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可扩展波形数字化PET读出的可实现分辨率以及与基于理想TDC读出的比较,并将深度神经网络应用于包括深度-of-interaction(DOI)在内的最优估计

Achievable resolution for scalable Waveform Digitizing PET readout and comparison with ideal TDC-based readout with an application of DNN to optimal estimation including DOI.

作者信息

Macchiarulo L, Chock C, Mostafanezhad I, Sabet H

机构信息

IEEE.

Martinos Center for Biomedical Imaging, Radiology, Massachusetts General Hospital & Harvard Medical School, Boston, MA, USA.

出版信息

IEEE Nucl Sci Symp Conf Rec (1997). 2024 Oct-Nov;2024. doi: 10.1109/NSS/MIC/RTSD57108.2024.10658167.

Abstract

Time Of Flight PET (TOF-PET) is a transformative technology for PET systems, but reaping its fullest potential requires achieving very high spatial and timing resolution, and overcoming dependency on signal variability at the level of the individual pixels. In this study, we try to quantify the advantage of using custom waveform sampling devices versus TDC methodology using realistic estimates of noise and other non-idealities. With the use of DNN methodology we show achievable gain for current and future acquisition systems, and also demonstrate the feasibility of DOI estimation from single side readout. We conclude by arguing for the scalability of such a system based on our experience with compact waveform digitizer design.

摘要

飞行时间正电子发射断层扫描(TOF-PET)是一种用于PET系统的变革性技术,但要充分发挥其潜力,需要实现非常高的空间和时间分辨率,并克服对单个像素级信号变异性的依赖。在本研究中,我们尝试使用噪声和其他非理想情况的实际估计值,量化使用定制波形采样设备相对于TDC方法的优势。通过使用深度神经网络(DNN)方法,我们展示了当前和未来采集系统可实现的增益,并且还证明了从单面读出进行深度-of-Interaction(DOI)估计的可行性。基于我们在紧凑型波形数字化仪设计方面的经验,我们论证了这种系统的可扩展性,并以此作为结论。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3418/12334274/fc897295d629/nihms-2096174-f0001.jpg

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