IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"4-D Cone-Beam CT Reconstruction via Diffusion Model and Motion Compensation","authors":"Xianghong Wang;Zhengwei Ou;Peng Jin;Jiayi Xie;Ze Teng;Lei Xu;Jichen Du;Mingchao Ding;Yang Chen;Tianye Niu","doi":"10.1109/TRPMS.2024.3449155","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3449155","url":null,"abstract":"4-Dcone-beam computed tomography (4-D CBCT) has recently been recognized as a proficient technique in mitigating motion artifacts attributed to respiratory organ movement. The primary challenges in 4-D CBCT reconstruction encompass the precision in projection grouping, the efficacy in reconstructing from sparsely sampled data, and the accuracy in deformation field estimation. To surmount these challenges, we propose an innovative approach that integrates meticulous respiratory curve extraction for projection grouping and utilizes a diffusion model network with motion compensation (MoCo) techniques targeted at significantly enhancing image quality. An object detection network is employed to ascertain the exact position of the diaphragm, which is then normalized to formulate the respiratory curve. Further, we employ a U-Net architecture-based diffusion model, which integrates attention mechanisms to enhance sparse-view reconstruction and reduce artifacts through Guided-Diffusion. Deviating from conventional optical flow methods, our approach introduces an unsupervised registration network for deformation vector field (DVF) in phase-enhanced images. This DVF is then utilized in a motion-compensated, ordered-subset, simultaneous algebraic reconstruction technique, culminating in the generation of 4-D CBCT images. The efficacy of this method has been substantiated through validation on both simulated and clinical datasets, with the results from comparative experiments indicating promising outcomes.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"191-201"},"PeriodicalIF":4.6,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10644124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106266","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":"By Any Other Name: Searching for the Right Plasma Nomenclature","authors":"Caroline Corcoran;Rachel Bennett;Vandana Miller;Fred Krebs;Will Dampier","doi":"10.1109/TRPMS.2024.3447551","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3447551","url":null,"abstract":"Nonthermal plasma, cold plasma, and atmospheric-pressure plasma are few terms used to describe the plasma used in plasma medicine research. The resulting ambiguity hampers literature searches, confuses discussion, and complicates collaborations. To assess the full breadth of this problem, we designed a natural language processing (NLP) model that surveyed approximately 15 000 papers in response to the query “plasma medicine” indexed in PubMed between 2020 and 2022. Our NLP was constructed and executed using the Hugging Face transformers API and PubMed BERT pretrained model. We used this model to determine the prevalence and to assess the utility of each term for searching literature relevant to plasma medicine. The effectiveness of each term was measured by precision, the ability to discriminate relevant and irrelevant literature; and recall, the ability to retrieve relevant literature. Each term was given a combined effectiveness score of 0-1 (<inline-formula> <tex-math>$1{=}$ </tex-math></inline-formula> ideal effectiveness) accounting for precision, recall, sample size, and model confidence. Our model showed that of the 12 commonly used terms analyzed, none received a combined effectiveness score over 0.025. We concluded that there is no universal term for “plasma” that provides a satisfactory representation of literature. These results highlight the need for standardization of nomenclature in plasma medicine.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"388-394"},"PeriodicalIF":4.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553284","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":"Non-Negative Matrix Factorization Using Partial Prior Knowledge for Radiation Dosimetry","authors":"Boby Lessard;Frédéric Marcotte;Arthur Lalonde;François Therriault-Proulx;Simon Lambert-Girard;Luc Beaulieu;Louis Archambault","doi":"10.1109/TRPMS.2024.3442773","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3442773","url":null,"abstract":"Hyperspectral unmixing aims at decomposing a given signal into its spectral signatures and its associated fractional abundances. To improve the accuracy of this decomposition, algorithms have included different assumptions depending on the application. The goal of this study is to develop a new unmixing algorithm that can be applied for the calibration of multipoint scintillation dosimeters used in the field of radiation therapy. This new algorithm is based on a non-negative matrix factorization. It incorporates a partial prior knowledge on both the abundances and the endmembers of a given signal. It is shown herein that, following a precise calibration routine, it is possible to use partial prior information about the fractional abundances, as well as on the endmembers, in order to perform a simplified yet precise calibration of these dosimeters. Validation and characterization of this algorithm is made using both simulations and experiments. The experimental validation shows an improvement in accuracy compared to previous algorithms with a mean spectral angle distance (SAD) on the estimated endmembers of 0.0766, leading to an average error of <inline-formula> <tex-math>$(0.25 pm 0.73)$ </tex-math></inline-formula>% on dose measurements.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"247-258"},"PeriodicalIF":4.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106263","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":"An FPGA-Based 64-Channel Readout Electronics for High-Resolution TOF-PET Detectors","authors":"Xiang Zhang;Yonggang Wang;Mingchen Wang;Xiaoguang Kong","doi":"10.1109/TRPMS.2024.3443831","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3443831","url":null,"abstract":"Field programmable logic array (FPGA)-based readout electronics has shown its capability of channel-by-channel signal readout for time-of-flight positron emission tomography (TOF-PET) detectors. However, for detectors that rely on light sharing to achieve subpixel resolution, the high-linear measurement dynamic range of the readout electronics is highly required. In this article, the problems with dynamic range in our previously proposed FPGA-based fast linear discharge circuit are investigated and corresponding methods are proposed to enhance its small signal measurement capability and improve the timing performance as well. A practical 64-channel TOF-PET detector module was constructed and evaluated. The readout electronics test results demonstrated a 240x measurement dynamic range with 99.5% conversion linearity. In the case that the \u0000<inline-formula> <tex-math>$8times 8$ </tex-math></inline-formula>\u0000 silicon photomultiplier (SiPM) array in the detector combines with an \u0000<inline-formula> <tex-math>$8times 8$ </tex-math></inline-formula>\u0000 LYSO crystal (each \u0000<inline-formula> <tex-math>$3.2times 3.2times 10$ </tex-math></inline-formula>\u0000 mm3) array, the average energy and coincidence time resolution of the detector are measured as 10.68% (511 keV) and 364.9 ps, respectively. To demonstrate the benefit of large dynamic range to high-resolution detectors, the crystal array in the detector was replaced by a \u0000<inline-formula> <tex-math>$24times 24$ </tex-math></inline-formula>\u0000 LYSO array (each \u0000<inline-formula> <tex-math>$1.04times 1.04times 15$ </tex-math></inline-formula>\u0000 mm3) and achieved 1-mm resolution. The test results confirm that the proposed FPGA-based readout circuit is practical for laboratory instrumentation","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"11-19"},"PeriodicalIF":4.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912439","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":"Improved 2-D Chest CT Image Enhancement With Multi-Level VGG Loss","authors":"Ayush Chaturvedi;Ritvik Prabhu;Mukund Yadav;Wu-Chun Feng;Guohua Cao","doi":"10.1109/TRPMS.2024.3439010","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3439010","url":null,"abstract":"Chest CT scans play an important role in diagnosing abnormalities associated with the lungs, such as tuberculosis, sarcoidosis, pneumonia, and, more recently, COVID-19. However, because conventional normal-dose chest CT scans require a much larger amount of radiation than x-rays, practitioners seek to replace conventional CT with low-dose CT (LDCT). LDCT often generates a low-quality CT image that poses noise and, in turn, negatively affects the accuracy of diagnosis. Therefore, in the context of COVID-19, due to the large number of affected populations, efficient image-denoising techniques are needed for LDCT images. Here, we present a deep learning (DL) model that combines two neural networks to enhance the quality of low-dose chest CT images. The DL model leverages a previously developed densenet and deconvolution-based network (DDNet) for feature extraction and extends it with a pretrained VGG network inside the loss function to suppress noise. Outputs from selected multiple levels in the VGG network (ML-VGG) are leveraged for the loss calculation. We tested our DDNet with ML-VGG loss using several sources of CT images and compared its performance to DDNet without VGG loss as well as DDNet with an empirically selected single-level VGG loss (DDNet-SL-VGG) and other state-of-the-art DL models. Our results show that DDNet combined with ML-VGG (DDNet-ML-VGG) achieves state-of-the-art denoising capabilities and improves the perceptual and quantitative image quality of chest CT images. Thus, DDNet with multilevel VGG loss could potentially be used as a post-acquisition image enhancement tool for medical professionals to diagnose and monitor chest diseases with higher accuracy.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"304-312"},"PeriodicalIF":4.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553300","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":"HYPR4D Kernel Method With an Unsupervised 2.5SD+0.5TD Deep Learning Assisted Kernel Matrix","authors":"Ju-Chieh Kevin Cheng;Erik Reimers;Vesna Sossi","doi":"10.1109/TRPMS.2024.3442690","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3442690","url":null,"abstract":"We describe a deep learning (DL) assisted HYPR4D kernelized reconstruction which produces low-noise voxel-level time-activity-curves (TACs) while preserving quantification within small structures as well as consistent spatiotemporal patterns/features within measured data. The proposed method consists of the following advantages over other methods: 1) unsupervised single subject network training scheme independent of positron emission tomography (PET) tracers; 2) training data generated on-the-fly during reconstruction; 3) intrinsic spatiotemporal consistency provided by minimizing the \u0000<inline-formula> <tex-math>$L_{2}$ </tex-math></inline-formula>\u0000 loss using pseudo 4-D (i.e., 2.5 Spatial Dimension + 0.5 Temporal Dimension or 2.5SD+0.5TD) patches between kernelized OSEM subset estimates; and 4) a final tuning step which minimizes over-smoothing from the network output within the kernel matrix. Contrast phantom, human [18F]FDG and [11C]RAC data acquired on GE SIGNA PET/MR were used for evaluations. The proposed DL HYPR4D kernel method outperformed the standard HYPR4D kernel method as well as TOF-OSEM and TOF-BSREM (Q.Clear) in terms contrast recovery versus noise. The proposed final tuning reduced the underestimation bias due to over-smoothing within a 4-mm target structure from ~15% to ~2% while maintaining low-noise voxel-level TACs. In addition, the proposed unsupervised DL assisted reconstruction also outperformed the supervised DL version in terms of bias reduction along the TACs and kinetic model fittings.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"20-28"},"PeriodicalIF":4.6,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912554","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":"List-Mode PET Image Reconstruction Using Dykstra-Like Splitting","authors":"Kibo Ote;Fumio Hashimoto;Yuya Onishi;Yasuomi Ouchi","doi":"10.1109/TRPMS.2024.3441526","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3441526","url":null,"abstract":"Convergence of the block iterative method in image reconstruction for positron emission tomography (PET) requires careful control of relaxation parameters, which is a challenging task. The automatic determination of relaxation parameters for list-mode reconstructions also remains challenging. Therefore, a different approach would be desirable. In this study, we propose a list-mode maximum-likelihood Dykstra-like splitting PET reconstruction (LM-MLDS) that reduces the limit-cycle amplitude by adding the distance from an initial image as a penalty term into an objective function. LM-MLDS uses a two-step approach because its performance depends on the quality of the initial image. The first step uses a uniform image as the initial image, whereas the second step uses a reconstructed image after one main iteration as the initial image. In a simulation study, LM-MLDS provided a better tradeoff curve between noise and contrast than the other methods. In a clinical study, LM-MLDS removed the false hotspots at the edge of the axial field of view and improved the image quality of slices covering the top of the head to the cerebellum. List-mode proximal splitting reconstruction is useful not only for optimizing nondifferential functions but also for mitigating the limit-cycle phenomenon in block iterative methods.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"29-39"},"PeriodicalIF":4.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912497","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":"Improving Reconstruction Speed of Positron Emission Particle Tracking by Efficient Gradient Calculation","authors":"Eunsik Choi;Yeseul Kim;Wonmo Sung","doi":"10.1109/TRPMS.2024.3440344","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3440344","url":null,"abstract":"The development of molecular imaging algorithms and tools has advanced our understanding of the molecular dynamics in complex systems, such as tracking cells in vivo. One of these advancements, the positron emission particle tracking (PEPT) algorithm, allows particles to be tracked through a positron emission tomography (PET) scanner. The spatiotemporal B-spline reconstruction (SBSR) method of the PEPT algorithm is capable of tracking a single particle, such as a cell using PET with high accuracy. However, its slow computational speed, particularly with large data, results in time-intensive hyperparameter tuning, which is a limitation in real-world applications. This study introduces a novel approach, employing the backpropagation algorithm, commonly used in deep learning, to enhance the efficiency of gradient computation during particle trajectory reconstruction. Comparisons of the computational speed of the previous and current algorithms on a PEPT benchmark dataset show that the novel approach significantly increased the computational speed without compromising the tracking accuracy. Notably, we found that the difference in computation time between the current and previous algorithms increased as the size of the data increased. In conclusion, we have improved the SBSR method by efficiently computing the gradients, making it faster and more efficient. Even with bigger data, our approach keeps up, showing an improvement in computational speed.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"40-46"},"PeriodicalIF":4.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912506","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":"Deep Learning-Based Fast Volumetric Image Generation for Image-Guided Proton Radiotherapy","authors":"Chih-Wei Chang;Yang Lei;Tonghe Wang;Sibo Tian;Justin Roper;Liyong Lin;Jeffrey Bradley;Tian Liu;Jun Zhou;Xiaofeng Yang","doi":"10.1109/TRPMS.2024.3439585","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3439585","url":null,"abstract":"Very fast imaging techniques can enhance the precision of image-guided radiation therapy, which can be useful for external beam radiation therapy. This work aims to develop a deep learning (DL)-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization for treating lung cancer patients with gating, and it is presented in the context of FLASH which leverages ultrahigh dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. The proposed framework comprises four modules, including orthogonal kV X-ray projection acquisition, DL-based volumetric image generation, image quality analyses, and proton water equivalent thickness (WET) evaluation. We investigated volumetric image reconstruction using kV projection pairs with four different source angles. Thirty patients with lung targets were identified from an institutional database, each patient having a 4-D computed tomography (CT) dataset with ten respiratory phases. Considering all evaluation metrics, the kV projections with source angles of 135° and 225° yielded the optimal volumetric images. The patient-averaged mean absolute error, peak signal-to-noise ratio, structural similarity index measure, and WET error were \u0000<inline-formula> <tex-math>$75pm 22$ </tex-math></inline-formula>\u0000 hounsfield unit, \u0000<inline-formula> <tex-math>$19pm 3$ </tex-math></inline-formula>\u0000.7 dB, \u0000<inline-formula> <tex-math>$0.938pm 0.044$ </tex-math></inline-formula>\u0000, and −1.3%±4.1%. The proposed framework can rapidly deliver volumetric images to potentially guide proton FLASH treatment delivery systems.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"973-983"},"PeriodicalIF":4.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587625","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":"Experimental Measurement of Secondary Particle Count for Real-Time Proton Range Verification","authors":"Chuan Huang;Zhengguo Hu;Wei Lv;Yucong Chen;Xiuling Zhang;Zhiguo Xu;Faming Luo;Xinle Lang;Zulong Zhao;Ruishi Mao;Yongzhi Yin;Zhongming Wang;Di Wang;Guoqing Xiao","doi":"10.1109/TRPMS.2024.3439517","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3439517","url":null,"abstract":"The real-time positioning of the particle beam range during treatment is a critical technology for improving the quality of the patient treatment. This article presents a scheme for the real-time proton range verification, and an experimental prototype is built at the Xi’an proton application facility (XiPAF) terminal. The experiment utilized a 150 MeV passive proton beam delivery mode to bombard the polymethyl methacrylate (PMMA) target for the real-time proton range verification. This scheme utilizes the secondary particle counts generated per monitor unit (MU) of primary particles and does not require identification of the secondary particle species, only its deposition energy in the cerium bromide (CeBr3) scintillator module exceeding 73.24 keV. The accuracy of range verification was evaluated at various acquisition periods by establishing the relationship between the secondary particle counts generated per MU of primary particles and the proton range. The range verification accuracy after one spill (\u0000<inline-formula> <tex-math>$sim ~1.67times 10$ </tex-math></inline-formula>\u00009 protons) delivery was measured at \u0000<inline-formula> <tex-math>$0.01~pm ~0$ </tex-math></inline-formula>\u0000.29 mm. The accuracy of range verification within milliseconds is mainly affected by the statistical fluctuations in the secondary particle counts caused by the accumulation of activation products. Under constrained conditions, the range verification accuracy was measured at \u0000<inline-formula> <tex-math>$0.16~pm ~0$ </tex-math></inline-formula>\u0000.69 mm within 110 ms acquisition time and \u0000<inline-formula> <tex-math>$0.16~pm ~0$ </tex-math></inline-formula>\u0000.94 mm within 55 ms acquisition time. The experimental results confirm the feasibility of the scheme for the real-time range verification practice. The study hopes to provide a new reference scheme for reducing the impact of range uncertainty on the patient treatment quality.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"984-989"},"PeriodicalIF":4.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587539","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}