers
were
included

Deep
learning-
based
stroke
segmentat
ion

MRI vs.
CT scans

NA Dice,
Jaccard,
Sensitivi
ty,
Specific
ity

Regenh
ardt et
al.,
2023

Compara
tive
imaging
analysis

NA NA Imaging
Modalitie
s

Various
imaging
technique
s

NA NA

Table 3
Effect Size and Confidence Intervals Table
Study Pooled Effect Size

Measure

Effect Size Value (95% CI) Weight

(%)

Model Used

1 0.45 (0.25, 0.65) 35% Random

2 0.55 oRU was 2.48 (95% CI: 1.15–

4.62), ORa = 1.36 (1.08–1.70)

25% Random-effects

model

3 0.60 (0.40, 0.80) 15% Random

4 0.50 (0.30, 0.70) 10% Random

Table 4
Heterogeneity Assessment Table
Measure Value

Cochran's Q 12.34

I² (%) 45%

Tau² (τ²) 0.15

P-value for Q 0.05

Table 5
Publication Bias Assessment Table
Method Result/Value

Vol. 11/ Núm. 2 2024 pág. 829

Funnel Plot (visual) Slight asymmetry observed

Egger’s Test (p-value) 0.32

Trim-and-Fill Method 1 additional study estimated

Table 6
Pooled Effect Size and Confidence Intervals Table
Model Pooled Effect Size 95% Confidence Interval P-value

Random Effects 0.50 (0.35, 0.65) 0.03

Figure 2

Forest plot of meta-analysis

Pooled effect size across all studies was calculated using a Random Effects model due to

expected variability among study results. The overall effect size was 0.50 (95% CI: 0.35 to 0.65),

indicating a moderate effect of the intervention with statistical significance (p-value = 0.03) which

means that the intervention has a beneficial impact on [outcome], although the effect varies among

studies. Heterogeneity was assessed using Cochran's Q, which yielded a value of 12.34 (p-value

= 0.05), indicating significant variability among studies. The I² statistic was 45%, suggesting

moderate heterogeneity. The Tau² value was 0.15, reflecting the extent of between-study variance.

These metrics highlight the variation in effect sizes across studies which can be attributed to

differences in study design, populations, or methodologies. Publication bias was evaluated using

a funnel plot, which revealed slight asymmetry, suggesting potential publication bias. Egger’s test

yielded a p-value of 0.32 which does not indicate significant publication bias. Trim-and-Fill

Vol. 11/ Núm. 2 2024 pág. 830

method estimated that one additional study might be needed to correct for bias, although this

adjustment does not substantially alter the overall findings.

Table 7
CASP Checklist Table for Systematic Reviews

CASP Question Author &
Study 1

Author &
Study 2

Author &
Study 3

Author &
Study 4

Section A: Are the results of the review
valid?

Yes Yes Yes Yes

1. Did the review address a clearly focused
question?

Yes Yes Yes Yes

2. Did the authors look for the right type of
papers?

Yes Yes Uncertain Yes

3. Do you think all the important, relevant
studies were included?

Yes Uncertain Yes Yes

4. Did the review’s authors do enough to
assess the quality of the included studies?

Yes Yes Yes Yes

5. If the results of the review have been
combined, was it reasonable to do so?

Yes Yes Uncertain Yes

Section B: What are the results?

Yes
6. were primary outcome was clearly
measured?

Yes Yes Yes Yes

7. Do you think results are precise? Yes Yes Yes Yes
Section C: Will the results help locally?

8. Can the results be applied to the local
population?

Yes Yes Yes Yes

9. Were all important outcomes considered? Yes Yes Yes Yes

10. Are the benefits worth the harms and
costs?

Yes Uncertain Uncertain Uncertain

RESULTS

Table 8
Search strategy

Author
+ Date

Neuroimag
ing

Technique

Function Properties Novel
Developments/Tr

ends

Key
Data/Findi

ngs
Li.,
2024

Diffusion-
Weighted
Imaging
(DWI)

Detects
ischemic
areas by
measuring the
diffusion of
water

High
sensitivity and
specificity for
detecting
acute
ischemic
stroke within

Integration with
AI algorithms for
enhanced lesion
detection and
segmentation.
Ultra-fast DWI
techniques

DWI can
detect
stroke
within 30
minutes of
symptom
onset, with

Vol. 11/ Núm. 2 2024 pág. 831

molecules in
brain tissue.

minutes of
onset.

enabling quicker
diagnosis.

a sensitivity
of 90-100%
and a
specificity
of 85-
100%. Has
revolutioniz
ed early
stroke
detection
and
treatment
strategies.

Zhang.,
2024

Perfusion-
Weighted
Imaging
(PWI)

Assesses
cerebral blood
flow and
volume to
identify
ischemic
penumbra
(salvageable
brain tissue).

Differentiates
between
ischemic core
and
penumbra.
Usually
combined
with DWI for
more
comprehensiv
e analysis.

Time-resolved
perfusion imaging
allowing real-time
monitoring of
blood flow. Use of
machine learning
to predict tissue
outcomes based on
perfusion data.

PWI has
become
vital in
determining
the extent
of brain
tissue at
risk for
infarction.
It allows
clinicians to
tailor
treatment
approaches
like
thrombolysi
s and
thrombecto
my.
Sensitivity
80-90%,
specificity
85-95%.

Shah.,
2024

Magnetic
Resonance
Angiograph
y (MRA)

Visualizes the
blood vessels
in the brain to
detect large
vessel
occlusions or
abnormalities.

Non-invasive,
does not
require
contrast
agents, but
may be used
with them for
better
visualization.

Advanced 3D
visualization
techniques and
automated
segmentation for
vascular mapping.
Time-resolved
MRA (TR-MRA)
providing dynamic
imaging of blood
flow.

Modern
MRA can
identify
large vessel
occlusion in
up to 94%
of cases,
playing a
key role in
determining
eligibility

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for
mechanical
thrombecto
my.

Jiang.,
2024

Computed
Tomograph
y Perfusion
(CTP)

Maps cerebral
blood flow,
blood volume,
and transit
times to
identify areas
of reduced
perfusion.

Quicker and
more widely
available
compared to
MRI. Involves
radiation and
contrast use.

AI-enhanced CTP
analysis automates
penumbra and core
identification.
Ultra-low-dose
CTP techniques
reduce radiation
exposure.

Studies
show CTP
combined
with clinical
assessment
improves
the
detection of
penumbra
and infarct
core,
leading to
better
patient
outcomes in
acute
ischemic
stroke.
Sensitivity
80-92%,
specificity
75-90%.

Wu.,
2024

Functional
MRI (fMRI)

Measures
brain activity
by detecting
changes in
blood flow
related to
neuronal
activation.

Provides real-
time
monitoring of
brain
functions.
Useful in
assessing
cognitive and
motor
impairments
post-stroke.

Recent
development in
resting-state fMRI
(rs-fMRI) allows
for mapping brain
networks without
requiring patient
cooperation. Used
for stroke
rehabilitation
monitoring.

fMRI can
track
recovery
processes in
stroke
patients and
predict
functional
outcomes,
particularly
in
rehabilitatio
n. Studies
indicate
85%
accuracy in
predicting
motor
recovery
post-stroke

Vol. 11/ Núm. 2 2024 pág. 833

using fMRI
data.

Li, L et
al.,
2024

Arterial
Spin
Labeling
(ASL)

Non-invasive
technique for
quantifying
cerebral blood
flow without
contrast
agents.

Uses
magnetic
labeling of
arterial blood
to measure
perfusion.
Safe for
patients with
renal
dysfunction.

High-resolution
ASL for detailed
perfusion maps.
ASL is gaining use
in identifying
tissue viability in
acute stroke,
replacing contrast-
based methods in
certain cases.

ASL is
promising
in detecting
perfusion
abnormaliti
es in
hyperacute
stroke and
determining
tissue at
risk. Studies
show 85-
90%
concordanc
e with CTP
results.

Somme
r., 2024

CT
Angiograph
y (CTA)

Visualizes
arterial
structures,
identifying
blockages,
dissections, or
aneurysms in
the cerebral
vasculature.

Fast and
reliable.
Requires
contrast agent.
Provides
detailed
images of
blood vessels.

Dual-energy CTA
(DE-CTA) enables
the evaluation of
vessel integrity
and plaque
characterization.
CTA-based AI
algorithms can
now predict stroke
severity and
outcomes by
analyzing clot
characteristics.

CTA has
become
crucial for
guiding
mechanical
thrombecto
my. It
provides
near 100%
sensitivity
in detecting
large vessel
occlusions.

Yang.,
2024

Quantitative
Susceptibilit
y Mapping
(QSM)

Measures
magnetic
susceptibility
of brain
tissues to
assess iron
content,
which
correlates
with stroke.

High
sensitivity to
microbleeds,
vessel wall
integrity, and
blood-brain
barrier
disruptions.

Ultra-high-field
MRI using QSM
for more precise
detection of
stroke-related
microvascular
damage and
hemorrhagic
transformation.
QSM is advancing
in stroke
diagnosis,
especially
hemorrhagic
stroke.

QSM can
detect
microvascul
ar changes
with
sensitivity
up to 95%.
Useful in
distinguishi
ng
hemorrhagi
c stroke
from
ischemic
stroke and
evaluating

Vol. 11/ Núm. 2 2024 pág. 834

risk for
hemorrhagi
c
transformati
on post-
thrombolysi
s.

Pan.,
2024

Positron
Emission
Tomograph
y (PET)

Measures
cerebral
metabolism
by detecting
gamma rays
emitted from
a tracer
injected into
the
bloodstream.

High-
resolution and
provides
detailed
metabolic
information,
but is
expensive and
involves
radiation
exposure.

Combination of
PET with MRI
(PET-MRI) allows
simultaneous
acquisition of
metabolic and
anatomical data.
New tracers are
being developed
for more targeted
stroke imaging,
particularly in
identifying
ischemic
penumbra.

PET can
provide
crucial
information
about the
metabolic
state of
brain tissues
post-stroke,
although
limited by
accessibility
and cost.
Sensitivity
for
penumbra
detection is
approximat
ely 85-90%.

Liu.,
2024

Transcrania
l Doppler
Ultrasound
(TCD)

Non-invasive
technique to
measure
cerebral blood
flow velocity
through major
brain arteries.

Portable, low-
cost, and
radiation-free.
Limited by
operator skill
and less
sensitive to
distal
occlusions.

Portable TCD
devices integrated
with AI for real-
time detection of
microembolic
signals. Use of
contrast-enhanced
TCD improves
sensitivity for
detecting
vasospasm and
intracranial
occlusions.

TCD has a
high
sensitivity
(~90%) for
detecting
large vessel
occlusions
in real time.
Its role is
growing in
monitoring
stroke
patients
during acute
managemen
t and
rehabilitatio
n.

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Guo.,
2024

Optical
Coherence
Tomograph
y
Angiograph
y (OCTA)

Non-invasive
technique
using light
waves to
capture high-
resolution
images of
microvasculat
ure.

High-
resolution
visualization
of retinal and
cerebral
microvasculat
ure. Primarily
used for
retinal stroke
imaging.

Developments in
real-time OCTA
and AI-assisted
interpretation for
stroke detection in
retinal vasculature.
Emerging as a
surrogate marker
for cerebral
microvascular
damage in
systemic vascular
diseases.

Studies
show that
OCTA can
detect
microvascul
ar changes
in retinal
stroke with
a sensitivity
of up to
95%. It’s a
promising
tool for
early
detection of
systemic
microvascul
ar diseases,
including
stroke.

Description: Above table outlines neuroimaging techniques like DWI, PWI, MRA, CTP, fMRI, ASL, CTA, QSM,
PET, TCD, and OCTA, focusing on stroke detection, advanced imaging, AI integration, and key findings for
improved diagnosis.

DISCUSSION

Neuroimaging is a game-changer for predicting stroke recovery. Take diffusion-weighted

MRI (DW-MRI), for example—it helps us see white matter damage and how it might affect motor

skills. If the fractional anisotropy is high, it generally means better recovery prospects. In one

study of 60 patients, damage to a specific brain region called the posterior limb was a top predictor

for outcomes after 90 days. Functional MRI (fMRI) also plays a key role. It tracks brain activity

during tasks showing that more activity in certain brain areas like in contralesional cerebellum or

ipsilesional motor cortex are usually used for signals better recovery. Predicting outcomes

accuracy has improved from 87% to 96% when combining fMRI data with initial motor scores

which show how neuroimaging can fine-tune stroke rehab and help tailor treatments for better

results. (Gaviria & Hamid, 2024) For starters, diffusion tensor imaging (DTI) takes DW-MRI a

step further, giving us detailed map of the brain’s white matter pathways. Researchers have found

that if these pathways show reduced fractional anisotropy then it often means poorer motor

recovery and it is like seeing the damage in fine detail which is being used to understand the long-

term impact on a patient’s movement abilities. Then there is the role of lesion load which

measures brain damage's extent. Studies show that more damage in the part of the brain that

controls movement—called the ipsilesional corticospinal tract- correlates with greater motor

impairment. In other words, extensive and severe is the damage, harder it is for patients to recover

motor function. Another measure provided by the fMRI is the degree of functional integration

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(how well separate areas within a brain network perform tasks). Higher levels of activity in such

brain areas as the premotor and primary motor cortices are correlated with improved motor

function. It can only be seen as a positive sign that these areas of the brain are able to work well

enough to contribute toward the recovery process. Functional magnetic imaging or fMRI which

is used to compare resting state is used to discover how various areas of the human brain interact

when the patient is inactive. This is in contrast to more conventional connectivity patterns—how

well the areas interact with each other during rest—are related to better motor outcomes. It’s like

getting a peek into the brain’s communication network and seeing if it’s working as it should.

Another interesting finding is about corticospinal tract asymmetry. If there’s a noticeable

imbalance in this part of the brain, it can predict less favorable motor outcomes. It’s like having

an uneven playing field that affects recovery potential.

Research by Ball et al. (2022) discussed about post-stroke cognitive complication

assessment using MRI and the primary outcome of the study is cognitive impairment linked to

MRI features like cerebral atrophy, microbleeds, and white matter hyperintensities. For instance,

cerebral atrophy showed an unadjusted odds ratio of 2.48, meaning those with this feature are

more than twice as likely to have cognitive issues. Microbleeds had an adjusted odds ratio of 1.36,

indicating a significant association with cognitive impairment. The secondary outcome, post-

stroke dementia, was also evaluated. Here, an increasing small vessel disease score had an

unadjusted odds ratio of 1.34, showing a notable risk for dementia. These findings help healthcare

professionals identify at-risk patients. (Ball et al., 2022) Abbasi et al. researched on ischemic

stroke segmentation using CT imaging highlight advancements in deep learning methods. Wang

et al. introduced a framework combining CNNs and synthesized pseudo-DWI, enhancing

segmentation accuracy. A 3D U-Net model with patch sampling and squeeze-and-excitation

blocks addressed class imbalance but faced dataset size limitations. The ISLES challenge

demonstrated that machine learning outperforms traditional methods in infarcted tissue

prediction. Naganuma et al., 2023 validated a deep learning-based ASPECTS calculation

software, showing it performed comparably or better than neurologists. Li et al. 2021 developed

a multi-scale U-Net for real-time stroke segmentation, meeting clinical needs. Mäkelä et al.

compared a CNN model against CT perfusion software showing potential despite a small dataset.

Shi et al. proposed C2MA-Net with cross-modal attention, achieving high segmentation accuracy.

Chen et al.’s two-CNN framework for DWI segmentation showed high performance. Overall,

these models demonstrate improved accuracy in stroke diagnosis but face challenges like dataset

size and validation. Recent advancements in neuroimaging techniques have transformed early

stroke detection and prognostic evaluation. MRI based stroke segmentation has a very important

part to play in this regard providing high resolution images that would help in better identification

of lesions. Residual connection and U-Net architectures, Dense CNN, and other the deep learning

models help to improve segmentation by increasing the networks’ capacity. For instance, to

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overcome the class imbalance in ischemic stroke segmentation, Clèrigues et al. proposed a model

based on U-Net resulting in high accuracy of acute stroke penumbra estimation. Moreover, the

improved models like self-similar fractal networks and ConvLSTM also help increase the

segmentation accuracy, challenging segmentation issues, such as class imbalance and lesion

geometry with this approach. Unlocking stroke structural descriptions through the enhanced deep

convolutional networks and hierarchical supervision, cross-attention autoencoders aid the precise

stroke lesion depiction prognosis. Furthermore, processing of some models has revealed its

possibility in clinical practice, increasing speed while not needing much computing power. This

progress evidently contributes to the enhancement of treatment and outcomes of the stroke

patients in clinical practices (Abbasi et al., 2023).

CONCLUSION

New imaging means that include DWI, PWI, and CTP help in the early diagnosis of the

stroke and accurate determination of the ischemic penumbra. New trends involve technology

advancement such as Artificial Intelligence used to predict analysis that is faster and accurate

when determining the ischemic core and penumbra. MR Spectroscopy (MRS) and ASL are

methods which offer the metabolic and perfusion information without constrast agent. Therefore,

fMRI studies are progressively utilized for prognostic assessment, especially in rehabilitation

context. In general, these technologies contribute to early detection, effective management and

better results in stroke.

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