IEEE Transactions on Radiation and Plasma Medical Sciences最新文献_第2页

D3Net: A Distribution-Driven Deep Network for Radiotherapy Dose Prediction

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2025-01-03 DOI: 10.1109/TRPMS.2025.3525732

Lu Wen;Jianghong Xiao;Zhenghao Feng;Xiao Chen;Jiliu Zhou;Xingchen Peng;Yan Wang

{"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}

引用次数: 0

IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors IEEE辐射与等离子体医学科学汇刊作者信息

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2025-01-02 DOI: 10.1109/TRPMS.2024.3519397

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IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information IEEE辐射与等离子体医学科学汇刊信息

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2025-01-02 DOI: 10.1109/TRPMS.2024.3519395

引用次数: 0

Timing Performance With Broadcom Metal Trench Silicon Photomultipliers

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-12-16 DOI: 10.1109/TRPMS.2024.3518479

Seungeun Lee;Woon-Seng Choong;Ryan Heller;Joshua W. Cates

{"title":"Timing Performance With Broadcom Metal Trench Silicon Photomultipliers","authors":"Seungeun Lee;Woon-Seng Choong;Ryan Heller;Joshua W. Cates","doi":"10.1109/TRPMS.2024.3518479","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3518479","url":null,"abstract":"Single photon time resolution (SPTR), photon detection efficiency (PDE), and correlated noise rate are important performance characteristics of modern silicon photomultipliers (SiPMs) in consideration of advances in time-of-flight positron emission tomography (TOF-PET). Commercially available Broadcom near-ultraviolet metal-trench (NUV-MT) SiPMs feature metal-filled trench technology to suppress optical crosstalk. We investigated the achievable SPTR and coincidence time resolution (CTR) of NUV-MT SiPMs with various sizes and scintillation crystals, employing low-noise high-frequency readout electronics. The achievable intrinsic SPTRs of <inline-formula> <tex-math>$2times 2$ </tex-math></inline-formula>, <inline-formula> <tex-math>$4times 4$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$6times 6$ </tex-math></inline-formula>-mm2 devices were estimated using a picosecond-pulse laser setup. 2- and 4-mm SiPMs were coupled with <inline-formula> <tex-math>$2times 2times 3$ </tex-math></inline-formula> mm3 and <inline-formula> <tex-math>$3times 3times 20$ </tex-math></inline-formula>-mm3 LGSO and BGO crystals to assess achievable CTRs. The intrinsic SPTRs of 2-, 4-, and 6-mm SiPMs biased with 48 V were estimated to be 45, 55, and 137 ps in full width at half maximum (FWHM), respectively. The detector comprised a 2-mm SiPM and a <inline-formula> <tex-math>$2times 2times 3$ </tex-math></inline-formula>-mm3 BGO achieved 111-ps CTR FWHM. The results demonstrated a significant influence of superior SPTR of 2-mm SiPM for the Cherenkov event portion compared to scintillation-based events. The suppressed noise of NUV-MT enabled stable operation at high-bias voltage, offering excellent SPTR and PDE for breakthroughs in the timing resolution of TOF-PET detectors.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"406-411"},"PeriodicalIF":4.6,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761355","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}

引用次数: 0

ConvNeXt-2U: A 3-D Deep Learning-Based Segmentation Model for Unified and Automatic Segmentation of Lungs, Normal Liver and Tumors in Y-90 Radioembolization Dosimetry

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-12-11 DOI: 10.1109/TRPMS.2024.3510587

Gefei Chen;Haiyan Wang;Zhonglin Lu;Tung-Hsin Wu;Ko-Han Lin;Greta S. P. Mok

{"title":"ConvNeXt-2U: A 3-D Deep Learning-Based Segmentation Model for Unified and Automatic Segmentation of Lungs, Normal Liver and Tumors in Y-90 Radioembolization Dosimetry","authors":"Gefei Chen;Haiyan Wang;Zhonglin Lu;Tung-Hsin Wu;Ko-Han Lin;Greta S. P. Mok","doi":"10.1109/TRPMS.2024.3510587","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3510587","url":null,"abstract":"Y-90 radioembolization (RE) is an effective treatment for inoperable liver tumors. Pretreatment planning using Tc-99m-macroaggregated albumin (MAA) SPECT/CT requires segmentations of lung, normal liver and tumor, which could be delineated on low dose CT (LDCT), CT arterial portography (CTAP) and CT hepatic arteriography (CTHA). This study aims to develop a deep learning-based method for automatic lung, normal liver, and tumor segmentation for Y-90 RE treatment planning. Sixty-four sets of Tc-99m-MAA SPECT/CT, CTAP and CTHA images were retrospectively collected. Ground truth maps were provided by an experienced radiologist. We proposed ConvNeXt-2U, utilizing two U-Nets with connected skip connections and 3-D ConvNeXt blocks for joint segmentations. The LDCT, CTAP and CTHA were input to the two U-Nets. U-Net, attention U-Net, ResU-Net, MedNeXt, UNETR and Swin-UNETR were implemented for comparison. The segmentation performance was evaluated using Dice, Hausdorff distance (HD)95% and volume similarity (VS), and Y-90 RE dosimetrics, i.e., tumor-to-normal-liver ratio, lung-shunt fraction (LSF), absorbed dose (AD) of lungs, normal liver and tumors, and injected activity (IA). ConvNeXt-2U achieved the best performance in all segmentation indices and dosimetrics, except for HD95% of normal liver. It achieved mean Dice of 0.99, 0.93 and 0.77 in lungs, normal liver and tumors. ConvNeXt-2U provides a one-stop platform for unified segmentations for Y-90 RE treatment planning.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"468-477"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761357","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}

引用次数: 0

Enhanced Risk Stratification of Gastrointestinal Stromal Tumors Through Cross-Modality Synthesis from CT to [¹⁸F]-FDG PET Images

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-12-11 DOI: 10.1109/TRPMS.2024.3514779

Kun Huang;Mengmeng Gao;Emanuele Antonecchia;Li Zhang;Ziling Zhou;Xianghui Zou;Zhen Li;Wei Cao;Yuqing Liu;Nicola D’Ascenzo

{"title":"Enhanced Risk Stratification of Gastrointestinal Stromal Tumors Through Cross-Modality Synthesis from CT to [¹⁸F]-FDG PET Images","authors":"Kun Huang;Mengmeng Gao;Emanuele Antonecchia;Li Zhang;Ziling Zhou;Xianghui Zou;Zhen Li;Wei Cao;Yuqing Liu;Nicola D’Ascenzo","doi":"10.1109/TRPMS.2024.3514779","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3514779","url":null,"abstract":"Risk stratification algorithms for gastrointestinal stromal tumors (GISTs) are mainly based on computed tomography (CT) data. Though [18F]-fluorodeoxyglucose positron emission tomography ([18F]-FDG PET) imaging may improve their performance, challenges in image interpretation in the gastrointestinal tract still limit the widespread integration of PET into routine clinical protocols, causing a poor availability of PET data to develop and train stratification models. To solve this issue, we propose to enrich existing [18F]-FDG PET GIST datasets with pseudo-images generated with a novel conditional PET generative adversarial network (CPGAN), which employs a weighted fusion of CT images and tumor masks, embedding also clinical data. As for GIST assessment, we propose the transformer-based multimodal network for GIST risk stratification (TMGRS), which is trained on the enriched dataset and exploits the properties of transformers to process simultaneously PET and CT images. The training and validation of the models were conducted on a multicenter dataset comprising 208 patients. In comparison with the existing stratification methods, CPGAN-synthesized PET images show a peak signal-to-noise ratio increased on average by 18% and improve risk stratification, which achieves a remarkable accuracy of 0.937 when TMGRS network is used. Results underscore the potential of CPGAN network in providing more reliable GIST predictions.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"487-496"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761571","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}

引用次数: 0

Genotype Characterization in Primary Brain Gliomas via Unsupervised Clustering of Dynamic PET Imaging of Short-Chain Fatty Acid Metabolism

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-12-09 DOI: 10.1109/TRPMS.2024.3514087

Marianna Inglese;Tommaso Boccato;Matteo Ferrante;Shah Islam;Matthew Williams;Adam D. Waldman;Kevin O’Neill;Eric O. Aboagye;Nicola Toschi

{"title":"Genotype Characterization in Primary Brain Gliomas via Unsupervised Clustering of Dynamic PET Imaging of Short-Chain Fatty Acid Metabolism","authors":"Marianna Inglese;Tommaso Boccato;Matteo Ferrante;Shah Islam;Matthew Williams;Adam D. Waldman;Kevin O’Neill;Eric O. Aboagye;Nicola Toschi","doi":"10.1109/TRPMS.2024.3514087","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3514087","url":null,"abstract":"The impact of genetics on the diagnosis, treatment, and survival outcomes for patients with brain glioma is significant. At present, isocitrate dehydrogenase (IDH) mutation, the key biomarker in brain glioma with considerably better-survival rates, lacks a distinct radiologic signature. In this study, we targeted the glioma specific mechanism involving short chain fatty acid (SCFA) transcellular flux (TF) for energy production using 18F-fluoropivalate (FPIA) PET tracer and used this information to characterize the genetic profile of 10 patients with brain gliomas (5 IDH-mutant and 5 wild-type). We discerned four unique SCFA metabolic profiles by applying k-means clustering to an average of <inline-formula> <tex-math>$25202~(pm ~14337$ </tex-math></inline-formula>) time activity curves (TACs) extracted from dynamic 18F-FPIA PET scans. Using deep learning (DL), the TACs from the first two clusters accurately differentiated between mutant and wild-type gliomas (<inline-formula> <tex-math>$96.75pm 3.24$ </tex-math></inline-formula>% accuracy, <inline-formula> <tex-math>$0.96pm 0.04$ </tex-math></inline-formula> AUC). The third cluster, the one with the lowest-FPIA SUV, showed the worst performance (<inline-formula> <tex-math>$23.67pm 16.83$ </tex-math></inline-formula>% accuracy, <inline-formula> <tex-math>$0.31pm 0.17$ </tex-math></inline-formula> AUC), suggesting that only a subset of SCFA-TF profiles define the genetic status of the tumor. Finally, disregarding the heterogeneity of SCFA-TF significantly reduced our model’s effectiveness, with accuracies dropping to <inline-formula> <tex-math>$67.40pm 22.87$ </tex-math></inline-formula>% and <inline-formula> <tex-math>$70.42pm 16.25$ </tex-math></inline-formula>% when tested using static SUV PET data and the full range of FPIA TACs, respectively.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"460-467"},"PeriodicalIF":4.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761570","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}

引用次数: 0

Progressively Prompt-Guided Models for Sparse-View CT Reconstruction

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-12-04 DOI: 10.1109/TRPMS.2024.3512172

Jiajun Li;Wenchao Du;Huanhuan Cui;Hu Chen;Yi Zhang;Hongyu Yang

{"title":"Progressively Prompt-Guided Models for Sparse-View CT Reconstruction","authors":"Jiajun Li;Wenchao Du;Huanhuan Cui;Hu Chen;Yi Zhang;Hongyu Yang","doi":"10.1109/TRPMS.2024.3512172","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3512172","url":null,"abstract":"While sparse-view computed tomography (CT) remarkably reduces the ionizing radiation dose, the reconstructed images have been compromised by streak-like artifacts, affecting clinical diagnostics. The deep unrolled methods have achieved promising results by integrating powerful regularization terms with deep learning technologies into iterative reconstruction algorithms. However, leading works focus on designing powerful regularization term to capture image and noise priors, which always requires carefully designed blocks, and leads to heavy computational burden while bringing over-smoothness into results. In this article, we integrate the idea of prompt learning into the general regularization terms, and propose a progressively prompt-guided model (shorted by PPM) to alleviate above problems. More specifically, we inject a prompting module into each unrolled block to perceive more native priors in a self-adaptive manner, which would capture more effective image and noise priors to guide high-quality CT reconstruction. Furthermore, we propose a progressively guiding strategy to facilitate high-quality prompt generation while speeding model convergence. Extensive experiments on multiple sparse-view CT reconstruction benchmarks demonstrate that our PPM achieves state-of-the-art performance in terms of artifact reduction and structure preservation while with fewer parameters and higher-inference efficiency.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"447-459"},"PeriodicalIF":4.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10778259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761354","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}

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2024 Index IEEE Transactions on Radiation and Plasma Medical Sciences Vol. 8 2024 Index IEEE Transactions on Radiation and Plasma Medical Sciences Vol.

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-11-25 DOI: 10.1109/TRPMS.2024.3483528

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Development and Performance Optimization of a Multiplexed DOI-Encoding PET Detector Using the TOFPET2 ASIC

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-11-22 DOI: 10.1109/TRPMS.2024.3505135

Francisco E. Enríquez-Mier-y-Terán;Walter Pineda-Valencia;Martin S. Judenhofer;Steven R. Meikle;Andre Z. Kyme

{"title":"Development and Performance Optimization of a Multiplexed DOI-Encoding PET Detector Using the TOFPET2 ASIC","authors":"Francisco E. Enríquez-Mier-y-Terán;Walter Pineda-Valencia;Martin S. Judenhofer;Steven R. Meikle;Andre Z. Kyme","doi":"10.1109/TRPMS.2024.3505135","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3505135","url":null,"abstract":"Depth-of-interaction (DOI) positron emission tomography (PET) detectors are essential in high-resolution small animal PET. The dual-ended readout detector configuration provides high DOI resolution; however, signal multiplexing schemes are often needed to reduce manufacturing costs. TOFPET2 is a low-cost, multichannel application-specific integrated circuit (ASIC) designed for individual silicon photomultiplier (SiPM) readout, with performance in multiplexed configurations not well known. This study evaluates a highly multiplexed, high-resolution, dual-ended readout PET detector using TOFPET2. The PET detector was constructed with a <inline-formula> <tex-math>$23times 23$ </tex-math></inline-formula> array of LYSO crystals (<inline-formula> <tex-math>$0.785times 0.785times $ </tex-math></inline-formula> 20 mm3) coupled to <inline-formula> <tex-math>$6times 6$ </tex-math></inline-formula> SiPM arrays (pitch <inline-formula> <tex-math>${=}3$ </tex-math></inline-formula>.3 mm) via 1.2 mm PMMA sheets. Custom boards reduced 72 SiPM signals to eight (four per end) before feeding into TOFPET2 channels. The detector was biased at 27, 27.5, and 28 V, with ASIC discriminator thresholds optimized based on flood map quality. Optimal flood maps were obtained at 27.5 V. The mean energy and DOI resolution were <inline-formula> <tex-math>$10.5~pm ~1.6$ </tex-math></inline-formula>% and <inline-formula> <tex-math>$2.3~pm ~0$ </tex-math></inline-formula>.3 mm, respectively, with a coincidence time resolution (CTR) of 1.3 ns between two detectors. This work demonstrates the feasibility of employing TOFPET2 to achieve high spatial, energy, and DOI resolution in a 9:1 multiplexed system.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"395-405"},"PeriodicalIF":4.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10764789","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761465","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}

引用次数: 0