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首页> 外文期刊>Medical image analysis >Improving low-dose blood-brain barrier permeability quantification using sparse high-dose induced prior for Patlak model
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Improving low-dose blood-brain barrier permeability quantification using sparse high-dose induced prior for Patlak model

机译：使用稀疏大剂量诱导的Patlak模型改善低剂量血脑屏障通透性定量

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摘要

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.
机译：使用Patlak模型从灌注计算机断层扫描（PCT）中提取的血脑屏障通透性（BBBP）测量值可以作为预测急性卒中患者出血性转化的重要指标。不幸的是，基于标准Patlak模型的PCT需要过多的辐射暴露，这引起了人们对辐射安全性的关注。最小化辐射剂量在临床实践中具有很高的价值，但是由于引入的严重噪声会降低图像质量。这项工作的目的是通过利用不同个体之间的大脑结构相似性以及高剂量和低剂量地图之间的关系，从低剂量PCT数据构建高质量的BBBP图。拟议的稀疏大剂量诱导（shd-Patlak）模型通过建立具有一组位置自适应词典的Patlak模型的先验大剂量诱导后执行，然后使用先验的正则化Patlak模型对BBBP图进行优化估计来执行。用模拟的低剂量临床脑PCT数据集进行的评估清楚地表明，与标准的Patlak模型相比，shd-Patlak模型可以实现更大的收益，其视觉质量得到改善，对金标准的保真度更高，并且临床分析的细节更加准确。

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