IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"Semi-Supervised Medical Lesion Image Segmentation Based on a Contrast-Guided Diffusion Model","authors":"Chunyuan Liu;Tongyuan Huang;Yunze He;Huayu Chen;Zipeng Wu;Yihan Yang","doi":"10.1109/TRPMS.2025.3560267","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3560267","url":null,"abstract":"Medical lesion segmentation plays a crucial role in computer-aided diagnosis, yet acquiring fully annotated images remains a significant challenge. Semi-supervised learning has shown great potential in scenarios with limited labeled data. However, pseudo-labels, commonly used for unlabeled data, may adversely affect model performance due to their inherent inaccuracies. To address this issue, we propose a semi-supervised lesion segmentation framework based on a contrast-guided diffusion model (CGDM). To mitigate the impact of inaccurate pseudo-labels, we exploit the contrastive relationship between lesion and healthy images, restoring lesion regions to a healthy-like appearance. By directly incorporating this contrastive semantic information during training, we alleviate the model’s over-reliance on pseudo-labels and mitigate its detrimental effects on model performance. Furthermore, we introduce a structural similarity contrast (SSC) loss function to balance supervised and unsupervised learning. This function constructs sample pairs for contrastive learning, maximizing the disparity between paired lesion and healthy images while minimizing the resemblance of lesion regions in unpaired lesion images. Experimental results on the BUSI, BraTS2018, and KiTS19 datasets demonstrate that CGDM achieves superior performance compared to state-of-the-art semi-supervised segmentation methods.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1036-1050"},"PeriodicalIF":3.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435710","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":"The Alpha-SPECT-Mini: A Small-Animal SPECT System Based on Hyperspectral Compound-Eye Gamma Cameras","authors":"Elena Maria Zannoni;Can Yang;Ling Cai;Matthew D. Wilson;Chin-Tu Chen;Ling-Jian Meng","doi":"10.1109/TRPMS.2025.3560558","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3560558","url":null,"abstract":"There is a rising interest in single-photon emission computed tomography (SPECT) imaging systems with improved energy resolution to facilitate multifunctional molecular imaging applications, such as alpha-emitter radiopharmaceutical therapy (<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>-RPT). In this article, we report the design and evaluation of the Alpha-SPECT-Mini system that offers an ultrahigh energy resolution and high sensitivity for small animal studies. The Alpha-SPECT-Mini system is constructed based on small-pixel CdTe detectors that offers sub-1-keV full-width-half-maximum (FWHM) energy resolution for single pixel events and an average ~2.5-keV energy resolution at 122 keV and ~3.5 keV at 218 keV over 153 600 pixels in the system. This allows to easily identify X- and gamma-ray contributions in densely populated spectra, such as from the Ac-225 decay chain. The system uses a 96-loft-hole collimator and six stationary detection panels in a full ring geometry. Finally, the system performance is demonstrated using Tc-99m- and Ac-225-filled resolution and image quality (IQ) phantoms. We have experimentally demonstrated that the Alpha-SPECT-Mini is a high-performance imaging system capable of imaging alpha-emitters in preclinical applications.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1107-1117"},"PeriodicalIF":3.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435706","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":"Submillimeter Pixelated SPECT Detector Using GAGG:Ce and Light Guide With Optical Barrier Slits","authors":"Zerui Yu;Zhenlei Lyu;Peng Fan;Jing Wu;Yaqiang Liu;Tianyu Ma","doi":"10.1109/TRPMS.2025.3559095","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3559095","url":null,"abstract":"In nuclear medicine imaging systems, intrinsic spatial resolution of the detector is one of the most important performance metrics. In this work, we aim to develop a high-resolution single photon emission computed tomography (SPECT) detector using pixelated Ce-doped gadolinium aluminum gallium garnet (GAGG:Ce) scintillators and silicon photomultiplier (SiPM) arrays. Special attention is paid to improving the resolving capability of edge crystals. We propose to place optical barrier (OB) slits onto the light guide that enhances the difference in light distribution for edge crystals. We experimentally optimize OB designs for two scintillator arrays, named as Array-ESR and Array-BaSO4, which uses enhanced specular reflector (ESR) film and barium sulfate (BaSO4) as the reflectors, respectively. Both arrays have <inline-formula> <tex-math>$31times 31~0$ </tex-math></inline-formula>.8 mm <inline-formula> <tex-math>$times 0$ </tex-math></inline-formula>.8 mm <inline-formula> <tex-math>$times $ </tex-math></inline-formula> 6 mm GAGG:Ce crystals. We introduce the flood map quality (FMQ) parameter to assess the separation of responses of neighboring crystals. The results demonstrate that for Array-ESR, an optimal light guide with two 7° OB slits and two 11° OB slits resolves 92.40% crystals with an energy resolution of 13.19% <inline-formula> <tex-math>$pm ~0.68$ </tex-math></inline-formula>%. The FMQ is <inline-formula> <tex-math>$1.52~pm ~0.38$ </tex-math></inline-formula>. For Array-BaSO4, the optimal design is a light guide with four 7° OB slits. 98.75% crystals are resolvable with an energy resolution of 15.33% <inline-formula> <tex-math>$pm ~0.96$ </tex-math></inline-formula>% and FMQ parameter of <inline-formula> <tex-math>$1.81~pm ~0.45$ </tex-math></inline-formula>. Overall, Array-BaSO4 is more suitable for building SPECT detector for its good crystal resolving performance and fabrication convenience. This study proposes a practical submillimeter pixelated SPECT detector design with no detection dead space and compact electronics. It is promising for being used to build large-scale detectors for high resolution SPECT systems.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1015-1024"},"PeriodicalIF":3.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435702","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 Performance Evaluation of a Benchtop Small-Animal PET/MRI Scanner","authors":"Xin Yu;Zhijun Zhao;Han Liu;Da Liang;Wenjing Zhu;Ying Lin;Jiayang Zeng;Chenxuan Liu;Jianfeng Xu;Siwei Xie;Weimin Wang;Qiyu Peng","doi":"10.1109/TRPMS.2025.3557789","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3557789","url":null,"abstract":"This study aims to develop a compact, low-cost, and high-performance benchtop small-animal PET/MRI scanner that achieves functional and anatomical image fusion. The system is designed to address challenges in cost reduction, spatial resolution, sensitivity, image quality (IQ), and quantitative accuracy. The PET/MRI system was developed with a parallel configuration, integrating a custom-designed PET scanner and a 0.5-T permanent magnet MRI system. Quantitative assessments included spatial resolution, sensitivity, IQ, and quantitative accuracy, as well as signal-to-noise ratio (SNR), geometric distortion (GD), and image uniformity (IU) for MRI. The spatial resolution at the axial center is 1.31 (axial), 1.26 (radial), and 1.22 mm (tangential), with a center sensitivity of 8.05% under a wide energy window. Image quality (IQ) tests using an IQ phantom demonstrated a uniformity of 10.08% standard deviation, recovery coefficients (RC) ranging from 0.23 to 0.96, and spill-over ratios (SOR) of 0.08 and 0.18 in air and water regions, respectively. The MRI system achieved an SNR of 14.16 in phantom tests, a GD of less than 1%, and IU of 90.13%. Fusion imaging of PET and MRI demonstrated high registration accuracy in both phantom and mouse studies, with complementary functional and anatomical information. The proposed PET/MRI system achieves high spatial resolution, sensitivity, IQ, and quantitative accuracy while maintaining a simple, low-cost design. The parallel configuration facilitates precise PET/MRI image fusion and allows for efficient multianimal imaging. The results highlight the potential of this system for preclinical research and its feasibility for future in-vehicle imaging applications. Further optimization of the MRI system and data transmission methods will enhance its performance in high-activity studies and broaden its application scope, with potential applications in preclinical research and in-vehicle imaging.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1118-1126"},"PeriodicalIF":3.5,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435709","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":"Proton Range Verification Realized via a Multislit Prompt Gamma Imaging System","authors":"Hongyang Zhang;Bo Zhao;Peng Fan;Shi Wang;Wenzhuo Lu;Yancheng Yu;Zhaoxia Wu;Tianyu Ma;Hui Liu;Yaqiang Liu","doi":"10.1109/TRPMS.2025.3553133","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3553133","url":null,"abstract":"Proton therapy is one of the most advanced radiotherapy techniques. Despite its advantages in dose delivery, it has not yet achieved significant clinical benefits for patients due to uncertainties in proton range. Accurate, real-time monitoring of proton dose and range is crucial for ensuring the precision of proton therapy. In prior work, a dual-head prompt gamma imaging system was proposed and evaluated through Monte Carlo simulations, demonstrating high spatial resolution and sufficient detection efficiency for proton pencil beam imaging at clinical doses. This study focuses on the assembly, calibration, and testing of one of the detectors in this system. Spatial resolution and detection efficiency were evaluated using a 22Na point source, while range shift detection and accuracy were assessed with 60 and 100 MeV proton beams under low proton count conditions. The single-head system achieved a detection efficiency of 0.22% and a full-width at half-maximum (FWHM) spatial resolution of 2.8 mm at the center of the field of view (FOV). The system was able to detect a 1 mm range shift by identifying the most distal edge position (MDEP) of the prompt gamma profile. The detector demonstrated a range accuracy of less than 1 mm at typical count levels for a single spot in proton pencil beam scanning. The results suggest that this system performs well in terms of both detection efficiency and spatial resolution, and the system could achieve real-time range verification with high accuracy.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1127-1134"},"PeriodicalIF":3.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435694","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":">Member Get-a-Member (MGM) Program","authors":"","doi":"10.1109/TRPMS.2025.3552178","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3552178","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"529-529"},"PeriodicalIF":4.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761356","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.2025.3552176","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3552176","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"528-528"},"PeriodicalIF":4.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947674","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761466","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.2025.3552150","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3552150","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"C2-C2"},"PeriodicalIF":4.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947672","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761439","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 Information for Authors","authors":"","doi":"10.1109/TRPMS.2025.3552148","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3552148","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761399","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 Unified CT Reconstruction: Federated Metadata Learning With Personalized Condition-Modulated iRadonMAP","authors":"Hao Wang;Mingqiang Li;Shixuan Chen;Mingqiang Meng;Ji He;Jianhua Ma;Dong Zeng","doi":"10.1109/TRPMS.2025.3574209","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3574209","url":null,"abstract":"Recent advances in deep-learning-based methods have shown great potential in improving low-dose CT image quality. Meanwhile, these methods are constructed based on a large, centralized, and diverse CT dataset from multiple institutions that is difficult to collect and share due to the high-cost acquisition and data privacy regulations. Previously developed federated learning (FL)-based methods enable collaborative and decentralized training without exchanging local data to preserve data privacy. In this work, we focus on analyzing the robustness of FL-based methods against dataset shifts (i.e., the datasets among multiple institutions are from different scanners, different protocols, or different sampling conditions). The results show that the FL-based CT reconstruction methods are sensitive to domain shifts, which can be attributed to the data heterogeneity among multiple institutions. Based on these findings, we propose a unified CT reconstruction method that leverages high-quality metadata (e.g., low-dose images and their corresponding normal-dose counterparts) stored on the cloud server to address the challenge of multi-institutional domain shifts. For simplicity, we refer to the proposed method as FM-iRadonMAP, representing federated metadata learning (FMDL) with a personalized condition-modulated iRadonMAP (CM-iRadonMAP). Specifically, the FM-iRadonMAP consists of two modules, i.e., CM-iRadonMAP and FMDL. CM-iRadonMAP introduces the knowledge of client-specific sampling conditions, i.e., imaging geometries and scan protocols, into iRadonMAP reconstruction network at each client to modulate the reconstruction effectively. FMDL trains a supervised meta model using high-quality metadata in an additional round and then adaptively unifies the network parameters of the meta model with those of the local models from all clients for broadcasting, addressing the issue of data heterogeneity. A large-scale multi-institutional CT dataset is used to validate and evaluate the reconstruction performance of the FM-iRadonMAP. The experimental results demonstrate the feasibility of the FM-iRadonMAP for multi-institutional CT reconstruction with severe data heterogeneity.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 2","pages":"169-180"},"PeriodicalIF":3.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11017337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116838","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}