Tensor total-variation regularized deconvolution kegularlzea ueconvolution for efficient low-dose CT perfusion.

Area of Science:

• Medical Imaging
• Computational Neuroscience
• Radiology

Background:

• Acute brain diseases like stroke are leading causes of death, making rapid diagnosis critical.
• Current CT perfusion (CTP) imaging requires high radiation doses, raising safety concerns.
• Low-dose CTP imaging introduces noise and artifacts, complicating accurate hemodynamic parameter estimation.

Purpose of the Study:

• To develop an efficient and accurate computational framework for deconvolution in low-dose CT perfusion.
• To reduce radiation dosage in CTP while maintaining diagnostic accuracy for acute cerebrovascular diseases.
• To improve the estimation of cerebral blood flow (CBF) and mean transit time (MTT).

Main Methods:

• Proposed a novel framework utilizing tensor total-variation (TTV) regularization for deconvolution.
• Implemented an efficient computational algorithm for fast convergence and solution finding.
• Evaluated the method's performance under normal and reduced sampling rates.

Main Results:

• Reduced radiation dose to 8% of the original level.
• Outperformed state-of-the-art algorithms with a 40% reduction in estimation error.
• Corrected over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT).

Conclusions:

• The proposed TTV regularization framework enables efficient and accurate hemodynamic parameter estimation from low-dose CTP.
• This approach enhances patient safety by significantly reducing radiation exposure.
• The method offers a promising solution for timely and reliable diagnosis in acute cerebrovascular disease treatment.