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/MIC42677.2020.9507759
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/MIC42677.2020.9507924
N. Zeraatkar, Kesava S. Kalluri, Benjamin Auer, Neil C. Momsen, Micaehla May, R. Garrett Richards, L. Furenlid, P. Kuo, Matt A. King
Multiplexing of projection images is a potential solution to increasing detection sensitivity in multi-pinhole (MPH) SPECT systems. However, the ambiguity caused by overlapped projections can generate artefacts in the reconstructed images. Therefore, multiplexing has been generally avoided in MPH SPECT systems at the cost of sensitivity loss. We are developing a new-generation brain-dedicated stationary SPECT scanner, AdaptiSPECT-C. In this study, we employed a prototype design of the AdaptiSPECT-C consisting of 25 square detectors arranged in a truncated spherical geometry. Each detector is equipped with an MPH collimator having 5 pinhole apertures. Each aperture can be independently opened or closed utilizing a shuttering mechanism. There is intentionally a significant amount of multiplexing when multiple apertures are opened. In this study, we propose an innovative approach to demultiplex projection data from multiple pinholes in the AdaptiSPECT-C. We used our MPH analytic simulation and iterative reconstruction software to investigate two acquisition schemes for an XCAT phantom emulating N-isopropyl-p-(I -123)iodoamphetamine (I-123-IMP) brain-perfusion agent distribution. In this approach, a small portion of imaging time (herein, 20%) is used for acquiring a set of non-multiplexed data by opening only central pinholes in each MPH collimator. The proposed algorithm can then demultiplex the projections acquired thereafter using an estimate of the activity distribution reconstructed from the non-multiplexed data. The results are promising for demultiplexing the projections when compared with simulated non-multiplexed ground truth. We expect this demultiplexing will result in substantial enhancement of the reconstructed images. This and variations in the acquisition schemes will be explored in our future studies.
{"title":"Demultiplexing of Projection Data in Adaptive Brain SPECT with Multi-Pinhole Collimation","authors":"N. Zeraatkar, Kesava S. Kalluri, Benjamin Auer, Neil C. Momsen, Micaehla May, R. Garrett Richards, L. Furenlid, P. Kuo, Matt A. King","doi":"10.1109/NSS/MIC42677.2020.9507924","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507924","url":null,"abstract":"Multiplexing of projection images is a potential solution to increasing detection sensitivity in multi-pinhole (MPH) SPECT systems. However, the ambiguity caused by overlapped projections can generate artefacts in the reconstructed images. Therefore, multiplexing has been generally avoided in MPH SPECT systems at the cost of sensitivity loss. We are developing a new-generation brain-dedicated stationary SPECT scanner, AdaptiSPECT-C. In this study, we employed a prototype design of the AdaptiSPECT-C consisting of 25 square detectors arranged in a truncated spherical geometry. Each detector is equipped with an MPH collimator having 5 pinhole apertures. Each aperture can be independently opened or closed utilizing a shuttering mechanism. There is intentionally a significant amount of multiplexing when multiple apertures are opened. In this study, we propose an innovative approach to demultiplex projection data from multiple pinholes in the AdaptiSPECT-C. We used our MPH analytic simulation and iterative reconstruction software to investigate two acquisition schemes for an XCAT phantom emulating N-isopropyl-p-(I -123)iodoamphetamine (I-123-IMP) brain-perfusion agent distribution. In this approach, a small portion of imaging time (herein, 20%) is used for acquiring a set of non-multiplexed data by opening only central pinholes in each MPH collimator. The proposed algorithm can then demultiplex the projections acquired thereafter using an estimate of the activity distribution reconstructed from the non-multiplexed data. The results are promising for demultiplexing the projections when compared with simulated non-multiplexed ground truth. We expect this demultiplexing will result in substantial enhancement of the reconstructed images. This and variations in the acquisition schemes will be explored in our future studies.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"72 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":"84165447","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/MIC42677.2020.9507805
Joshua Moo, P. Marsden, K. Vyas, A. Reader
The challenge in delineating the boundary between cancerous and healthy tissue during cancer resection surgeries can be addressed with the use of intraoperative probes to detect cancer cells labelled with radiotracers to facilitate excision. In this study, deep learning algorithms for background gamma ray signal rejection were explored for an intraoperative probe utilising CMOS monolithic active pixel sensors optimised towards the detection of internal conversion electrons from 99mTc. Two methods utilising convolutional neural networks (CNNs) were explored for beta-gamma discrimination: 1) classification of event clusters isolated from the sensor array outputs (SAOs) from the probe and 2) semantic segmentation of event clusters within an acquisition frame of an SAO. The feasibility of the methods in this study was explored with several radionuclides including 14C, 57Co and 99mTc. Overall, the classification deep network is able to achieve an improved area under the curve (AUC) of the receiver operating characteristic (ROC), giving 0.93 for 14C beta and 99mTc gamma clusters, compared to 0.88 for a more conventional feature-based discriminator. Further optimisation of the lower left region of the ROC by using a customised AUC loss function during training led to an improvement of 33% in sensitivity at low false positive rates compared to the conventional method. The segmentation deep network is able to achieve a mean dice score of 0.93. Through the direct comparison of all methods, the classification method was found to have a better performance in terms of the AUC.
{"title":"Deep Learning Signal Discrimination for Improved Sensitivity at High Specificity for CMOS Intraoperative Probes","authors":"Joshua Moo, P. Marsden, K. Vyas, A. Reader","doi":"10.1109/NSS/MIC42677.2020.9507805","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507805","url":null,"abstract":"The challenge in delineating the boundary between cancerous and healthy tissue during cancer resection surgeries can be addressed with the use of intraoperative probes to detect cancer cells labelled with radiotracers to facilitate excision. In this study, deep learning algorithms for background gamma ray signal rejection were explored for an intraoperative probe utilising CMOS monolithic active pixel sensors optimised towards the detection of internal conversion electrons from 99mTc. Two methods utilising convolutional neural networks (CNNs) were explored for beta-gamma discrimination: 1) classification of event clusters isolated from the sensor array outputs (SAOs) from the probe and 2) semantic segmentation of event clusters within an acquisition frame of an SAO. The feasibility of the methods in this study was explored with several radionuclides including 14C, 57Co and 99mTc. Overall, the classification deep network is able to achieve an improved area under the curve (AUC) of the receiver operating characteristic (ROC), giving 0.93 for 14C beta and 99mTc gamma clusters, compared to 0.88 for a more conventional feature-based discriminator. Further optimisation of the lower left region of the ROC by using a customised AUC loss function during training led to an improvement of 33% in sensitivity at low false positive rates compared to the conventional method. The segmentation deep network is able to achieve a mean dice score of 0.93. Through the direct comparison of all methods, the classification method was found to have a better performance in terms of the AUC.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"42 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":"84360647","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/MIC42677.2020.9507748
D. Fiorina
In the framework of the HL-LHC project, the upgrade of the CMS Muon System foresees the installation of three new muon stations based on the GEM technology, named as GE1/1, GE2/1 and ME0. The CMS GEM Group has developed a novel construction design of GE1/1 triple-GEM detectors; especially, a new self-stretching technique has been introduced to mechanically stretch the GEM foils without using spacer grids or glue inside the gas volume. As has been observed, the PCB boards get deformed under the internal gas overpressure, introducing irregularities in the planarity of the detector, which could potentially affect the uniformity of the detector performance. New solutions and design upgrades have been implemented to prevent such effects in future GE2/1 and ME0 upgrade projects. The contribution will focus on the novel design solutions based on the PCB pillars and their impact on the performance of the detector. Furthermore, the early results of the R&D campaign will be presented regarding the optimization of the detector for the very high hit rate environment and the reduction of the discharge probability.
{"title":"Novel Triple-GEM Mechanical Design for the CMS-ME0 Detector, Preliminary Performance and R&D Results","authors":"D. Fiorina","doi":"10.1109/NSS/MIC42677.2020.9507748","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507748","url":null,"abstract":"In the framework of the HL-LHC project, the upgrade of the CMS Muon System foresees the installation of three new muon stations based on the GEM technology, named as GE1/1, GE2/1 and ME0. The CMS GEM Group has developed a novel construction design of GE1/1 triple-GEM detectors; especially, a new self-stretching technique has been introduced to mechanically stretch the GEM foils without using spacer grids or glue inside the gas volume. As has been observed, the PCB boards get deformed under the internal gas overpressure, introducing irregularities in the planarity of the detector, which could potentially affect the uniformity of the detector performance. New solutions and design upgrades have been implemented to prevent such effects in future GE2/1 and ME0 upgrade projects. The contribution will focus on the novel design solutions based on the PCB pillars and their impact on the performance of the detector. Furthermore, the early results of the R&D campaign will be presented regarding the optimization of the detector for the very high hit rate environment and the reduction of the discharge probability.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"31 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":"83695368","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/MIC42677.2020.9507757
X. Ren, S. Lee
This paper presents a comparative study of the effects of using joint sparse coding (JSC) for regularized super-resolution (SR) PET reconstruction. With an assumption that a limited number of high-resolution (HR) PET images are available for a joint training dataset for JSC, we attempt to improve the accuracy of sparse coding (SC) based SR reconstruction in conventional non-HR PET imaging. Here we also assume that the anatomical (CT or MR) and PET images acquired from the same patient lie in coupled feature spaces. The images in one feature space can be transformed into corresponding images in the other feature space by a common mapping function. In this case, the images in the coupled feature spaces have a common sparse representation in terms of the specific dictionaries that are jointly trained, which is the main key to the JSC method. We implemented the penalized-likelihood SR reconstruction algorithm whose penalty term is modeled as JSC and compared with our previous method using the single dictionary-based SC penalty. The experimental results demonstrate that our proposed JSC method clearly outperforms the standard SC method by more accurately restoring the fine details that are often missed by the standard SC method.
{"title":"Joint Sparse Coding-Based Super-Resolution PET Image Reconstruction","authors":"X. Ren, S. Lee","doi":"10.1109/NSS/MIC42677.2020.9507757","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507757","url":null,"abstract":"This paper presents a comparative study of the effects of using joint sparse coding (JSC) for regularized super-resolution (SR) PET reconstruction. With an assumption that a limited number of high-resolution (HR) PET images are available for a joint training dataset for JSC, we attempt to improve the accuracy of sparse coding (SC) based SR reconstruction in conventional non-HR PET imaging. Here we also assume that the anatomical (CT or MR) and PET images acquired from the same patient lie in coupled feature spaces. The images in one feature space can be transformed into corresponding images in the other feature space by a common mapping function. In this case, the images in the coupled feature spaces have a common sparse representation in terms of the specific dictionaries that are jointly trained, which is the main key to the JSC method. We implemented the penalized-likelihood SR reconstruction algorithm whose penalty term is modeled as JSC and compared with our previous method using the single dictionary-based SC penalty. The experimental results demonstrate that our proposed JSC method clearly outperforms the standard SC method by more accurately restoring the fine details that are often missed by the standard SC method.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"10 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":"90528750","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/MIC42677.2020.9507916
Bo Wu, Yonggang Wang, Qiang Cao, Xiaoyu Zhou
High precision time-of-flight (TOF) measurements in modern high-energy physics experiments have often a high demand to measure both pulse timing and pulse width at the same time. The pulse width from such TOF detectors can be as narrow as 1 ns, which poses great challenges to current design of time-to-digital converters (TDCs) based on field programmable gate array (FPGA). In this paper, we propose a novel FPGA-based TDC design which can measure nuclear signals with extremely narrow pulse width outputting timestamps for both the rising edge and falling edge simultaneously. The discriminated digital signal with both timings from the rising edge and falling edge is directly transmitted along the tapped-delay-line (TDL) of the TDC. Relying on the proposed powerful and efficient encoding logic, the two timestamps are precisely extracted out from the TDL status in one time of measurement. The TDC measurement dead time is only two system clock cycle, and the minimum measurable pulse width is only limited by the performance of LVDS receiver of FPGA, which was tested as low as 400 ps in our case of implementing the TDC in a Virtex Ultrascale+ FPGA. Using one TDC channels to measure given pulse width, the RMS precision is evaluated as 3.0 ps. Given the pulse widths ranging from 0.4 ns to 1.5 ns, the measured pulse width by the TDC is highly consistent with the readout values from the oscilloscope. In addition to the excellent performance, compared with previous TDC designs for pulse width measurement, the structure of the proposed TDC is much compact with low logic resource consumption, which is very helpful for multi-channel integration in high-energy physics experiments.
{"title":"An FPGA-based, high-precision, narrow pulse width measurement time-to-digital converter","authors":"Bo Wu, Yonggang Wang, Qiang Cao, Xiaoyu Zhou","doi":"10.1109/NSS/MIC42677.2020.9507916","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9507916","url":null,"abstract":"High precision time-of-flight (TOF) measurements in modern high-energy physics experiments have often a high demand to measure both pulse timing and pulse width at the same time. The pulse width from such TOF detectors can be as narrow as 1 ns, which poses great challenges to current design of time-to-digital converters (TDCs) based on field programmable gate array (FPGA). In this paper, we propose a novel FPGA-based TDC design which can measure nuclear signals with extremely narrow pulse width outputting timestamps for both the rising edge and falling edge simultaneously. The discriminated digital signal with both timings from the rising edge and falling edge is directly transmitted along the tapped-delay-line (TDL) of the TDC. Relying on the proposed powerful and efficient encoding logic, the two timestamps are precisely extracted out from the TDL status in one time of measurement. The TDC measurement dead time is only two system clock cycle, and the minimum measurable pulse width is only limited by the performance of LVDS receiver of FPGA, which was tested as low as 400 ps in our case of implementing the TDC in a Virtex Ultrascale+ FPGA. Using one TDC channels to measure given pulse width, the RMS precision is evaluated as 3.0 ps. Given the pulse widths ranging from 0.4 ns to 1.5 ns, the measured pulse width by the TDC is highly consistent with the readout values from the oscilloscope. In addition to the excellent performance, compared with previous TDC designs for pulse width measurement, the structure of the proposed TDC is much compact with low logic resource consumption, which is very helpful for multi-channel integration in high-energy physics experiments.","PeriodicalId":6760,"journal":{"name":"2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"67 4","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":"91471349","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/MIC42677.2020.9508052
Chen Hu, Nan Lu, Liyuan Zhang, R. Zhu, Adi Bornheim, L. Narváez, J. Trevor, M. Spiropulu
The barrel timing layer for the CMS HL-LHC precision timing detector will be constructed using LYSO+SiPM modules. The barrel in HL-LHC beam intensities is expected to be exposed under an ionization dose rate of up to 200 rad/h and a neutron flux of up to 3x106neq/cm2/s. We present results from measurements of photocurrent in the LYSO+SiPM packages induced by Co-60 γ-rays and Cf-252 neutrons. The γ-ray induced readout noise is found to be about 30 keV, which is negligible compared to the 4.2 MeV signal from minimum ionization particles. The neutron induced noise is about 7 keV, which is more than a factor of 4 smaller than that from the ionization dose.
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