IEEE Transactions on Radiation and Plasma Medical Sciences最新文献_第7页

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IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-09-06 DOI: 10.1109/TRPMS.2024.3453689

引用次数: 0

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IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-09-06 DOI: 10.1109/TRPMS.2024.3453691

引用次数: 0

Synthetic CT Generation via Variant Invertible Network for Brain PET Attenuation Correction 基于可变可逆网络的脑PET衰减校正合成CT生成

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-09-05 DOI: 10.1109/TRPMS.2024.3453009

Yu Guan;Bohui Shen;Shirui Jiang;Xinchong Shi;Xiangsong Zhang;Bingxuan Li;Qiegen Liu

{"title":"Synthetic CT Generation via Variant Invertible Network for Brain PET Attenuation Correction","authors":"Yu Guan;Bohui Shen;Shirui Jiang;Xinchong Shi;Xiangsong Zhang;Bingxuan Li;Qiegen Liu","doi":"10.1109/TRPMS.2024.3453009","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3453009","url":null,"abstract":"Attenuation correction (AC) is essential for the generation of artifact-free and quantitatively accurate positron emission tomography (PET) images. Nowadays, deep-learning-based methods have been extensively applied to PET AC tasks, yielding promising results. Therefore, this article develops an innovative approach to generate continuously valued CT images from nonattenuation corrected PET images for AC on brain PET imaging. Specifically, an invertible neural network combined with the variable augmentation strategy that can achieve the bidirectional inference processes is proposed for synthetic CT generation. On the one hand, invertible architecture ensures a bijective mapping between the PET and synthetic CT image spaces, which can potentially improve the robustness of the prediction and provide a way to validate the synthetic CT by checking the consistency of the inverse mapping. On the other hand, the variable augmentation strategy enriches the training process and leverages the intrinsic data properties more effectively. Therefore, the combination provides for superior performance in PET AC by preserving information throughout the network and by better handling of the data variability inherent PET AC. To evaluate the performance of the proposed algorithm, we conducted a comprehensive study on a total of 1480 2-D slices from 37 whole-body 18F-FDG clinical patients using comparative algorithms (such as cycle-generative adversarial network and Pix2pix). Perceptual analysis and quantitative evaluations illustrate that the invertible network for PET AC outperforms other existing AC models, which demonstrates the feasibility of achieving brain PET AC without additional anatomical information.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"325-336"},"PeriodicalIF":4.6,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666843","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553033","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

Segmentation-Based X-Ray Multiobjective Quality Assessment Network 基于分割的x射线多目标质量评估网络

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-09-02 DOI: 10.1109/TRPMS.2024.3452683

Qianyi Yang;Demin Xu;Zhenxing Huang;Wenbo Li;Guanxun Cheng;Tianye Niu;Hairong Zheng;Dong Liang;Fei Feng;Zhanli Hu

{"title":"Segmentation-Based X-Ray Multiobjective Quality Assessment Network","authors":"Qianyi Yang;Demin Xu;Zhenxing Huang;Wenbo Li;Guanxun Cheng;Tianye Niu;Hairong Zheng;Dong Liang;Fei Feng;Zhanli Hu","doi":"10.1109/TRPMS.2024.3452683","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3452683","url":null,"abstract":"X-ray imaging is crucial in orthopedic disease detection and diagnosis, but it can impact the body significantly. Ensuring imaging quality is vital for accurate diagnoses and reducing repeat scans. However, quality inspection can decrease efficiency and be influenced by subjectivity when handling large data volumes, affecting evaluation outcomes. Current deep learning methods for medical image quality assessment rely on extensive labeled data, posing privacy and resource challenges. Our research aims to develop a quality assessment network for X-ray imaging independent of complex labels and large datasets, tailored for multi-index quality assessment. We propose an X-ray imaging quality assessment network based on segmentation priors, utilizing the “segment anything model” (SAM) for mask segmentation and a dual-feature extraction network to process prior information. Through a channel fully connected module, we transform the regression problem into a multiclassification problem, improving convergence speed and performance. Comparative analysis demonstrates the superiority of our proposed algorithm. Our X-ray imaging quality assessment network achieves accurate and efficient quality assessment without relying on extensive labeled data. <uri>https://github.com/OPMZZZ/SAM-DRIQA/</uri>","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"202-214"},"PeriodicalIF":4.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106267","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

BTMuda: A Bi-Level Multisource Unsupervised Domain Adaptation Framework for Breast Cancer Diagnosis 乳腺癌诊断的双层次多源无监督域自适应框架

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-09-02 DOI: 10.1109/TRPMS.2024.3453401

Yuxiang Yang;Xinyi Zeng;Pinxian Zeng;Binyu Yan;Xi Wu;Jiliu Zhou;Yan Wang

{"title":"BTMuda: A Bi-Level Multisource Unsupervised Domain Adaptation Framework for Breast Cancer Diagnosis","authors":"Yuxiang Yang;Xinyi Zeng;Pinxian Zeng;Binyu Yan;Xi Wu;Jiliu Zhou;Yan Wang","doi":"10.1109/TRPMS.2024.3453401","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3453401","url":null,"abstract":"Deep learning has revolutionized the early detection of breast cancer, resulting in a significant decrease in mortality rates. However, difficulties in obtaining annotations and huge variations in distribution between training sets and real scenes have limited their clinical applications. To address these limitations, unsupervised domain adaptation (UDA) methods have been used to transfer knowledge from one labeled source domain to the unlabeled target domain, yet these approaches suffer from severe domain shift issues and often ignore the potential benefits of leveraging multiple relevant sources in practical applications. To address these limitations, in this work, we construct a three-branch mixed extractor and propose a bi-level multisource UDA method called BTMuda for breast cancer diagnosis. Our method addresses the problems of domain shift by dividing domain shift issues into two levels: 1) intradomain and 2) interdomain. To reduce the intradomain shift, we jointly train a convolutional neural network and a Transformer as two paths of a domain mixed feature extractor to obtain robust representations rich in both low-level local and high-level global information. As for the interdomain shift, we redesign the Transformer delicately to a three-branch architecture with cross-attention and distillation, which learns domain-invariant representations from multiple domains. Besides, we introduce two alignment modules—one for feature alignment and one for classifier alignment—to improve the alignment process. Extensive experiments conducted on three public mammographic datasets demonstrate that our BTMuda outperforms state-of-the-art methods.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"313-324"},"PeriodicalIF":4.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663460","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553042","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

A Method to Locate Radio-Frequency Coils Using a CT-Based Template for a More Accurate Photon Attenuation Correction in PET/MRI 一种基于ct模板定位射频线圈的方法，用于PET/MRI中更精确的光子衰减校正

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-08-28 DOI: 10.1109/TRPMS.2024.3450833

Emily Anaya;Paul Schleyer;Craig Levin

{"title":"A Method to Locate Radio-Frequency Coils Using a CT-Based Template for a More Accurate Photon Attenuation Correction in PET/MRI","authors":"Emily Anaya;Paul Schleyer;Craig Levin","doi":"10.1109/TRPMS.2024.3450833","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3450833","url":null,"abstract":"In simultaneous positron emission tomography and magnetic resonance (PET/MR) imaging, MR radio-frequency (RF) coils are placed on the top of the patient to receive the MR signal. These coils can produce an undesirable photon attenuation of the PET signal by as much as 17% in certain local regions of a reconstructed PET cylindrical phantom. Currently, photon attenuation of RF body coils is not typically accounted for in the attenuation correction (AC) procedure in commercial PET/MR systems. To correct for this coil attenuation, the position of the coils and their most attenuating components, such as the preamplifier housings must be accurately determined. This work proposes a simple and effective solution to this problem by using three optical cameras placed just outside the field-of-view (FOV) of the PET/MR system. The cameras are used to determine the positions of markers attached to the RF coils. An average marker location error of 7.7 mm was achieved over eight markers placed on a flexible RF coil draped over a cylindrical PET phantom. Quantification of reconstructed PET signal error due to inaccurate assessment of flexible RF coil location on a phantom is presented. Given the coil location accuracy of this method, the PET signal attenuation error is reduced from 17% to less than 3%. Our method can also be extended to correct for other attenuating objects in the FOV of the PET/MR system.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"182-190"},"PeriodicalIF":4.6,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106269","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

4-D Cone-Beam CT Reconstruction via Diffusion Model and Motion Compensation 基于扩散模型和运动补偿的四维锥束CT重建

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-08-23 DOI: 10.1109/TRPMS.2024.3449155

Xianghong Wang;Zhengwei Ou;Peng Jin;Jiayi Xie;Ze Teng;Lei Xu;Jichen Du;Mingchao Ding;Yang Chen;Tianye Niu

{"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}

引用次数: 0

By Any Other Name: Searching for the Right Plasma Nomenclature 任何其他名称：寻找正确的等离子体命名法

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-08-21 DOI: 10.1109/TRPMS.2024.3447551

Caroline Corcoran;Rachel Bennett;Vandana Miller;Fred Krebs;Will Dampier

{"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}

引用次数: 0

Non-Negative Matrix Factorization Using Partial Prior Knowledge for Radiation Dosimetry 基于部分先验知识的非负矩阵分解在辐射剂量测定中的应用

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-08-16 DOI: 10.1109/TRPMS.2024.3442773

Boby Lessard;Frédéric Marcotte;Arthur Lalonde;François Therriault-Proulx;Simon Lambert-Girard;Luc Beaulieu;Louis Archambault

{"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}

引用次数: 0

An FPGA-Based 64-Channel Readout Electronics for High-Resolution TOF-PET Detectors 基于fpga的64通道读出电子器件用于高分辨率TOF-PET探测器

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-08-16 DOI: 10.1109/TRPMS.2024.3443831

Xiang Zhang;Yonggang Wang;Mingchen Wang;Xiaoguang Kong

{"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}

引用次数: 0