2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)最新文献

{"title":"Optimizing Converter Layer and Active Volume Thickness for Gallium Nitride Neutron Detectors","authors":"Zhong-Fen Zhang, M. Aspinall","doi":"10.1109/NSS/MIC42677.2020.9507948","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507948","url":null,"abstract":"Gallium nitride (GaN) is a direct energy gap semiconductor material with a wide bandgap, high thermal conductivity, high chemical stability, and strong resistance to radiation. It has broad prospects in the application of optoelectronics, high temperature and high power devices, and particle detectors. In this work, an early-stage GaN radiation-hardened neutron detector is described. Monte Carlo simulations using Geant4 10.6 are used to investigate and optimize the converter layers and active volume for the detector and the suggested thickness needed to achieve the highest detection efficiency is given. Further, the gamma rejection ability for GaN has been studied, and the spatial distribution of the partial reaction type of gamma rays with GaN are shown for the first time. This work will aid the design and fabrication of radiation-resistant GaN neutron detectors and will benefit reactor monitoring, high-energy physics experiments, and nuclear fusion research.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"20 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73146770","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}

{"title":"Low Level Radioactivity Measurement using Bayesian Method","authors":"H. Arahmane, J. Dumazert, E. Barat, T. Dautremer, N. Dufour, F. Carrel, F. Lainé","doi":"10.1109/NSS/MIC42677.2020.9508031","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9508031","url":null,"abstract":"The paper introduces original Bayesian algorithm developed by the CEA LIST for the measurement of low-activity uranium contaminations using high-resolution gamma-ray spectrometry based on a high purity germanium diode detector. Such measurement indeed provides access to an indirect estimation of surface activity, assuming that the ratio between the number of alpha particles to be quantified and the number of gamma-rays that are detected is known. The Bayesian approach allows to lower detection limits in low count rates and exploit a richer time-energy information structure than the algorithms used in conventional detection procedures. The performance evaluation and characterization of Bayesian statistical tests is performed using classical receiver operating characteristic curves by comparison to frequentist hypothesis tests. The results indicate that the Bayesian approach, in conjunction with HPGe detector has a superior detection performance of the low-activity uranium contamination up to 50% than that achieved within the frequentist tests. Furthermore, it ensure a significant compromise between the true detection rate, the false alarm rate and the response time.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"36 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":"73986898","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}

{"title":"Analysis of the performance of square photomultiplier tubes with 6 µm pore microchannel plates","authors":"J. Milnes, T. Conneely, A. Duran, C. Slatter, P. Hink","doi":"10.1109/NSS/MIC42677.2020.9507873","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507873","url":null,"abstract":"Microchannel plate (MCP) based photomultiplier tubes (PMT) are used in applications where sub nanosecond timing and/or the ability to work in strong magnetic fields are critical, such as inertial confinement fusion diagnostics or Cherenkov based particle identification systems. Both aspects are improved by reducing the size of the pores in the MCP. Results have previously been presented with the Photek MAPMT253, a 53×53 mm active area square PMT configured with 8×8 anode pads and 15 µm pore MCPs. Here we present results analyzing the performance of the first square PMTs that use 6 µm pore MCPs. The detectors will be evaluated for single photon timing accuracy, gain, uniformity, magnetic field susceptibility, and count rate capability compared to the standard device.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"48 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":"73530230","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}

{"title":"Liquid Scintillator Development for the SABRE Detector Experiment","authors":"M. S. Rahman, W. Hutchison, L. Bignell, G. Lane, Nathan J. Spinks, T. Truong, Ethan Crosby","doi":"10.1109/NSS/MIC42677.2020.9508101","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9508101","url":null,"abstract":"The SABRE (Sodium-iodide with Active Background REjection) experiment consists of 50 kg of ultrapure NaI(Tl) scintillators contained within a 10.5 tons liquid scintillator (LS) veto detector, and will search for dark matter interactions in the inner NaI(Tl) detector. SABRE will be housed in a new Australian underground laboratory at Stawell, Victoria. Linear Alkyl Benzene (LAB) will be used as the LS solvent, together with PPO (2,5-Diphenyloxazole) and Bis-MSB (4-Bis (2-methylstyryl) benzene) as primary and secondary fluorophores, in the SABRE veto detector. The SABRE physics goals require LAB of high chemical purity to maximise the light yield and optical attenuation length of the veto detector's scintillator. This study focuses on analysis of LAB samples purified using vacuum distillation and a LAB sample prepared separately though column purification. The analysis includes attenuation length measurement with UV-Vis spectroscopy, the identification of organic impurities using the gas chromatography-mass spectrometry (GC-MS) and light yield measurements. The UV-Vis and GC-MS results for LAB samples confirmed that recursive distillation reduced the organic impurities in the wavelength region 330 to 500 nm. The chemical identity of three organic impurities were determined tentatively with GC-MS. The purification of LAB improved the scintillation light yield by as much as 13%, compared to scintillator that used unpurified LAB. In conclusion, the study provides very useful information in regard to LAB purification, light yield, and optical transparency improvement both for the SABRE and future research experiments in the area of particle physics and nuclear science.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"1 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73749200","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}

{"title":"HGCROC-Si and HGCROC-SiPM: the front-end readout ASICs for the CMS HGCAL","authors":"G. Bombardi, A. Marchioro, T. Vergine, F. Bouyjou, F. Guilloux, S. Callier, F. Dulucq, M. Berni, C. de La Taille, L. Raux, D. Thienpont, S. Extier, M. Firlej, T. Fiutowski, M. Idzik, J. Moroń, K. Swientek","doi":"10.1109/NSS/MIC42677.2020.9508012","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9508012","url":null,"abstract":"1 Abstract—The two variants of HGCROC are the ASICs designed to readout the more than 6 million channels of the future HGCAL of CMS, which will consist of hexagonal silicon sensors for a large part but also SiPM-on-scintillators tiles. The SiPM version of the chip was made from the silicon version by adapting only the first amplifier stage. The first aspect is on the performance for both versions in terms of noise, charge and timing, the DAQ and Trigger paths, as well as results from irradiation qualification with total ionizing dose and heavy ions for single-event effects. The third version of HGCROC chip is a major digital release, with RadHard solutions and an additional buffer.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"63 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":"74408851","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}

{"title":"Proton Radiography for a Small-Animal Irradiation Platform Based on a Miniaturized Timepix Detector","authors":"M. Würl, Katrin Schnürle, J. Bortfeldt, C. Oancea, C. Granja, E. Verroi, F. Tommasino, K. Parodi","doi":"10.5282/UBM/EPUB.74258","DOIUrl":"https://doi.org/10.5282/UBM/EPUB.74258","url":null,"abstract":"Pre-treatment proton radiography and computed tomography can improve precision of proton therapy. A compact imaging setup for small-animal proton radiography, based on a miniaturized Timepix detector is presented along with results from proof-of-concept experiments. The MiniPIX detector was placed behind a µ-CT calibration phantom with 10 different tissue-equivalent inserts. The intensity of the 70MeV proton beam was adjusted such that pixel signal clusters from individual protons on the detector could be resolved. Analysis and event filtering on various cluster properties were used to suppress unwanted events. The energy deposition of the selected clusters was converted to water-equivalent thickness (WET) of the traversed material using a conversion curve based on Monte Carlo simulations and measured clusters of protons after traversing PMMA slabs of known thickness. Despite a systematic underestimation of up to 3%, retrieved WET values are in good agreement with ground truth values from literature. The achieved spatial resolution ranges from 0.3 to 0.7 mm for phantom-detector-distances of 1 to 5 cm. Applicability to living animals is currently limited by the relatively long acquisition time of up to 20 minutes per radiography. This obstacle can however be overcome with the latest detector generation Timepix3, allowing to handle higher particle rates and thus requiring shorter irradiation times.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"37 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74524905","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}

{"title":"Noise2Void Denoising of PET Images","authors":"Tzu-An Song, J. Dutta","doi":"10.1109/NSS/MIC42677.2020.9507875","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507875","url":null,"abstract":"Qualitative and quantitative interpretation of PET images is often a challenging task due to high levels of noise in the images. While deep learning architectures based on convolutional neural networks have produced unprecedented accuracy at denoising PET images, most existing approaches require large training datasets with corrupt and clean image pairs, which are often unavailable for many clinical applications. The Noise2Noise technique obviates the need for clean target images but instead introduces the requirement for two noise realizations for each corrupt input. In this paper, we present a denoising technique for PET based on the Noise2Void paradigm, which requires only a single noisy image for training thus ensuring wider applicability and adoptability. During the training phase, a single noisy PET image serves as both the input and the target. The method was validated on simulation data based on the BrainWeb digital phantom. Our results show that it generates comparable performance at the training and validation stages for varying noise levels. Furthermore, its performance remains robust even when the validation inputs have different count levels than the training inputs.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"1 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":"75568204","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}

{"title":"Monte Carlo Simulation of Diverging Collimator Geometries for Ring SPECT/MR","authors":"Erik Reimers, T. Farncombe","doi":"10.1109/NSS/MIC42677.2020.9507770","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507770","url":null,"abstract":"The potential of a diverging fan beam collimator for use in a multimodal SPECT-MR system has been investigated. Collimation was designed for use with a stationary ring of gamma camera modules each comprised of a 32 × 32 pixel CZT detector. The collimators provide a desired field of view (FOV) of 25.0 cm at the center of the bore. Eleven collimator designs were compared, yielding between 13 to 23 modules per ring. Each design was evaluated using reconstructed resolution and sensitivity metrics. The designs were simulated with the Monte Carlo software, GEANT4 Application for the Tomographic Emission (GATE) and tomographic reconstruction was performed with a maximum-likelihood expectation maximization (ML-EM) algorithm in MATLAB. The results showed that a practical SPECT/MR design using 18 detectors per ring with a 3.83 cm length collimator gave equivalent tomographic resolution to that of a clinical SPECT/CT system but with 7.0 times greater detection sensitivity compared to the conventional rotating dual-head camera. Resolution across the reconstructed 25 cm x 25 cm FOV did show slight non-uniformity, with resolution improving around the periphery of the FOV as much as two-fold. A smearing artifact was seen in the corners of the FOV likely due to undersampling within those regions. A reconstructed hot-rod resolution phantom matched the previous results, giving similar resolution performance. However, the simulation also showed that the system suffers from aliasing effects when reconstructing features of 7.9 mm or less. To further investigate how the design choices affected the tomographic resolution, parameters for collimator hole size, detector pixel size, and number of projection angles were explored. Both the reduction of hole size and pixel size each allowed for improved resolvability down to 7.9 mm and 6.4 mm respectively. Increasing the number of projection angles was found to remove smearing artifacts from the image, however it did not significantly change the resolution. The resolution is therefore believed to be limited by the 2.46 mm pixel size and associated pixel matched collimator. These are promising results that show that a diverging fan beam collimator could be a viable choice for a SPECT/MR system.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"40 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76993426","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}

{"title":"Feasibility of Population-Based Input Function for Kinetic Analysis of [11C]-DPA-713","authors":"S. Gauthier, C. Henchcliffe, M. Akerele, S. Zein, S. Pandya, A. Nikolopoulou, A. Raj, P. Mozley, N. Karakatsanis, Ajay Gupta, J. Babich, S. Nehmeh","doi":"10.1109/NSS/MIC42677.2020.9508079","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9508079","url":null,"abstract":"Quantitative PET studies of neurodegenerative diseases typically require the measurement of arterial input function (AIF), an invasive and risky procedure. The aim of this study was to assess the accuracy of population-based input function (PBIF) for [11C]DPA-713 PET kinetic analysis. The final goal is to possibly eliminate the need for AIF. Eighteen subjects from two [11C]-DPA-713 PET protocols, including six (6) healthy and twelve (12) Parkinson Disease (PD) subjects, were included in this study. Each subject underwent 90min dynamic PET imaging on a Siemens Biograph mCT™ scanner. Five of the six healthy subjects underwent a Test/Retest within the same day to assess the reproducibility of the kinetic parameters. Kinetic modeling was carried out with 2-tissue compartment model (2TCM) as well as with the Logan VT model using the PBIF, and again with the patient-specific AIF (PSAIF, gold standard). Using the leave-one-out cross validation method, we generated a PBIF for each subject from the remaining 17 subjects after normalizing the PSAIFs by three techniques: (a) patient weight×injected dose (b) Area Under AIF Curve (AUC), and (c) weight×AUC. The variability in the total distribution volume (VT) and non-displaceable binding potential (BPND) due to the use of PBIF was assessed for some brain regions of interest using Bland-Altman analysis, and for the three normalization approaches. Systematic bias was noticed with the test-retest scans, but this was removed by normalizing with gray matter. Better repeatability was obtained with the Logan VT model where the 95% limits of agreement (LoA) lie within ±20% for all the brain regions. Also, % relative difference between PBIF and PSAIF is significantly different across the normalization techniques, with the normalization by weight×AUC yielding the least % relative difference. For the Bland-Altman analysis, the mean % difference for VT lies within ±2% and the 95% LOA lies within ±40%. For the BPND, the mean difference lies within ±4% and the corresponding 95% LOA is ±80%. In all cases, the variability between PBIF and PSAIF lie within the test-retest repeatability. This study shows that PBIF-based kinetic modelling is feasible, and that better repeatability is achieved with Logan VTmodelling.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"40 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77260373","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}

{"title":"Automatic Generation of MR-based Attenuation Map using Conditional Generative Adversarial Network for Attenuation Correction in PET/MR","authors":"Emily Anaya, C. Levin","doi":"10.1109/NSS/MIC42677.2020.9507903","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507903","url":null,"abstract":"Attenuation correction is an important correction for quantitative PET image reconstruction. Current PET/MR attenuation correction methods involve segmenting MR images acquired with zero-time echo (ZTE) or Dixon sequences and assigning known attenuation coefficients to different tissues. This work builds upon our previous work where we explore a novel deep learning method of attenuation map (µ-map) generation using a conditional generative adversarial network (cGAN) that allows for continuous attenuation coefficients [1]. We develop the use of a cGAN network to directly convert MR images to CT images (pseudo CT) through registered training data. A straightforward bilinear conversion can be applied to the pseudo CT images to obtain attenuation maps at 511keV for PET attenuation correction of the head and neck region, including brain. The overall average MAE of the pseudo CT compared to the real CT test images was found to be 88.2 ± 32.7 HU. Future work includes applying the correction on PET data and comparing the reconstructed PET image with CT-based attenuation correction at 511keV as the gold standard.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"53 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":"76347894","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}