Clinical Application of Scatter Correction with Artificial Neural Network in Myocardial and Brain SPECT

本文提出了一种新的基于人工神经网络的散射校正算法，并在双同位素SPECT中验证了该算法的有效性。首先，我们将人工神经网络设置为一个输入层，该输入层由10个单元组成，需要10个能量窗口进行数据采集。该方法在体模实验中SPECT定量的误差在4%以内。针对现有伽马相机系统信噪比不高，能量设置不合理等问题，设计了一种新的神经网络，该网络包含一个输入层，三个输入单元，用于三个能量窗的采集。将人工神经网络应用于脑SPECT和心肌SPECT的临床试验，同时评价静息和负荷脑灌注、心肌灌注和脂肪酸代谢、心肌灌注和心交感神经功能。该方法能将锝-99m和碘-123图像清晰地分离，并能获得临床可接受的噪声干扰图像，使我们能够在一次采集中获得包含两种不同信息的图像。

We have developed a novel scatter correction algorithm with an artificial neural network(ANN) and it was validated in dual isotope SPECT for separating the primary photons of Tc-99m from those of I-123.Initially, we arranged an ANN with one input layer comprising 10 units that requires 10 energy windows for data acquisition. This method resulted in errors within 4% in SPECT quantification in phantom experiments. However, the signal-to-noise ratios were not satisfactory, and the energy setting is not achievable in current gamma camera systems.A renewed ANN containing one input layer with 3 units for 3 energy window acquisition was designed to overcome the above problems. Phantom experiments yielded images with reduced noise, and errors of SPECT quantification within 5%.Clinical trials of the ANN were conducted in brain and myocardial SPECT for the simultaneous evaluation of rest and stress brain perfusion, myocardial perfusion and fatty acid metabolism, and myocardial perfusion and cardiac sympathetic nerve function. Technetium-99m and I-123 images were clearly separated and images with clinically acceptable noises were obtained.This method enables us to obtain images including two different pieces of information of various kinds by just one-time acquisition.