Pub Date : 2020-10-31DOI: 10.1109/NSS/MIC42677.2020.9507753
Ruxue Hu, Hongkai Wang, T. Ristaniemi, Wentao Zhu, Ling Chen, Hui Shen, Fan Rao
Pulmonary lobes segmentation of pulmonary CT images is important for assistant therapy and diagnosis of pulmonary disease in many clinical tasks. Recently supervised deep learning methods are applied widely in fast automatic medical image segmentation including pulmonary lobes segmentation of pulmonary CT images. However, they require plenty of ground truth due to their supervised learning scheme, which are always difficult to realize in practice. To address this issue, in this study we extend an existed unsupervised learning network with an extra pulmonary mask constraint to develop a deformable pulmonary lobes atlas and apply it for fast automatic segmentation of pulmonary lobes in pulmonary CT images. The experiment on 40 pulmonary CT images shows that our method can segment the pulmonary lobes in seconds, and achieve average Dice of 0.906 ± 0.044 and average surface distance of 0.495 ± 0.380 mm, which outperforms the state-of-the-art methods in segmentation accuracy. Our method successfully combines the advantages of both deformable atlas and unsupervised learning for automatic segmentation and ensures the consistent and topology preserving of pulmonary lobes without any postprocessing.
{"title":"Automatic Segmentation of Pulmonary Lobes in Pulmonary CT Images using Atlas-based Unsupervised Learning Network","authors":"Ruxue Hu, Hongkai Wang, T. Ristaniemi, Wentao Zhu, Ling Chen, Hui Shen, Fan Rao","doi":"10.1109/NSS/MIC42677.2020.9507753","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507753","url":null,"abstract":"Pulmonary lobes segmentation of pulmonary CT images is important for assistant therapy and diagnosis of pulmonary disease in many clinical tasks. Recently supervised deep learning methods are applied widely in fast automatic medical image segmentation including pulmonary lobes segmentation of pulmonary CT images. However, they require plenty of ground truth due to their supervised learning scheme, which are always difficult to realize in practice. To address this issue, in this study we extend an existed unsupervised learning network with an extra pulmonary mask constraint to develop a deformable pulmonary lobes atlas and apply it for fast automatic segmentation of pulmonary lobes in pulmonary CT images. The experiment on 40 pulmonary CT images shows that our method can segment the pulmonary lobes in seconds, and achieve average Dice of 0.906 ± 0.044 and average surface distance of 0.495 ± 0.380 mm, which outperforms the state-of-the-art methods in segmentation accuracy. Our method successfully combines the advantages of both deformable atlas and unsupervised learning for automatic segmentation and ensures the consistent and topology preserving of pulmonary lobes without any postprocessing.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"32 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":"81922233","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/MIC42677.2020.9508085
F. Mele, M. Gandola, M. Grassi, C. Labanti, P. Malcovati, G. Bertuccio
We present the design of a multichip Application Specific Integrated Circuit (ASIC), named ORION, for the front-end readout of the X-Gamma Imaging Spectrometer (XGIS) on-board the Transient High Energy Sky and Early Universe Surveyor (THESEUS) space mission. The XGIS instrument is composed by two cameras that operate as a wide field deep sky monitors with a broad energy range from 2 keV to 20 MeV, and it is based on a position sensitive double-detection mechanism for image reconstruction, in which a single pixel is constituted by a Thallium activated Cesium Iodide (CsI(Tl)) scintillator crystals and two Silicon Drift Detectors (SDDs) glued at both crystal ends, whose signal is collected, reconstructed and digitized by the presented ORION chipset. In each camera, the ORION chipset is organized in a constellation of 12 800 analog front-end chips (ORION-FE), closely connected to the SDD anodes, and 800 mixed signal multi-channel back-end chips (ORION-BE) for signal processing and digitalization, for a total 25 600 ORION-FE and 1600 ORION-BE in the complete instrument. The back-end chips have two parallelized X and Gamma signal processors, for low-energy and high-energy photons respectively, which allow a tailored optimization on the noise and energy range requirements for each type of event. The chipset has an input dynamic range of 32 fC that allows to process signals with a linearity error below ±1.2% on the Gamma processor, and below ±0.1% on the X processor. The nominal Equivalent Noise Charge (ENC) of the system at -20 °C for an estimated detector leakage current of 0.7 pA is 12.5 el. r.m.s at 1 µs peaking time for the X processor, and 32.9 el. r.m.s. at 3 µs peaking time for the Gamma processor. The simulated power consumption is of 1.55 mW per pixel.
{"title":"The ORION Chipset for the X-Gamma Imaging Spectrometer Onboard of the THESEUS Space Mission","authors":"F. Mele, M. Gandola, M. Grassi, C. Labanti, P. Malcovati, G. Bertuccio","doi":"10.1109/NSS/MIC42677.2020.9508085","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9508085","url":null,"abstract":"We present the design of a multichip Application Specific Integrated Circuit (ASIC), named ORION, for the front-end readout of the X-Gamma Imaging Spectrometer (XGIS) on-board the Transient High Energy Sky and Early Universe Surveyor (THESEUS) space mission. The XGIS instrument is composed by two cameras that operate as a wide field deep sky monitors with a broad energy range from 2 keV to 20 MeV, and it is based on a position sensitive double-detection mechanism for image reconstruction, in which a single pixel is constituted by a Thallium activated Cesium Iodide (CsI(Tl)) scintillator crystals and two Silicon Drift Detectors (SDDs) glued at both crystal ends, whose signal is collected, reconstructed and digitized by the presented ORION chipset. In each camera, the ORION chipset is organized in a constellation of 12 800 analog front-end chips (ORION-FE), closely connected to the SDD anodes, and 800 mixed signal multi-channel back-end chips (ORION-BE) for signal processing and digitalization, for a total 25 600 ORION-FE and 1600 ORION-BE in the complete instrument. The back-end chips have two parallelized X and Gamma signal processors, for low-energy and high-energy photons respectively, which allow a tailored optimization on the noise and energy range requirements for each type of event. The chipset has an input dynamic range of 32 fC that allows to process signals with a linearity error below ±1.2% on the Gamma processor, and below ±0.1% on the X processor. The nominal Equivalent Noise Charge (ENC) of the system at -20 °C for an estimated detector leakage current of 0.7 pA is 12.5 el. r.m.s at 1 µs peaking time for the X processor, and 32.9 el. r.m.s. at 3 µs peaking time for the Gamma processor. The simulated power consumption is of 1.55 mW per pixel.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"3 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":"84249005","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/MIC42677.2020.9507982
R. Giordano, A. Aloisio, S. Massarotti, G. Tortone, Y. Lai, S. Korpar, R. Pestotnik, L. Šantelj, A. Lozar, M. Shoji, S. Nishida
High Energy Physics experiments usually require radiation-tolerant electronics for on-detector operation. When possible, it is preferable to use commercial off-the-shelf components. For purely digital functions, such as data aggregation, processing and transfer, static RAM-based Field Programmable Gate Arrays (SRAM-based FPGAs) are increasingly being used on outer sub-detectors. While these devices offer great advantages in terms of flexibility and performance, they pose important issues related to single-event upsets (SEUs) in their configuration. These upsets need to be corrected, i.e. scrubbed, and their rate is valuable information for choosing the proper mitigation strategy. If possible, dedicated in situ measurements should be performed to this aim. In this work, we present a system for SEU monitoring in FPGAs, which we installed in proximity of the Belle II detector at the SuperKEKB electron-positron collider of the KEK laboratory (Tsukuba, JP). As part of the system, we also describe our design of a robust-yet-flexible configuration scrubber, portable over Xilinx Virtex-5 and 7- Series FPGA families. We discuss the measured FPGA configuration error rate and the device power consumption. We compare our results across the tested FPGA families. We compare our scrubber to the Xilinx Soft Error Mitigation controller in terms of reliability by means of proton beam tests conducted at INFN Laboratori Nazionali del Sud (Catania, Italy). In order to show the flexibility of our scrubber, we briefly describe its usage in the Belle II aerogel ring imaging Cherenkov counter.
高能物理实验通常需要耐辐射的电子设备来进行探测器上的操作。在可能的情况下，最好使用商业现成的组件。对于纯数字功能，如数据聚合、处理和传输，基于静态ram的现场可编程门阵列(SRAM-based fpga)越来越多地用于外部子探测器。虽然这些设备在灵活性和性能方面具有很大的优势，但它们在配置中存在与单事件干扰(seu)相关的重要问题。这些干扰需要纠正，即清除，其速率是选择适当缓解策略的宝贵信息。如果可能，应为此目的进行专门的现场测量。在这项工作中，我们提出了一种在fpga中监测SEU的系统，我们将其安装在KEK实验室(筑波，日本)的SuperKEKB正电子对撞机的Belle II探测器附近。作为系统的一部分，我们还描述了我们设计的强大而灵活的配置洗涤器，可移植到Xilinx Virtex-5和7系列FPGA家族上。我们讨论了测量的FPGA配置错误率和器件功耗。我们比较了测试FPGA系列的结果。通过在意大利卡塔尼亚国家南方实验室(INFN laboratory Nazionali del Sud)进行的质子束测试，我们将我们的洗涤器与赛灵思软误差缓解控制器在可靠性方面进行了比较。为了展示我们的洗涤器的灵活性，我们简要介绍了它在Belle II气凝胶环成像切伦科夫计数器中的使用情况。
{"title":"On-the-Fly Self-Reconfiguring FPGAs for Single Event Upset Monitoring at Belle II","authors":"R. Giordano, A. Aloisio, S. Massarotti, G. Tortone, Y. Lai, S. Korpar, R. Pestotnik, L. Šantelj, A. Lozar, M. Shoji, S. Nishida","doi":"10.1109/NSS/MIC42677.2020.9507982","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507982","url":null,"abstract":"High Energy Physics experiments usually require radiation-tolerant electronics for on-detector operation. When possible, it is preferable to use commercial off-the-shelf components. For purely digital functions, such as data aggregation, processing and transfer, static RAM-based Field Programmable Gate Arrays (SRAM-based FPGAs) are increasingly being used on outer sub-detectors. While these devices offer great advantages in terms of flexibility and performance, they pose important issues related to single-event upsets (SEUs) in their configuration. These upsets need to be corrected, i.e. scrubbed, and their rate is valuable information for choosing the proper mitigation strategy. If possible, dedicated in situ measurements should be performed to this aim. In this work, we present a system for SEU monitoring in FPGAs, which we installed in proximity of the Belle II detector at the SuperKEKB electron-positron collider of the KEK laboratory (Tsukuba, JP). As part of the system, we also describe our design of a robust-yet-flexible configuration scrubber, portable over Xilinx Virtex-5 and 7- Series FPGA families. We discuss the measured FPGA configuration error rate and the device power consumption. We compare our results across the tested FPGA families. We compare our scrubber to the Xilinx Soft Error Mitigation controller in terms of reliability by means of proton beam tests conducted at INFN Laboratori Nazionali del Sud (Catania, Italy). In order to show the flexibility of our scrubber, we briefly describe its usage in the Belle II aerogel ring imaging Cherenkov counter.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"42 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":"84265056","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/MIC42677.2020.9508053
E. Ronchieri, M. Canaparo, Yue Yang, A. Costantini, D. C. Duma, D. Salomoni
Predicting changes proneness in software modules is an open area of research. This activity implies dealing with code changes datasets that are typically either incomplete or absent. To obtain a change dataset properly constructed, a new dictionary of software changes terms has been defined by leveraging our experience with the High Energy Physics (HEP) software. Our new dictionary includes various terms that classify a “code change” like warning, fixed bug, minor fix and optimization. Each term has been opportunely used to label each software module analyzed. The derived categories range from code development to performance improvements and refer to single pieces of the considered software. The resulting code-change dataset has been used to build a prediction model able to monitor software evolution and assess its maintainability over time. The present article gives details of the designed procedure that has been followed and presents the obtained results. The designed dictionary can be used with other, non-HEP, software as long as researchers can rely on well documented code changes. In such respect, our prediction model can be tested against these new datasets in order to improve both reliability and performance.
{"title":"A New Code Change Prediction Dataset: A Case Study Based on HEP Software","authors":"E. Ronchieri, M. Canaparo, Yue Yang, A. Costantini, D. C. Duma, D. Salomoni","doi":"10.1109/NSS/MIC42677.2020.9508053","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9508053","url":null,"abstract":"Predicting changes proneness in software modules is an open area of research. This activity implies dealing with code changes datasets that are typically either incomplete or absent. To obtain a change dataset properly constructed, a new dictionary of software changes terms has been defined by leveraging our experience with the High Energy Physics (HEP) software. Our new dictionary includes various terms that classify a “code change” like warning, fixed bug, minor fix and optimization. Each term has been opportunely used to label each software module analyzed. The derived categories range from code development to performance improvements and refer to single pieces of the considered software. The resulting code-change dataset has been used to build a prediction model able to monitor software evolution and assess its maintainability over time. The present article gives details of the designed procedure that has been followed and presents the obtained results. The designed dictionary can be used with other, non-HEP, software as long as researchers can rely on well documented code changes. In such respect, our prediction model can be tested against these new datasets in order to improve both reliability and performance.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"220 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":"76972239","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/MIC42677.2020.9507857
Shouyi Wei, Lemise Saleh, Michael Salerno, Jules A. Cohen, A. Stopeck, L. Baer, Paul Fisher, D. Franceschi, Patricia Thompson, P. Vaska
High-resolution PET imaging has considerable potential to improve management of breast cancer, especially if it could be acquired simultaneously with the clinical standard of breast MRI. In this multimodal approach, PET contributes critical information on specific molecular subtypes and heterogeneity, while avoiding the challenge of reproducibly positioning the breast which confronts technologists when PET and MRI images are acquired separately. Using a compact, high-resolution and MR-compatible PET system (VersaPET) mounted into a breast MRI table, we have begun to assess the feasibility of this approach by collecting preliminary FDG data on primary tumors in breast cancer patients. In order to augment this approach to examine nodal involvement, we also performed a simulation study that incorporates novel detector geometries to expand the FOV to include axillary lymph nodes which are critical for diagnosing metastasis. We evaluated scanner geometries with limited angle sampling and features including time of flight (TOF) and depth of interaction (DOI) readouts, using GATE simulation and detection-based tasks using channelized Hotelling observer (CHO). Our simulation result indicates superior performance for detection of low-grade (3:1 lesion to tissue contrast), small (3 mm diameter) lesions using the proposed scanners compared to whole-body PET. We show that the incorporation of a DOI resolution of 2 mm substantially improves the detection tasks for the proposed scanner designs, while TOF capability is less impactful.
{"title":"Characterizing Primary Breast Cancer and Nodal Involvement with High-Resolution PET/MRI: Novel PET Configurations and Preliminary Results","authors":"Shouyi Wei, Lemise Saleh, Michael Salerno, Jules A. Cohen, A. Stopeck, L. Baer, Paul Fisher, D. Franceschi, Patricia Thompson, P. Vaska","doi":"10.1109/NSS/MIC42677.2020.9507857","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507857","url":null,"abstract":"High-resolution PET imaging has considerable potential to improve management of breast cancer, especially if it could be acquired simultaneously with the clinical standard of breast MRI. In this multimodal approach, PET contributes critical information on specific molecular subtypes and heterogeneity, while avoiding the challenge of reproducibly positioning the breast which confronts technologists when PET and MRI images are acquired separately. Using a compact, high-resolution and MR-compatible PET system (VersaPET) mounted into a breast MRI table, we have begun to assess the feasibility of this approach by collecting preliminary FDG data on primary tumors in breast cancer patients. In order to augment this approach to examine nodal involvement, we also performed a simulation study that incorporates novel detector geometries to expand the FOV to include axillary lymph nodes which are critical for diagnosing metastasis. We evaluated scanner geometries with limited angle sampling and features including time of flight (TOF) and depth of interaction (DOI) readouts, using GATE simulation and detection-based tasks using channelized Hotelling observer (CHO). Our simulation result indicates superior performance for detection of low-grade (3:1 lesion to tissue contrast), small (3 mm diameter) lesions using the proposed scanners compared to whole-body PET. We show that the incorporation of a DOI resolution of 2 mm substantially improves the detection tasks for the proposed scanner designs, while TOF capability is less impactful.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"9 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":"77094667","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/MIC42677.2020.9507840
K. Knecht, D. Hellfeld, R. Pavlovsky, B. Quiter, T. Joshi, T. Torii, Y. Furuta, K. Vetter
Portable radiation detection systems can be equipped with contextual sensors to allow free-moving 3D gamma-ray source mapping and imaging through a method called scene data fusion (SDF). The scene information provided by the contextual sensors can be used to enable 3D mapping and constrain gamma-ray image reconstruction to improve accuracy and computational efficiency. SDF can be a useful tool in a wide range of radiological and nuclear safety and security applications such as radiation mapping for contamination remediation. To demonstrate SDF for this application, Polaris-Lamp,a commercially available detector that has been integrated with contextual sensors, was hand-carried in a parking lot containing vehicles used during remediation efforts following the March 2011 Fukushima Daiichi Nuclear Power Station accident. In order to detect and map potential contamination of the vehicles, proximity mapping and Compton imaging techniques have been applied to data collected over a series of short measurements, each covering different areas of the parking lot. Proximity mapping successfully identified which vehicles are contaminated, but Compton imaging further improved localization of intensity to key vehicle features on top of distributed contamination, demonstrating the utility of SDF in radiation mapping of unknown distributed source environments. Methods to stitch multiple reconstructions together were also developed, allowing the creation of large area radiation maps that are globally consistent. The work presented here illustrates the utility of SDF with an contextual-sensor enhanced commercial radiation detection and imaging system deployed in a hand-portable format to effectively map extended areas and localize radiological contamination within minutes which is impossible to achieve with conventional means with hand-portable radiation detectors or statically deployed gamma-ray imagers.
{"title":"Evaluating 3D Gamma-ray Imaging Techniques for Distributed Sources at the Fukushima Daiichi Nuclear Power Station","authors":"K. Knecht, D. Hellfeld, R. Pavlovsky, B. Quiter, T. Joshi, T. Torii, Y. Furuta, K. Vetter","doi":"10.1109/NSS/MIC42677.2020.9507840","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507840","url":null,"abstract":"Portable radiation detection systems can be equipped with contextual sensors to allow free-moving 3D gamma-ray source mapping and imaging through a method called scene data fusion (SDF). The scene information provided by the contextual sensors can be used to enable 3D mapping and constrain gamma-ray image reconstruction to improve accuracy and computational efficiency. SDF can be a useful tool in a wide range of radiological and nuclear safety and security applications such as radiation mapping for contamination remediation. To demonstrate SDF for this application, Polaris-Lamp,a commercially available detector that has been integrated with contextual sensors, was hand-carried in a parking lot containing vehicles used during remediation efforts following the March 2011 Fukushima Daiichi Nuclear Power Station accident. In order to detect and map potential contamination of the vehicles, proximity mapping and Compton imaging techniques have been applied to data collected over a series of short measurements, each covering different areas of the parking lot. Proximity mapping successfully identified which vehicles are contaminated, but Compton imaging further improved localization of intensity to key vehicle features on top of distributed contamination, demonstrating the utility of SDF in radiation mapping of unknown distributed source environments. Methods to stitch multiple reconstructions together were also developed, allowing the creation of large area radiation maps that are globally consistent. The work presented here illustrates the utility of SDF with an contextual-sensor enhanced commercial radiation detection and imaging system deployed in a hand-portable format to effectively map extended areas and localize radiological contamination within minutes which is impossible to achieve with conventional means with hand-portable radiation detectors or statically deployed gamma-ray imagers.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"29 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":"77916716","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/MIC42677.2020.9507744
Maria Ruiz-Gonzalez, L. Furenlid
One relatively inexpensive way a gamma-ray imaging system can be upgraded is by updating the read-out electronics system and, as a consequence, modernizing the digitization and data-processing methods. The objective of this project is to replace the front-end electronics of modular gamma-ray cameras utilized in multiple small-animal PET and SPECT systems developed at the University of Arizona within the last 20 years. We have previously presented the new front-end board, which utilizes 1-bit sigma-delta modulation (SDM) for energy estimation and a non-uniform 2-bit SDM architecture for timing estimation and triggering. One advantage of this digitization method is that instead of ADC integrated circuits, only a few analog components per channel are utilized, which reduces the complexity and power consumption of the system. The board also includes, among other resources, a Xilinx FPGA combined with an ARM-based processor, DDR3 SDRAM and QSPI flash memory. This project presents the comparison between the original and the new SDM-based front-end electronics board, implemented in a 9-channel modular gamma-ray camera, by obtaining the spectrum of each individual photomultiplier tube (PMT) with both frontend boards. The results show an improvement of 1.5x to 2x in the PMT spectrum resolution with the new approach.
{"title":"Comparison of Conventional and SDM-Based Read-Out Systems for Gamma-Ray Imaging","authors":"Maria Ruiz-Gonzalez, L. Furenlid","doi":"10.1109/NSS/MIC42677.2020.9507744","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507744","url":null,"abstract":"One relatively inexpensive way a gamma-ray imaging system can be upgraded is by updating the read-out electronics system and, as a consequence, modernizing the digitization and data-processing methods. The objective of this project is to replace the front-end electronics of modular gamma-ray cameras utilized in multiple small-animal PET and SPECT systems developed at the University of Arizona within the last 20 years. We have previously presented the new front-end board, which utilizes 1-bit sigma-delta modulation (SDM) for energy estimation and a non-uniform 2-bit SDM architecture for timing estimation and triggering. One advantage of this digitization method is that instead of ADC integrated circuits, only a few analog components per channel are utilized, which reduces the complexity and power consumption of the system. The board also includes, among other resources, a Xilinx FPGA combined with an ARM-based processor, DDR3 SDRAM and QSPI flash memory. This project presents the comparison between the original and the new SDM-based front-end electronics board, implemented in a 9-channel modular gamma-ray camera, by obtaining the spectrum of each individual photomultiplier tube (PMT) with both frontend boards. The results show an improvement of 1.5x to 2x in the PMT spectrum resolution with the new approach.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"14 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":"72880518","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/MIC42677.2020.9507783
J. Wetzel, E. Tiras, O. Koseyan, N. Bostan, B. Bilki, D. Winn, Y. Onel
We report preliminary results from in situ monitoring of an optical scintillating fiber while being exposed to a cesium-173 gamma radiatior. We measured the degradation of fiber transmittance across the visible spectrum as a function of time. We observed that the region below 500 nm was degraded quickly and thoroughly while wavelengths above 500 nm lost clarity more slowly.
{"title":"Results from In Situ Monitoring of Radiation Damage of Scintillation Fibers","authors":"J. Wetzel, E. Tiras, O. Koseyan, N. Bostan, B. Bilki, D. Winn, Y. Onel","doi":"10.1109/NSS/MIC42677.2020.9507783","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507783","url":null,"abstract":"We report preliminary results from in situ monitoring of an optical scintillating fiber while being exposed to a cesium-173 gamma radiatior. We measured the degradation of fiber transmittance across the visible spectrum as a function of time. We observed that the region below 500 nm was degraded quickly and thoroughly while wavelengths above 500 nm lost clarity more slowly.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"185 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":"73377503","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/MIC42677.2020.9507977
H. Arabi, Isaac Shiri, E. Jenabi, M. Becker, H. Zaidi
Accurate delineation of the gross tumor volume (GTV) is critical for treatment planning in radiation oncology. This task is very challenging owing to the irregular and diverse shapes of malignant lesions. Manual delineation of the GTVs on PET images is not only time-consuming but also suffers from inter- and intra-observer variability. In this work, we developed deep learning-based approaches for automated GTV delineation on PET images of head and neck cancer patients. To this end, V-Net, a fully convolutional neural network for volumetric medical image segmentation, and HighResNet, a 20-layer residual convolutional neural network, were adopted. 18F-FDG-PET/CT images of 510 patients presenting with head and neck cancer on which manually defined (reference) GTVs were utilized for training, evaluation and testing of these algorithms. The input of these networks (in both training or evaluation phases) were 12×12×12 cm sub-volumes of PET images containing the whole volume of the tumors and the neighboring background radiotracer uptake. These networks were trained to generate a binary mask representing the GTV on the input PET subvolume. Standard segmentation metrics, including Dice similarity and precision were used for performance assessment of these algorithms. HighResNet achieved automated GTV delineation with a Dice index of 0.87±0.04 compared to 0.86±0.06 achieved by V-Net. Despite the close performance of these two approaches, HighResNet exhibited less variability among different subjects as reflected in the smaller standard deviation and significantly higher precision index (0.87±0.07 versus 0.80±0.10). Deep learning techniques, in particular HighResNet algorithm, exhibited promising performance for automated GTV delineation on head and neck PET images. Incorporation of anatomical/structural information, particularly MRI, may result in higher segmentation accuracy or less variability among the different subjects.
准确描述肿瘤总体积(GTV)是放射肿瘤学治疗计划的关键。由于恶性病变的形状不规则且多样，这项任务非常具有挑战性。人工圈定PET图像上的gtv不仅耗时，而且存在观察者之间和观察者内部的可变性。在这项工作中，我们开发了基于深度学习的方法，用于对头颈癌患者的PET图像进行自动GTV描绘。为此，采用体积医学图像分割的全卷积神经网络V-Net和20层残差卷积神经网络HighResNet。对510例头颈癌患者的18F-FDG-PET/CT图像，使用人工定义的(参考)gtv对这些算法进行训练、评估和测试。这些网络(在训练或评估阶段)的输入是含有整个肿瘤体积和邻近背景放射性示踪剂摄取的PET图像的12×12×12 cm亚体积。对这些网络进行训练，生成一个表示输入PET子卷上GTV的二进制掩码。使用标准分割指标，包括骰子相似度和精度来评估这些算法的性能。HighResNet实现了自动GTV描绘，其Dice指数为0.87±0.04，而V-Net的Dice指数为0.86±0.06。尽管这两种方法的性能接近，但HighResNet在不同受试者之间的可变性较小，反映在较小的标准差和显著更高的精度指数上(0.87±0.07 vs 0.80±0.10)。深度学习技术，特别是HighResNet算法，在头颈部PET图像的自动GTV描绘方面表现出了很好的性能。结合解剖/结构信息，特别是MRI，可能导致更高的分割准确性或减少不同受试者之间的差异。
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Pub Date : 2020-10-31DOI: 10.1109/NSS/MIC42677.2020.9508087
E. Ronconi, N. Corna, S. Salgaro, F. Garzetti, N. Lusardi, L. Bucci, A. Geraci
In this contribution we present a novel implementation of a firmware and software bundle for the computation of real-time histograms based on a System-on-Chip (SoC) Linux-based platform. Histograms are basic instruments that turn out to be of fundamental help when it comes not only to single-shot events, but also to collection and elaboration of big amount of data, their shaping and statistical insights coming from the collected measures. Industry and Academia have already proposed many solutions to this need, both in full-custom Application-Specific Integrated Circuits (ASICs) and Field-Programmable Gate Arrays (FPGAs) IP-Cores. However, despite being mostly satisfying in performance, these solutions often lack ease of use, upgrade and interfacing. Moreover, in this particular application, large storage capabilities are needed, in order to guarantee the user the possibility to build large enough histograms. To solve these issues, we present a hybrid hardware and software implementation of a Histogram Maker in an FPGA-based SoC. Its main features are the large available memory accessible through a Direct Memory Access (DMA), the low amount of consumed FPGA resources of the actual hardware Histogram (Histo-Pack), the real-time behavior and the simplified, yet efficient, interface to the ARM core in the Xilinx SoC, hosting a Linux-based Operating System. A set of IP-Cores and libraries relaxes the effort for the interfacing between the two worlds, so that the user-friendly Processing System can be connected to the programmable logic part to exploit its high-performance in an easy and flexible way. The system has been successfully validated on Xilinx Zynq-7000 and Zynq UltraScale+ devices. This opens new opportunities for simple data transfer through advanced interfaces and protocols, data elaboration and analysis, with no need for complex hardware on the Programmable Logic part. The system is able to receive up to 0.3 Gsps with a refresh rate of 1ms.
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