Wu Hao, Eck Brendan L, Levi Jacob, Fares Anas, Li Yuemeng, Wen Di, Bezerra Hiram G, Wilson David L
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
Department of Physics, Case Western Reserve University, Cleveland, OH, 44106, USA.
Proc SPIE Int Soc Opt Eng. 2018 Feb;10578. doi: 10.1117/12.2293829. Epub 2018 Mar 12.
There are several computational methods for estimating myocardial blood flow (MBF) using CT myocardial perfusion imaging (CT-MPI). Previous work has shown that model-based deconvolution methods are more accurate and precise than model-independent methods such as singular value decomposition and max-upslope. However, iterative optimization is computationally expensive and models are sensitive to image noise, thus limiting the utility of low x-ray dose acquisitions. We propose a new processing method, SLICR, which segments the myocardium into super-voxels using a modified simple linear iterative clustering (SLIC) algorithm and quantifies MBF via a robust physiologic model (RPM). We compared SLICR against voxel-wise SVD and voxel-wise model-based deconvolution methods (RPM, single-compartment and Johnson-Wilson). We used image data from a digital CT-MPI phantom to evaluate robustness of processing methods to noise at reduced x-ray dose. We validate SLICR in a porcine model with and without partial occlusion of the LAD coronary artery with known pressure-wire fractional flow reserve. SLICR was ~50 times faster than voxel-wise RPM and other model-based methods while retaining sufficient resolution to show all clinically interesting features (e.g., a flow deficit in the endocardial wall). SLICR showed much better precision and accuracy than the other methods. For example, at simulated MBF=100 mL/min/100g and 100 mAs exposure (50% of nominal dose) in the digital simulator, MBF estimates were 101 ± 12 mL/min/100g, 160 ± 54 mL/min/100g, and 122 ± 99 mL/min/100g for SLICR, SVD, and Johnson-Wilson, respectively. SLICR even gave excellent results (103 ± 23 ml/min/100g) at 50 mAs, corresponding to 25% nominal dose.
使用CT心肌灌注成像（CT-MPI）估算心肌血流量（MBF）有几种计算方法。先前的研究表明，基于模型的去卷积方法比诸如奇异值分解和最大斜率等与模型无关的方法更准确、更精确。然而，迭代优化计算成本高昂，且模型对图像噪声敏感，因此限制了低剂量X射线采集的实用性。我们提出了一种新的处理方法SLICR，它使用改进的简单线性迭代聚类（SLIC）算法将心肌分割成超体素，并通过稳健的生理模型（RPM）对MBF进行量化。我们将SLICR与逐体素SVD和基于体素的基于模型的去卷积方法（RPM、单室和约翰逊-威尔逊方法）进行了比较。我们使用来自数字CT-MPI体模的图像数据来评估处理方法在降低X射线剂量时对噪声的鲁棒性。我们在有和没有已知压力导丝血流储备分数的LAD冠状动脉部分闭塞的猪模型中验证了SLICR。SLICR比逐体素RPM和其他基于模型的方法快约50倍，同时保留了足够的分辨率以显示所有临床上感兴趣的特征（例如，心内膜壁的血流不足）。SLICR的精度和准确性比其他方法好得多。例如，在数字模拟器中模拟的MBF = 100 mL/min/100g和100 mAs曝光（标称剂量的50%）下，SLICR、SVD和约翰逊-威尔逊方法的MBF估计值分别为101±12 mL/min/100g、160±54 mL/min/100g和122±99 mL/min/100g。在50 mAs（相当于标称剂量的25%）时，SLICR甚至给出了出色的结果（103±23 ml/min/100g）。
