Hamilton D, Riley P J, Miola U J, Amro A A
Department of Medical Physics, Clinical and Bio-Engineering, Riyadh Al Kharj Hospital Programme, Kingdom of Saudi Arabia.
Eur J Nucl Med. 1995 Feb;22(2):108-15. doi: 10.1007/BF00838939.
Identification of hypoperfused areas in myocardial perfusion single-photon emission tomography studies can be aided by bull's-eye representation of raw counts, lesion extent and lesion severity, the latter two being produced by comparison of the raw bull's-eye data with a normal data base. An artificial intelligence technique which is presently becoming widely popular and which is particularly suitable for pattern recognition is that of artificial neural network. We have studied the ability of feed forward neural networks to extract patterns from bull's-eye data by assessing their capability to predict lesion presence without direct comparison with a normal data base. Studies were undertaken on both simulation data and on real stress-rest data obtained from 410 male patients undergoing routine thallium-201 myocardial perfusion scintigraphy. The ability of trained neural networks to predict lesion presence was quantified by calculating the areas under receiver operating characteristic curves. Figures as high as 0.96 for non-preclassified patient data were obtained, corresponding to an accuracy of 92%. The results demonstrate that neural networks can accurately classify patterns from bull's-eye myocardial perfusion images and detect the presence of hypoperfused areas without the need for comparison with a normal data base. Preliminary work suggests that this technique could be used to study perfusion patterns in the myocardium and their correlation with clinical parameters.
在心肌灌注单光子发射断层扫描研究中，通过原始计数、病变范围和病变严重程度的靶心图表示法有助于识别灌注不足区域，后两者是通过将原始靶心数据与正常数据库进行比较得出的。目前正变得广泛流行且特别适用于模式识别的一种人工智能技术是人工神经网络。我们通过评估前馈神经网络在不与正常数据库直接比较的情况下预测病变存在的能力，研究了其从靶心数据中提取模式的能力。我们对模拟数据以及从410名接受常规铊-201心肌灌注闪烁扫描的男性患者获得的真实应激-静息数据进行了研究。通过计算受试者操作特征曲线下的面积来量化训练后的神经网络预测病变存在的能力。对于未预先分类的患者数据，获得了高达0.96的数值，对应于92%的准确率。结果表明，神经网络可以准确地对靶心心肌灌注图像中的模式进行分类，并检测出灌注不足区域的存在，而无需与正常数据库进行比较。初步工作表明，该技术可用于研究心肌灌注模式及其与临床参数的相关性。
