Perfusion imaging is extensively utilized to assess hemodynamic status and tissue perfusion in various organs. Computed tomography perfusion (CTP) imaging plays a key role in the early assessment and planning of stroke treatment. While CTP provides essential perfusion parameters to identify abnormal blood flow in the brain, the use of contrast agents in CTP can lead to allergic reactions and adverse side effects, along with costing USD 4.9 billion worldwide in 2022. To address these challenges, we propose a novel deep learning framework called Multitask Automated Generation of Intermodal CT perfusion maps (MAGIC). This framework combines generative artificial intelligence and physiological information to map non-contrast computed tomography (CT) imaging to multiple contrast-free CTP imaging maps. We demonstrate enhanced image fidelity by incorporating physiological characteristics into the loss terms. Our network was trained and validated using CT image data from patients referred for stroke at UF Health and demonstrated robustness to abnormalities in brain perfusion activity. A double-blinded study was conducted involving seven experienced neuroradiologists and vascular neurologists. This study validated MAGIC's visual quality and diagnostic accuracy showing favorable performance compared to clinical perfusion imaging with intravenous contrast injection. Overall, MAGIC holds great promise in revolutionizing healthcare by offering contrast-free, cost-effective, and rapid perfusion imaging.

翻译：灌注成像广泛应用于评估各器官的血流动力学状态和组织灌注情况。计算机断层扫描灌注（CTP）成像在卒中治疗的早期评估和规划中发挥着关键作用。虽然CTP能提供识别脑部异常血流所必需的灌注参数，但CTP中对比剂的使用可能导致过敏反应和不良副作用，且2022年全球相关成本高达49亿美元。为应对这些挑战，我们提出了一种名为多模态CT灌注图自动生成多任务网络（MAGIC）的新型深度学习框架。该框架结合生成式人工智能与生理信息，将非对比计算机断层扫描（CT）成像映射至多幅无对比剂CTP成像图。我们通过将生理特征融入损失函数项，证明了图像保真度的提升。我们的网络使用来自UF Health卒中转诊患者的CT图像数据进行训练和验证，并展现出对脑灌注活动异常的鲁棒性。一项双盲研究邀请了七位经验丰富的神经放射科医师和血管神经科医师参与。该研究验证了MAGIC的视觉质量与诊断准确性，结果显示其相较于静脉注射对比剂的临床灌注成像具有更优性能。总体而言，MAGIC通过提供无对比剂、经济高效且快速的灌注成像，在革新医疗保健领域展现出巨大潜力。