S. Di Giacomo;M. Ronchi;G. Borghi;D. R. Schaart;M. Carminati;C. Fiorini
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The specific target is the reconstruction of the position of interaction of gamma-rays in the scintillator crystal of Anger cameras used for PET and SPECT. This chip can be used stand-alone or monolithically integrated within the application specific integrated circuit (ASIC) for the filtering of current signals from arrays of silicon photomultipliers (SiPMs). Computation is performed in charge domain by means of crossbar arrays of programmable capacitor. The architecture of the 64-input ASIC and the training of the NN are presented, discussing the impact of weight quantization on 5 bits. From MATLAB and circuit simulations, consistent with ASIC topology and operations, the NN capabilities were tested using two different datasets, obtained from both simulated data and experimental data, both based on PET detector composed by a monolithic scintillator crystal readout by an \n<inline-formula> <tex-math>$8\\times 8$ </tex-math></inline-formula>\n array of SiPMs. Simulations show an achievable spatial resolution better than 2-mm full-width-at-half-maximum with a 10-mm thick crystal, a max. count rate of 200kHz and the energy efficiency per inference is estimated to be of 93.5GOP/J, i.e., competitive with digital counterparts, with an energy consumption of 38nJ per inference and area of 23mm2.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Implementing an Integrated Neural Network for Real-Time Position Reconstruction in Emission Tomography With Monolithic Scintillators\",\"authors\":\"S. 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引用次数: 0
摘要
随着视野范围的不断扩大(即通道数增加),核成像扫描仪的复杂性也在不断增加,将信号处理嵌入辐射探测器前端是应对这种情况的一种方法。机器学习(ML)是内在图像重建性能和计算能力之间的良好折衷。虽然大多数用于 ML 的硬件加速器都基于数字电路,因此需要对来自光电探测器的所有单独信号进行模数转换,但模拟方法可以简化流水线。我们介绍了对模拟加速器的研究,该加速器在 0.35- ${\mu }$ m CMOS 工艺节点上实现了一个拥有 42 个神经元的神经网络 (NN)。具体目标是重建用于 PET 和 SPECT 的 Anger 相机闪烁晶体中伽马射线相互作用的位置。该芯片可独立使用,也可单片集成在专用集成电路(ASIC)中,用于过滤硅光电倍增管(SiPM)阵列发出的电流信号。计算是通过可编程电容器横条阵列在电荷域中进行的。介绍了 64 输入 ASIC 的结构和 NN 的训练,讨论了权重量化对 5 位的影响。根据 MATLAB 和电路仿真(与 ASIC 拓扑结构和操作一致),使用两个不同的数据集测试了 NN 的能力,这两个数据集均来自模拟数据和实验数据,均基于由单片闪烁晶体组成的 PET 检测器,该检测器由 8 美元/次的 SiPM 阵列读出。模拟结果表明,使用 10 毫米厚的晶体,可实现优于 2 毫米全宽半最大值的空间分辨率,最大计数率为 200kHz,每次推理的能效估计为 93.5GOP/J,即与数字同行相比具有竞争力,每次推理的能耗为 38nJ,面积为 23 平方毫米。
Implementing an Integrated Neural Network for Real-Time Position Reconstruction in Emission Tomography With Monolithic Scintillators
Embedding signal processing in the front-end of radiation detectors represents an approach to cope with the growing complexity of nuclear imaging scanners with increasing field of view (i.e., higher number of channels). Machine learning (ML) offers a good compromise between intrinsic image reconstruction performance and computational power. While most hardware accelerators for ML are based on digital circuits and, thus, require the analog-to-digital conversion of all individual signals from photodetectors, an analog approach allows to streamline the pipeline. We present the study of an analog accelerator implementing a neural network (NN) with 42 neurons in a 0.35-
${\mu }$
m CMOS process node. The specific target is the reconstruction of the position of interaction of gamma-rays in the scintillator crystal of Anger cameras used for PET and SPECT. This chip can be used stand-alone or monolithically integrated within the application specific integrated circuit (ASIC) for the filtering of current signals from arrays of silicon photomultipliers (SiPMs). Computation is performed in charge domain by means of crossbar arrays of programmable capacitor. The architecture of the 64-input ASIC and the training of the NN are presented, discussing the impact of weight quantization on 5 bits. From MATLAB and circuit simulations, consistent with ASIC topology and operations, the NN capabilities were tested using two different datasets, obtained from both simulated data and experimental data, both based on PET detector composed by a monolithic scintillator crystal readout by an
$8\times 8$
array of SiPMs. Simulations show an achievable spatial resolution better than 2-mm full-width-at-half-maximum with a 10-mm thick crystal, a max. count rate of 200kHz and the energy efficiency per inference is estimated to be of 93.5GOP/J, i.e., competitive with digital counterparts, with an energy consumption of 38nJ per inference and area of 23mm2.