IEEE Transactions on Radiation and Plasma Medical Sciences最新文献_第9页

Toward a Second Generation of Metascintillators Using the Purcell Effect 利用珀塞尔效应研究第二代超振荡子

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-10-03 DOI: 10.1109/TRPMS.2024.3471251

A. Shultzman;R. Schütz;Y. Kurman;N. Lahav;G. Dosovitskiy;C. Roques-Carmes;Y. Bekenstein;G. Konstantinou;R. Latella;L. Zhang;F. Loignon-Houle;A. J. Gonzalez;J. M. Benlloch;I. Kaminer;P. Lecoq

引用次数: 0

PrideDiff: Physics-Regularized Generalized Diffusion Model for CT Reconstruction CT重建的物理正则化广义扩散模型

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-10-01 DOI: 10.1109/TRPMS.2024.3471677

Zexin Lu;Qi Gao;Tao Wang;Ziyuan Yang;Zhiwen Wang;Hui Yu;Hu Chen;Jiliu Zhou;Hongming Shan;Yi Zhang

{"title":"PrideDiff: Physics-Regularized Generalized Diffusion Model for CT Reconstruction","authors":"Zexin Lu;Qi Gao;Tao Wang;Ziyuan Yang;Zhiwen Wang;Hui Yu;Hu Chen;Jiliu Zhou;Hongming Shan;Yi Zhang","doi":"10.1109/TRPMS.2024.3471677","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3471677","url":null,"abstract":"Achieving a lower radiation dose and a faster imaging speed is a pivotal objective of computed tomography (CT) reconstruction. However, these often come at the cost of compromised reconstruction quality. With the advent of deep learning, numerous CT reconstruction methods rooted in this field have significantly improved the reconstruction performance. Recently, diffusion models have further enhanced training stability and imaging quality for CT. However, many of these methods only focus on CT image domain features, ignoring the intrinsic physical information of the imaging process. Although compressive sensing-based iterative reconstruction algorithms utilize physical prior information, their intricate iterative process poses challenges in training, subsequently influencing their efficiency. Motivated by these observations, we introduce a novel physics-regularized generalized diffusion model for CT reconstruction (PrideDiff). On the one hand, our method further improves the quality of reconstructed images by fusing physics-regularized iterative reconstruction methods with diffusion models. On the other hand, we propose a prior extraction module embedded with temporal features, which effectively improves the performance of the iteration process. Extensive experimental results demonstrate that PrideDiff outperforms several state-of-the-art methods in low-dose and sparse-view CT reconstruction tasks on different datasets, with faster reconstruction speed. We further discuss the effectiveness of relevant components in PrideDiff and validate the stability of the iterative reconstruction process, followed by detailed analysis of computational cost and inference time.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"157-168"},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10701005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106286","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}

引用次数: 0

Three-Gamma Imaging in Nuclear Medicine: A Review 核医学中的三伽马成像：综述

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-09-30 DOI: 10.1109/TRPMS.2024.3470836

Hideaki Tashima;Taiga Yamaya

{"title":"Three-Gamma Imaging in Nuclear Medicine: A Review","authors":"Hideaki Tashima;Taiga Yamaya","doi":"10.1109/TRPMS.2024.3470836","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3470836","url":null,"abstract":"Three-gamma imaging is attracting attention as a futuristic diagnostic imaging method that surpasses positron emission tomography (PET). Its conceptual key is using \u0000<inline-formula> <tex-math>$beta ^{+}$ </tex-math></inline-formula>\u0000-\u0000<inline-formula> <tex-math>$gamma $ </tex-math></inline-formula>\u0000 nuclides that simultaneously emit a prompt gamma ray with the positron decay. In this review, we have categorized the utilizations of prompt gamma rays into three categories: 1) multiple positron emitter imaging; 2) reconstruction-less positron emission imaging; and 3) positronium lifetime imaging. Multiple positron emitter imaging utilizes the prompt gamma ray as a trigger to discriminate from signals of pure positron emitters to enable simultaneous injection and imaging of two different radioisotopes. Reconstruction-less positron emission imaging combines PET and Compton imaging technologies to estimate the source position as almost a point for each triple coincidence event. Positronium lifetime imaging utilizes the prompt gamma ray as a starting signal to measure the time difference between positronium formation and annihilation for each triple coincidence event as its lifetime. This is because the positronium lifetime is affected by the surrounding microenvironment of electrons, it is expected to provide new information regarding biological conditions, such as the hypoxia state. In this review we introduce the principles of the three categories of three-gamma imaging methods, prototype development, and demonstration experiments.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"853-866"},"PeriodicalIF":4.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10700810","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587582","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}

引用次数: 0

Unified Feature Consistency of Under-Performing Pixels and Valid Regions for Semi-Supervised Medical Image Segmentation 半监督医学图像分割中欠表现像素和有效区域的统一特征一致性

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-09-23 DOI: 10.1109/TRPMS.2024.3465561

Tao Lei;Yi Wang;Xingwu Wang;Xuan Wang;Bin Hu;Asoke K. Nandi

{"title":"Unified Feature Consistency of Under-Performing Pixels and Valid Regions for Semi-Supervised Medical Image Segmentation","authors":"Tao Lei;Yi Wang;Xingwu Wang;Xuan Wang;Bin Hu;Asoke K. Nandi","doi":"10.1109/TRPMS.2024.3465561","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3465561","url":null,"abstract":"Existing semi-supervised medical image segmentation methods based on the teacher-student model often employ unweighted pixel-level consistency loss, neglecting the varying difficulties of different pixels and resulting in significant deficits in segmenting challenging regions. Additionally, consistency learning often excludes pixels with high uncertainty, which destroys the semantic integrity of a medical image. To address these issues, we propose a novel unified feature consistency (UFC) of under-performing pixels (UPPs) and valid regions for semi-supervised medical image segmentation: 1) high-performing pixels (HPPs) and UPPs are distinguished by confidence differences between the student and teacher models, and then UPPs are mapped into a latent feature space to improve consistency learning effect (UPPFC); 2) in order to obtain richer semantic information from a medical image, vectors of valid regions are selected from both image- and patch-level class feature vectors by using the output probabilities of the teacher model; and 3) these vectors are mapped into the latent feature space for class feature consistency (CFC) learning as a supplement to UPPFC which only focuses on challenging regions for pixel-level consistency learning, thereby enhancing the model’s ability to learn structured semantic information from images themselves. Experimental results demonstrate that the proposed UFC achieves sufficient learning for challenging regions and retains the semantic integrity of medical images. Encouragingly, our proposed UFC provides better-segmentation results than the current state-of-the-art methods on three publicly available datasets. Our codes will be released at: <uri>https://github.com/SUST-reynole</uri>.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"169-181"},"PeriodicalIF":4.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10685517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106268","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}

引用次数: 0

Finite Element Method-Based Hybrid MRI/CBCT Generation to Improve Liver Stereotactic Body Radiation Therapy Targets Localization Accuracy 基于有限元法的MRI/CBCT混合生成提高肝脏立体定向放射治疗靶点定位精度

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-09-23 DOI: 10.1109/TRPMS.2024.3466184

Zeyu Zhang;Mark Chen;Ke Lu;Dongyang Guo;Zhuoran Jiang;Hualiang Zhong;Jason Molitoris;Phuoc T. Tran;Fang-Fang Yin;Lei Ren

{"title":"Finite Element Method-Based Hybrid MRI/CBCT Generation to Improve Liver Stereotactic Body Radiation Therapy Targets Localization Accuracy","authors":"Zeyu Zhang;Mark Chen;Ke Lu;Dongyang Guo;Zhuoran Jiang;Hualiang Zhong;Jason Molitoris;Phuoc T. Tran;Fang-Fang Yin;Lei Ren","doi":"10.1109/TRPMS.2024.3466184","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3466184","url":null,"abstract":"Cone-beam CT (CBCT) is commonly used in treatment imaging, but its limited soft tissue contrast presents challenges for liver tumor localization. As a result, indirect localization methods relying on the liver’s boundary are commonly utilized, which have limited accuracy for tumor localization. On-board MRI offers superior soft tissue contrast but is limited by the cost. To address this, we devised a method to generate onboard virtual MRI by integrating pretreatment MRI with onboard CBCT, enhancing liver stereotactic body radiation therapy (SBRT) tumor localization accuracy. We employed a finite element method (FEM) for deformable mapping, deforming prior liver MR images onto CBCT geometry to create a virtual MRI. This hybrid virtual-MRI/CBCT (hMRI-CBCT) approach was evaluated in a pilot study involving 48 patients. The hMRI-CBCT demonstrated superb soft-tissue contrast with clear tumor visualization. Registration accuracy of hMRI-CBCT to planning CT significantly surpasses the onboard CBCT to planning CT registration, particularly for tumors not near the liver boundary, with an average error reduction of <inline-formula> <tex-math>$1.53~pm ~2$ </tex-math></inline-formula>.16 mm. Our study demonstrated that hybrid MRI/CBCT can apparently reduce localization errors in liver SBRT, potentially improving tumor control and reducing toxicities, and opening avenues for further margin reduction and dose escalation.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"372-381"},"PeriodicalIF":4.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553150","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}

引用次数: 0

Data Regularization for Streak Artifacts Removal in Self-Supervised Micro-CT Denoising 自监督微ct去噪中条带伪影去除的数据正则化

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-09-17 DOI: 10.1109/TRPMS.2024.3462732

Jiaming Liu;Guang Li;Qingxian Zhao;Shouhua Luo

{"title":"Data Regularization for Streak Artifacts Removal in Self-Supervised Micro-CT Denoising","authors":"Jiaming Liu;Guang Li;Qingxian Zhao;Shouhua Luo","doi":"10.1109/TRPMS.2024.3462732","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3462732","url":null,"abstract":"Lens-coupled micro-CT imaging is widely used for its high resolution and noninvasive characteristics. However, due to the low efficiency of its optical system, the reconstructed images suffer from a low signal-to-noise ratio, and it is challenging to acquire sufficient high-quality images. We adapt Noise2Noise for denoising and obtain paired data by dividing a single scan into odd and even projection subsets for separate reconstruction. This process results in undesirable sparse angle artifacts and image structural discrepancies between the noisy pairs. Networks trained on this data tend to mistakenly overfit these discrepancies and introduce streak artifacts during inference. In this article, we propose a self-supervised data regularization-based model that utilizes a unique symmetrical phantom to create pairs of data differing only in noise. This data is input into the network and functions as a regularization mechanism to prevent overfitting. Specifically, we design a fine-tuning strategy based on extremely limited sample data and a mixed datasets training strategy. Both approaches do not need high-quality images. Experimental results show that our method achieves satisfactory denoising effect without introducing artifacts and outperforms the comparison method. This method also generalizes well to unseen samples and various network architectures.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"627-638"},"PeriodicalIF":4.6,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900512","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}

引用次数: 0

Test-Time Adaptation via Orthogonal Meta-Learning for Medical Imaging 基于正交元学习的医学影像学考试时间适应

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-09-17 DOI: 10.1109/TRPMS.2024.3462542

Zhiwen Wang;Zexin Lu;Tao Wang;Ziyuan Yang;Hui Yu;Zhongxian Wang;Yinyu Chen;Jingfeng Lu;Yi Zhang

{"title":"Test-Time Adaptation via Orthogonal Meta-Learning for Medical Imaging","authors":"Zhiwen Wang;Zexin Lu;Tao Wang;Ziyuan Yang;Hui Yu;Zhongxian Wang;Yinyu Chen;Jingfeng Lu;Yi Zhang","doi":"10.1109/TRPMS.2024.3462542","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3462542","url":null,"abstract":"Deep learning (DL) models, which have significantly promoted medical imaging, typically assume that training and testing data come from the same domain and distribution. However, these models struggle with unseen testing variations, like different imaging scanners or protocols, leading to suboptimal results from distribution mismatches between training and testing data. Despite extensive research, the issue of distribution mismatch in DL-based medical imaging has been largely overlooked in current literature. To improve the performance with mismatched testing data, this article proposes an orthogonal meta-learning (OML) framework for test-time adaptation (TTA) in medical imaging. Specifically, during training, we develop supervised meta-training reconstruction tasks to guide the self-supervised meta-testing task. Additionally, we introduce an orthogonal learning strategy to enforce orthogonality of pretrained parameters during training, which accelerates convergence during TTA and enhances performance. During the testing stage, the fine-tuned meta-learned parameters effectively reconstruct new, unseen testing data. Extensive experiments on magnetic resonance imaging and computed tomography datasets were conducted to validate our method’s effectiveness against other state-of-the-art methods, including supervised ones, in various mismatch scenarios.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"215-227"},"PeriodicalIF":4.6,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106335","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}

引用次数: 0

Implementation of a FPGA-Based Time-to-Digital Converter Utilizing Opposite-Transition Propagation and Virtual Bin Method 基于fpga的时间-数字转换器的反向转换传播和虚拟Bin方法实现

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-09-10 DOI: 10.1109/TRPMS.2024.3457618

Daehee Lee;Sun Il Kwon

{"title":"Implementation of a FPGA-Based Time-to-Digital Converter Utilizing Opposite-Transition Propagation and Virtual Bin Method","authors":"Daehee Lee;Sun Il Kwon","doi":"10.1109/TRPMS.2024.3457618","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3457618","url":null,"abstract":"We propose an field-programmable gate array (FPGA)-based time-to-digital converter (TDC) that utilizes a virtual bin (VB) approach with opposite-transition (OT) inputs on two tapped-delay lines (TDLs) to obtain less-correlated time bins. The VBs from the proposed OT TDC were obtained by comparing and segmenting the less-correlated bins collected from the two TDLs. The OT TDC was implemented on a 7-series FPGA (Xilinx) to verify performance. A conventional monotonic-transition (MT) TDC, which used identical transition inputs (0-to-1 or 1-to-0 transition) for the two TDLs, was also implemented as a control group. The results were compared with those from the MT TDC and other studies. The proposed method effectively improves time resolution and integral linearity while keeping resource usage low by exploiting these characteristics. The average bin size and RMS value were 5.5 and 4.2 ps, respectively. Moreover, the proposed method exhibits stable performance under temperature variations and implementation location changes. The VB OT TDC, which applies the VB method to the OT TDC, successfully measures detection time differences of two signals from two Cerenkov radiator integrated microchannel plate photomultiplier tubes (CRI-MCP-PMTs) with a high-timing precision of sub-100 ps. The VB OT TDC can be used for next-generation applications that require fast-timing measurements.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"148-156"},"PeriodicalIF":4.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106285","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}

引用次数: 0

A Silicon-Pixel Paradigm for PET PET的硅像素范式

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-09-09 DOI: 10.1109/TRPMS.2024.3456241

Aleix Boquet-Pujadas;Jihad Saidi;Mateus Vicente;Lorenzo Paolozzi;Jonathan Dong;Pol Del Aguila Pla;Giuseppe Iacobucci;Michael Unser

引用次数: 0

IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information 电气和电子工程师学会辐射与等离子体医学科学杂志》（IEEE Transactions on Radiation and Plasma Medical Sciences）出版信息

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-09-06 DOI: 10.1109/TRPMS.2024.3449313

引用次数: 0