IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"Deep Convolutional Backbone Comparison for Automated PET Image Quality Assessment","authors":"Jessica B. Hopson;Anthime Flaus;Colm J. McGinnity;Radhouene Neji;Andrew J. Reader;Alexander Hammers","doi":"10.1109/TRPMS.2024.3436697","DOIUrl":"10.1109/TRPMS.2024.3436697","url":null,"abstract":"Pretraining deep convolutional network mappings using natural images helps with medical imaging analysis tasks; this is important given the limited number of clinically annotated medical images. Many 2-D pretrained backbone networks, however, are currently available. This work compared 18 different backbones from 5 architecture groups (pretrained on ImageNet) for the task of assessing [18F]FDG brain positron emission tomography (PET) image quality (reconstructed at seven simulated doses), based on three clinical image quality metrics (global quality rating, pattern recognition, and diagnostic confidence). Using 2-D randomly sampled patches, up to eight patients (at three dose levels each) were used for training, with three separate patient datasets used for testing. Each backbone was trained five times with the same training and validation sets, and with six cross-folds. Training only the final fully connected layer (with ~6000–20000 trainable parameters) achieved a test mean-absolute-error (MAE) of ~0.5 (which was within the intrinsic uncertainty of clinical scoring). To compare “classical” and over-parameterized regimes, the pretrained weights of the last 40% of the network layers were then unfrozen. The MAE fell below 0.5 for 14 out of the 18 backbones assessed, including two that previously failed to train. Generally, backbones with residual units (e.g., DenseNets and ResNetV2s), were suited to this task, in terms of achieving the lowest MAE at test time (~0.45–0.5). This proof-of-concept study shows that over-parameterization may also be important for automated PET image quality assessments.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"893-901"},"PeriodicalIF":4.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477477","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":"Detector Characterization of a High-Resolution Ring for PET Imaging of Mice Heads With Sub-200-ps TOF","authors":"Celia Valladares;John Barrio;Neus Cucarella;Marta Freire;Luis F. Vidal;José M. Benlloch;Antonio J. González","doi":"10.1109/TRPMS.2024.3432194","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3432194","url":null,"abstract":"Positron emission tomography (PET) stands out as a highly specific molecular imaging technique. However, its detection sensitivity remains a challenge. The implementation of time-of-flight (TOF) PET technology enhances sensitivity by precisely measuring the time lapse between the annihilation photons. Moreover, by characterizing scattered (Compton) events, the effective sensitivity of PET imaging might significantly be enhanced. In this work, we present the scatter subsystem of a 2 layers preclinical TOF-PET scanner for mice head imaging. The scatter subsystem is composed of eight identical modules based on analog silicon photomultipliers (SiPMs) coupled to crystal arrays of \u0000<inline-formula> <tex-math>$24times 24$ </tex-math></inline-formula>\u0000 LYSO pixels with 0.95 mm \u0000<inline-formula> <tex-math>$times 0$ </tex-math></inline-formula>\u0000.95 mm \u0000<inline-formula> <tex-math>$times $ </tex-math></inline-formula>\u0000 3 mm dimensions. The system has 29-mm bore and 50.8-mm axial length. An average CTR of \u0000<inline-formula> <tex-math>$192~pm ~1$ </tex-math></inline-formula>\u0000 ps was obtained for the whole subsystem at the photopeak energy range after energy and timing corrections, and CTR values as good as 155 ps were found for some individual pixels. The transit time spread at the SiPM level was also studied and corrected, achieving a mean value of 41 ps of maximum time difference at the sensor corners with respect to the center. Voronoi diagrams were implemented to correct for position decoding.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"876-885"},"PeriodicalIF":4.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10606942","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of List-Mode-Based Intraframe Motion Correction in Dynamic Brain PET Imaging","authors":"Amal Tiss;Yanis Chemli;Nicolas Guehl;Thibault Marin;Keith Johnson;Georges El Fakhri;Jinsong Ouyang","doi":"10.1109/TRPMS.2024.3432322","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3432322","url":null,"abstract":"Motion is unavoidable in dynamic [18F]-MK6240 positron emission tomography (PET) imaging, especially in Alzheimer’s disease (AD) research requiring long scan duration. To understand how motion correction affects quantitative analysis, we investigated two approaches: intra- and inter- frame motion correction (II-MC), which corrects for both the interframe and intraframe motion, and interframe only motion correction (IO-MC), which only corrects for the interframe motion. These methods were applied to 83 scans from 34 subjects, and we calculated distribution volume ratios (DVRs) using the multilinear reference tissue model with the two parameters (MRTM2) in tau-rich brain regions. Most of the studies yielded similar DVR results for both II-MC and IO-MC. However, in one scan of an AD subject, the inferior temporal region showed 14% higher DVR with II-MC compared to IO-MC. This difference was reasonable given the AD diagnosis, although similar results were not observed in other regions. Although discrepancies between IO-MC and II-MC results were rare, they underscore the importance of incorporating intraframe motion correction for more accurate and dependable PET quantitation, particularly in the context of dynamic imaging. These findings suggest that while the overall impact of intraframe motion correction may be subtle, it can improve the reliability of longitudinal PET data, ultimately enhancing our understanding of tau protein distribution in AD pathology.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"950-958"},"PeriodicalIF":4.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587634","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":"IRDNet: Iterative Relation-Based Dual-Domain Network via Metal Artifact Feature Guidance for CT Metal Artifact Reduction","authors":"Huamin Wang;Shuo Yang;Xiao Bai;Zhe Wang;Jiayi Wu;Yang Lv;Guohua Cao","doi":"10.1109/TRPMS.2024.3424941","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3424941","url":null,"abstract":"The metal artifacts in computed tomography (CT) images not only affect diagnosis and treatment but also present a classic nonlinear inverse problem in CT reconstruction. In this study, we propose an iterative relation-based dual-domain network (IRDNet) that utilizes metal artifact feature guidance to reduce such artifacts in CT images. To the best of our knowledge, IRDNet leverages metal artifact features as guidance of the dual-domain network for the first time to reduce metal artifacts. Our framework incorporates artifact-corrupted and precorrected images (linear-interpolated images) as well as metal artifact features to effectively reduce metal artifacts for a high-quality prior CT image and corresponding prior sinogram. The prior image and prior sinogram are then iteratively recovered sinogram using the residual learning strategy and mitigate the artifacts of CT image with a metal-location guidance framework. We construct IRDNet in an unrolling manner to accurately optimize anatomical structures. Compared to the state-of-the-art algorithms, IRDNet consistently produces reasonable CT images with reduced metal artifacts, as evaluated both quantitatively and qualitatively across different-sized metal implant samples and different metal materials. It generalized different artifacts caused by metals of various sizes and materials and successfully recovered surrounding tissues. The experimental results demonstrate the potential of incorporating metal inherent features as priors in the dual-domain network for reducing metal artifacts.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"959-972"},"PeriodicalIF":4.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10589441","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors","authors":"","doi":"10.1109/TRPMS.2024.3405098","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3405098","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584412","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Member Get-A-Member (MGM) Program","authors":"","doi":"10.1109/TRPMS.2024.3421769","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3421769","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"708-708"},"PeriodicalIF":4.6,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584434","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information","authors":"","doi":"10.1109/TRPMS.2024.3405100","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3405100","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"C2-C2"},"PeriodicalIF":4.6,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584413","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Investigations of Two Channel Readout Configurations on the Cross-Strip Cadmium Zinc Telluride Detector","authors":"Emily Enlow;Yuli Wang;Greyson Shoop;Shiva Abbaszadeh","doi":"10.1109/TRPMS.2024.3411522","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3411522","url":null,"abstract":"In a detector system where the number of channels exceeds the number of channels available on an application-specific integrated circuit (ASIC), there is a need to configure channels among the multiple ASICs to achieve the lowest electronic noise and highest count rate. In this work, two board configurations were designed to experimentally assess which one provides the more favorable performance. In the half-half configuration, contiguous channels from one edge to the center of CZT detector are read by one ASIC, and the other half are read by the other ASIC. In the alternate configuration, the CZT channels are read by alternating ASICs. A lower electronic noise level, better FWHM energy resolution performance (5.35% \u0000<inline-formula> <tex-math>$pm ~1.08$ </tex-math></inline-formula>\u0000% compared to 7.84% \u0000<inline-formula> <tex-math>$pm ~0.98$ </tex-math></inline-formula>\u0000%), and higher count rate was found for the anode electrode strips with the half-half configuration. Cross-talk between the ASICs and deadtime play a role in the different performances, and the total count rate of the half-half configuration has a count rate 18.1% higher than that of the alternate configuration.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"886-892"},"PeriodicalIF":4.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587583","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":"Co-Learning Multimodality PET-CT Features via a Cascaded CNN-Transformer Network","authors":"Lei Bi;Xiaohang Fu;Qiufang Liu;Shaoli Song;David Dagan Feng;Michael Fulham;Jinman Kim","doi":"10.1109/TRPMS.2024.3417901","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3417901","url":null,"abstract":"<italic>Background:</i>\u0000 Automated segmentation of multimodality positron emission tomography—computed tomography (PET-CT) data is a major challenge in the development of computer-aided diagnosis systems (CADs). In this context, convolutional neural network (CNN)-based methods are considered as the state-of-the-art. These CNN-based methods, however, have difficulty in co-learning the complementary PET-CT image features and in learning the global context when focusing solely on local patterns. \u0000<italic>Methods:</i>\u0000 We propose a cascaded CNN-transformer network (CCNN-TN) tailored for PET-CT image segmentation. We employed a transformer network (TN) because of its ability to establish global context via self-attention and embedding image patches. We extended the TN definition by cascading multiple TNs and CNNs to learn the global and local contexts. We also introduced a hyper fusion branch that iteratively fuses the separately extracted complementary image features. We evaluated our approach, when compared to current state-of-the-art CNN methods, on three datasets: two nonsmall cell lung cancer (NSCLC) and one soft tissue sarcoma (STS). \u0000<italic>Results:</i>\u0000 Our CCNN-TN method achieved a dice similarity coefficient (DSC) score of 72.25% (NSCLC), 67.11% (NSCLC), and 66.36% (STS) for segmentation of tumors. Compared to other methods the DSC was higher for our CCNN-TN by 4.5%, 1.31%, and 3.44%. \u0000<italic>Conclusion:</i>\u0000 Our experimental results demonstrate that CCNN-TN, when compared to the existing methods, achieved more generalizable results across different datasets and has consistent performance across various image fusion strategies and network backbones.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"814-825"},"PeriodicalIF":4.6,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143751","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":"Toward Sub-100 ps TOF-PET Systems Employing the FastIC ASIC With Analog SiPMs","authors":"A. Mariscal-Castilla;S. Gómez;R. Manera;J. M. Fernández-Tenllado;J. Mauricio;N. Kratochwil;J. Alozy;M. Piller;S. Portero;A. Sanuy;D. Guberman;J. J. Silva;E. Auffray;R. Ballabriga;G. Ariño-Estrada;M. Campbell;D. Gascón","doi":"10.1109/TRPMS.2024.3414578","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3414578","url":null,"abstract":"Time of Flight positron emission tomography (TOF-PET) scanners demand electronics that are power-efficient, low-noise, cost-effective, and possess a large bandwidth. Recent developments have demonstrated sub-100 ps time resolution with elevated power consumption per channel, rendering this unfeasible to build a scanner. In this work, we evaluate the performance for the TOF-PET of the FastIC front-end using different scintillators and silicon photomultipliers (SiPMs). FastIC is an eight-channel application specific integrated circuit developed in CMOS 65 nm capable of measuring the energy and the arrival time of a detected pulse with 12 mW per channel. Using Hamamatsu SiPMs (S13360-3050PE) coupled to LSO:Ce:0.2%Ca crystals of \u0000<inline-formula> <tex-math>$2times 2times $ </tex-math></inline-formula>\u0000 3 mm\u0000³\u0000 and LYSO:Ce:0.2%Ca of \u0000<inline-formula> <tex-math>$3.13times 3.13times $ </tex-math></inline-formula>\u0000 20 mm\u0000³\u0000, we measured a coincidence time resolution (CTR) of (\u0000<inline-formula> <tex-math>$95~pm ~3$ </tex-math></inline-formula>\u0000) and \u0000<inline-formula> <tex-math>$156~pm ~4$ </tex-math></inline-formula>\u0000) ps full width half maximum (FWHM), respectively. With Fondazione Bruno Kessler NUV-HD LF2 M0 SiPMs coupled to the same crystals, we obtained a CTR of (\u0000<inline-formula> <tex-math>$76~pm ~2$ </tex-math></inline-formula>\u0000) and (\u0000<inline-formula> <tex-math>$127~pm ~3$ </tex-math></inline-formula>\u0000) ps FWHM. We employed FastIC with a TlCl pure Cherenkov emitter, demonstrating time resolutions comparable to those achieved with the high-power-consuming electronics. These findings shows that the FastIC represents a cost-effective alternative that can significantly enhance the time resolution of the current TOF-PET systems while maintaining low power consumption.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"718-733"},"PeriodicalIF":4.6,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557761","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}