Improving low-dose blood–brain barrier permeability quantification using sparse high-dose induced prior for Patlak model

• Interweaving the standard Patlak model with sparse high-dose induced prior.
• Construct location adaptive dictionary based on anatomical structure of the brain.
• Learn sparse high-dose induced prior from different patients without the need of accurate registration.
• Propose EM style iterative optimization for sparse high-dose induced Patlak model.
• Outperform Patlak model in low-dose BBBP estimation in terms of different metrics.

Blood–brain barrier permeability (BBBP) measurements extracted from the perfusion computed tomography (PCT) using the Patlak model can be a valuable indicator to predict hemorrhagic transformation in patients with acute stroke. Unfortunately, the standard Patlak model based PCT requires excessive radiation exposure, which raised attention on radiation safety. Minimizing radiation dose is of high value in clinical practice but can degrade the image quality due to the introduced severe noise. The purpose of this work is to construct high quality BBBP maps from low-dose PCT data by using the brain structural similarity between different individuals and the relations between the high- and low-dose maps. The proposed sparse high-dose induced (shd-Patlak) model performs by building a high-dose induced prior for the Patlak model with a set of location adaptive dictionaries, followed by an optimized estimation of BBBP map with the prior regularized Patlak model. Evaluation with the simulated low-dose clinical brain PCT datasets clearly demonstrate that the shd-Patlak model can achieve more significant gains than the standard Patlak model with improved visual quality, higher fidelity to the gold standard and more accurate details for clinical analysis.

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