IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"IEEE DataPort","authors":"","doi":"10.1109/TRPMS.2026.3674616","DOIUrl":"https://doi.org/10.1109/TRPMS.2026.3674616","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"640-640"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11472784","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588292","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.2026.3674559","DOIUrl":"https://doi.org/10.1109/TRPMS.2026.3674559","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"C2-C2"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11472783","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588294","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":"YRT-PET: An Open-Source GPU-Accelerated Image Reconstruction Engine for Positron Emission Tomography","authors":"Yassir Najmaoui;Yanis Chemli;Maxime Toussaint;Yoann Petibon;Baptiste Marty;Kathryn Fontaine;Jean-Dominique Gallezot;Gašper Razdevšek;Matic Orehar;Maeva Dhaynaut;Nicolas Guehl;Rok Dolenec;Rok Pestotnik;Keith Johnson;Jinsong Ouyang;Marc Normandin;Marc-André Tétrault;Roger Lecomte;Georges El Fakhri;Thibault Marin","doi":"10.1109/TRPMS.2025.3619872","DOIUrl":"10.1109/TRPMS.2025.3619872","url":null,"abstract":"Image reconstruction for positron emission tomography (PET) requires an accurate model of the PET scanner geometry and degrading factors to produce high-quality and clinically meaningful images. It is typically implemented by scanner manufacturers, with proprietary software designed specifically for each scanner. This limits the ability to perform direct comparisons between scanners or to develop advanced image reconstruction algorithms. Open-source image reconstruction software can offer an alternative to manufacturer implementations, allowing more control and portability. Several existing software packages offer a wide range of features and interfaces, but there is still a need for an engine that simultaneously offers reusable code, fast implementation and convenient interfaces for interoperability and extensibility. In this work, we introduce Yale reconstruction toolkit for positron emission tomography, an open-source toolkit for PET image reconstruction that aims for flexibility, reproducibility, speed, and interoperability with existing research software. The toolkit is implemented in C++ with CUDA-enabled graphics processing unit (GPU) acceleration, relies on a plugin system to facilitate the use with multiple scanners, and offers Python bindings to enable the development of advanced algorithms. It includes support for list-mode/histogram data formats, multiple PET projectors, incorporation of time-of-flight (TOF) information, event-by-event rigid motion correction, point spread function modeling. It can incorporate correction factors, such as normalization, randoms and scatter, obtained from scanner-specific plugins or provided by the user. The toolkit also includes an experimental module for scatter estimation without TOF. To evaluate the capabilities of the software, two different scanners in four different contexts were tested: dynamic imaging, motion correction, deep image prior, and reconstruction for a limited-angle scanner geometry with TOF. Comparisons with existing tools demonstrated good agreement in image quality and the effectiveness of the correction methods. The proposed software toolkit offers high versatility and potential for research, including the development of novel reconstruction algorithms and new PET scanner systems.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"535-546"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145805973","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":"First Positronium Imaging Using 44Sc With the J-PET Scanner: A Case Study on the NEMA-Image Quality Phantom","authors":"Manish Das;Sushil Sharma;Ermias Yitayew Beyene;Aleksander Bilewicz;Jarosław Choiński;Neha Chug;Catalina Curceanu;Eryk Czerwiński;Kavya Valsan Eliyan;Jakub Hajduga;Sharareh Jalali;Krzysztof Kacprzak;Tevfik Kaplanoglu;Łukasz Kapłon;Kamila Kasperska;Aleksander Khreptak;Grzegorz Korcyl;Tomasz Kozik;Karol Kubat;Deepak Kumar;Anoop Kunimmal Venadan;Edward Lisowski;Filip Lisowski;Justyna Medrala-Sowa;Simbarashe Moyo;Wiktor Mryka;Szymon Niedźwiecki;Piyush Pandey;Szymon Parzych;Alessio Porcelli;Bartłomiej Rachwal;Elena Perez del Rio;Martin Rädler;Axel Rominger;Kuangyu Shi;Magdalena Skurzok;Anna Stolarz;Tomasz Szumlak;Pooja Tanty;Keyvan Tayefi Ardebili;Satyam Tiwari;Rafał Walczak;Ewa Ł. Stepien;Paweł Moskal","doi":"10.1109/TRPMS.2025.3621554","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3621554","url":null,"abstract":"Positronium lifetime imaging (PLI), an emerging extension of conventional positron emission tomography (PET) imaging, offers a novel window for probing the submolecular properties of biological tissues by imaging the mean lifetime of the positronium atom. Currently, the method is under rapid development in terms of reconstruction and detection systems. Recently, the first in vivo positronium lifetime imaging (PLI) of the human brain was performed using the J-PET scanner utilizing the 68Ga isotope. However, this isotope has limitations due to its comparatively low prompt gamma yields, which is crucial for positronium lifetime measurement. Among alternative radionuclides, 44Sc stands out as a promising isotope for PLI, characterized by a clinically suitable half-life (4.04 h) emitting 1157 keV prompt gamma in 100% cases after the emission of the positron. This study reports the first experimental demonstration of PLI with 44Sc, carried out on a NEMA-image quality (IQ) phantom using the Modular J-PET tomograph—the first plastic scintillators-based PET scanner.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"593-602"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11203778","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588215","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":"Multi-Input Interaction Positioning and Signal Demultiplexing With Deep Learning in Semi-Monolithic Detectors","authors":"Francis Loignon-Houle;Fiammetta Pagano;Antonio J. Gonzalez","doi":"10.1109/TRPMS.2025.3613981","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3613981","url":null,"abstract":"Deep learning is increasingly transforming medical imaging by enabling more accurate data interpretation and reconstruction from complex detector signals. In positron emission tomography (PET), accurate localization of photon interactions within detectors is crucial for improving image resolution and diagnostic value. This work focuses on semi-monolithic scintillation detectors, which balance pixelated and monolithic designs, and applies deep learning methods that exploit scintillation light patterns across photosensors to improve interaction positioning. We evaluated how different neural network architectures and combinations of input signals affect positioning accuracy using experimental data from two types of detector arrays: 1) a <inline-formula> <tex-math>$1times 8$ </tex-math></inline-formula> module used in the IMAS total-body scanner and 2) a <inline-formula> <tex-math>$1times 16$ </tex-math></inline-formula> module from a brain-dedicated PET, both coupled to 64-channel photosensor matrices. We compared multilayer perceptron and convolutional neural network, using either reduced 16-channel inputs (obtained via row and column summation) or the full 64-channel configuration, along with energy, time, and engineered features. Results show that deeper architectures and richer inputs improve positioning performance—especially in the depth-of-interaction direction, with gains of around 20%—by better exploiting light-sharing effects between slabs. Finally, we introduced a deep-learning-based signal demultiplexing approach which accurately reconstructs full 64-channel signals from reduced 16-channel measurements with structural similarity index measure above 0.98. This enables the combination of simplified hardware design crucial for data throughput together with the benefits of higher resolution positioning when using 64 signals. This work shows how deep learning, when combined with multiple signal inputs and the learned recovery of full signals from multiplexed data, can enhance the performance of PET instrumentation.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"547-557"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11177553","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588277","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":"Active Eye Lens Dosimetry With Dosepix: Influence of Measurement Position and Lead Glass Shielding","authors":"Leonie Ullmann;Florian Beißer;Rolf Behrens;Stefan Funk;Gerhard Hilgers;Oliver Hupe;Jürgen Roth;Tom Tröltzsch;Hayo Zutz;Thilo Michel","doi":"10.1109/TRPMS.2025.3609894","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3609894","url":null,"abstract":"In this work, the effect of the measurement position on measurements of Hp(3) of a new active eye lens dosemeter prototype (ELD) based on the Dosepix detector is examined. A comparison between measuring directly in front of the eye and measuring at the side of the head of an Alderson phantom showed no significant influence on the resulting Hp(3) for different radiation qualities and angles. In addition, to account for the absorption effect of radiation safety glasses, pieces of lead glass were attached to the front and side of the dosemeter. The challenging geometry of the human face, when additionally equipped with absorbing glasses and irradiated at different angles of radiation incidence, led to a complex irradiation setup, whereby the dosemeter cannot be placed at the real point of interest, i.e., directly in front of the eye. Corresponding effects and consequences for radiation protection measurements have been investigated by using a human like Alderson head phantom as well as thermoluminescent dosemeters (TLDs) and the active ELD based on the Dosepix detector. For specific angles, the radiation bypassed the radiation safety glasses and lead glass pieces, leading to an increase in the measured Hp(3). Compared to TLDs behind radiation safety glasses, measurements with the lead glass shielded prototype resulted in lower values for Hp(3). Furthermore, the results did not reproduce previous findings where larger dose values were found for Dosepix behind lead glass pieces than for the TLDs behind radiation safety glasses. A possible reason might be that the dimensions of the lead glass pieces are not representative of the radiation safety glasses in front of the eye but, ultimately, it is not yet clear what the main reason for the deviation is. Therefore, it is advisable to test the same methodology in future investigations with other eyewear models and lead glass pieces to investigate whether similar behaviors occur.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"619-628"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588263","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":"Effects of Short- and Long-Lived ROS Generated by a Cold Atmospheric Plasma Jet on Human Xenograft A2058 Melanoma and Associated Side Effects in a Nude Mouse Model","authors":"Tung Ku;Chia-Hung Dylan Tsai;Chao-Yu Chen;Ivan Gomez Ho;Chih-Tung Liu;Ru-Tsun Mai;Yun-Chien Cheng","doi":"10.1109/TRPMS.2025.3597795","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3597795","url":null,"abstract":"This study evaluated the therapeutic efficacy of long-lived and short-lived reactive oxygen species (ROS) generated by argon cold atmospheric plasma (Ar-CAP) under water-soaking conditions in a murine model of treating cutaneous melanoma. Potential side effects associated with Ar-CAP treatment in mice were also assessed. While direct cold atmospheric plasma (CAP) treatment and indirect treatment via injection of plasma-treated solution have been reported in previous studies, the distinct modalities employed have made it challenging to delineate the specific contributions of long-lived and short-lived ROS in vivo. In this study, direct and indirect CAP treatments were applied to melanoma xenografts under comparable conditions. Both modalities effectively inhibited tumor progression, with direct treatment leading to a more pronounced tumor volume reduction in the early stages. Over a six-month observation of CAP-treated nude mice, no significant adverse effects were noted in pain response, skin appearance, blood cell counts, or histopathological evaluation, aside from slight elevations in liver enzymes (AST and ALT). These results suggest that argon CAP (Ar-CAP) treatment is both effective and well-tolerated, with short-lived ROS playing a key role in enhancing antitumor activity.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"629-639"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588275","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":"Alleviating Low-Level Information Loss in Multiscale Blocks for Effective Posterior Extraocular Muscles Segmentation","authors":"Sanghyuck Lee;Mingi Kim;Haesung Oh;Jeong Kyu Lee;Jaesung Lee","doi":"10.1109/TRPMS.2025.3579362","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3579362","url":null,"abstract":"Accurate segmentation of the posterior extraocular muscles is important for diagnosing thyroid-associated orbitopathy. Given that this region occupies a small area in computed tomography (CT) images, effectively leveraging low-level information within high-resolution feature maps is crucial. However, such information is often lost in conventional networks because of the excessive expansion of the receptive field in multiscale blocks, leading to less accurate pixel-wise classification. To address these challenges, we propose a layer-wise residual-learning approach for multiscale blocks. The residual connections are integrated between each layer within the stacked convolutional layers of the multiscale block, thereby preserving low-level information and enhancing the high-level feature extraction for small objects. The proposed model was evaluated using CT images from 232 patients at Chung-Ang University Hospital (2009–2022) and compared with 11 state-of-the-art networks. Four datasets corresponding to the medial, lateral, superior, and inferior rectus muscles of the left eye, using six measures, were used for this evaluation. The results showed that the proposed model achieved the highest performance in 15 of the 24 cases and the second-highest performance in eight cases. Our ablation study and qualitative analysis demonstrate the efficacy of the proposed model in segmenting small muscle regions.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"474-491"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588286","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":"Development and Initial Evaluation of 3D-Printed High Resolution Brain Phantom for PET","authors":"Cynthia Lo;Ekaterina Shanina;Paul Gravel;Yifan Hu;Steven A. Lucero;Sean Martins;Tim Mulnix;Kathryn Fontaine;Seyed Faraz Nejati;Xishan Sun;Hongdi Li;Jinyi Qi;Simon R. Cherry;Richard E. Carson;Takuya Toyonaga","doi":"10.1109/TRPMS.2025.3638588","DOIUrl":"10.1109/TRPMS.2025.3638588","url":null,"abstract":"Phantoms are essential for assessing positron emission tomography (PET) system performance, but most existing brain phantoms do not provide the anatomical fidelity required for the next-generation high-resolution PET systems. To address this limitation, we developed HYDRA-OL (HYDrodynamically filled Realistic Anatomy of the Occipital Lobe) phantoms using high-resolution 3-D printing based on the BigBrain atlas. In this study, two phantom types were created, HYDRA-OLG (gray matter (GM) only) and HYDRA-OLGW (gray and white matter (WM)), enabling contrast modulation between GM and WM. Precise anatomical fidelity and repositioning accuracy were confirmed via CT. A custom closed-loop filling apparatus ensured efficient and reproducible phantom preparation. Residual air in the phantom after 15 min was estimated to be less than 0.01% of the fillable space. To compare spatial resolution between scanners, the HYDRA-OLG phantom was imaged on four different PET systems (NeuroEXPLORER (NX), high resolution research tomograph (HRRT), Vision, and Focus220), with NX and Focus showing superior spatial resolution compared to the other two systems. To generate variable GM:WM contrast, list mode datasets obtained on the NX for both HYDRA-OLG and HYDRA-OLGW phantoms were down-sampled and merged to form one list mode data. Reconstructed images were successfully produced with GM:WM contrast ratio of 4:1, 2:1, and 4:3. Overall, the HYDRA phantom platform is a versatile tool for accurately representing human brain anatomy. It is likely to be more useful than previous phantoms for evaluating image resolution and optimizing reconstruction parameters for next-generation brain PET systems.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"603-610"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145828742","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":"ResPF: Residual Poisson Flow Generative Model for Efficient and Physically Consistent Sparse-View CT Reconstruction","authors":"Changsheng Fang;Yongtong Liu;Bahareh Morovati;Shuo Han;Yu Shi;Li Zhou;Shuyi Fan;Hengyong Yu","doi":"10.1109/TRPMS.2025.3615836","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3615836","url":null,"abstract":"Sparse-view computed tomography (CT) reduces radiation dose but leads to an ill-posed inverse problem that challenges accurate reconstruction. While deep learning and diffusion-based methods have shown promising results, they often lack physical consistency or suffer from high-computational cost due to iterative sampling from random noise. Recent advances in generative modeling, especially poisson flow generative models (PFGMs), offer high-fidelity synthesis by modeling the full data distribution via deterministic ordinary differential equation (ODE) trajectories. In this work, we propose residual poisson flow (ResPF), the first application of PFGM++ to sparse-view CT reconstruction. ResPF introduces the four key innovations: 1) a conditional PFGM++ trained on paired sparse/full-view data; 2) a hijacking strategy that skips early sampling steps to accelerate convergence; 3) a data-consistency module embedded at each iteration to enforce fidelity to measured projections; and 4) to preserve the stability of the generative path, we further propose a residual fusion mechanism to combine generative outputs with physics-consistent updates. Extensive experiments on synthetic and clinical datasets demonstrate that ResPF achieves state-of-the-art performance, reaching 0.964 structural similarity (SSIM) and 39.8 dB peak signal-to-noise ratio (PSNR) on 63-view reconstructions (from 1000-view groundtruth), significantly outperforming existing baselines. Moreover, ResPF reduces sampling time to just 1.5 s per image that is over <inline-formula> <tex-math>$15{times }$ </tex-math></inline-formula> faster than conventional diffusion-based methods, highlighting its practicality for real-world applications.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"520-534"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588230","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}