IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information","authors":"","doi":"10.1109/TRPMS.2025.3530622","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3530622","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870459","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106264","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":"Multibranch Generative Models for Multichannel Imaging With an Application to PET/CT Synergistic Reconstruction","authors":"Noel Jeffrey Pinton;Alexandre Bousse;Catherine Cheze-Le-Rest;Dimitris Visvikis","doi":"10.1109/TRPMS.2025.3532176","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3532176","url":null,"abstract":"This article presents a novel approach for learned synergistic reconstruction of medical images using multibranch generative models. Leveraging variational autoencoders (VAEs), our model learns from pairs of images simultaneously, enabling effective denoising and reconstruction. Synergistic image reconstruction is achieved by incorporating the trained models in a regularizer that evaluates the distance between the images and the model. We demonstrate the efficacy of our approach on both Modified National Institute of Standards and Technology (MNIST) and positron emission tomography (PET)/computed tomography (CT) datasets, showcasing improved image quality for low-dose imaging. Despite challenges, such as patch decomposition and model limitations, our results underscore the potential of generative models for enhancing medical imaging reconstruction.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"654-666"},"PeriodicalIF":4.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900606","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":"A Statistical Reconstruction Algorithm for Positronium Lifetime Imaging Using Time-of-Flight Positron Emission Tomography","authors":"Hsin-Hsiung Huang;Zheyuan Zhu;Slun Booppasiri;Zhuo Chen;Shuo Pang;Chien-Min Kao","doi":"10.1109/TRPMS.2025.3531225","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3531225","url":null,"abstract":"Positron emission tomography (PET) is an important modality for diagnosing diseases, such as cancer and Alzheimer’s disease, capable of revealing the uptake of radiolabeled molecules that target specific pathological markers of the diseases. Recently, positronium lifetime imaging (PLI) that adds to traditional PET the ability to explore properties of the tissue microenvironment beyond tracer uptake has been demonstrated with time-of-flight (TOF) PET and the use of nonpure positron emitters. However, achieving accurate reconstruction of lifetime images from data acquired by systems having a finite TOF resolution still presents a challenge. This article focuses on the 2-D PLI, introducing a maximum-likelihood estimation (MLE) method that employs an exponentially modified Gaussian (EMG) probability distribution that describes the positronium lifetime data produced by TOF PET. We evaluate the performance of our EMG-based MLE method against approaches using exponential likelihood functions and penalized surrogate methods. Results from computer-simulated data reveal that the proposed EMG-MLE method can yield quantitatively accurate lifetime images. We also demonstrate that the proposed MLE formulation can be extended to handle PLI data containing multiple positron populations.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"478-486"},"PeriodicalIF":4.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10844904","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761358","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":"Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model","authors":"Weijie Gan;Huidong Xie;Carl von Gall;Günther Platsch;Michael T. Jurkiewicz;Andrea Andrade;Udunna C. Anazodo;Ulugbek S. Kamilov;Hongyu An;Jorge Cabello","doi":"10.1109/TRPMS.2025.3528728","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3528728","url":null,"abstract":"Anatomically guided positron emission tomography (PET) reconstruction using magnetic resonance imaging (MRI) information has been shown to have the potential to improve PET image quality. However, these improvements are limited to PET scans with paired MRI information. In this work, we employed a diffusion probabilistic model (DPM) to infer T1-weighted-MRI (deep-MRI) images from FDG-PET brain images. We then use the DPM-generated T1w-MRI to guide the PET reconstruction. The model was trained with brain FDG scans, and tested in datasets containing multiple levels of counts. Deep-MRI images appeared somewhat degraded and in some cases showed inaccuracies compared to the acquired MRI images. Regarding PET image quality, volume of interest analysis in different brain regions showed that both PET reconstructed images using the acquired and the deep-MRI images improved image quality compared to ordered subset expected maximum (OSEM). Same conclusions were found analysing the decimated datasets. A subjective evaluation performed by two physicians confirmed that OSEM scored consistently worse than the MRI-guided PET images and no significant differences were observed between the MRI-guided PET images. This proof of concept shows that it is possible to infer DPM-based MRI imagery to guide the PET reconstruction, enabling the possibility of changing reconstruction parameters, such as the strength of the prior on anatomically guided PET reconstruction in the absence of MRI.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"412-420"},"PeriodicalIF":4.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761569","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":"Experimental Validation of ANNA: Analog Neural Network ASIC for Event Positioning in Monolithic Scintillation Detectors","authors":"S. Di Giacomo;M. Ronchi;M. Amadori;G. Borghi;M. Carminati;C. Fiorini","doi":"10.1109/TRPMS.2025.3530774","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3530774","url":null,"abstract":"machine learning (ML) accelerators represent an attractive area of research, offering the potential to streamline algorithmic complexity and handle massively parallel in-memory computations, with substantial improvements in energy efficiency and speed related to data transmission and processing. Analog computing can further boost ML acceleration due to its superior computational density compared to digital platforms and its ability to deal with analog data acquired from sensors. The analog approach to edge computing can be beneficial for signal processing in long-axial field-of-view (LA-FOV) scintillation detectors used in nuclear medical tomographic imaging (PET and SPECT). In such scenarios, the deployment of analog computations in close proximity to the sensors would significantly diminish the volume of data that must be digitized and transmitted, and ML reconstruction algorithms, such as neural networks (NNs), could enhance the image reconstruction process. We present an ASIC fabricated in 0.35-<inline-formula> <tex-math>$mathrm { {mu }text {m}}$ </tex-math></inline-formula> CMOS technology implementing an analog NN featuring 64 inputs, two hidden layers of 20 neurons each, and two outputs. It is intended for use in the reconstruction of the 2-D position of interaction of gamma photons inside a monolithic scintillator crystal readout by a matrix of silicon photomultipliers (SiPMs) for PET/SPECT applications. This chip can interact directly with analog signals originating from the photosensors, and is able to provide the predicted interaction coordinates of the gamma-ray at its output. The vector-matrix multiplications for inference are executed in the charge domain using programmable switched capacitors (SC) organized in crossbar arrays. Experimental measurements of this first proof-of-concept prototype ASIC are reported, demonstrating the correct functionality of the NN circuit. With an energy efficiency of <inline-formula> <tex-math>$50~{mathrm {GOPS/W}}$ </tex-math></inline-formula> and power consumption of <inline-formula> <tex-math>$17~{mathrm {mW}}$ </tex-math></inline-formula> per inference, the achieved results are promising for the integration of the ASIC with the photodetector front-end for in situ analog computing.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"542-552"},"PeriodicalIF":4.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900602","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":"Semi-Supervised MVCT Enhancement Using Diffusion Model Refined With KVCT Priors","authors":"Mengxun Zheng;Long Tang;Peiwen Liang;Shuang Jin;Xiaotong Xu;Zhe Su;Hua Zhang","doi":"10.1109/TRPMS.2025.3529582","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3529582","url":null,"abstract":"Megavoltage computed tomography (MVCT) on the tomotherapy system has been widely used as a tomographic imaging modality for image-guided radiotherapy. However, the quality of MVCT images is often compromised by poor tissue contrast and significant noise. Conventional networks designed to enhance CT quality typically require the clean ground-truth images, which are not feasible for MVCT. In this study, we introduce a semi-supervised framework named Semi-Diff, which leverages the denoising diffusion probabilistic model and the prior information sourced from kilovoltage computed tomography (KVCT) to address challenges in MVCT enhancement. Specifically, employing a discriminative prior learning method, we first learn a mapping function to estimate MVCT noise and perform state matching. With this state matching dictionary, we then represent the MVCT image as a sample from an intermediate posterior distribution within the diffusion Markov chain, which enables the reverse conditional sampling process of the diffusion model to start directly from the noisy MVCT images. To fully explore the prior information from the plan KVCT images of the same patients, we introduce a novel diffusion base network called RefNet, whose dynamic feature aggregation module can extract and align the relevant features from reference KVCT image to enhance image restoration performance. Quantitative evaluations using simulated digital phantom data show that the proposed Semi-Diff model achieves the average FSIM score of 0.954, PSNR score of 33.22 dB, and RMSE value of 0.023, demonstrating improvements of approximately 2.16% in FSIM, 0.59% in PSNR, and a reduction of 3.58% in RMSE compared to the best-performing baseline method. Results from physical phantom and patient data further validate the model’s superior performance in noise suppression and structural preservation.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"667-679"},"PeriodicalIF":4.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900603","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":"Individual and Simultaneous Imaging of ⁹⁹mTc and ¹⁷⁷Lu With a Preclinical Broad Energy-Spectrum CZT-Based SPECT","authors":"Pedro M. C. C. Encarnação;Pedro M. M. Correia;Baharak Mehrdel;Isabella Bredwell;João F. C. A. Veloso;Javier Caravaca;Youngho Seo","doi":"10.1109/TRPMS.2025.3527874","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3527874","url":null,"abstract":"Radiopharmaceutical therapy has demonstrated a high efficacy in the treatment of various tumor types. One of the radionuclides already used in the clinic is 177Lu, a beta emitter that also emits several photons imageable with single photon emission computed tomography (SPECT). Quantitative imaging of 177Lu is critical for developing new radiopharmaceuticals. Energy resolution is an important factor when imaging multiple photon emissions. Solid-state detectors offer a superior performance over scintillators, that are commonly used in commercially available preclinical SPECT scanners. This study demonstrates the feasibility of 99mTc and 177Lu quantitative imaging in mouse phantoms, individually and simultaneously, with a SPECT prototype built with four CdZnTe (CZT) detector heads and a custom-designed and energy-optimized parallel-hole tungsten collimator. With a custom implementation of the one-step late (OSL) image reconstruction algorithm, the system is capable of imaging energies from ~70 to 250 keV. Above 250 keV, images were significantly affected by septal penetration, consistent with the collimator design. A recovery coefficient within 25% was obtained for activities as low as 2 kBq/mL for 99mTc and 45% for 177Lu. Compared to a commercial NaI-based preclinical SPECT (VECTor4/CT), our prototype showed a superior energy resolution (<5% at 140 keV), a similar uniformity with a high-compact design.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"564-577"},"PeriodicalIF":4.6,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900518","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":"Performance Evaluation of New PET/CT DigitMI 930","authors":"Bo Zhang;Bingxuan Li;Lei Fang;Xiaoyun Zhou;Ang Li;Xuan Zhang;Yang Liu;Zhuo Wang;Chien-Min Kao;Yuqing Liu;Xiaohua Zhu;Lin Wan;Peng Xiao;Xun Chen;Hidehiro Iida;Juhani Knuuti;Qingguo Xie","doi":"10.1109/TRPMS.2025.3526659","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3526659","url":null,"abstract":"The study evaluates the performance of the DigitMI 930 positron emission tomography (PET)/CT system, featuring detector modules with an 1:1:1 coupling of the scintillation crystal, the photosensor, and the electronic readout channel, in adherence to the NEMA NU 2-2018 standard. Moreover, brain and whole-body images were used to assess image quality. The radial, tangential, and axial resolutions at a radial offset of 1 cm were 3.9, 3.9, and 3.7 mm, respectively. The average sensitivity was measured at 16.2 cps/kBq. The peak noise-equivalent count rate was calculated as 412.5 kcps at 34.5 kBq/mL. At an activity concentration of 5.3 kBq/mL, the scatter fraction was 37.5%, and the time-of-flight (TOF) resolution was 248.6 ps. The contrast recovery coefficient ranged from 70.6% to 87.7% with TOF reconstruction. Despite increased noise in shorter whole-body scans, critical lesions remained identifiable at 20-s durations per bed position. The DigitMI 930 PET/CT system demonstrates a strong overall performance, particularly noteworthy for its low spatial resolution to crystal size ratio in comparison to other clinical PET systems. Moreover, the clinical studies indicate that the DigitMI 930 PET/CT system is capable of generating high-quality clinical images with high sensitivity for detecting small lesions, even at low injection doses or short scanning times.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"578-585"},"PeriodicalIF":4.6,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900638","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":"D3Net: A Distribution-Driven Deep Network for Radiotherapy Dose Prediction","authors":"Lu Wen;Jianghong Xiao;Zhenghao Feng;Xiao Chen;Jiliu Zhou;Xingchen Peng;Yan Wang","doi":"10.1109/TRPMS.2025.3525732","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3525732","url":null,"abstract":"Radiotherapy is a primary treatment for cancers to apply sufficient radiation dose to the planning target volume (PTV) while minimizing dose hazards to the organs at risk (OARs). Recently, convolutional neural network (CNN) has automated radiotherapy plan making by directly predicting the dose distribution maps. However, existing CNN-based methods ignore two critical dose distribution characteristics, i.e., 1) the spatial distribution of different dose values and 2) dose differences in the interior and exterior PTV, resulting in suboptimal predictions. In this article, we propose a distribution-driven deep network, named D3Net, to achieve automatic dose prediction by simultaneously considering its spatial distribution and dose differences. Concretely, D3Net is constructed by a traditional CNN framework embedded with a transformer encoder to extract both local and global dosimetric information. To investigate the spatial distribution of different dose values, we present an innovative discrete multidose constraint to measure multiple dose values in the predicted dose map with discrete dose masks. Besides, we design a PTV-guided triplet constraint to utilize the explicit geometry of PTV to refine dose feature representations in the interior and exterior PTV, thus facilitating the dose differences. The proposed method is validated on the two clinical datasets, achieving <inline-formula> <tex-math>$| {{Delta }{D}}_{98} |$ </tex-math></inline-formula> values of 1.87 Gy for rectum (REC) cancer and 1.08 Gy for cervical cancer. The experimental results surpass those of other state-of-the-art (SOTA) methods, verifying that the predicted dose distribution of our method is more closed to the clinically approved one.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"497-507"},"PeriodicalIF":4.6,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10824860","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761464","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.2024.3519397","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3519397","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"C2-C2"},"PeriodicalIF":4.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912397","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}