In order to study the possibility of improving the timing performance of the time of flight （TOF） systems, which are made of plastic scintillator counters, and read out by photomultiplier tubes （PMT） with mesh dyn...In order to study the possibility of improving the timing performance of the time of flight （TOF） systems, which are made of plastic scintillator counters, and read out by photomultiplier tubes （PMT） with mesh dynodes and conventional electronics, we have conducted a study using faster PMTs and ultra fast waveform digitizers to read out the plastic scintillators. Different waveform analysis methods are used to calculate the time resolution of such a system. Results are compared with the conventional discriminating method based on a threshold and pulse height. Our tests and analysis show that significant timing performance improvements can be achieved by using this new system.展开更多
To increase spatial resolution and signal-to-noise ratio in PET imaging,we present in this paper the design and performance evaluation of a PET detector module combining both depth-of-interaction(DOI) and time-offligh...To increase spatial resolution and signal-to-noise ratio in PET imaging,we present in this paper the design and performance evaluation of a PET detector module combining both depth-of-interaction(DOI) and time-offlight(TOF) capabilities.The detector module consists of a staggered dual-layer LYSO block with2 mm × 2 mm × 7 mm crystals.MR-compatible SiPM sensors(MicroFJ-30035-TSV,SensL) are assembled into an 8× 8 array.SiPM signals from both fast and slow outputs are read out by a 128-channel ASIC chip.To test its performance,a flood histogram is acquired with a ^(22)Na point source on top of the detector,and the energy resolution and the coincidence resolving time(CRT) value for each individual crystal are measured.The flood histogram shows excellent crystal separation in both layers.The average energy resolution at 511 keV is 14.0 and 12.7%at the bottom and top layers,respectively.The average CRT of a single crystal is 635 and 565 ps at the bottom and top layers,respectively.In conclusion,the compact DOI-TOF PET detector module is of excellent crystal identification capability,good energy resolution and reasonable time resolution and has promising application prospective in clinical TOF PET,PET/MRI,and brain PET systems.展开更多
针对犬类心力衰竭诊疗中呼吸频率连续监测的临床需求,开发一种混合式TOF(Time-of-Flight)传感的新型非接触式呼吸监测系统。提出单点-点阵混合式TOF传感架构,构建自适应空间监测模型以适应宠物不同姿态的监测。基于STM32嵌入式平台实现...针对犬类心力衰竭诊疗中呼吸频率连续监测的临床需求,开发一种混合式TOF(Time-of-Flight)传感的新型非接触式呼吸监测系统。提出单点-点阵混合式TOF传感架构,构建自适应空间监测模型以适应宠物不同姿态的监测。基于STM32嵌入式平台实现传感器时序协同控制与原始信号预处理,通过LabVIEW上位机开发实时呼吸波形解析算法,并光学校准测试优化点阵TOF测距参数。静态场景下对小型犬的测试表明,系统呼吸频率测量误差≤2 BPM(breaths per minute)。LabVIEW界面可实时显示呼吸波形与BPM值,验证了光学TOF传感在活体监测中的可行性。单点-点阵混合TOF系统通过光学测量优化与空间自适应策略,实现了高精度犬静态呼吸监测,为宠物医疗领域提供了小型化、低成本的生物光学测量新方案。展开更多
文章旨在减轻Time of Flight(ToF)相机在动态场景中产生的运动模糊,以提升图像质量和深度信息的准确性。提出了一种基于增益标定的矫正方法,通过增益标定精确测定每个像素的增益系数,并引入深度解算原理中的四位相移法以此优化曝光时间...文章旨在减轻Time of Flight(ToF)相机在动态场景中产生的运动模糊,以提升图像质量和深度信息的准确性。提出了一种基于增益标定的矫正方法,通过增益标定精确测定每个像素的增益系数,并引入深度解算原理中的四位相移法以此优化曝光时间,从而达到提升图像质量和深度测量精度的效果。在运动模糊测试平台上对改善效果进行一系列测试。结果表明,该方法能显著提高ToF相机在动态场景下的深度图像质量,在测试装置转速为100~200 r/min内时,由运动模糊产生的错误像素数减少百分比可达35%以上,最高可达41.46%。通过实验验证,基于增益标定的ToF相机运动模糊矫正方法能有效提升图像质量和深度信息的准确性,展现了其在动态环境下的广泛应用潜力。展开更多
Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,th...Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,the high hardware costs and data burden associated with the acquisition of neutron ToF signals pose significant challenges.Higher sampling rates increase the data volume,data processing,and storage hardware costs.Compressed sampling can address these challenges,but it faces issues regarding optimal sampling efficiency and high-quality reconstructed signals.This paper proposes a revolutionary deep learning-based compressed sampling(DL-CS)algorithm for reconstructing neutron ToF signals that outperform traditional compressed sampling methods.This approach comprises four modules:random projection,rising dimensions,initial reconstruction,and final reconstruction.Initially,the technique adaptively compresses neutron ToF signals sequentially using three convolutional layers,replacing random measurement matrices in traditional compressed sampling theory.Subsequently,the signals are reconstructed using a modified inception module,long short-term memory,and self-attention.The performance of this deep compressed sampling method was quantified using the percentage root-mean-square difference,correlation coefficient,and reconstruction time.Experimental results showed that our proposed DL-CS approach can significantly enhance signal quality compared with other compressed sampling methods.This is evidenced by a percentage root-mean-square difference,correlation coefficient,and reconstruction time results of 5%,0.9988,and 0.0108 s,respectively,obtained for sampling rates below 10%for the neutron ToF signal generated using an electron-beam-driven photoneutron source.The results showed that the proposed DL-CS approach significantly improves the signal quality compared with other compressed sampling methods,exhibiting excellent reconstruction accuracy and speed.展开更多
为提高植物三维重建的精度,更好地实现植物数字化研究,提出了基于TOF(time of flight)深度传感的植物三维点云数据获取与去噪方法。首先通过TOF深度传感来获取植物点云数据,采用直通滤波器对点云数据进行预处理,减少背景噪声;其次采用...为提高植物三维重建的精度,更好地实现植物数字化研究,提出了基于TOF(time of flight)深度传感的植物三维点云数据获取与去噪方法。首先通过TOF深度传感来获取植物点云数据,采用直通滤波器对点云数据进行预处理,减少背景噪声;其次采用改进密度分析的离群点去噪算法,该算法通过结合邻近点平均距离和邻域点数数量2个特征参数,对点云数据中的离群点噪声进行检测和去除;最后采用双边滤波算法对点云内部的小尺寸噪声进行检测和去除。以番茄植株进行相关试验,试验结果表明:与传统双边滤波算法比较,该文算法最大误差降低了11.2%,平均误差降低了23.2%;与拉普拉斯滤波算法比较,最大误差降低了20.6%,平均误差降低了39.2%,表明该文提出的算法在保持点云特征的情况下,能简单高效地去除植物三维点云数据中的不同尺度噪声。展开更多
基金Supported by National Natural Science Foundation of China(10979003)Main Direction Program of Knowledge Innovation Project of Chinese Academy of Sciences
文摘In order to study the possibility of improving the timing performance of the time of flight （TOF） systems, which are made of plastic scintillator counters, and read out by photomultiplier tubes （PMT） with mesh dynodes and conventional electronics, we have conducted a study using faster PMTs and ultra fast waveform digitizers to read out the plastic scintillators. Different waveform analysis methods are used to calculate the time resolution of such a system. Results are compared with the conventional discriminating method based on a threshold and pulse height. Our tests and analysis show that significant timing performance improvements can be achieved by using this new system.
基金supported in part by Fundamental Research Funds for the Central Universities(No.FRF-TP-15-114A1)National Natural Science Foundation of China(Nos.11375096,11505300)Tsinghua University Initiative Scientific Research Program(No.20131089289)
文摘To increase spatial resolution and signal-to-noise ratio in PET imaging,we present in this paper the design and performance evaluation of a PET detector module combining both depth-of-interaction(DOI) and time-offlight(TOF) capabilities.The detector module consists of a staggered dual-layer LYSO block with2 mm × 2 mm × 7 mm crystals.MR-compatible SiPM sensors(MicroFJ-30035-TSV,SensL) are assembled into an 8× 8 array.SiPM signals from both fast and slow outputs are read out by a 128-channel ASIC chip.To test its performance,a flood histogram is acquired with a ^(22)Na point source on top of the detector,and the energy resolution and the coincidence resolving time(CRT) value for each individual crystal are measured.The flood histogram shows excellent crystal separation in both layers.The average energy resolution at 511 keV is 14.0 and 12.7%at the bottom and top layers,respectively.The average CRT of a single crystal is 635 and 565 ps at the bottom and top layers,respectively.In conclusion,the compact DOI-TOF PET detector module is of excellent crystal identification capability,good energy resolution and reasonable time resolution and has promising application prospective in clinical TOF PET,PET/MRI,and brain PET systems.
文摘针对犬类心力衰竭诊疗中呼吸频率连续监测的临床需求,开发一种混合式TOF(Time-of-Flight)传感的新型非接触式呼吸监测系统。提出单点-点阵混合式TOF传感架构,构建自适应空间监测模型以适应宠物不同姿态的监测。基于STM32嵌入式平台实现传感器时序协同控制与原始信号预处理,通过LabVIEW上位机开发实时呼吸波形解析算法,并光学校准测试优化点阵TOF测距参数。静态场景下对小型犬的测试表明,系统呼吸频率测量误差≤2 BPM(breaths per minute)。LabVIEW界面可实时显示呼吸波形与BPM值,验证了光学TOF传感在活体监测中的可行性。单点-点阵混合TOF系统通过光学测量优化与空间自适应策略,实现了高精度犬静态呼吸监测,为宠物医疗领域提供了小型化、低成本的生物光学测量新方案。
文摘文章旨在减轻Time of Flight(ToF)相机在动态场景中产生的运动模糊,以提升图像质量和深度信息的准确性。提出了一种基于增益标定的矫正方法,通过增益标定精确测定每个像素的增益系数,并引入深度解算原理中的四位相移法以此优化曝光时间,从而达到提升图像质量和深度测量精度的效果。在运动模糊测试平台上对改善效果进行一系列测试。结果表明,该方法能显著提高ToF相机在动态场景下的深度图像质量,在测试装置转速为100~200 r/min内时,由运动模糊产生的错误像素数减少百分比可达35%以上,最高可达41.46%。通过实验验证,基于增益标定的ToF相机运动模糊矫正方法能有效提升图像质量和深度信息的准确性,展现了其在动态环境下的广泛应用潜力。
基金supported by the National Defense Technology Foundation Program of China(No.JSJT2022209A001-3)Sichuan Science and Technology Program(No.2021JDRC0011)+1 种基金Nuclear Energy Development Research Program of China(Research on High Energy X-ray Imaging of Nuclear Fuel)Scientific Research and Innovation Team Program of Sichuan University of Science and Engineering(No.SUSE652A001).
文摘Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,the high hardware costs and data burden associated with the acquisition of neutron ToF signals pose significant challenges.Higher sampling rates increase the data volume,data processing,and storage hardware costs.Compressed sampling can address these challenges,but it faces issues regarding optimal sampling efficiency and high-quality reconstructed signals.This paper proposes a revolutionary deep learning-based compressed sampling(DL-CS)algorithm for reconstructing neutron ToF signals that outperform traditional compressed sampling methods.This approach comprises four modules:random projection,rising dimensions,initial reconstruction,and final reconstruction.Initially,the technique adaptively compresses neutron ToF signals sequentially using three convolutional layers,replacing random measurement matrices in traditional compressed sampling theory.Subsequently,the signals are reconstructed using a modified inception module,long short-term memory,and self-attention.The performance of this deep compressed sampling method was quantified using the percentage root-mean-square difference,correlation coefficient,and reconstruction time.Experimental results showed that our proposed DL-CS approach can significantly enhance signal quality compared with other compressed sampling methods.This is evidenced by a percentage root-mean-square difference,correlation coefficient,and reconstruction time results of 5%,0.9988,and 0.0108 s,respectively,obtained for sampling rates below 10%for the neutron ToF signal generated using an electron-beam-driven photoneutron source.The results showed that the proposed DL-CS approach significantly improves the signal quality compared with other compressed sampling methods,exhibiting excellent reconstruction accuracy and speed.
文摘为提高植物三维重建的精度,更好地实现植物数字化研究,提出了基于TOF(time of flight)深度传感的植物三维点云数据获取与去噪方法。首先通过TOF深度传感来获取植物点云数据,采用直通滤波器对点云数据进行预处理,减少背景噪声;其次采用改进密度分析的离群点去噪算法,该算法通过结合邻近点平均距离和邻域点数数量2个特征参数,对点云数据中的离群点噪声进行检测和去除;最后采用双边滤波算法对点云内部的小尺寸噪声进行检测和去除。以番茄植株进行相关试验,试验结果表明:与传统双边滤波算法比较,该文算法最大误差降低了11.2%,平均误差降低了23.2%;与拉普拉斯滤波算法比较,最大误差降低了20.6%,平均误差降低了39.2%,表明该文提出的算法在保持点云特征的情况下,能简单高效地去除植物三维点云数据中的不同尺度噪声。
