Clement Christoph, Birindelli Gabriele, Pizzichemi Marco, Pagano Fiammetta, Kruithof-De Julio Marianna, Ziegler Sibylle, Rominger Axel, Auffray Etiennette, Shi Kuangyu
Department of Nuclear Medicine, Inselspital Bern, University of Bern, Bern, Switzerland.
EP Department, CERN, Geneva, Switzerland.
EJNMMI Phys. 2022 May 19;9(1):38. doi: 10.1186/s40658-022-00467-x.
Organs-on-Chips (OOCs), microdevices mimicking in vivo organs, find growing applications in disease modeling and drug discovery. With the increasing number of uses comes a strong demand for imaging capabilities of OOCs as monitoring physiologic processes within OOCs is vital for the continuous improvement of this technology. Positron Emission Tomography (PET) would be ideal for OOC imaging, however, current PET systems are insufficient for this task due to their inadequate spatial resolution. In this work, we propose the concept of an On-Chip PET system capable of imaging OOCs and optimize its design using a Monte Carlo Simulation (MCS).
The proposed system consists of four detectors arranged around the OOC device. Each detector is made of two monolithic LYSO crystals and covered with Silicon photomultipliers (SiPMs) on multiple surfaces. We use a Convolutional Neural Network (CNN) trained with data from a MCS to predict the first gamma-ray interaction position inside the detector from the light patterns that are recorded by the SiPMs on the detector's surfaces.
The CNN achieves a mean average prediction error of 0.80 mm in the best configuration. The proposed system achieves a sensitivity of 34.81% for 13 mm thick crystals and does not show a prediction degradation near the boundaries of the detector. We use the trained network to reconstruct an image of a grid of 21 point sources spread across the field-of-view and obtain a mean spatial resolution of 0.55 mm. We show that 25,000 Line of Responses (LORs) are needed to reconstruct a realistic OOC phantom with adequate image quality.
We demonstrate that it is possible to achieve a spatial resolution of almost 0.5 mm in a PET system made of multiple monolithic LYSO crystals by directly predicting the scintillation position from light patterns created with SiPMs. We observe that a thinner crystal performs better than a thicker one, that increasing the SiPM size from 3 mm to 6 mm only slightly decreases the prediction performance, and that certain surfaces encode significantly more information for the scintillation-point prediction than others.
芯片上器官（OOC）是模仿体内器官的微型器件，在疾病建模和药物发现中的应用越来越广泛。随着应用数量的增加，对OOC成像能力的需求也日益强烈，因为监测OOC内的生理过程对于该技术的持续改进至关重要。正电子发射断层扫描（PET）对于OOC成像将是理想的，然而，由于当前PET系统的空间分辨率不足，无法胜任这项任务。在这项工作中，我们提出了一种能够对OOC进行成像的芯片上PET系统的概念，并使用蒙特卡罗模拟（MCS）对其设计进行优化。
所提出的系统由围绕OOC设备布置的四个探测器组成。每个探测器由两块单片LYSO晶体制成，并在多个表面覆盖有硅光电倍增管（SiPM）。我们使用一个通过MCS数据训练的卷积神经网络（CNN），根据探测器表面SiPM记录的光模式来预测探测器内第一个伽马射线相互作用位置。
在最佳配置下，CNN的平均预测误差为0.80毫米。所提出的系统对于13毫米厚的晶体实现了34.81%的灵敏度，并且在探测器边界附近没有显示出预测性能下降。我们使用训练好的网络重建了一个分布在视野范围内的21个点源网格的图像，获得了0.55毫米的平均空间分辨率。我们表明，需要25,000条响应线（LOR）来重建具有足够图像质量的逼真OOC体模。
我们证明，通过直接根据SiPM产生的光模式预测闪烁位置，在由多个单片LYSO晶体组成的PET系统中可以实现近0.5毫米的空间分辨率。我们观察到，较薄的晶体比较厚的晶体表现更好，将SiPM尺寸从3毫米增加到6毫米只会略微降低预测性能，并且某些表面对于闪烁点预测编码的信息比其他表面显著更多。
