Advanced neuroimaging in stroke: The integration of CT perfusion and Artificial intelligence

Abstract

Stroke is still the second-leading cause of death and a main reason for people being disabled all around the world. There were 12.2 million cases of stroke and 6.55 million deaths from stroke in 2019. This big problem of stroke includes types, such as stroke that happens because of a blockage, in a blood vessel, which is called ischaemic stroke and this makes up 62.4 percent of all stroke cases. Then there is haemorrhage, which is bleeding inside the brain and this makes up 27.9 percent of stroke cases. Lastly there is subarachnoid haemorrhage, which is bleeding around the brain and this makes up 9.7 percent of stroke cases. While neuroimaging is a critical component of clinical care, conventional imaging has limits that necessitate the use of sophisticated techniques to improve patient outcomes as the global burden increases .The objective of this study is to assess current developments in stroke neuroimaging, tracing the progression from Computed Tomography Perfusion (CTP) to the use of artificial intelligence (AI) in clinical procedures. Important Developments: CTP-based core–penumbra mapping has advanced significantly, enabling the identification of brain tissue that can be saved in patients even six to twenty-four hours after the beginning of symptoms. Perfusion imaging has been shown in large-scale trials to enable effective endovascular thrombectomy in late time windows for ischaemic stroke by identifying a mismatch between the ischaemic core and hypoperfused tissue .Additionally, image reconstruction and interpretation for both ischaemic and haemorrhagic stroke are being revolutionised by AI-driven automation, particularly supervised machine learning and deep learning. Automated software has improved multimodal imaging to enable immediate, multidimensional data analysis that simplifies acute stroke patient diagnosis and treatment. The direction of the future: The use of AI for personalised and real-time stroke management in neuroimaging has great promise for improving clinical processes and removing potential image interpretation problems. The accuracy of diagnosis and therapy selection for both ischaemic and haemorrhagic disease kinds will probably improve with additional growth of AI models

Keywords

Introduction

Stroke Epidemiology Worldwide and, in India Stroke is a problem that causes a lot of deaths and disabilities. In fact it is the leading thing that causes death and the third leading thing that causes death and disability combined in the world. This happened in 2019. So stroke causes a lot of sickness and death worldwide. In 2019 stroke caused 6.55 million deaths and 12.2 million new stroke cases worldwide. Age-standardized stroke-related mortality is substantially higher in low-income countries (3.6 times higher than high-income groups), and the burden continues to rise in these regions due to a lack of primary prevention, although specific incidence data for India is not isolated in the sources provided. The Value of “Time is Brain” The adage “time is brain” highlights how vulnerable brain tissue is to ischemia, with irreparable damage frequently happening in a matter of minutes at extremely low cerebral blood flow. The stringent time windows for thrombolysis were created by this idea. But a ”physiology is brain” paradigm is emerging in the area, acknowledging that some patients sustain a significant ischaemic penumbra—salvageable tissue sustained by collateral circulation—well beyond conventional time limits. Conventional MRI and Non-Contrast CT’s Drawbacks Non-contrast CT (NCCT): Although NCCT has low sensitivity for early ischaemic alterations, it is the gold standard for ruling out haemorrhage. In the hyperacute period, findings are frequently normal, and obvious hypo attenuation usually indicates irreparable damage. Traditional MRI: MRI is hampered by longer acquisition times and high susceptibility to motion artefacts, despite its great sensitivity for the infarct core. Furthermore, when evaluating early haemorrhage in the basal cisterns, CT is more accurate than sequences like FLAIR. Its hyperacute application is further limited by logistical issues, such as the scanners’ distance from emergency rooms. Although MRI is useful, current guidelines stress that it shouldn’t postpone urgent therapies. Make the switch from visual evaluation to quantitative and AI-based imaging Objective, quantitative analysis is replacing subjective eye assessment in imaging. Post-processing is currently used to create haemodynamic maps of cerebral blood flow (CBF) and volume (CBV) since manual evaluation of raw images can be deceptive. This shift has been transformed by the use of automated AI software (e.g., RAPID), which offers standardised, quick assessment of the ischaemic core and hypo perfused tissue. By eliminating observer bias, this automation enables accurate “mismatch” estimates between the penumbra and core. Justification for Combining AI with CT Perfusion to find “slow progressors” that might benefit from intervention in longer time periods, CT perfusion (CTP) and AI are used. Selecting individuals based on CTP/AI-defined mismatch—rather not time alone—leads to considerably superior functional results in patients treated 6 to 16 hours after onset, according to major clinical trials like EXTEND-IA and DEFUSE 3 . AI guarantees the timely and accurate completion of these intricate physiological evaluations, allowing thrombectomy for a larger patient group.

Pathophysiology

Ischemic stroke:

An ischemic stroke, which has a central core of irreversibly infarcted tissue with markedly reduced blood flow, is brought on by arterial obstruction. This core is surrounded by the ischemic penumbra, a region of hypoperfused but potentially recoverable tissue that is functionally impaired but not yet dead. The penumbra's fate is determined by the rapidity of reperfusion therapy and the efficiency of the collateral circulation, or alternate circulatory routes. The main aim of acute imaging is to precisely separate the penumbra from the core.

Haemorrhagic Stroke:

 (Intracerebral haemorrhage) Haemorrhagic stroke occurs when a blood vessel within the brain bursts. Key pathophysiology includes hematoma expansion (HHE), which frequently occurs within the first hours and worsens outcome, perihematomal edema that leads to secondary injury, and secondary ischemia in the tissue adjacent to the haemorrhage because of compression and diminished blood flow. Consequently, imaging should not only demonstrate the haemorrhage but also supply prognostic information as to the likely course of its evolution and associated squeals

3. Neuroimaging Techniques

3.1 Non-Contrast CT (NCCT)

Early Ischaemic Signs: Baseline unenhanced CT is the standard imaging selection tool used in the initial triage of patients with acute ischaemic stroke (AIS). In order to assess if intravenous or intra-arterial therapies are appropriate, it is used to offer an immediate and emergency examination of the brain. Alberta Stroke Program Early CT Score (ASPECTS) is a 10-point quantitative instrument used to assess early ischaemic alterations (such as loss of the insular ribbon or sulcal effacement) on NCCT. It could be a good idea to independently confirm these clinical grading standards.

3.2 MRI

DWI, FLAIR Mismatch: Fluid-Attenuated Inversion Recovery (FLAIR) and Diffusion-Weighted Imaging (DWI) are used in conventional MRI protocols. Tissue cellularity is measured by calculating Apparent Diffusion Coefficient (ADC) maps using DWI. While these sequences are employed for tumour grading and general brain imaging. GRE/SWI for Haemorrhage: In neuroimaging, haemorrhage is identified as a region of magnetic field distortion induced by paramagnetic blood breakdown products. This is commonly represented as regions of hypointensity in axial EPI T2-weighted images (which operate similarly to GRE or SWI).

3.3 CT Angiography

Large Vessel Occlusion (LVO) Detection: An essential part of the urgent imaging triage for AIS is single-phase CT angiography (CTA) of the head and neck. It is critical for identifying vascular targets for intra-arterial treatments, such as detecting an LVO.  Collateral Assessment: Multiphase CTA is a reliable method that generates time-resolved pictures of the pial arteries. By using a six-point ordinal scale to score pial arterial fullness, it enables the evaluation of collaterals. Research reveals that multiphase CTA has strong interrater reliability and is better to single-phase CTA or perfusion CT in predicting clinical outcomes for stroke patients.

4. CTP Perfusion Imaging: Quantifying tissue validity

4.1 CTP Parameters

Through a number of crucial factors, CTP (Computed Tomography Perfusion) offers vital information on brain haemodynamic: Cerebral Blood Flow (CBF) and Cerebral Blood Volume (CBV): These metrics are crucial to characterising pathological situations in brain haemodynamic.

Tmax: This measure indicates perfusion delay; in particular, hypoperfused tissue is identified by a Tmax > 6 seconds. Mean Transit Time (MTT): MTT is a typical haemodynamic metric used with CBF and CBV to evaluate brain perfusion.

4.2 core vs Penumbra Quantification

One of the major objectives of CTP is to assess the difference between the ischaemic penumbra (potentially recoverable tissue) and the ischaemic core (irreversibly damaged tissue). This is achieved through the use of threshold-based decision making: Infarct Core Thresholds: The most accurate threshold for identifying the acute infarct core has been shown to be a relative CBF (rCBF) of 45% compared to the contralateral hemisphere. Other software tools, such as RAPID, employ a requirement of CBF < 30%to designate the ischemia core.

Perfusion Mismatch: The penumbra is often characterised by an area of delayed perfusion (e.g., Tmax > 6 seconds) that is greater than the core indicated by lower CBF. In clinical selection, patients who might benefit from intervention are identified by a mismatch ratio of 1.8 or more (volume of hypoperfused tissue divided by the core volume).

4.3Role in Acute Stroke Management

CTP is useful in choosing patients for acute therapies, particularly when they present outside the normal time window: Thrombolysis and Treatment Selection: Perfusion imaging offers doctors with a clearer understanding of the advantages and cons of various procedures to assist in picking the best method for specific clinical scenarios .  DAWN Trial: Mechanical Thrombectomy After 6 Hours (DAWN and DEFUSE-3): As long as there is a mismatch between clinical deficit (severity of symptoms) and the actual infarct volume, this investigation showed that mechanical thrombectomy is effective 6 to 24 hours after the patient was last known well.

DEFUSE 3 Trial: This experiment increased the window for thrombectomy to 6 to 16 hours for patients with proximal middle-cerebral-artery or internal-carotid-artery occlusions who exhibit a favourable mismatch on perfusion imaging.

4.4 Limitations of CTP

CTP has a number of acknowledged drawbacks despite its usefulness: Threshold Uncertainty and Software Variability: various software tools may produce various results, and research on the accuracy of particular thresholds in determining the acute infarct core is still underway. Motion artefacts (which can seriously impair map quality and accuracy), contrast nephropathy (risk related to the use of iodinated contrast), and radiation exposure (due to the repetitive scanning required for dynamic imaging).

5. Advanced MRI techniques

Advanced MRI provides a potent supplementary repertoire, but CT-based procedures predominate in hyperacute imaging. Dynamic Susceptibility Contrast (DSC) or non-contrast Arterial Spin Labelling (ASL) methods can be used to do MR Perfusion. Diffusion Tensor Imaging (DTI) provides predictive data for motor recovery by mapping the integrity of the white matter tract. Brain reorganisation during recovery can be evaluated using functional MRI (fMRI). A new method called vessel wall imaging can show artery wall pathology (such as inflammation and dissection) that is not seen on lumenographic tests like CTA.

6. Ai in stroke neuroimaging

By examining symptoms of patients and clinical information from electronic health records, machine learning (ML), a computer framework intended to emulate human insight, is especially helpful in enhancing stroke diagnosis models. Clinical processes in the emergency department (ED) can incorporate machine learning (ML)-enabled screening tools to recommend a "StrokeAlert" to patients who exhibit moderate or unusual symptoms .The Deep Learning method: The automated detection of key findings in head CT scans, such as different forms of intracranial haemorrhage (ICH), calvarial fractures, and midline displacement, are detected using sophisticated deep learning algorithms. By combining early acute ischaemic stroke imaging data with clinical characteristics such as age and NIHSS scores, these models also help in functional outcome prediction .

Convolutional Neural Networks (CNNs): CNN architectures are used for specific imaging applications. For example, DeepSymNet automatically detects large vascular occlusions (LVOs) and infarct core volume on CT angiography (CTA) source images by identifying intracerebral vasculature.

6.1 AI Applications in Ischemic Stroke

Automated ASPECTS Scoring: By quickly and impartially calculating ASPECTS from NCCT, AI systems can minimise inter-rater variability. • Core-Penumbra Segmentation: AI systems can automatically analyse CTP data to quantify and segment core and penumbra volumes, producing mismatch maps in a matter of minutes. • Large Vessel Occlusion (LVO) Detection: AI is capable of autonomously analysing CTA pictures to identify suspected LVO and notify doctors, causing thrombectomy teams to be activated early. • Outcome Prediction: AI models are being developed to predict functional outcomes (e.g., modified Ranking Scale score at 90 days) by combining imaging information with clinical data.

6.2 AI Application in hemorrhagic stroke

AI applications include the prediction of haematoma growth risk based on imaging biomarkers such as the "spot sign" and perihematomal oedema features, as well as the automated computation of haematoma volume from NCCT, which is more accurate and repeatable than manual techniques.

6.3 FDA/CE approved AI tools

A new clinical AI tool ecosystem has surfaced. RAPID (iSchemaView) is a popular tool for automated CTA and CTP analysis. In order to speed up diagnosis and treatment, Viz.ai and Aidoc provide platforms that automatically identify suspected LVO and other important findings, then send out mobile alerts to specialist teams. Brain mix offers e-ASPECTS and e-CTA analysis solutions driven by artificial intelligence. As demonstrated by Viz.ai's provision of an all-in-one solution that "autodetects suspected diseases across a wide range of therapeutic areas in seconds," these platforms represent the transition towards AI-powered care coordination.

7. AI and CT Perfusion for Clinical Decision Support

Artificial intelligence (AI) is a crucial clinical decision support tool for stroke imaging, especially in neuroradiology, which is well-suited for machine learning because of the large amounts of data generated. In order to simulate cognitive processes, deep learning and supervised machine learning find patterns in high-dimensional data for analysis, interpretation, and prediction on fresh datasets. These methods are essential to the image-based diagnosis and treatment of stroke.

Optimising Workflow and Reducing Door-to-Needle Time AI developments in stroke imaging place a strong emphasis on image acquisition, reconstruction, and workflow optimisation. AI can speed up imaging analysis, a crucial part of clinical management, by using patterns discovered from labelled “ground truth” data. The sources stress that AI’s main focus is on improving the workflow and interpretation in patients with acute stroke, even though they do not specifically use the term “door-to-needle time,” which naturally supports the objective of rapid treatment administration.

Automated TriageAI methods help in automated triage by quickly diagnosing and interpreting images. These technologies can help physicians evaluate acute situations more effectively than manual evaluation alone because supervised machine learning can find intricate patterns in high-dimensional datasets. Integration of Telestrokes Through hub and spoke model, stroke systems of care are increasingly including

 Tele-stroke (stroke telemedicine) in addition to automated analysis. In order to deliver specialist-led or neurologist-led treatment in a variety of contexts, these models enable protocol-driven care and reorganise the current hospital infrastructure. By guaranteeing that organised stroke care is available, telemedicine integration is a crucial tactic for reducing morbidity and death.

8. Role of Imaging in Prognosis and Outcome Prediction

Modern neuroimaging offers important prognostic biomarkers. Strong indicators of the functional outcome following thrombectomy include the size of the ischemic core and the existence of strong collaterals. Imaging characteristics in ischemic stroke can be used to predict the likelihood of hemorrhagic transition following reperfusion treatments. The use of serial imaging with sophisticated modalities, such as fMRI or DTI, to track stroke recovery and customize rehabilitation plans is growing.

9. Challenges, Ethical issues and limitations

Despite its potential, this technological innovation is not without its challenges. AI algorithms may encounter data bias if they are trained on non-diverse populations, which could impair performance across various ethnic groups. There is currently a lack of standardization in AI outputs and CTP post-processing. Cost and accessibility are major barriers, especially in low-resource settings like some parts of India, which could result in a "neuroimaging divide." AI outcomes need to be properly evaluated by doctors and shouldn't be the sole consideration for making decisions. AI use also raises moral and legal questions about data privacy and decision-making accountability.

10. FUTURE DIRECTION

Further integration is indicated by the path. To enable genuinely personalized stroke therapy, future systems will probably use AI to combine multidimensional imaging results (CT, MRI, and angiography) with clinical and genomic data. Diagnosis could be accelerated by actual time AI analysis at the point-of-care, possibly on portable imaging devices. Using new technologies, such as telemedicine platforms, to enhance stroke care in remote and rural locations and democratize access to cutting-edge diagnostics is a major frontier.

CONCLUSION

The way we understand what is going on in the body with CT perfusion and the ability to analyse things with intelligence have really changed how we look at stroke neuroimaging. Artificial intelligence and CT perfusion have made it easier to diagnose stroke. Have given doctors more time to treat it.They also help doctors choose the treatment for each patient who has had a stroke whether it is an ischemic stroke or a hemorrhagic stroke. These new developments are very good for patients because they make a difference, in their care. It is very important to remember that artificial intelligence is a tool that helps doctors do their jobs better it does not replace them. Artificial intelligence is meant to work with doctors not of them. Large-scale, prospective clinical trials, worldwide standardization initiatives, and global accessibility plans are desperately needed to fully realise its potential and guarantee fair benefit.

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