IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"Simulating Sinogram-Domain Motion and Correcting Image-Domain Artifacts Using Deep Learning in HR-pQCT Bone Imaging","authors":"Farhan Sadik;Christopher L. Newman;Stuart J. Warden;Rachel K. Surowiec","doi":"10.1109/TRPMS.2025.3617225","DOIUrl":"10.1109/TRPMS.2025.3617225","url":null,"abstract":"Rigid-motion artifacts, such as cortical bone streaking and trabecular smearing, hinder in vivo assessment of bone microstructures in high-resolution peripheral quantitative computed tomography (HR-pQCT). Despite various motion grading techniques, no motion correction methods exist due to the lack of standardized degradation models. We optimize a conventional sinogram-based method to simulate motion artifacts in high-resolution pQCT (HR-pQCT) images, creating paired datasets of motion-corrupted images and their corresponding ground truth, which enables seamless integration into supervised learning frameworks for motion correction. As such, we propose an edge-enhanced self-attention Wasserstein generative adversarial network with gradient penalty (ESWGAN-GP) to address motion artifacts in both simulated (source) and real-world (target) datasets. The model incorporates edge-enhancing skip connections to preserve trabecular edges and self-attention mechanisms to capture long-range dependencies, facilitating motion correction. A visual geometry group (VGG)-based perceptual loss is used to reconstruct fine micro-structural features. The ESWGAN-GP achieves a mean signal-to-noise ratio (SNR) of 26.78, structural similarity index measure (SSIM) of 0.81, and visual information fidelity (VIF) of 0.76 for the source dataset, while showing improved performance on the target dataset with an SNR of 29.31, SSIM of 0.87, and VIF of 0.81. The proposed methods address a simplified representation of real-world motion that may not fully capture the complexity of in vivo motion artifacts. Nevertheless, because motion artifacts present one of the foremost challenges to more widespread adoption of this modality, these methods represent an important initial step toward implementing deep learning-based motion correction in HR-pQCT.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 5","pages":"697-711"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145432618","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":"Impact of DOI Capability on Detector Performance: A Comparative Study of DOI and Non-DOI Detectors for High-Resolution and Sensitivity Organ-Specific PET Inserts","authors":"Lukas U. Rauscher;Magdelena S. Allen;Martin S. Judenhofer;Larry Byars;Michele Scipioni;José M. Udias;Ciprian Catana;Bernd J. Pichler;Fabian P. Schmidt","doi":"10.1109/TRPMS.2025.3624609","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3624609","url":null,"abstract":"High-sensitivity organ-specific positron emission tomography (PET) inserts employing long scintillation crystals in combination with small ring diameters require depth of interaction (DOI) encoding to ensure uniform spatial resolution across the field of view. In this study, we have developed high-resolution, DOI-capable detectors based on the Hamamatsu C13500 TOF-PET modules, using <inline-formula> <tex-math>$1.6times 1.6$ </tex-math></inline-formula> mm2 LSO crystals coupled to <inline-formula> <tex-math>$4times 4$ </tex-math></inline-formula> mm2 silicon photomultiplier. Detector configurations were designed either for a brain PET/MR insert (26 mm long crystals with a top light guide (TLG) for DOI encoding via light spread) or for a breast PET/MR insert (15 mm long with a TLG; 15 mm long with a stacked dual-layer configuration). Corresponding reference configurations without DOI capability were included to assess the impact of DOI implementation on detector performance. The 26 mm long brain detector configuration with TLG achieved an energy resolution of 12.1% and a coincidence time resolution (CTR) of 684 ps, with a degradation of 148 ps compared to the non-DOI variant. A newly developed DOI parameter calculation method improved DOI resolution from 9.1 ± 1.7 to 7.1 ± 1.8 mm. The 15 mm long single layer breast detector configuration with TLG yielded an energy resolution of 10.8%, a CTR of 460 ps, and a DOI resolution of 6.3 ± 1.8 mm. The 15 mm dual layer detector configuration provided comparable performance (11.5% energy resolution, 605 ps CTR) while eliminating the need for DOI calibration. For both the 15 and 26 mm single layer crystal block configurations, introducing a TLG preserved crystal identification and energy resolution. Overall, the DOI-capable designs demonstrated only minor performance tradeoffs, supporting the feasibility of both approaches for integration into organ-specific PET/MR inserts.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 5","pages":"753-763"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147808140","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":"DuDoCROP: Dual-Domain CLIP-Assisted Residual Optimization Perception Model for CT Metal Artifact Reduction","authors":"Xinrui Zhang;Ailong Cai;Shaoyu Wang;Linyuan Wang;Zhizhong Zheng;Lei Li;Bin Yan","doi":"10.1109/TRPMS.2025.3628646","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3628646","url":null,"abstract":"MA in computed tomography (CT) imaging pose significant challenges to accurate clinical diagnosis. The presence of high-density metallic implants results in artifacts that deteriorate image quality, manifesting in the forms of streaking, blurring, or beam hardening effects, etc. Nowadays, various deep-learning-based approaches, particularly generative models, have been proposed for metal artifact reduction (MAR). However, these methods exhibit limited perception ability in the diverse morphologies of different metal implants with artifacts, which may generate spurious anatomical structures and exhibit inferior generalization capability. To address the issues, we leverage visual-language model (VLM) to identify these morphological features and introduce them into a dual-domain CLIP-assisted residual optimization perception model (DuDoCROP) for MAR. Specifically, a dual-domain CLIP (DuDoCLIP) is fine-tuned using contrastive learning to extract semantic descriptions of clean images and metal-affected regions in both image domain and sinogram domain. Subsequently, a diffusion model is guided by the embeddings of DuDoCLIP, thereby enabling the dual-domain prior generation. In addition, we design prompt engineering for more precise image-text descriptions that can enhance the model’s perception capability. Then, a downstream task is devised for the one-step residual optimization and integration of dual-domain priors. Ultimately, a new perceptual indicator (PI) is proposed to validate the model’s perception and generation performance. With the assistance of DuDoCLIP, our DuDoCROP exhibits at least 63.7% PI higher compared to the baseline model. Numerical experiments demonstrate that the proposed method can generate more realistic image structures and outperform other SOTA approaches both qualitatively and quantitatively.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 5","pages":"683-696"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147808125","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":"Self-Gelling Multifunctional Plasma-Activated AVC Powder for the Treatment of Chronic Diabetic Wounds","authors":"Hao Zhang;Shiyao Wang;Xixi Jing;Zewei Wang;Jishen Zhang;Li Guo;Zifeng Wang;Dingxin Liu;Mingzhe Rong;Qi Chang","doi":"10.1109/TRPMS.2025.3605875","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3605875","url":null,"abstract":"Chronic diabetic wounds represent one of the most prevalent complications of diabetes, often necessitating amputation in severe cases of nonhealing wounds. Cold atmospheric plasma technology has shown promise in promoting the healing of chronic diabetic wounds, with plasma-generated reactive species playing a key role. However, direct plasma treatment is limited by uneven treatment intensity, the risk of thermal injury, and a short effective duration. To address these limitations, we propose the use of lyophilized plasma-activated acryloyldimethylammonium taurate/VP copolymer (PA-AVC) dressings. These dressings have good stability, strong activity, and a long shelf-life and can absorb wound exudate, provide physical protection, and load/release plasma-generated reactive species to promote wound healing. In vitro studies have demonstrated that lyophilized plasma-activated acryloyldimethyl taurate/VP copolymer (PA-AVC) dressings efficiently load air discharge plasma-generated reactive species and exhibit excellent antimicrobial and biosafety properties. In vivo wound healing studies in diabetic ICR mice have shown that lyophilized PA-AVC dressings significantly enhance wound healing by reducing bacterial infection, controlling local inflammatory responses, and promoting the release of growth factors to stimulate vascular regeneration, and no significant systemic toxicity was observed. These results suggest that lyophilized PA-AVC dressings have great potential for the treatment of chronic diabetic wounds.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 5","pages":"825-835"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147808159","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":"Adaptive Diffusion Models for Sparse-View Motion-Corrected Head Cone-beam CT","authors":"Antoine De Paepe;Alexandre Bousse;Clémentine Phung-Ngoc;Youness Mellak;Dimitris Visvikis","doi":"10.1109/TRPMS.2025.3637124","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3637124","url":null,"abstract":"Cone-beam computed tomography (CBCT) is an imaging modality widely used in head and neck diagnostics due to its accessibility and lower radiation dose. However, its relatively long acquisition times make it susceptible to patient motion, especially under sparse-view settings used to reduce dose, which can result in severe image artifacts. In this work, we propose a novel framework combining joint reconstruction and motion estimation (JRM) with an adaptive diffusion model (ADM) that simultaneously addresses motion compensation and sparse-view reconstruction in head cone-beam computed tomography (CBCT). Leveraging recent advances in diffusion-based generative models, our method integrates a wavelet-domain diffusion prior into an iterative reconstruction pipeline to guide the solution toward anatomically plausible volumes while estimating rigid motion parameters in a blind fashion. We evaluate our method on simulated motion-affected CBCT data derived from real clinical computed tomography (CT) volumes. Experimental results demonstrate that JRM-ADM achieves consistent quantitative improvements over both traditional and learning-based baselines. In highly undersampled cases, JRM-ADM improves peak signal-to-noise ratio (PSNR) by more than 4 dB and structural similarity index measure (SSIM) by 0.10 compared to the baseline motion-corrected (MC) reconstruction method. These results highlight the potential of our approach to enable motion-robust, low-dose CBCT imaging, paving the way for improved clinical viability. The project page is available at <uri>https://antoinedepaepe.github.io/jrm-adm-io/</uri>.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 5","pages":"662-672"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147808162","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":"Liver Couinaud Segmentation: A Review on Existing Segmentation Techniques of the Couinaud Classification of Liver","authors":"Dalamchwami Chen Lyngdoh;Sarada Prasad Dakua;Gajendra Kumar Mourya","doi":"10.1109/TRPMS.2025.3623914","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3623914","url":null,"abstract":"Liver Couinaud segmentation is crucial in biomedical image processing for treatment planning and surgical interventions of the liver parenchyma. This is because the liver Couinaud segments help provide clinicians with a clearer and specific diagnosis of the location of the diseased areas within the liver tissue. Technological advancements in the form of machine learning (ML) and deep learning (DL) models have greatly enhanced the process of liver Couinaud segmentation. The efficiency of ML and DL models showcases their performance with incredible results. Hence, this article discusses the state-of-the-art techniques for liver Couinaud segmentation from 2006 to 2024. The segmentation techniques are divided into semi-automatic and automatic. Additionally, the online medical imaging datasets suitable for developing algorithms and frameworks for liver Couinaud segmentation are presented with their descriptions, advantages, and limitations. Furthermore, the performance metrics that are commonly used for evaluating the performance of segmentation algorithms are thoroughly discussed. Finally, the research challenges and recommendations are accentuated for future researchers who intend to bridge the highlighted gaps in liver Couinaud segmentation.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 5","pages":"642-661"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147808151","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.2026.3655045","DOIUrl":"https://doi.org/10.1109/TRPMS.2026.3655045","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 2","pages":"C3-C3"},"PeriodicalIF":3.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11372203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116807","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.2026.3655047","DOIUrl":"https://doi.org/10.1109/TRPMS.2026.3655047","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 2","pages":"C2-C2"},"PeriodicalIF":3.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11372527","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116922","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":"Investigation of a Novel Snout-Mounted Quad-Compton Camera system for In Vivo Proton Dose Verification via Prompt Gamma Imaging.","authors":"Vijay R Sharma, E Shakeri, Z Jiang, S Mossahebi, A Chalise, M K Gobbert, S W Peterson, J C Polf, L Ren","doi":"10.1109/trpms.2026.3659770","DOIUrl":"10.1109/trpms.2026.3659770","url":null,"abstract":"<p><p>This study presents a novel simulation study for integrating four Compton Cameras (CC) into proton therapy systems by mounting them directly on the snout of the treatment gantry for prompt gamma imaging (PGI). This compact, fixed geometry simplifies detector integration, maintains alignment with the proton beam axis, and supports in vivo proton range verification. A Geant4-based Monte Carlo framework, PJ-MC, was used to simulate prompt gamma emissions (PGE) in water and patient-specific geometry. PG image reconstruction was performed using the Kernel Weighted Back Projection (KWBP) algorithm. To improve range accuracy, we introduced a post-processing method called Point Source Normalization (PSN), which uses precomputed PGI responses for monoenergetic gamma point sources at different depths to normalize PGI images and correct distortions from attenuation, geometric factors, and detector responses from PGE detected curve. Simulations with monoenergetic sources confirmed the influence of depth-dependent attenuation and inverse square losses, especially at higher proton energies. Clinical beam energies of 70, 150, and 200 MeV were tested. KWBP reconstructions showed increasing range discrepancies with energy, reaching 176 mm (water) and 144 mm (patient) at 200 MeV. PSN correction reduced these errors by up to 70%, bringing PG hotspots closer to the Bragg peak <math><mo>(</mo> <msub><mrow><mtext>D</mtext></mrow> <mrow><mtext>max</mtext></mrow> </msub> <mo>)</mo></math> . Simulations with anterior/posterior shifts showed that the PSN-corrected system could detect millimeter-scale range deviations from setup uncertainties and beam shifts. This is the first study to simulate and evaluate a snout-mounted CC system for PGI in proton therapy. Results support future validation under clinical beam conditions in phantoms and patients.</p>","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13008334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147515586","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 Virtual Clinical Trial to Detect Changes in Tumor Uptake with PET using Lesion Embedding.","authors":"Majid Kazemi Kozani, Min Gao, Joel S Karp, Suleman Surti","doi":"10.1109/trpms.2026.3656152","DOIUrl":"10.1109/trpms.2026.3656152","url":null,"abstract":"<p><p>We conducted a virtual clinical trial (VCT) using patient data from the PennPET Explorer scanner and embedded lesions to model tumor response to therapy as measured by a change in the standardized uptake value (SUV). Two patient data sets (high and low BMI) were bootstrapped prior to embedding data from separately acquired sphere-in-air data sets. In each bootstrapped dataset, 20 small lesions (1 cm diameter) were embedded, 10 each in lung and liver organs. Multiple scans were reconstructed using list-mode time of flight-ordered subset expectation maximization (TOF-OSEM) for varying scan durations and pre-defined lesion uptake values. The resulting SUV measurements were utilized to construct receiver operating characteristic (ROC) curves to evaluate the system's ability to discriminate between different values of lesion uptake corresponding to therapy-induced changes. The area under the ROC curve (AUC) was used as a summary metric of this discrimination performance. Our results demonstrate that long axial field of view (LAFOV) PET scanners with high sensitivity may effectively detect early tumor response to therapy, even with brief scan durations (1 minute scan) or, equivalently, with reduced activity. The AUC values change very slowly with increasing scan duration, suggesting that performance is not primarily limited by count statistics within the studied range. The AUC values are also not very sensitive to the patient BMI or the type of SUV metrics (i.e. SUV_mean and SUV_max), and are relatively independent of the local background (organ) uptake. The primary factor determining the AUC values seems to be the absolute change in lesion uptake that will be sensitive to partial volume effects as determined by the scanner spatial resolution. Hence, a standard axial field-of-view scanner with similar spatial resolution will likely perform as well as a LAFOV scanner after appropriate compensation for the sensitivity differences.</p>","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147624133","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}