Pub Date : 2020-10-31DOI: 10.1109/NSS/MIC42677.2020.9508013
C. Delplancke, M. Gurnell, J. Latz, P. Markiewicz, C. Schönlieb, Matthias Joachim Ehrhardt
Positron Emission Tomography (PET) image reconstruction presents challenges related to the large scale of data to be processed, which affects reconstruction speed, and the need to include regularizers to improve image quality. Among the methods proposed to overcome these challenges, the recently introduced Stochastic Primal Dual Hybrid Gradient (SPDHG) algorithm combines the ability to deal with regularizers like Total Variation and to process large datasets by random subsampling. We present two contributions regarding the step-sizes of SPDHG: i) larger step-sizes facilitated by a new formula, and ii) a numerical method to calibrate, in the context of PET reconstruction, the tradeoff between primal and dual progression, which is common to all primal-dual algorithms. We validate improvements in speed reconstruction on real PET data from the Siemens Biograph mMR.
{"title":"Improving a Stochastic Algorithm for Regularized PET Image Reconstruction","authors":"C. Delplancke, M. Gurnell, J. Latz, P. Markiewicz, C. Schönlieb, Matthias Joachim Ehrhardt","doi":"10.1109/NSS/MIC42677.2020.9508013","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9508013","url":null,"abstract":"Positron Emission Tomography (PET) image reconstruction presents challenges related to the large scale of data to be processed, which affects reconstruction speed, and the need to include regularizers to improve image quality. Among the methods proposed to overcome these challenges, the recently introduced Stochastic Primal Dual Hybrid Gradient (SPDHG) algorithm combines the ability to deal with regularizers like Total Variation and to process large datasets by random subsampling. We present two contributions regarding the step-sizes of SPDHG: i) larger step-sizes facilitated by a new formula, and ii) a numerical method to calibrate, in the context of PET reconstruction, the tradeoff between primal and dual progression, which is common to all primal-dual algorithms. We validate improvements in speed reconstruction on real PET data from the Siemens Biograph mMR.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"427 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76487442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-31DOI: 10.1109/NSS/MIC42677.2020.9507759
Mehdi Amini, M. Nazari, Isaac Shiri, G. Hajianfar, M. Deevband, H. Abdollahi, H. Zaidi
To provide a comprehensive characterization of intra-tumor heterogeneity, this study proposes multi-level multimodality radiomic models derived from 18F-FDG PET and CT images by feature- and image-level fusion. Specifically, we developed fusion radiomic models to improve overall survival prediction of NSCLC patients. In this work, a NSCLC dataset including patients from two different institutions (86 patients used as training and 95 patients used as testing) was included. By extracting 225 features from CT, PET, and fused images, radiomics analysis was used to build single-modality and multimodality models where the fused images are constructed by 3D-wavelet transform fusion (WF). Two models were also developed using two feature-level fusion strategies of feature concatenation (ConFea) and feature averaging (AvgFea). Cox proportional hazard (Cox PH) regression was utilized for survival analysis. Spearman's correlation was utilized as a measure of redundancy, and then best combination of 10 most related features (ranked by univariate Cox PH) were fed into multivariate Cox model. Moreover, the median prognostic score captured from training cohort was used as an untouched threshold in the test cohort to stratify patients into low- and high-risk groups. The difference between groups was assessed using log-rank test. Among all models, WF (C-index=0.708) had the highest index and significantly outperformed CT and PET (C-index = 0.616, 0.572, respectively). Image-level fusion model also outperformed feature-level fusion models ConFea and AvgFea (C-indices = 0.581, 0.641, respectively). Our results demonstrate that multimodal radiomics models especially models fused at image-level have the potential to improve prognosis by combining information from different tumor characteristics, including anatomical and metabolic captured by different imaging modalities.
{"title":"Multi-Level PET and CT Fusion Radiomics-based Survival Analysis of NSCLC Patients","authors":"Mehdi Amini, M. Nazari, Isaac Shiri, G. Hajianfar, M. Deevband, H. Abdollahi, H. Zaidi","doi":"10.1109/NSS/MIC42677.2020.9507759","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507759","url":null,"abstract":"To provide a comprehensive characterization of intra-tumor heterogeneity, this study proposes multi-level multimodality radiomic models derived from 18F-FDG PET and CT images by feature- and image-level fusion. Specifically, we developed fusion radiomic models to improve overall survival prediction of NSCLC patients. In this work, a NSCLC dataset including patients from two different institutions (86 patients used as training and 95 patients used as testing) was included. By extracting 225 features from CT, PET, and fused images, radiomics analysis was used to build single-modality and multimodality models where the fused images are constructed by 3D-wavelet transform fusion (WF). Two models were also developed using two feature-level fusion strategies of feature concatenation (ConFea) and feature averaging (AvgFea). Cox proportional hazard (Cox PH) regression was utilized for survival analysis. Spearman's correlation was utilized as a measure of redundancy, and then best combination of 10 most related features (ranked by univariate Cox PH) were fed into multivariate Cox model. Moreover, the median prognostic score captured from training cohort was used as an untouched threshold in the test cohort to stratify patients into low- and high-risk groups. The difference between groups was assessed using log-rank test. Among all models, WF (C-index=0.708) had the highest index and significantly outperformed CT and PET (C-index = 0.616, 0.572, respectively). Image-level fusion model also outperformed feature-level fusion models ConFea and AvgFea (C-indices = 0.581, 0.641, respectively). Our results demonstrate that multimodal radiomics models especially models fused at image-level have the potential to improve prognosis by combining information from different tumor characteristics, including anatomical and metabolic captured by different imaging modalities.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"1 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79846956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-31DOI: 10.1109/NSS/MIC42677.2020.9507926
Yuli Wang, S. Abbaszadeh
Optical properties modulation based ionization radiation detection method was used as a potential method for the TOF-PET detection, which is with the potential to improve the coincidence timing performance of current PET to picosecond. In this article, we present our simulation works on optical properties modulation based method and demonstrate the feasibility of the optical method for the future PET applications. We first simulate (using Monte Carlo simulation) the energy deposition track of 511 keV photons in the detector crystal, and use Matlab to convert it to the number of ionized charges generated. Then, we (using finite element calculation tools) simulate the distribution of carrier density and electric field modulation caused by ionized charges. Finally, we calculated the change in optical refractive index (n) caused by Pockels effect modulation. Cadmium Telluride (CdTe) was chosen as the detector for the simulation work. Based on our simulation conditions, a 511 keV photon depositing 350 keV energy, the induced optical photon-refraction modulation is around 4.9E-04 (in CdTe), 4.1E-04 refractive index measurement has been achieved by modern optics method. Therefore, the feasibility of the optical properties modulation method has been demonstrated.
基于光学性质调制的电离辐射检测方法是一种潜在的TOF-PET检测方法，具有提高当前PET到皮秒的符合定时性能的潜力。在本文中，我们介绍了基于光学特性调制方法的仿真工作，并证明了光学方法在未来PET应用中的可行性。我们首先模拟(使用蒙特卡罗模拟)511 keV光子在探测器晶体中的能量沉积轨迹，并使用Matlab将其转换为产生的电离电荷数。然后，我们(利用有限元计算工具)模拟了由电离电荷引起的载流子密度分布和电场调制。最后，我们计算了波克尔斯效应调制引起的光学折射率(n)变化。模拟工作选择碲化镉(CdTe)作为探测器。在我们的模拟条件下，一个511 keV的光子沉积350 keV的能量，诱导光学光子折射调制约为4.90 e -04(在CdTe中)，用现代光学方法获得了4.1E-04的折射率测量。因此，证明了光学性质调制方法的可行性。
{"title":"Detection sensitivity of optical property-based radiation detection for PET: refraction index modulation","authors":"Yuli Wang, S. Abbaszadeh","doi":"10.1109/NSS/MIC42677.2020.9507926","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507926","url":null,"abstract":"Optical properties modulation based ionization radiation detection method was used as a potential method for the TOF-PET detection, which is with the potential to improve the coincidence timing performance of current PET to picosecond. In this article, we present our simulation works on optical properties modulation based method and demonstrate the feasibility of the optical method for the future PET applications. We first simulate (using Monte Carlo simulation) the energy deposition track of 511 keV photons in the detector crystal, and use Matlab to convert it to the number of ionized charges generated. Then, we (using finite element calculation tools) simulate the distribution of carrier density and electric field modulation caused by ionized charges. Finally, we calculated the change in optical refractive index (n) caused by Pockels effect modulation. Cadmium Telluride (CdTe) was chosen as the detector for the simulation work. Based on our simulation conditions, a 511 keV photon depositing 350 keV energy, the induced optical photon-refraction modulation is around 4.9E-04 (in CdTe), 4.1E-04 refractive index measurement has been achieved by modern optics method. Therefore, the feasibility of the optical properties modulation method has been demonstrated.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"7 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81073832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-31DOI: 10.1109/NSS/MIC42677.2020.9507779
Nina Mürschberger, Maximilian P. Reymann, P. Ritt, T. Kuwert, A. Vija, M. Cachovan, A. Maier
In this work we investigate the usage of deep learning techniques on SPECT data solving a multi-organ segmentation problem. We extract projections from 21 Lu-177 MELP SPECT scans and obtain the corresponding ground truth labels from the accompanied CT scans by forward-projection of 3D CT organ segmentations. We train a U-Net to predict the area of the kidney, spleen, liver, and background seen in the projection data, using a weighted dice loss between prediction and target labels to account for class imbalance. With our method we achieved a mean dice coefficient of 72 % on the test set, encouraging us to perform further experiments using the U-Net.
{"title":"U-Net for Multi-Organ Segmentation of SPECT Projection Data","authors":"Nina Mürschberger, Maximilian P. Reymann, P. Ritt, T. Kuwert, A. Vija, M. Cachovan, A. Maier","doi":"10.1109/NSS/MIC42677.2020.9507779","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507779","url":null,"abstract":"In this work we investigate the usage of deep learning techniques on SPECT data solving a multi-organ segmentation problem. We extract projections from 21 Lu-177 MELP SPECT scans and obtain the corresponding ground truth labels from the accompanied CT scans by forward-projection of 3D CT organ segmentations. We train a U-Net to predict the area of the kidney, spleen, liver, and background seen in the projection data, using a weighted dice loss between prediction and target labels to account for class imbalance. With our method we achieved a mean dice coefficient of 72 % on the test set, encouraging us to perform further experiments using the U-Net.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"4 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80286569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-31DOI: 10.1109/NSS/MIC42677.2020.9507945
C. Chrysostomou
A novel method for sinogram denoise based on Generative Adversarial Networks (GANs) in the field of SPECT imaging is presented. Projection data from software phantoms were used to train the proposed model. For evaluation of the efficacy of the method Shepp Logan based phantom, with various noise levels added where used. The resulting denoised sinograms are reconstructed using Ordered Subset Expectation Maximization (OSEM) and compared to the reconstructions of the original noised sinograms. As the results show, the proposed method significantly denoise the sinograms and significantly improves the reconstructions. Finally, to demonstrate the efficacy and capability of the proposed method results from real-world DAT-SPECT sinograms are presented.
{"title":"Sinogram Denoise Based on Generative Adversarial Networks","authors":"C. Chrysostomou","doi":"10.1109/NSS/MIC42677.2020.9507945","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507945","url":null,"abstract":"A novel method for sinogram denoise based on Generative Adversarial Networks (GANs) in the field of SPECT imaging is presented. Projection data from software phantoms were used to train the proposed model. For evaluation of the efficacy of the method Shepp Logan based phantom, with various noise levels added where used. The resulting denoised sinograms are reconstructed using Ordered Subset Expectation Maximization (OSEM) and compared to the reconstructions of the original noised sinograms. As the results show, the proposed method significantly denoise the sinograms and significantly improves the reconstructions. Finally, to demonstrate the efficacy and capability of the proposed method results from real-world DAT-SPECT sinograms are presented.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"16 2 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82892796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-31DOI: 10.1109/NSS/MIC42677.2020.9507754
D. Doering, M. Kwiatkowski, Umanath Kamath, C. Tamma, L. Rota, L. Ruckman, R. Herbst, B. Reese, P. Caragiulo, G. Blaj, C. Kenney, G. Haller, A. Dragone
LCLS-II, a Free Electron Laser (FEL) X-ray light source, started operations at SLAC National lab in 2020. This new machine will produce X-ray pulses with a repetition rate up to 1,000,000 times per second. To take advantage of its high pulse rate, detectors and readout systems that can operate at the same frequency and cope with the generated data volumes are being developed. In this paper, we present the readout system for the first generation of ePixHR high rate detectors including the first readout ASIC, namely, ePixHR10k, the first prototype of this family. This system aims at sustaining readout rates higher than 5,000 frames per second for the matrix of the ePixHR10k sensor/ASIC module (288 rows x 384 columns and 2 bytes per pixel). The proposed electronic system uses an FPGA to perform data capture and transmission to a host computer. The firmware is developed using a custom public library called SURF where building blocks such as ASIC register access, high speed communication links, protocols for data stream, and register configuration exist. The user interface uses a companion framework called ROGUE, which implements the software modules for the elements that exist in SURF. Initial results from X-ray tests using Fe55 source are presented.
{"title":"Readout System for ePixHR X-ray Detectors: A Framework and Case Study","authors":"D. Doering, M. Kwiatkowski, Umanath Kamath, C. Tamma, L. Rota, L. Ruckman, R. Herbst, B. Reese, P. Caragiulo, G. Blaj, C. Kenney, G. Haller, A. Dragone","doi":"10.1109/NSS/MIC42677.2020.9507754","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507754","url":null,"abstract":"LCLS-II, a Free Electron Laser (FEL) X-ray light source, started operations at SLAC National lab in 2020. This new machine will produce X-ray pulses with a repetition rate up to 1,000,000 times per second. To take advantage of its high pulse rate, detectors and readout systems that can operate at the same frequency and cope with the generated data volumes are being developed. In this paper, we present the readout system for the first generation of ePixHR high rate detectors including the first readout ASIC, namely, ePixHR10k, the first prototype of this family. This system aims at sustaining readout rates higher than 5,000 frames per second for the matrix of the ePixHR10k sensor/ASIC module (288 rows x 384 columns and 2 bytes per pixel). The proposed electronic system uses an FPGA to perform data capture and transmission to a host computer. The firmware is developed using a custom public library called SURF where building blocks such as ASIC register access, high speed communication links, protocols for data stream, and register configuration exist. The user interface uses a companion framework called ROGUE, which implements the software modules for the elements that exist in SURF. Initial results from X-ray tests using Fe55 source are presented.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"13 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85158825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-31DOI: 10.1109/NSS/MIC42677.2020.9507967
Seowung Leem, Byeong-Yeol Yu, H. Cha, Kyeyoung Cho, R. Miyaoka, Cheolung Kang, Jongmyoung Lee, Seungbin Bae, Hakjae Lee, Kisung Lee
Crystal area segmentation is one of the critical procedures for decoding the detector module coupled with scintillation crystal. However, the blurring effect makes the decoding procedure challenging. For precise decoding, we propose a crystal area segmentation method based on convolutional neural network (CNN). The method is divided into training stage and evaluation stage. In the training stage, data set was extracted from five flood maps in blocks. These blocks went over preprocessing with bandpass filter (BPF) and thresholding. Then the processed blocks were used to train and test the CNN. In evaluation stage, flood map from 2 positron emission tomography (PET) scanners were tested. The method showed 99.5% and 99.4% of peak detection accuracy for each test samples while existing method achieved 91.1% and 95.4%. The proposed algorithm detected center peaks almost perfectly and improved detectability of boundary peaks. Also, the whole decoding process was done in short amount of time. However, the algorithm proposed in this paper only considered the spatial information of the peaks in flood map. In further studies we will develop improved algorithm with using both spatial and energy information to develop more precise and practical decoding algorithm.
{"title":"Crystal Area Segmentation for a Scintillation Detector based on Convolutional Neural Network","authors":"Seowung Leem, Byeong-Yeol Yu, H. Cha, Kyeyoung Cho, R. Miyaoka, Cheolung Kang, Jongmyoung Lee, Seungbin Bae, Hakjae Lee, Kisung Lee","doi":"10.1109/NSS/MIC42677.2020.9507967","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507967","url":null,"abstract":"Crystal area segmentation is one of the critical procedures for decoding the detector module coupled with scintillation crystal. However, the blurring effect makes the decoding procedure challenging. For precise decoding, we propose a crystal area segmentation method based on convolutional neural network (CNN). The method is divided into training stage and evaluation stage. In the training stage, data set was extracted from five flood maps in blocks. These blocks went over preprocessing with bandpass filter (BPF) and thresholding. Then the processed blocks were used to train and test the CNN. In evaluation stage, flood map from 2 positron emission tomography (PET) scanners were tested. The method showed 99.5% and 99.4% of peak detection accuracy for each test samples while existing method achieved 91.1% and 95.4%. The proposed algorithm detected center peaks almost perfectly and improved detectability of boundary peaks. Also, the whole decoding process was done in short amount of time. However, the algorithm proposed in this paper only considered the spatial information of the peaks in flood map. In further studies we will develop improved algorithm with using both spatial and energy information to develop more precise and practical decoding algorithm.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"12 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89693568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-31DOI: 10.1109/NSS/MIC42677.2020.9507810
P. Bhattacharya, C. Brown, C. Sosa, S. Miller, C. Brecher, V. Nagarkar, R. Riedel
High data rate detectors are needed for neutron reflectometer instruments and diffractometers require high spatial resolution detectors. Scintillators are the predominant material for the neutron converters in single crystal detector instruments, with GS20 being the most common choice. Here we report on the development and properties of Ce3+ activated 6LiI crystal scintillator grown with the aim of minimizing the decay time to support the high data rate applications while providing high brightness and high efficiency compared to GS20. The LiI crystals doped with Ce3+ were grown by the vertical Bridgman technique using 95%-enriched 6Li. It demonstrates two decay components with the primary decay of 43- 50 ns (93%) and the secondary decay of ~300 ns (7%), significantly faster than the Eu2+ doped 6LiI decay (~1 µs). Light yield for thermal neutron interactions was measured to be ~18,500 photons/interaction, which is a factor of 3 higher than the GS20. The X-ray excited radioluminescence spectrum of Ce3+ activator in LiI at room temperature shows three well-defined emission bands in the range of 400 to 700 nm, peaking at 430, 474, and 590 nm, which are due to 4f-5d transitions of Ce, The crystals also demonstrate high gamma equivalent energy (GEE) of nearly 3 MeV, thereby permitting effective pulse height neutron/gamma discrimination
{"title":"High Brilliance Fast Scintillator for Neutron Detection and Imaging","authors":"P. Bhattacharya, C. Brown, C. Sosa, S. Miller, C. Brecher, V. Nagarkar, R. Riedel","doi":"10.1109/NSS/MIC42677.2020.9507810","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507810","url":null,"abstract":"High data rate detectors are needed for neutron reflectometer instruments and diffractometers require high spatial resolution detectors. Scintillators are the predominant material for the neutron converters in single crystal detector instruments, with GS20 being the most common choice. Here we report on the development and properties of Ce3+ activated 6LiI crystal scintillator grown with the aim of minimizing the decay time to support the high data rate applications while providing high brightness and high efficiency compared to GS20. The LiI crystals doped with Ce3+ were grown by the vertical Bridgman technique using 95%-enriched 6Li. It demonstrates two decay components with the primary decay of 43- 50 ns (93%) and the secondary decay of ~300 ns (7%), significantly faster than the Eu2+ doped 6LiI decay (~1 µs). Light yield for thermal neutron interactions was measured to be ~18,500 photons/interaction, which is a factor of 3 higher than the GS20. The X-ray excited radioluminescence spectrum of Ce3+ activator in LiI at room temperature shows three well-defined emission bands in the range of 400 to 700 nm, peaking at 430, 474, and 590 nm, which are due to 4f-5d transitions of Ce, The crystals also demonstrate high gamma equivalent energy (GEE) of nearly 3 MeV, thereby permitting effective pulse height neutron/gamma discrimination","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"16 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89294368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-31DOI: 10.1109/NSS/MIC42677.2020.9507740
J. Segal, C. Kenney, R. Patti, Benjamin Parpillon, Sangki Hong
Low Gain Avalanche Detectors (LGADs) have recently been studied for applications in high energy physics. They provide the advantages of built-in gain and fast read-out. However, radiation hardness remains a problem, reduced effective boron doping concentration (acceptor removal) after hadron irradiation that dramatically reduces LGAD gain. We propose a new LGAD process flow that allows for creation of a very steep boron profile in the multiplication region, reducing the fractional acceptor removal and resulting performance degradation. The new LGAD process flow requires a low temperature silicon-silicon wafer bonding process, which is currently under development. TCAD process simulations are used to demonstrate feasibility of the new structure, and TCAD device simulations are used to characterize LGAD performance before and after irradiation.
{"title":"New Radiation Tolerant LGAD for High Energy Physics","authors":"J. Segal, C. Kenney, R. Patti, Benjamin Parpillon, Sangki Hong","doi":"10.1109/NSS/MIC42677.2020.9507740","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507740","url":null,"abstract":"Low Gain Avalanche Detectors (LGADs) have recently been studied for applications in high energy physics. They provide the advantages of built-in gain and fast read-out. However, radiation hardness remains a problem, reduced effective boron doping concentration (acceptor removal) after hadron irradiation that dramatically reduces LGAD gain. We propose a new LGAD process flow that allows for creation of a very steep boron profile in the multiplication region, reducing the fractional acceptor removal and resulting performance degradation. The new LGAD process flow requires a low temperature silicon-silicon wafer bonding process, which is currently under development. TCAD process simulations are used to demonstrate feasibility of the new structure, and TCAD device simulations are used to characterize LGAD performance before and after irradiation.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"51 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87024741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-31DOI: 10.1109/NSS/MIC42677.2020.9507961
Faruk Logoglu, M. Flaska
Detection of fast neutrons is of utmost importance in many scenarios including calibration of neutron sources, neutron imaging and detection of special nuclear materials (SNM) [1], [2]. Neutron detection frequently relies on converting neutrons to charged particles via elastic scattering or neutron capture. The resulting charged particles interact with the surrounding atoms through Coulomb interactions and deposit their energies in the medium. In this work, efficiency of micro-layered scintillating neutron detectors is investigated with extensive Geant4 simulations. Micro-layer geometries can improve the neutron detection efficiency while decreasing the gamma sensitivity. The proposed detection module consists of neutron capture layers made of boron metal enriched to 95% in 10B, scintillating crystals (ZnO) covering each 10B layer from both sides for light production, and neutron moderators placed between individual 10B/ZnO sandwiches to thermalize fast neutrons. The moderator must be optically transparent so that the light created in the scintillators can travel to photosensors without any significant attenuation. Polyethylene is chosen as the moderator in this work due to its low-Z content and optically transparent nature. Photosensors are placed at four corners of the detector module to detect optical photons. After optimizing the detector components, neutron detection efficiency for 1 MeV neutrons was estimated to be 6.8%, 3.2%, and 1.5% for 5, 10, and 20 photon thresholds at the photosensors, respectively. Finally, the gamma sensitivity of the detector module was estimated to be in the range of 10−3-10−4 for 1 MeV gamma rays.
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