Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization.
Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization.
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DOI：
10.1109/tmi.2015.2405015
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发表时间：
2015-07
影响因子：
10.6
通讯作者：
Sanelli PC
中科院分区：
文献类型：
--
作者：
Ruogu Fang;Shaoting Zhang;Tsuhan Chen;Sanelli PC
急性脑部疾病，如急性中风和短暂性脑缺血发作，是全球致死和致残的主要原因，每年占总死亡人数的9%。“时间就是大脑”是急性脑血管疾病治疗中被广泛接受的概念。高效且准确的血流动力学参数估计计算框架可为溶栓治疗节省关键时间。同时，计算机断层扫描灌注（CTP）中连续图像采集导致的累积辐射剂量过高引发了对患者安全和公共卫生的担忧。然而，低辐射会导致噪声和伪影增加，这需要更复杂且耗时的算法来进行稳健估计。在本文中，我们专注于开发一个稳健且高效的框架，以便在低辐射剂量下准确估计灌注参数。具体而言，我们提出了一种张量全变分（TTV）技术，它融合了血管结构的空间相关性和血液信号流的时间连续性。还提出了一种高效算法，以实现快速收敛并降低计算复杂度。我们从对噪声水平的敏感度、估计准确性、对比度保持等方面进行了广泛评估，并在数字灌注模型估计以及体内临床受试者上进行了测试。我们的框架将所需辐射剂量降低至原始水平的仅8%，并且性能优于现有最先进的算法，峰值信噪比提高了32%。它减少了残差函数的波动，纠正了脑血流量（CBF）的高估和平均通过时间（MTT）的低估，并保持了缺损区域和正常区域之间的差异。
Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. ‘Time is brain’ is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in-vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions.