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Updated: Feb 7, 2026

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Diagnostically Competitive Performance of a Physiology-Informed Generative Multi-Task Network for Contrast-Free CT

Wasif Khan1, John Rees2, Kyle B See1

  • 1J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.

Arxiv
|February 6, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning framework, MAGIC, generates contrast-free computed tomography perfusion (CTP) maps from non-contrast CT scans. This innovation offers a cost-effective and rapid alternative for assessing brain perfusion, crucial for stroke treatment.

Keywords:
Brain perfusionDiagnostic evaluationGenerative adversarial networkIschemic strokeNon-contrast CTNon-contrast perfusion

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Brain Slice Stimulation Using a Microfluidic Network and Standard Perfusion Chamber
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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Computed tomography perfusion (CTP) imaging is vital for stroke assessment but relies on contrast agents.
  • Contrast agents in CTP can cause allergic reactions, adverse effects, and significant costs.
  • There is a need for safer, more cost-effective perfusion imaging techniques.

Purpose of the Study:

  • To introduce Multitask Automated Generation of Intermodal CT perfusion maps (MAGIC), a deep learning framework.
  • To generate contrast-free CTP imaging maps from non-contrast CT scans.
  • To improve image fidelity and diagnostic accuracy in perfusion imaging.

Main Methods:

  • Developed a novel deep learning framework (MAGIC) using generative AI and physiological information.
  • Mapped non-contrast CT imaging to multiple contrast-free CTP maps.
  • Incorporated physiological characteristics into loss terms to enhance image fidelity.
  • Trained and validated the network on stroke patient data from UF Health.

Main Results:

  • Demonstrated robustness to brain perfusion abnormalities.
  • A double-blinded study with neuroradiologists validated MAGIC's visual quality and diagnostic accuracy.
  • MAGIC showed favorable performance compared to traditional contrast-enhanced CTP.

Conclusions:

  • MAGIC offers a promising contrast-free, cost-effective, and rapid solution for perfusion imaging.
  • This technology has the potential to revolutionize stroke assessment and treatment planning.
  • The framework enhances healthcare by providing safer and more accessible perfusion diagnostics.