IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"A Scalable Dynamic TOT Circuit for a 100 ps TOF-PET Detector Design to Improve Energy Linearity and Dynamic Range","authors":"Shirin Pourashraf;Joshua W. Cates;Craig S. Levin","doi":"10.1109/TRPMS.2023.3344399","DOIUrl":"https://doi.org/10.1109/TRPMS.2023.3344399","url":null,"abstract":"This article focuses on adapting linearization strategies for annihilation photon energy measurement for a time-of-flight (TOF) positron emission tomography (PET) system that achieves \u0000<inline-formula> <tex-math>$sim 100$ </tex-math></inline-formula>\u0000 picosecond (ps) full-width at half maximum (FWHM) coincidence time resolution (CTR). We have adapted the method of dynamic TOT (DynTOT) for our scalable TOF-PET detector readout electronics to linearize the energy spectra while maintaining \u0000<inline-formula> <tex-math>$sim 100$ </tex-math></inline-formula>\u0000 ps FWHM CTR. The linear response of the resulting DynTOT circuit facilitates improved energy performance compared with conventional time-over-threshold (TOT). Our detector design has the capability to position the 3-D coordinates of one or more 511-keV photon interactions. To facilitate this goal, DynTOT’s linearity across the entire energy range enables accurate measurement of low-energy interactions that is required for more accurate positioning of intercrystal scatter events. This DynTOT block is implemented by off-the-shelf discrete components and consumes only 11 mW power per detector layer unit design comprising 24:1 multiplexed energy and timing channels. We first validated the performance of DynTOT using single \u0000<inline-formula> <tex-math>$3times 3times10$ </tex-math></inline-formula>\u0000 mm3 LGSO scintillation crystals side-coupled to arrays of three \u0000<inline-formula> <tex-math>$3times3$ </tex-math></inline-formula>\u0000 mm2 SiPMs which achieved 511-keV photopeak energy resolutions of 13.6 ± 0.4%, 13.0 ± 0.8%, and 17.1 ± 0.6% for conventional pulse height, DynTOT, and conventional TOT methods, respectively. Then, we stretched by roughly 7-fold the DynTOT digital pulses (energy) generated from side-coupling \u0000<inline-formula> <tex-math>$2times4$ </tex-math></inline-formula>\u0000 array of \u0000<inline-formula> <tex-math>$3times 3times10$ </tex-math></inline-formula>\u0000 mm3 crystals to 24 SiPMs, and achieved 511-keV photopeak energy resolutions of 11.8 ± 0.7% with a dynamic range from less than 60 to 1274 keV, making that suitable for methods of accurate 3-D positioning of intercrystal-scatter interactions. Moreover, CTR with a highly multiplexed timing circuit was measured using these extended DynTOT pulses for energy gating, resulting in an average 108 ± 1.3 ps FWHM CTR.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140031610","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":"A Low Cost, Flexible Atmospheric Pressure Plasma Jet Device With Good Antimicrobial Efficiency","authors":"Fellype do Nascimento;Aline da Graça Sampaio;Noala Vicensoto Moreira Milhan;Aline Vidal Lacerda Gontijo;Philipp Mattern;Torsten Gerling;Eric Robert;Cristiane Yumi Koga-Ito;Konstantin Georgiev Kostov","doi":"10.1109/TRPMS.2023.3342709","DOIUrl":"10.1109/TRPMS.2023.3342709","url":null,"abstract":"Plasma sources suitable to generate low-temperature plasmas has been fundamental for the advances in plasma medicine. In this research field, plasma sources must comply with stringent conditions for clinical applications. The main requirement to be met is the patient and operator’s safety and the ethical requirement of effectivity, which encompasses the electrical regulations, potential device toxicity, and effectiveness in relation to the desired treatment. All these issues are addressed by the German prestandard DIN SPEC 91315:2014–06 (DINSpec), which deals with the safety limits, risk assessment, and biological efficacy of plasma sources aimed for medical applications. In this work, a low cost, user-friendly, and flexible atmospheric pressure plasma jet (APPJ) device was characterized following the DINSpec guidelines. The device, which is still under development, proved to be safe for medical applications. It is capable of producing an APPJ with low patient leakage current and ultraviolet emission, gas temperature lower than 40 °C, production of harmful gases within the safety limits and low cytotoxicity. The most differentiating feature is that the device presented good antimicrobial efficacy even operating at frequency of the order of just a few hundred Hz, a value below that of most devices reported in the literature.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139310856","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":"CT Image Denoising and Deblurring With Deep Learning: Current Status and Perspectives","authors":"Yiming Lei;Chuang Niu;Junping Zhang;Ge Wang;Hongming Shan","doi":"10.1109/TRPMS.2023.3341903","DOIUrl":"https://doi.org/10.1109/TRPMS.2023.3341903","url":null,"abstract":"This article reviews the deep learning methods for computed tomography image denoising and deblurring separately and simultaneously. Then, we discuss promising directions in this field, such as a combination with large-scale pretrained models and large language models. Currently, deep learning is revolutionizing medical imaging in a data-driven manner. With rapidly evolving learning paradigms, related algorithms and models are making rapid progress toward clinical applications.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139676142","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":"Rotational Augmented Noise2Inverse for Low-Dose Computed Tomography Reconstruction","authors":"Hang Xu;Alessandro Perelli","doi":"10.1109/TRPMS.2023.3340955","DOIUrl":"10.1109/TRPMS.2023.3340955","url":null,"abstract":"In this work, we present a novel self-supervised method for low-dose computed tomography (LDCT) reconstruction. Reducing the radiation dose to patients during a computed tomography (CT) scan is a crucial challenge since the quality of the reconstruction highly degrades because of low photons or limited measurements. Supervised deep learning DL methods have shown the ability to remove noise in images but require accurate ground truth which can be obtained only by performing additional high-radiation CT scans. Therefore, we propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN). Based on the noise2inverse (N2I) method, we enforce in the training loss the equivariant property of rotation transformation, which is induced by the CT imaging system, to improve the quality of the CT image in a lower dose. Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented noise2inverse (RAN2I) method keeps better-image quality over a different range of sampling angles. Finally, the quantitative results demonstrate that RAN2I achieves higher-image quality compared to N2I, and experimental results of RAN2I on real projection data show comparable performance to supervised learning.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138959144","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":"Temperature Control Strategies of Atmospheric Plasma Jet for Tissue Treatment","authors":"Bingkai Wang;Xu Yan;Zilan Xiong","doi":"10.1109/TRPMS.2023.3340154","DOIUrl":"https://doi.org/10.1109/TRPMS.2023.3340154","url":null,"abstract":"Besides the charged particles and neutral reactive species, the temperature effect is another significant issue needs to concern during the plasma treatment of biological tissue, which has effects on therapeutic efficacy and the risk of burns. Due to the influence of multiple factors on the temperature effect, it’s a complex nonlinear problem. In this study, temperature rise and distribution uniformity under different parameters and moving trajectories on porcine skin were investigated, and then a surface temperature control strategy was proposed. A 3-D electric motor control platform was constructed for the jet moving during the treatment. First, the effects of factors, such as distance, voltage, and flow rate on temperature variation over porcine skin surface, were analyzed, and the trends of temperature variation under single-factor influence were summarized. Then, the temperature distribution of fixed-point treatment and the temperature superposition effect on the tissue surface under different trajectories were explored, and a trajectory scheme for achieving homogeneous temperature distribution was proposed. Finally, a closed-loop control model was designed to achieve the control objectives of constant temperature holding over a certain surface area and resistance to high-temperature interference in real time. This control scheme also has reference significance for other surface treatments such as material processing.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081229","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":"PET Synthesis via Self-Supervised Adaptive Residual Estimation Generative Adversarial Network","authors":"Yuxin Xue;Lei Bi;Yige Peng;Michael Fulham;David Dagan Feng;Jinman Kim","doi":"10.1109/TRPMS.2023.3339173","DOIUrl":"https://doi.org/10.1109/TRPMS.2023.3339173","url":null,"abstract":"Positron emission tomography (PET) is a widely used, highly sensitive molecular imaging in clinical diagnosis. There is interest in reducing the radiation exposure from PET but also maintaining adequate image quality. Recent methods using convolutional neural networks (CNNs) to generate synthesized high-quality PET images from “low-dose” counterparts have been reported to be “state-of-the-art” for low-to-high-image recovery methods. However, these methods are prone to exhibiting discrepancies in texture and structure between synthesized and real images. Furthermore, the distribution shift between low-dose PET and standard PET has not been fully investigated. To address these issues, we developed a self-supervised adaptive residual estimation generative adversarial network (SS-AEGAN). We introduce 1) an adaptive residual estimation mapping mechanism, AE-Net, designed to dynamically rectify the preliminary synthesized PET images by taking the residual map between the low-dose PET and synthesized output as the input and 2) a self-supervised pretraining strategy to enhance the feature representation of the coarse generator. Our experiments with a public benchmark dataset of total-body PET images show that SS-AEGAN consistently outperformed the state-of-the-art synthesis methods with various dose reduction factors.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342795","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":"Effects of Loss Functions and Supervision Methods on Total-Body PET Denoising","authors":"Si Young Yie;Keon Min Kim;Sangjin Bae;Jae Sung Lee","doi":"10.1109/TRPMS.2023.3334276","DOIUrl":"https://doi.org/10.1109/TRPMS.2023.3334276","url":null,"abstract":"Introduction of the total-body positron emission tomography (TB PET) system is a remarkable advancement in noninvasive imaging, improving annihilation photon detection sensitivity and bringing the quality of positron emission tomography (PET) images one step closer to that of anatomical images. This enables reduced scan times or radiation doses and can ultimately improve other PET images through denoising. This study investigated the effect of loss functions: mean squared error (MSE), Poisson negative log-likelihood derived from the Poisson statistics of radiation activity, and L1 derived from the histogram of count differences between the full and partial scans. Furthermore, the effect of supervision methods, comparing supervised denoising, self-supervised denoising, and interpolation of input and self-supervised denoising based on dependency relations of the partial and full scans are explored. The supervised denoising method using the L1 norm loss function shows high-denoising performance regardless of harsh denoising conditions, and the interpolated self-supervised denoising using MSE loss preserves local features.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342796","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":"Unified Noise-Aware Network for Low-Count PET Denoising With Varying Count Levels","authors":"Huidong Xie;Qiong Liu;Bo Zhou;Xiongchao Chen;Xueqi Guo;Hanzhong Wang;Biao Li;Axel Rominger;Kuangyu Shi;Chi Liu","doi":"10.1109/TRPMS.2023.3334105","DOIUrl":"https://doi.org/10.1109/TRPMS.2023.3334105","url":null,"abstract":"As positron emission tomography (PET) imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. However, low-count PET scans often suffer from high-image noise, which can negatively impact image quality and diagnostic performance. Recent advances in deep learning have shown great potential for recovering underlying signal from noisy counterparts. However, neural networks trained on a specific noise level cannot be easily generalized to other noise levels due to different noise amplitude and variances. To obtain optimal denoised results, we may need to train multiple networks using data with different noise levels. But this approach may be infeasible in reality due to limited data availability. Denoising dynamic PET images presents additional challenge due to tracer decay and continuously changing noise levels across dynamic frames. To address these issues, we propose a unified noise-aware network (UNN) that combines multiple subnetworks with varying denoising power to generate optimal denoised results regardless of the input noise levels. Evaluated using large-scale data from two medical centers with different vendors, presented results showed that the UNN can consistently produce promising denoised results regardless of input noise levels, and demonstrate superior performance over networks trained on single noise level data, especially for extremely low-count data.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10323300","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342666","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":"A Total-Body Ultralow-Dose PET Reconstruction Method via Image Space Shuffle U-Net and Body Sampling","authors":"Gaoyu Chen;Sheng Liu;Wenxiang Ding;Li Lv;Chen Zhao;Fenghua Weng;Yong Long;Yunlong Zan;Qiu Huang","doi":"10.1109/TRPMS.2023.3333839","DOIUrl":"https://doi.org/10.1109/TRPMS.2023.3333839","url":null,"abstract":"Low-dose positron emission tomography (PET) reconstruction algorithms manage to reduce the injected dose and/or scanning time in PET examination while maintaining the image quality, and thus has been extensively studied. In this article, we proposed a novel ultralow-dose reconstruction method for total-body PET. Specifically, we developed a deep learning model named ISS-Unet based on U-Net and introduced 3-D PixelUnshuffle/PixelShuffle pair in image space to reduce the training time and GPU memory. We then introduced two body sampling methods in the training patch preparation step to improve the training efficiency and local metrics. We also reported the misalignment artifacts that were often neglected in 2-D training. The proposed method was evaluated on the MICCAI 2022 Ultralow-Dose PET Imaging Challenge dataset and won the first prize in the first-round competition according to the comprehensive score combining global and local metrics. In this article, we disclosed the implementation details of the proposed method followed by the comparison results with three typical methods.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10320380","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342754","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":"Deep-Learning-Aided Intraframe Motion Correction for Low-Count Dynamic Brain PET","authors":"Erik Reimers;Ju-Chieh Cheng;Vesna Sossi","doi":"10.1109/TRPMS.2023.3333202","DOIUrl":"https://doi.org/10.1109/TRPMS.2023.3333202","url":null,"abstract":"Data-driven intraframe motion correction of a dynamic brain PET scan (with each frame duration on the order of minutes) is often achieved through the co-registration of high-temporal-resolution (e.g., 1-s duration) subframes to estimate subject head motion. However, this conventional method of subframe co-registration may perform poorly during periods of low counts and/or drastic changes in the spatial tracer distribution over time. Here, we propose a deep learning (DL), U-Net-based convolutional neural network model which aids in the PET motion estimation to overcome these limitations. Unlike DL models for PET denoising, a nonstandard 2.5-D DL model was used which transforms the high-temporal-resolution subframes into nonquantitative DL subframes which allow for improved differentiation between noise and structural/functional landmarks and estimate a constant tracer distribution across time. When estimating motion during periods of drastic change in spatial distribution (within the first minute of the scan, ~1-s temporal resolution), the proposed DL method was found to reduce the expected magnitude of error (+/−) in the estimation for an artificially injected motion trace from 16 mm and 7° (conventional method) to 0.7 mm and 0.6° (DL method). During periods of low counts but a relatively constant spatial tracer distribution (60th min of the scan, ~1-s temporal resolution), an expected error was reduced from 0.5 mm and 0.7° (conventional method) to 0.3 mm and 0.4° (DL method). The use of the DL method was found to significantly improve the accuracy of an image-derived input function calculation when motion was present during the first minute of the scan.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081230","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}