IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"A Silicon-Pixel Paradigm for PET","authors":"Aleix Boquet-Pujadas;Jihad Saidi;Mateus Vicente;Lorenzo Paolozzi;Jonathan Dong;Pol Del Aguila Pla;Giuseppe Iacobucci;Michael Unser","doi":"10.1109/TRPMS.2024.3456241","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3456241","url":null,"abstract":"Positron emission tomography (PET) scanners use scintillation crystals to stop high-energy photons. The ensuing lower-energy photons are then detected via photomultipliers. We study the performance of a stack of monolithic silicon-pixel detectors as an alternative to the combination of crystals and photomultipliers. The resulting design allows for pitches as small as <inline-formula> <tex-math>$100~ {mu }$ </tex-math></inline-formula>m and greatly mitigates depth-of-interaction problems. We develop a theory to optimize the sensitivity of these and other scanners under design constraints. The insight is complemented by Monte Carlo simulations and reconstructions thereof. Experiments and theory alike suggest that our approach has the potential to move PET closer to the microscopic scale. The volumetric resolution is an order of magnitude better than that of the state of the art and the parallax error is very small. A small-animal scanner is now under construction.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"228-246"},"PeriodicalIF":4.6,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106262","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.2024.3449313","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3449313","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143580","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":"Investigating the Role of Active Air Ions and Hydroxyl Radicals on the Eradication of ESKAPE Bacteria Using Non-Equilibrium Atmospheric Air Cold Plasma","authors":"Ramavtar Jangra;Kiran Ahlawat;Ram Prakash","doi":"10.1109/TRPMS.2024.3454542","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3454542","url":null,"abstract":"Multidrug-resistant bacteria are becoming more common in clinical settings and are posing an increasing threat to hospitals across the globe. Considering their prevalent antibiotic resistance, the ESKAPE bacteria (i.e., Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are becoming a serious threat to public health and are the often cause of nosocomial infections. This study uses an in-house-developed non-equilibrium atmospheric air cold plasma (NE-AACP) source to eradicate ESKAPE bacteria in an enclosed environment. The antimicrobial properties of NE-AACP source arise from the generation of negative and positive air ions (in the range of 300–<inline-formula> <tex-math>$1.5times 10{^{{5}}}$ </tex-math></inline-formula> ions/cc) and on-site generated hydroxyl radicals (in the range of 15–<inline-formula> <tex-math>$75~mu $ </tex-math></inline-formula>M). These quasi-static equilibrium charges disrupt bacterial cell structures and metabolic processes. The ozone concentration generated from the NE-AACP source has also been measured and found to be 0.13 ppm, which is very low for bactericidal applications. The on-site generation of hydroxyl radicals is due to the plasma-produced highly energetic electrons (3–5 eV) and coating of <inline-formula> <tex-math>${mathrm { TiO}}_{2}$ </tex-math></inline-formula> nanoparticle catalysts onto one of the electrodes of the NE-AACP source. In 60 min of treatment, more than 99.9% ESKAPE bacterial inactivation has been achieved in an enclosed environment of <inline-formula> <tex-math>$sim ~28.3$ </tex-math></inline-formula> m3. This work elucidates the mechanism of cold plasma-induced bacterial inactivation and highlights its potential as a viable strategy against infections that are resistant to antibiotics. These findings have implications for infection control in healthcare facilities and other settings where ESKAPE bacteria provide a health concern to the general public.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"515-527"},"PeriodicalIF":4.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761400","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":"IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors","authors":"","doi":"10.1109/TRPMS.2024.3449311","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3449311","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"C2-C2"},"PeriodicalIF":4.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669129","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143652","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.2024.3453689","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3453689","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"850-850"},"PeriodicalIF":4.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143609","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 DataPort","authors":"","doi":"10.1109/TRPMS.2024.3453691","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3453691","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"851-851"},"PeriodicalIF":4.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143678","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":"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}

{"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}

{"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}

{"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}