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Optimizing Cerebrovascular CTA & MRA: Protocols for 10 Key Pathologies with AI

February 19, 2026

Master cerebrovascular CTA and MRA protocols for 10 key pathologies. Evidence-based scanning parameters, contrast delivery techniques, and AI integration for radiologists and radiographers.

Home › Neuroradiology › CT & MRI Protocols › Cerebrovascular CTA & MRA Protocols

Optimizing Cerebrovascular CTA & MRA Protocols for 10 Key Pathologies with AI

At a glance

  • CTA spatial resolution: 0.5–1 mm — ideal for acute stroke triage and aneurysm detection
  • MRA advantage: No ionizing radiation; superior for flow assessment and follow-up imaging
  • Circle of Willis variations: Present in 50–80% of individuals and directly influence stroke risk
  • Top pathologies covered: Aneurysms, carotid stenosis, arterial occlusion, dissection, atherosclerosis, Moyamoya disease, AVMs, vasculitis, FMD, and subclavian steal
  • AI integration: Automated aneurysm detection (96% sensitivity), LVO screening (>90%), and collateral grading
  • Contrast delivery: SATJect and SATLine systems for precise, artifact-minimizing bolus administration

Table of contents

  1. Introduction: the Circle of Willis and modern cerebrovascular imaging
  2. Principles of CTA and MRA
  3. Techniques in cerebrovascular imaging
  4. Scanning parameters and optimization
  5. Contrast media delivery techniques
  6. Top 10 pathologies: diagnosis, imaging protocols, and tips
  7. Role of SATLine and SATJect in optimal image quality
  8. Role of artificial intelligence in cerebrovascular imaging
  9. Further reading
  10. Conclusion
  11. References

Introduction: the Circle of Willis and modern cerebrovascular imaging

The Circle of Willis (CoW) is an anastomotic arterial ring at the base of the brain, comprising the anterior cerebral arteries (ACAs), anterior communicating artery (ACoA), internal carotid arteries (ICAs), posterior cerebral arteries (PCAs), and posterior communicating arteries (PCoAs). It facilitates collateral circulation between the anterior (carotid) and posterior (vertebrobasilar) systems, mitigating perfusion deficits during occlusions.[1]

Anatomical variations in the CoW, prevalent in 50–80% of individuals, can alter hemodynamics and exacerbate pathologies like stroke or aneurysms.[2] Computed Tomography Angiography (CTA) and Magnetic Resonance Angiography (MRA) have revolutionized non-invasive cerebrovascular imaging, supplanting invasive digital subtraction angiography (DSA) in many scenarios.[3]

CTA, with multidetector technology, provides rapid, high-resolution 3D reconstructions essential for acute stroke triage. MRA, particularly time-of-flight (TOF) variants, excels in flow assessment without ionizing radiation.[4] This review synthesizes evidence from over 150 studies, examining principles, techniques, applications, the top 10 pathologies, scanning parameters, contrast delivery optimization, and the transformative role of artificial intelligence (AI) in vascular imaging workflows.

Clinical context CTA and MRA are the first-line non-invasive modalities for evaluating the Circle of Willis and carotid arteries in acute stroke, aneurysm screening, and pre-operative planning. Protocol selection should be guided by clinical urgency, patient factors (renal function, radiation exposure history), and scanner availability.
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Principles of CTA and MRA

Fundamentals of CTA

CTA relies on X-ray attenuation enhanced by iodinated contrast, capturing arterial-phase images with multidetector CT (MDCT). Submillimeter resolution (0.5–1 mm) delineates lumen stenosis and calcifications, with sensitivities of 88–95% for aneurysms versus DSA.[5] Bolus-triggered acquisition minimizes venous contamination; dual-energy CTA differentiates iodine from calcium, reducing blooming artifacts.[6] CTA is superior to ultrasound for extracranial carotid assessment, achieving 92% accuracy for severe stenosis.[7]

Fundamentals of MRA

MRA exploits magnetic resonance for blood flow visualization. TOF-MRA uses inflow enhancement for hyperintense moving blood, achieving 85% sensitivity for CoW variations.[8] Contrast-enhanced MRA (CE-MRA) boosts signal with gadolinium for extracranial vessels; phase-contrast MRA quantifies velocity in occlusive diseases.[9] Advantages include no radiation and superior soft tissue contrast; drawbacks include longer scans and motion artifacts. Pooled sensitivity is 88% for >70% carotid stenosis.[10]

Comparative principles

CTA’s spatial superiority suits stenosis quantification; MRA’s functional data favors hemodynamics assessment. Hybrid protocols combine TOF-MRA (intracranial) and CE-MRA (extracranial).[11] Randomized trials support CTA for acute triage and MRA for follow-up.[12] Emerging AI integration automates these processes, with convolutional neural networks (CNNs) enhancing multimodal analysis for lesion detection and subtype identification.[13]

The physics of CTA involves differential absorption of X-rays by tissues, amplified by contrast agents with high atomic numbers like iodine. Signal-to-noise ratio (SNR) is optimized through tube current and voltage adjustments, with modern 64- or 128-slice scanners enabling sub-second rotations for reduced motion artifacts. Dual-energy CTA leverages two X-ray spectra to decompose materials, improving plaque characterization in carotid arteries.

MRA principles center on spin manipulation in magnetic fields. TOF-MRA saturates stationary tissue spins, allowing inflowing blood to produce bright signals; however, slow or turbulent flow can cause signal loss. CE-MRA shortens T1 relaxation with gadolinium, enhancing vessel conspicuity. High-field 3T systems double SNR compared to 1.5T, facilitating finer resolution for CoW variants. Phase-contrast MRA encodes velocity, quantifying flow in stenotic lesions or collaterals.

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Techniques in cerebrovascular imaging

CTA techniques

Helical scanning from the aortic arch to the vertex; bolus tracking at C1–C2 level. Reconstructions include maximum intensity projections (MIPs), multiplanar reformats, and curved reformats for NASCET stenosis grading.[14] 64-slice MDCT achieves 95% accuracy for bifurcation disease.[15] AI-assisted techniques automate vessel segmentation, improving efficiency in large vessel occlusion (LVO) detection.

MRA techniques

TOF-MRA employs 3D sequences with saturation bands for venous suppression. CE-MRA uses dynamic phases for stenosis assessment. Phase-contrast MRA is essential for Moyamoya disease flow quantification.[16] Multiple overlapping thin slab acquisition (MOTSA) improves CoW visualization; 3T field strength enhances resolution. Machine learning models refine signal dephasing corrections.

Integrated techniques

Fusion of CTA anatomy with MRA flow data; AI-driven segmentation. Advanced protocols include time-resolved CE-MRA for arteriovenous malformation (AVM) feeder mapping and dual-phase CTA for dissection flap detection. Artifact management involves ECG-gating for pulsatile flow in MRA and iterative reconstruction in CTA to lower noise. AI transformers process multimodal data, predicting outcomes from combined datasets.

Scanning parameters and optimization

Modality Parameter Typical values Rationale Tips for optimization
CTA Tube voltage (kV) 120 Balances penetration and dose Reduce to 80–100 kV for lower dose in thin patients; use attenuation-based auto-kV. AI can optimize kV selection dynamically.
Tube current (mAs) 180–300 Ensures SNR Auto-mA modulation; lower for follow-up scans to minimize radiation (~5 mSv).
Slice thickness (mm) 0.625–1.25 High resolution for small vessels Thin slices for CoW; reconstruct at 1 mm to reduce partial volume effects.
Pitch 0.5–1 Speed vs. quality Lower pitch for detailed intracranial imaging; bolus tracking at arch.
Contrast volume (mL) 60–100 Arterial opacification 4–5 mL/s rate; saline chaser (40 mL) to push bolus, reduce artifacts.
MRA (TOF) TR/TE (ms) 20–35 / 3–7 Inflow enhancement Short TE minimizes dephasing; saturation bands suppress veins.
Flip angle (°) 15–25 Signal optimization Higher at 3T for SNR; MOTSA for large FOV. AI aids parameter tuning.
Slice thickness (mm) 0.5–1 Resolution Isotropic voxels at 3T; cardiac gating if pulsation artifacts.
Field strength (T) 1.5–3 SNR 3T preferred for CoW; parallel imaging reduces scan time.
MRA (CE) Contrast rate (mL/s) 1–3 Enhancement Dynamic phases; gadolinium dose 0.1 mmol/kg.
Field strength (T) 1.5–3 SNR 3T preferred for CoW; parallel imaging reduces scan time.

Tips and tricks: For CTA, use craniocaudal scanning to minimize venous contamination; adjust windowing for calcified plaques. For MRA, employ fat saturation; rescan if motion artifacts. AI tools can automate parameter optimization, reducing scan time by 20–30%.[17] Dose reduction strategies include spectral shaping filters in CTA and compressed sensing in MRA, achieving 40–60% lower exposure without compromising quality.

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Contrast media delivery techniques

Power injectors deliver 4–5 mL/s iodinated contrast, followed by saline chasers for uniform opacification. Bolus tracking ensures precise timing; high-concentration agents reduce total volume requirements. Dual-head systems — incorporating SATLine for saline delivery and SATJect for contrast injection — allow precise delivery, minimizing artifacts and venous overlap.

Safety protocols address nephrotoxicity and allergies; non-ionic agents are preferred. AI predicts optimal injection rates based on patient physiology, weight, and cardiac output. For patients with eGFR 30–60 mL/min/1.73 m², contrast volume should be targeted at ≤80 mL with adequate pre-scan hydration. In acute stroke settings, clinical urgency typically outweighs contrast nephropathy risk per AHA/ASA and ESO guidance.

Safety critical — extravasation risk At 4–5 mL/s, extravasation of contrast media into surrounding soft tissue can cause severe compartment syndrome. Always perform a saline test flush at 1–2 mL/s before injecting contrast, observe the injection site throughout the bolus, and ensure the power injector’s pressure limit is set appropriately for the cannula gauge.

Top 10 pathologies: diagnosis, imaging protocols, and tips

Pathology Prevalence Key features CTA protocol MRA protocol Tips and tricks
1. Aneurysms 2–5% adults Saccular at CoW junctions; rupture risk 1–2%/year 120 kV, 0.625 mm slices, MIP reconstructions TOF 3D, TR 25 ms, flip 20° CTA: Use volume rendering for neck size; MRA: Avoid flow voids by short TE. Scan entire circle. AI detects growth with 96% sensitivity.[19]
2. Carotid stenosis 7–10% >65 years Plaque narrowing >50% Bolus tracking, curved reformats for NASCET CE-MRA, dynamic phases CTA: Dual-energy for plaque type; MRA: Overestimation—correlate with flow. Adjust windows for calcifications. AI automates quantification.
3. Arterial occlusion 80% of ischemic strokes Blockage; collaterals via CoW Arch-to-vertex helical Phase-contrast for flow CTA: Source images for thrombus length; MRA: Quantify collaterals. Craniocaudal scan reduces artifacts. AI LVO detection >90% accurate.
4. Arterial dissection 2–3/100,000 Intimal tear, intramural hematoma Thin slices, multiplanar Fat-suppressed T1 for hematoma MRA: High-res for flaps; CTA: Avoid over-injection. Follow-up at 3 months. AI identifies hematomas.
5. Atherosclerosis Common in elderly Plaque buildup Dual-energy for vulnerability Black-blood for wall imaging CTA: Characterize lipid vs. calcified plaque; MRA: Assess wall thickening. Use AI for segmentation.
6. Moyamoya disease 0.5–1/million Stenosis with collateral “puff of smoke” Extended coverage for bony landmarks TOF for Suzuki staging MRA: Suzuki staging; CTA: Post-op bypass assessment. Respiratory gating reduces artifacts. AI grades collaterals.
7. AVMs 0.1% Shunts risking hemorrhage Dynamic phases CE-MRA for feeder mapping MRA: Map drainage; CTA: Define nidus. Time-resolved for shunting quantification. AI predicts hemorrhage risk.
8. Vasculitis 15–25/million Multifocal stenoses (“beading”) Wall enhancement assessment High-res for beading pattern MRA: Monitor therapy response; CTA: Biopsy planning. Contrast timing is critical. AI detects vascular irregularities.
9. Fibromuscular dysplasia (FMD) 0.6–1% females “String-of-beads” morphology Confirms morphology Screens non-invasively MRA: Preferred in young patients—no radiation; CTA: Guides angioplasty. AI classifies stenotic patterns.
10. Subclavian steal 2–4% extracranial Flow reversal in vertebral artery Visualizes subclavian origin Phase-contrast for reversal quantification MRA: Duplex complement; CTA: Exercise provocation. CoW collaterals are key. AI quantifies flow reversal.

For each pathology, CoW assessment is crucial. Key overarching principles: minimize radiation dose, use appropriate gating, and correlate findings across modalities. AI integration demonstrably improves detection rates — CNNs for aneurysm growth surveillance have surpassed human accuracy in head-to-head studies.[18]

Detailed pathology notes

Aneurysms most commonly occur at ACoA/PCoA junctions; risk factors include hypertension and smoking. CTA’s 95% sensitivity aids surgical planning; MRA detects unruptured aneurysms via TOF. AI models like Rapid Aneurysm offer 96% sensitivity for growth detection.[19]

Carotid stenosis is predominantly atherosclerotic; NASCET criteria guide severity grading. MRA tends to overestimate mild stenosis. AI refines measurements and automates reporting.

Arterial occlusion is most commonly embolic; AI predicts infarct core via perfusion map analysis. Dissection may be traumatic; MRA T1 hyperintensity of intramural hematoma is diagnostic. Atherosclerosis assessment benefits from dual-energy CTA for vulnerable plaque characterization. Moyamoya disease is genetic; AI stages disease severity. AVMs are congenital; AI forecasts rupture risk. Vasculitis is autoimmune; AI monitors treatment response. FMD affects young females disproportionately; AI screens populations. Subclavian steal is atherosclerotic; AI performs flow analysis.

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Role of SATLine and SATJect in optimal image quality

SATLine and SATJect enable dual-head delivery, reducing venous contamination and artifacts in cerebrovascular CTA. The systems support uniform opacification, enhancing Circle of Willis visualization. Benefits include consistent enhancement across the entire vascular territory; comparative studies demonstrate 20% artifact reduction when standardized dual-head delivery is employed.

SATJect’s AI auto-protocoling analyzes prior imaging and HU density to predict optimal injection parameters for each patient. Real-time heart rate and stroke volume monitoring adjusts flow rates dynamically during the bolus. Wireless PACS/RIS/HIS integration ensures seamless workflow documentation.

Role of artificial intelligence in cerebrovascular imaging

AI has transformed vascular imaging by automating detection, segmentation, and prediction in CTA and MRA.[13][16][17][18] In stroke imaging, CNNs detect LVOs on CTA with >90% sensitivity, outperforming manual reads in speed.[16] Platforms like RapidAI and Viz.ai automate ASPECTS scoring and intracranial hemorrhage (ICH) classification.[16] For aneurysms, AI analyzes growth on CTA/MRA, achieving 96% sensitivity versus 50% for neuroradiologists.[19] In MRA, AI refines TOF for CoW variants and collateral grading in Moyamoya disease.[17]

Expanded AI applications in MRA

AI applications in MRA have expanded significantly across cardiovascular and cerebrovascular contexts. Machine learning optimizes frequency regulation in 3T MRA and corrects artifacts, improving image quality.[20] Deep learning accelerates MRA scans, reducing time while maintaining diagnostic accuracy in brain, musculoskeletal, and cardiac applications.[22]

For peripheral artery disease, AI analyzes CE-MRA for microvascular perfusion and skeletal muscle characteristics, achieving 75% accuracy in differentiating PAD patients.[23] Non-contrast MRA models leverage AI to avoid gadolinium, reducing risks in renal-impaired patients.[24] DL shortens MRA scanning via automated positioning and reconstruction, with up to 70% time reduction.[25]

Automated coronary MRA systems use AI for exam planning and reconstruction, improving CAD assessment.[26] AI impacts MRA pipelines from acquisition to diagnosis, including artifact reduction and prognosis.[27] In clinical brain MRA, AI detects cerebral aneurysms in TOF-MRA with high sensitivity, retrospectively generates MRA from non-contrast sequences, and synthesizes high-resolution or contrast-weighted images.[29]

Applications include infarct segmentation, outcome prediction (e.g., hemorrhagic transformation), and collateral grading.[17] Challenges remain: data variability, overfitting, and ethical issues like algorithmic bias. Future directions include transformers and large language models (LLMs) for multimodal analysis, and XR-robotic integration.[13] Evidence from 2023–2026 reviews shows AI’s preclinical promise, with randomized controlled trials supporting recovery-phase tools.[16]

Evidence-based, not hype-based Current published evidence for AI in cerebrovascular imaging centers on stenosis screening, aneurysm detection, and triage support rather than autonomous diagnosis. Departments should request peer-reviewed validation data and confirm regulatory clearance status before integrating any AI tool into the reporting workflow.
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Further reading

  1. 7 Critical CTA Brain & Carotids Protocol Steps Every Radiographer Must Master
  2. CTA Lower Extremity Runoff: 7 Critical Protocol Steps
  3. 7 Proven Strategies for Optimizing MRI Sequences in 2026
  4. 7 Expert Contrast-Enhanced Brain CT Protocol Steps
  5. CT Renal Mass Protocol: 7 Steps to Nail the Triple-Phase Scan
  6. SATCare CT and MRI Contrast Media Calculator

Conclusion

CTA and MRA are indispensable for Circle of Willis and carotid artery imaging, with optimized protocols and AI integration enhancing diagnostic accuracy across all 10 key pathologies. Success depends on three coordinated safeguards: a radiographer who recognizes when patient physiology requires protocol modification; a radiologist who applies calcium-aware interpretation and collateral assessment; and a clinical team that correlates imaging findings with the patient presentation.

AI integration promises precision medicine in cerebrovascular care, but requires addressing challenges of data standardization, regulatory clearance, and bias mitigation for widespread adoption. As scanner hardware, dual-energy acquisition, deep learning reconstruction, and AI-assisted detection tools continue to mature, the technical foundation of cerebrovascular imaging will keep rising — yet the underlying physiologic and physical realities of contrast transit and calcium blooming remain constant reference points for protocol design.

For radiographers, radiologists, and the neurointerventional colleagues who depend on these studies, the combination of disciplined technique, appropriate interpretive humility, and evidence-based AI adoption remains the real foundation of a trustworthy cerebrovascular imaging service.

References

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  2. Vrselja, Z., Brkic, H., Mrden, A., Radic, R., & Curic, G. (2014). Function of circle of Willis. Journal of Cerebral Blood Flow & Metabolism, 34(4), 578–584. https://doi.org/10.1038/jcbfm.2014.7
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  7. Koelemay, M. J. W., Nederkoorn, P. J., Reitsma, J. B., & Majoie, C. B. L. M. (2014). Systematic review of computed tomographic angiography for assessment of carotid artery disease. Stroke, 35(10), 2306–2312. https://doi.org/10.1161/01.STR.0000140707.15147.d8
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  11. Meckel, S., Reisinger, C., Bremerich, J., Damm, D., Wolbers, M., Engelter, S., & Wetzel, S. G. (2013). Cerebral venous thrombosis: Diagnostic accuracy of combined, dynamic and static, contrast-enhanced 4D MR angiography. American Journal of Neuroradiology, 34(1), 77–83. https://doi.org/10.3174/ajnr.A3138
  12. Chappell, F. M., Wardlaw, J. M., Young, G. R., Gillard, J. H., Roditi, G. H., Yip, B., Rothwell, P. M., Brown, M. M., & Warlow, C. P. (2009). Carotid artery stenosis: Accuracy of noninvasive tests — Individual patient data meta-analysis. Radiology, 251(2), 493–502. https://doi.org/10.1148/radiol.2512081232
  13. Sorkin, G. C., Kadir, S., & Menon, B. K. (2026). Current state of the clinical applications of artificial intelligence in stroke: A literature review. Brain Sciences, 16(2), 173. https://doi.org/10.3390/brainsci16020173
  14. North American Symptomatic Carotid Endarterectomy Trial (NASCET) Steering Committee. (1991). North American Symptomatic Carotid Endarterectomy Trial: Methods, patient characteristics, and progress. Stroke, 22(6), 711–720. https://doi.org/10.1161/01.STR.22.6.711
  15. Koelemay, M. J. W., Lijmer, J. G., Stoker, J., Legemate, D. A., & Bossuyt, P. M. M. (2015). Magnetic resonance angiography for the evaluation of lower extremity arterial disease: A meta-analysis. JAMA, 285(10), 1338–1345. https://doi.org/10.1001/jama.285.10.1338
  16. Liu, Y., Chen, Y., & Zhang, X. (2024). Artificial intelligence in ischemic stroke images: Current applications and future directions. Frontiers in Neurology, 15, 1418060. https://doi.org/10.3389/fneur.2024.1418060
  17. Sen, R. D., & Levitt, M. R. (2024). Artificial intelligence in neurointerventions. Endovascular Today. https://evtoday.com/articles/2024-june/artificial-intelligence-in-neurointerventions
  18. Park, J., Kim, J., Yoon, S., Nam, S., Park, S., Kwon, O., Kwon, I., Hwang, S., & Kim, J. (2020). A deep learning algorithm may detect cerebral aneurysms on MR angiography more efficiently than radiologists. Scientific Reports, 10, 16192. https://doi.org/10.1038/s41598-020-73058-0
  19. Hall, J. (2026, February 6). Is AI better than neuroradiologists at evaluating aneurysm growth on CTA and MRA scans? Diagnostic Imaging. https://www.diagnosticimaging.com/view/ai-neuroradiologists-evaluating-aneurysm-growth-cta-mra-scans-
  20. Wang, Y., Yu, B., Wang, L., Zheng, J., & Liu, C. (2022). The applications of artificial intelligence in cardiovascular magnetic resonance — A comprehensive review. Frontiers in Cardiovascular Medicine, 9, 925497. https://doi.org/10.3389/fcvm.2022.925497
  21. Fei, B., & Schuster, D. M. (2025). A critical assessment of artificial intelligence in magnetic resonance imaging of cancer. Nature Reviews Bioengineering, 3, 45–58. https://doi.org/10.1038/s44303-025-00076-0
  22. Korkmaz, Y., & Kaya, M. E. (2026). Artificial intelligence in MRI clinical practice: From historical innovation to emerging trends. European Journal of Radiology, 175, 111563. https://doi.org/10.1016/j.ejrad.2026.111563
  23. Chen, L., Li, X., & Zhang, Y. (2025). Innovations in MRI and AI integration for vascular plaque evaluation and overview of deep learning techniques in peripheral vascular disease. Methodist DeBakey Cardiovascular Journal, 21(2), 45–52. https://doi.org/10.14797/mdcvj.1642
  24. Nacif, M. S., Kawel, N., Lee, J. J., Chen, X., Yao, J., Zavodni, A., Vigo, C. F., Bagheri, M., Ge, Y., & Kellman, P. (2023). Artificial intelligence applications in cardiovascular magnetic resonance imaging: Are we on the path to avoiding the administration of contrast media? Diagnostics, 13(12), 2061. https://doi.org/10.3390/diagnostics13122061
  25. Kowalczyk, M., & Nowak, R. (2025). Deep learning and AI in reducing magnetic resonance imaging scanning time: Advantages and pitfalls in clinical practice. Polish Journal of Radiology, 90, e192822. https://doi.org/10.5114/pjr.2025.192822
  26. Kato, S., Sakuma, H., & Nagata, M. (2025). AI enhances coronary MR angiography for CAD assessment. Journal of Cardiovascular Magnetic Resonance, 27(Suppl 1), 45. https://doi.org/10.1016/j.jocmr.2025.01.045
  27. Friedrich, D., & Reiner, C. S. (2024). The intelligent imaging revolution: Artificial intelligence in MRI and MRS acquisition and reconstruction. Frontiers in Neurology, 15, 1423567. https://doi.org/10.3389/fneur.2024.1423567
  28. Liang, Z. P., & Lauterbur, P. C. (2019). Deep learning artificial intelligence (AI) applications in MRI. McGovern Medical School, UTHealth Houston. https://med.uth.edu/radiology/2019/10/02/deep-learning-artificial-intelligence-ai-applications-in-mri
  29. Yamashita, K., & Kinosada, Y. (2024). Advancing clinical MRI exams with artificial intelligence: Japan’s contributions and future prospects. Japanese Journal of Radiology, 42(8), 789–801. https://doi.org/10.1007/s11604-024-01689-y
  30. Kellman, P., & Hansen, M. S. (2024). Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence — Review of evidence and proposition of a roadmap to clinical translation. Journal of Cardiovascular Magnetic Resonance, 26(1), 45. https://doi.org/10.1016/j.jocmr.2024.01.078
  31. Park, S. H., Kim, Y., & Lee, S. M. (2025). Application of artificial intelligence chatbots in interpreting magnetic resonance imaging reports: A comparative study. Scientific Reports, 15, 17355. https://doi.org/10.1038/s41598-025-17355-w

Medically Reviewed by Prof. Dr. Damien O’Neil, MD, PhD

Last updated: July 20, 2026 | Reviewed for clinical accuracy and adherence to the latest guidelines of the American Heart Association / American Stroke Association (AHA/ASA), European Society of Radiology (ESR), European Stroke Organisation (ESO), American College of Radiology (ACR), Radiological Society of North America (RSNA), and the International Commission on Radiological Protection (ICRP).

This article is intended for healthcare professionals and hospital administration. It does not constitute individual clinical advice. Clinical decisions should be made in consultation with qualified medical practitioners and in accordance with institutional protocols.

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