IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"TlCl:Be,I: A High Sensitivity Scintillation and Cherenkov Radiator for TOF-PET","authors":"Nicolaus Kratochwil;Nathaniel Kaneshige;Giulia Terragni;Roberto Cala;Jared Schott;Edgar van Loef;Lakshmi Soundara Pandian;Emilie Roncali;Jaroslaw Glodo;Etiennette Auffray;Gerard Ariño-Estrada","doi":"10.1109/TRPMS.2024.3487359","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3487359","url":null,"abstract":"The material requirements for gamma-ray detectors for medical imaging applications are multifold and sensitivity is often overlooked. High effective atomic number (Z<inline-formula> <tex-math>$_{text {eff}}$ </tex-math></inline-formula>) Cherenkov radiators have raised the attention in the community due to their potential for harvesting prompt photons. A material with one of the highest Zeff and thus short gamma-ray attenuation length is thallium chloride (TlCl). By doping TlCl with beryllium (Be) or iodine (I), it becomes a scintillator and therefore produces scintillation photons upon gamma-ray interaction on the top of the prompt Cherenkov luminescence. The scintillation response of TlCl:Be,I is investigated in terms of intensity, energy resolution, kinetics, and timing capability with and without energy discrimination. The ratio of prompt to slow scintillation photons is used to derive the intrinsic number of produced Cherenkov photons and compared with analytic calculations avoiding complex Monte Carlo simulations. The experimentally determined number of Cherenkov photons upon 511 keV gamma excitation of <inline-formula> <tex-math>$17.9~pm ~4.6$ </tex-math></inline-formula> photons is in line with our simple calculations yielding 14.5 photons. We observe three scintillation decay time components with an effective decay time of 60 ns. The scintillation light yield of 0.9 ph/keV is sufficient to discriminate events with low energy deposition in the crystal which is used to improve the measured coincidence time resolution from 360-ps FWHM without energy selection down to 235-ps after energy discrimination and time walk correction for 2.8-mm thick TlCl:Be,I crystals, and from 580 to 402 ps for 15.2-mm thick ones. Already with the first generation of doped TlCl encouraging timing capability close to other materials with lower effective atomic number has been achieved.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"296-303"},"PeriodicalIF":4.6,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10740386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553121","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 New Ensemble Transfer Learning Approach With Rejection Mechanism for Tuberculosis Disease Detection","authors":"Seng Hansun;Ahmadreza Argha;Hamid Alinejad-Rokny;Roohallah Alizadehsani;Juan M. Gorriz;Siaw-Teng Liaw;Branko G. Celler;Guy B. Marks","doi":"10.1109/TRPMS.2024.3474708","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3474708","url":null,"abstract":"Transfer learning (TL) is a strategic solution to handle vast data volume requirements in deep learning (DL). It transfers knowledge learned from a large base dataset, as a pretrained model (PTM), to a new domain. In this study, we introduce an ensemble of classifiers trained on features extracted from some intermediate layers of a PTM for Tuberculosis (TB) detection task. We use different EfficientNet variants: EfficientNet-B0–EfficientNet-B3, as the PTM. Moreover, we introduce a rejection mechanism and implement post-hoc calibration methods to enhance the reliability and trustworthiness of the developed models. Additionally, we conduct analyses on domain-shift distribution, a topic rarely discussed in the context of TB detection. Through a fivefold cross-validation on two prominent chest X-ray datasets, the Montgomery County (MC) and Shenzhen (SZ), our ensemble approach achieved competitive results with accuracies of 94.89% (MC) and 92.75% (SZ). The incorporation of the devised rejection mechanism resulted in enhanced model accuracy, albeit with a coverage tradeoff. In domain-shift experiments, the proposed approach achieved an accuracy of 83.57% (63% coverage) when applying the MC-trained model on SZ, and an accuracy of 88.50% (82% coverage) when applying the SZ-trained model on MC.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"433-446"},"PeriodicalIF":4.6,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761568","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":"Semi-Supervised 3-D Medical Image Segmentation Using Multiconsistency Learning With Fuzzy Perception-Guided Target Selection","authors":"Tao Lei;Wenbiao Song;Weichuan Zhang;Xiaogang Du;Chenxia Li;Lifeng He;Asoke K. Nandi","doi":"10.1109/TRPMS.2024.3473929","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3473929","url":null,"abstract":"Semi-supervised learning methods based on the mean teacher model have achieved great success in the field of 3-D medical image segmentation. However, most of the existing methods provide auxiliary supervised signals only for reliable regions, but ignore the effect of fuzzy regions from unlabeled data during the process of consistency learning, which results in the loss of more valuable information. Besides, some of these methods only employ multitask learning to improve models’ performance, but ignore the role of consistency learning between tasks and models, thereby weakening geometric shape constraints. To address the above issues, in this article, we propose a semi-supervised 3-D medical image segmentation framework with multiconsistency learning for fuzzy perception-guided target selection. First, we design a fuzzy perception-guided target selection strategy from multiple perspectives and adopt the fusion method of fuzziness minimization and the fuzzy map momentum update to obtain a fuzzy region. By incorporating the fuzzy region into consistency learning, our model can effectively exploit more useful information from the fuzzy region of unlabeled data. Second, we design a multiconsistency learning strategy that employs intratask and intermodal mutual consistency learning as well as cross-model cross-task consistency learning to effectively learn the shape representation of fuzzy regions. The strategy can encourage the model to agree on predictions for different tasks in fuzzy regions. Experiments demonstrate that the proposed framework outperforms the current mainstream methods on two popular 3-D medical datasets, the left atrium segmentation dataset, and the brain tumor segmentation dataset. The code 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 4","pages":"421-432"},"PeriodicalIF":4.6,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761566","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":"Toward a Second Generation of Metascintillators Using the Purcell Effect","authors":"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","doi":"10.1109/TRPMS.2024.3471251","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3471251","url":null,"abstract":"This study focuses on advancing metascintillators to break the 100 ps barrier and approach the 10 ps target. We exploitnanophotonic features, specifically the Purcell effect, to shape and enhance the scintillation properties of the first-generation metascintillator. We demonstrate that a faster emission is achievable along with a more efficient conversionefficiency. This results in a coincidence time resolution improved by a factor of 1.3, crucial for TOF-PET applications.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"141-147"},"PeriodicalIF":4.6,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704688","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106284","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":"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}

{"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}

{"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}

{"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}

{"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}

{"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}