IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"Dynamic PET Image Reconstruction Using Kalman Inspired Network","authors":"Mengrui Chen;Hengjia Ran;Yiming Wan;Ao Ran;Min Guo;Huafeng Liu","doi":"10.1109/TRPMS.2025.3602938","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3602938","url":null,"abstract":"Positron Emission Tomography (PET) facilitates the visualization of the distribution of radioactive isotope-labeled compounds within biological organisms. Existing reconstruction algorithms, including iterative and deep learning methods that depend on the system matrix, often suffer from inaccuracies in the system matrix, resulting in artifacts or blurring in the reconstructed images. Inspired by Kalman Filtering, we propose a data-driven deep learning framework for PET image reconstruction, named the Kalman Inspired Network (KIN). The KIN framework divides the reconstruction problem into two phases: 1) state prediction and 2) state update, and consists of three core components: 1) prediction net; 2) projection net; and 3) Kalman Gain Net. By adopting a data-driven approach, KIN circumvents the reliance on the system matrix, thereby overcoming the limitations imposed by noise prior knowledge and the inversion of large-dimensional matrices typically required in traditional Kalman Filtering algorithms. We evaluated the proposed KIN network using simulated phantom and experimental rat datasets, benchmarking it against traditional algorithms as well as other deep-learning-based methods. The results demonstrate that KIN enhances the quality of dynamic PET scans, both in terms of image quality and quantitative indices, underscoring its potential for dynamic imaging applications that require extremely short frames and are typically challenged by high noise levels.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"492-503"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588245","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":"DOI Self-Calibration Method Based on Intrinsic Radiation for Positron Emission Tomography","authors":"Xin Yu;Haoyu Zou;Han Liu;Zhijun Zhao;Da Liang;Xiaoyin Zhang;Huiping Zhao;Jinyong Tao;Wenjing Zhu;Yibin Zhang;Jiayang Zeng;Xiaoying Tang;Jianfeng Xu;Qiyu Peng","doi":"10.1109/TRPMS.2025.3602383","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3602383","url":null,"abstract":"depth of interaction (DOI) decoding can reduce localization errors in the line-of-response (LOR), thereby improving positron emission tomography (PET) image quality, especially in high-resolution PET, long-axial PET, and small-aperture PET systems. Current PET detectors are capable of achieving high-resolution DOI decoding, but most methods rely on collimation calibration for each detector. Calibrating all detectors is labor-intensive and complicates subsequent system maintenance. We aim to achieve DOI collimation for PET detectors based on the intrinsic radiation of 176Lu, which allows us to omit the complex step of calibrating DOI for all detectors. This approach allows DOI calibration to be performed automatically during each system operation, simplifying system maintenance. The pixelated LYSO detector in this study consists of LYSO pixels with dimensions of <inline-formula> <tex-math>$ 2 times 2 times 20 , text {mm}^{3} $ </tex-math></inline-formula>. The detector is arranged in an <inline-formula> <tex-math>$8times 10$ </tex-math></inline-formula> array configuration. We obtained the relationship between intrinsic radiation and DOI through Monte Carlo simulations, and the relationship between the multi pixel photon counter (MPPC) signal ratio (the relative proportion of light detected by different MPPCs from the same crystal, which changes with DOI) and intrinsic radiation through experiments. This allowed us to establish a conversion function between DOI and the MPPC signal ratio. We verified the DOI resolution through collimation testing and demonstrated the improvement in image quality of the DOI self-calibration based on intrinsic radiation method using the Derenzo phantom experiment. We validated the DOI resolution of our method through mechanical collimation experiments at depths of 4, 10, and 16 mm, achieving an average DOI resolution full width at half maximum (FWHM) of 5.2 mm and a mean absolute error (MAE) of 3.6 mm. The images reconstructed with DOI correction obtained through self-collimation demonstrated significantly improved signal-to-noise ratio and spatial resolution compared to images without DOI correction. Specifically, the spatial resolution at an offset of 45 mm improved from 3.0 to 2.1 mm. The DOI self-calibration method based on intrinsic radiation provides a practical and efficient solution for calibrating PET detectors without the need to add complex hardware. It should be noted that this method is only applicable to PET systems with detectors employing LSO or LYSO, and therefore this method is based on spontaneous emission of <inline-formula> <tex-math>${}^{176}text {Lu}$ </tex-math></inline-formula>. This method has the potential to improve the image quality of PET systems and can be readily integrated into existing detector designs.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"574-581"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588213","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":"Light Propagation Modeling in a Mie-Scattering Dominated Scintillator","authors":"A. Dahmane;M. Bongrand;C. Bourgeois;D. Breton;M. Briere;A. Cabrera;V. Chaumat;R. Gazzini;D. Giovagnoli;F. Haddad;A. Hourlier;G. Hull;M. Hussein;P. Lanièce;F. Lefevre;P. Loaiza;J. Maalmi;Y. Mellak;T. Merlin;R. Mastrippolito;C. Marquet;L. Ménard;D. Navas-Nicolàs;P. Pillot;L. Simard;D. Stocco;M.-A. Verdier;D. Visvikis;F. Yermia;D. Brasse","doi":"10.1109/TRPMS.2025.3614151","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3614151","url":null,"abstract":"We present an analytical approach to model light propagation in a highly scattering, opaque organic scintillator, as used in the novel positron emission tomography detector concept LiquidO. In this detection scheme, annihilation photons undergo multiple Compton scatterings, each producing a localized light emission. The scintillating medium being opaque, the scintillation light is confined near its emission point, mainly by Mie scattering. Each annihilation photon interaction is characterized by a succession of light balls, collected by a lattice of optical fibers that are read out at both ends. Simulating optical photons using Monte-Carlo (MC) methods, as implemented in <sc>Geant4</small> and <sc>Gate</small>, requires substantial computational resources and introduces statistical noise. To address this, we developed an analytical model (AM) to predict the spatial and temporal probability density function of optical photons in a scintillating, scattering and absorbing medium. The model combines isotropic random walk dynamics with a 2-D polynomial parameterization to account for anisotropic scattering, by correcting the transport parameters for a double-lobe Henyey–Greenstein phase function with opposite asymmetry factors and a tunable forward-to-backward lobe ratio. Validated against MC simulations, the model predicts spatial and temporal photon distributions with an average relative deviation of 1.60% for the mean radius per time slice <inline-formula> <tex-math>$r(t)$ </tex-math></inline-formula> and 0.42% for the mean time per radial slice <inline-formula> <tex-math>$t(r)$ </tex-math></inline-formula>, while being between 289 and 967 times faster than MC simulations. Its validity spans optical parameters with scattering mean free path of 2–5 mm, asymmetry factor between 0.3–0.7, and forward-to-backward scattering ratio of 0.25–1. The proposed AM can be used to speed up optical simulations, with applications in detector design, event reconstruction, optimization of medium optical properties, and modeling of highly scattering media.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"558-565"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588258","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":"Spatial Dosimetry Verification of Conformal Beams in Proton Radiotherapy by Spectral-Sensitive Imaging","authors":"Carlos Granja;Jan Swakon;Pawel Olko","doi":"10.1109/TRPMS.2025.3603137","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3603137","url":null,"abstract":"Particle radiotherapy with conformal beams aims to tailor the delivered radiation and dose with spatial customization. For treatments using beam shaping collimators it is desired to verify the delivered doses and examine the characteristics of the produced fields with imaging response. For this purpose, we use the semiconductor pixel detector Timepix which provides time-resolved spectral and imaging response with high-spatial resolution and particle-type resolving power. Precise measurements are performed in-field at the isocenter on selected regions (<inline-formula> <tex-math>$leq $ </tex-math></inline-formula> 14 mm) of delivered beams of low intensity (<inline-formula> <tex-math>$leq $ </tex-math></inline-formula> pA). Detailed information in wide dynamic range is produced in the form of field composition (particle-type species), spectral (deposited energy, linear-energy-transfer (LET) spectra) and spatial dose distribution at the sub-mm scale (<inline-formula> <tex-math>$approx ~200$ </tex-math></inline-formula>-<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>m spatial resolution). Results are presented for measurements of two conformal beam collimators used for eye treatment with 55 MeV protons. The produced 2-D maps and 1-D profile distributions can be used for experimental verification of spatially fractionated radiation therapy (grid therapy), and in particle therapy for verification of Monte Carlo transport calculation, including LET distribution at the target isocenter position.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"611-618"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588269","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":"Feature Space-Guided Denoising of Noisy 4-D Data: Applications to Dynamic PET Imaging and Dual-Calibrated Functional MRI","authors":"Connor W. J. Bevington;Ju-Chieh Cheng;Rebecca J. Williams;Jing Zhang;Wen-Ming Luh;G. Bruce Pike;Vesna Sossi","doi":"10.1109/TRPMS.2025.3608506","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3608506","url":null,"abstract":"4-D neuroimaging methods, including dynamic positron emission tomography (PET) and functional MRI, capture the spatiotemporal behavior of physiological processes. Subsequent analysis, such as kinetic modeling of dynamic PET data, can provide parametric images related to unique aspects of physiology. However, 4-D data is often exceptionally noisy, thus requiring post-processing denoising for reliable quantitative results. Several proposed 4-D denoising algorithms reduce noise via signal averaging of physiologically similar voxels, but often have the tradeoff of reduced accuracy. Furthermore, many do not fully exploit nonlocal voxel similarity, due to computational constraints and/or a suboptimal heuristic for identifying physiological similarity. In this work, we propose a denoising framework that uses a low-dimensional feature space representation of the data to identify similar voxels. This permits targeted nonlocal denoising to produce an initial denoised product, which is then passed to the HighlY constrained backPRojection (HYPR) algorithm to ensure data consistency. We optimize the feature space for data from different PET tracers and demonstrate cross-modality applicability, using dual-calibrated fMRI as a proof-of-concept. Additionally, we show our proposed method is superior to comparable denoising algorithms in terms of quantitative accuracy and precision of parametric images computed from the denoised data—thus demonstrating improvements relevant for research and clinical applications.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 4","pages":"504-519"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588287","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.2026.3663808","DOIUrl":"https://doi.org/10.1109/TRPMS.2026.3663808","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 3","pages":"C3-C3"},"PeriodicalIF":3.5,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11421172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147352560","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.2026.3663806","DOIUrl":"https://doi.org/10.1109/TRPMS.2026.3663806","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 3","pages":"C2-C2"},"PeriodicalIF":3.5,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11421173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147352565","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.2026.3663800","DOIUrl":"https://doi.org/10.1109/TRPMS.2026.3663800","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 3","pages":"472-472"},"PeriodicalIF":3.5,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11421171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147352537","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":"Evaluation of a PET Insert for Trimodal Imaging: A Step Toward PET/MRI-Guided Focused Ultrasound","authors":"F. Lopez-Berenguer;A. Gonzalez-Montoro;M. Freire;A. González-Tamarit;S. Jiménez-Serrano;L. F. Vidal;M. Gil;F. Loignon-Houle;I. Torres;J. L. Sachs;J. V. Rispoli;N. Tustison;S. S. Berr;M. B. Williams;D. Andrés;J. J. Rodríguez-García;J. L. Alonso-Ramos;N. Jiménez;A. Carrión;F. Camarena;T. Guallart-Naval;J. M. Algarín;R. Bosch;E. Pallás;J. Alonso;N. D. Sheybani;S. Maslova;T. Sherlock;A. Thede;Z. E. F. Demir;M. Hoch;J. He;J. M. Benlloch;M. J. Rodríguez-Álvarez;A. J. González","doi":"10.1109/TRPMS.2025.3615467","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3615467","url":null,"abstract":"Combining positron emission tomography (PET) with magnetic resonance imaging (MRI) and focused ultrasound (FUS) has emerged as a promising hybrid technique in the medical imaging field. Despite the potential benefits of using simultaneous PET and MRI acquisitions to monitor therapeutic effects and ensure precision and safety during FUS applications, no commercial or academic PET/MRI-guided FUS system (referred in the following as trimodal PET-MRI-FUS) exists. This work presents the design and evaluation of a preclinical PET insert for simultaneous operation with MRI and FUS systems. The proposed PET insert is based on monolithic LYSO crystals arranged in two octagonal rings that define inner and outer diameters of 72 and 114 mm, respectively, with an axial length of 67 mm. The system performance was evaluated according to the NEMA NU 4 2008 protocol. It achieves a uniform spatial resolution of ~0.9 mm along the whole PET Field of View (FOV) when enabling Depth of Interaction (DOI) capabilities. Moreover, we found a sensitivity peak of 3.8% for an energy window of 255–766 keV and a peak noise equivalent count rate peak of 80 kcps at an activity of 19 MBq. The compatibility of the PET insert with both low- and high-field MRI systems, as well as with custom and commercially available FUS devices, was studied using phantoms and in vivo experiments, respectively. The results of the trimodal tests have proven that the PET insert works simultaneously with both MRI and FUS systems.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 5","pages":"789-798"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184334","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147808124","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":"Partial Volume Correction of SPECT Images With Deep Learning Trained on Synthetic Data","authors":"Théo Kaprélian;Ane Etxebeste;David Sarrut","doi":"10.1109/TRPMS.2025.3634484","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3634484","url":null,"abstract":"Partial volume effects (PVEs) cause blurring and inaccuracies in activity measurements in single photon emission computed tomography (SPECT) imaging. They are a major challenge, especially for SPECT-based dosimetry in radionuclide therapies using <inline-formula> <tex-math>${}^{177}text {Lu}$ </tex-math></inline-formula>. This study aims to develop and evaluate new deep-learning (DL) methods to correct PVEs. The proposed methods aim to enhance the accuracy of SPECT quantification by applying DL techniques trained on synthetic data. We developed three distinct DL approaches to correct PVEs: PVCNet-sino which operates in the projection domain, PVCNet-img that works in the image domain and PVCNet-hybrid that combines information from both domains. The training data included a variety of organ shapes, lesions, and activity distributions. The performance of these networks was compared to resolution modeling (RM) and the iterative Yang (iY) methods, using a large synthetic test dataset, real phantom measurements, Monte Carlo simulations, and patient data. We showed that all of the proposed DL methods allowed to reduce PVEs compared to RM. Under some conditions on the inputs and training framework, PVCNet-sino was the most capable of quantifying the activity in the 22/28/37 mm spheres on real NEMA IEC phantom measurements (with recovery coefficients (RCs) of 0.97/0.99/1.08) close to or better than iY (1.02/1.02/1.09) and better than PVCNet-img (0.78/0.86/1.07) and RM (0.53/0.65/0.76). But this method leads to noisy images and reconstruction artifacts, mitigated by the PVCNet-hybrid framework, also leading to improved RCs (0.78/0.86/1.07). The better noise/resolution tradeoff obtained by the PVCNet-hybrid approach was confirmed on the patient MC simulation, where normalized root mean squared error was reduced by 33% and background noise by 80%. We demonstrate that DL based partial volume correction (PVC) trained on synthetic data can reduce PVE in SPECT images without any segmentation requirement. This work also shows that using both the sinogram and the image domain is a suitable alternative to post-reconstruction PVC. While applied to <inline-formula> <tex-math>${}^{177}text {Lu}$ </tex-math></inline-formula>, the method is generic and can be used with other radionuclides.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 5","pages":"673-682"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147808104","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}