IEEE Transactions on Radiation and Plasma Medical Sciences最新文献_第3页

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

Low-Dose CT Using a Nonlocal and Nonlinear Principal Component Analysis for Image Restoration 基于非局部非线性主成分分析的低剂量CT图像恢复

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2025-01-01 DOI: 10.1109/TRPMS.2024.3523366

Erfan Ebrahim Esfahani;Andishe Gouran

{"title":"Low-Dose CT Using a Nonlocal and Nonlinear Principal Component Analysis for Image Restoration","authors":"Erfan Ebrahim Esfahani;Andishe Gouran","doi":"10.1109/TRPMS.2024.3523366","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3523366","url":null,"abstract":"Computed tomography (CT) is a widely used medical imaging modality which provides invaluable visual representation of various conditions ranging from neurological lesions, such as haemorrhage, stroke, tumors, etc., to cardiovascular disorders like calcium deposits, pulmonary embolism, and many other pathologies. However, the ionizing radiation from the CT machine’s X-ray tube has to be kept in check, because overexposure is related to elevated risks for genetic mutation or cancer development. In this work, we attempt to reduce the radiation exposure required for high-quality CT image formation by establishing rank sparsity in principal components’ domain and developing a compressed sensing framework based on a novel nonlocal and nonlinear low-rank principal component analysis technique in image denoising, which will be subsequently incorporated as a building block for a sparse-view CT image reconstruction framework under the umbrella of convex analysis. Experiments will show that the proposed strategy provides a viable solution for low-dose CT, outperforming other well-known nonlocal image restoration models in both denoising and reconstruction tasks. In particular, the proposed method will offer 4%–10% improvement in root-mean-squared error relative to other nonlocal methods at little extra computational time.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"639-653"},"PeriodicalIF":4.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900589","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

Experimental Evaluation of Maximum-Likelihood-Based Data Preconditioning for DE-SPECT: A Clinical SPECT System Constructed With CZT Imaging Detectors 基于最大似然的DE-SPECT数据预处理的实验评估：一个由CZT成像探测器构建的临床SPECT系统

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-12-25 DOI: 10.1109/TRPMS.2024.3520668

Yifei Jin;E. M. Zannoni;Ling-Jian Meng

{"title":"Experimental Evaluation of Maximum-Likelihood-Based Data Preconditioning for DE-SPECT: A Clinical SPECT System Constructed With CZT Imaging Detectors","authors":"Yifei Jin;E. M. Zannoni;Ling-Jian Meng","doi":"10.1109/TRPMS.2024.3520668","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3520668","url":null,"abstract":"This study introduces a novel maximum-likelihood-based data preconditioning method for a 3-D position sensitive cadmium zinc telluride (CZT) detector used in the dynamic extremity-single photon emission computed tomography imaging system, an organ-dedicated Single-Photon Emission computed tomography system optimized for imaging peripheral vascular diseases in lower extremities. The 3-D CZT detectors offer subpixel resolution of ~0.5 mm FWHM in X-Y-Z directions and an ultrahigh energy resolution of 3 keV at 200 keV, 4.5 keV at 450 keV, and 5.4 keV at 511 keV. Given the intrinsic challenges posed by pixel boundary issues, spatial distortions, and nonuniformity inherent in large-volume, high-resolution CZT detectors, we proposed a Maximum-Likelihood-based preconditioning technique to reconstruct the projection, which effectively mitigates the pixel boundary issue and deconvolves the distortions and nonuniformity in detector responses. To facilitate the preconditioning step, we used sheet-beam scanning to measure the distortion map of the CZT detectors. We have evaluated our data preconditioning technique through extensive experimental evaluations, including Tc-99m sheet-beam scanning and image reconstruction of an image quality phantom. These results not only demonstrated the efficacy of the technique in reducing the impact of pixel boundary issues and correcting for spatial distortions. The proposed data preconditioning technique could potentially be applied across various types of imaging sensors.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"553-563"},"PeriodicalIF":4.6,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900608","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

Timing Performance With Broadcom Metal Trench Silicon Photomultipliers Broadcom金属沟槽硅光电倍增管的时序性能

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

Energy Scale-Factor Estimation for Use in Photomultiplier Tube Energy Calibration Using C-SPECT 利用C-SPECT进行光电倍增管能量校准的能量标度因子估计

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-12-13 DOI: 10.1109/TRPMS.2024.3517421

Dale J. Stentz;Poopalasingam Sankar;Lindsay C. Johnson;Scott D. Metzler

{"title":"Energy Scale-Factor Estimation for Use in Photomultiplier Tube Energy Calibration Using C-SPECT","authors":"Dale J. Stentz;Poopalasingam Sankar;Lindsay C. Johnson;Scott D. Metzler","doi":"10.1109/TRPMS.2024.3517421","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3517421","url":null,"abstract":"An array of photomultiplier tubes (PMTs) provides energy readout for gamma cameras, leading to event selection and positioning. However, operational and environmental changes, such as temperature, can cause PMTs to “drift” away from their nominal energy readouts and, therefore, require a correction procedure to return to their reference energies. We present two methods for determining the energy-scale change of each PMT using data collected on C-SPECT, a dedicated cardiac single-photon emission computational tomography (SPECT) scanner. A scan of a vertical line source of 99mTc provides the data from which we produce an energy histogram for each of the 130 PMTs. Each energy histogram is composed of events passing an energy-fraction selection to give events closest to the PMT center. We consider energy fractions ranging from 0.25% to 5.00%. For our analysis, we use bootstrapping to create data realizations as well as emulating energy-scale changes (simultaneously and independently for all PMTs) in the data. Using the average relative error as a measurement of the accuracy and the standard deviation from taking bootstrapped replicates of our data as a measurement of our precision, we determine the energy-scaling to within -0.05% <inline-formula> <tex-math>$pm ~0.03$ </tex-math></inline-formula>% (mean and standard deviation, respectively).","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"531-541"},"PeriodicalIF":4.6,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900516","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

Noise-Generating Mechanism-Driven Implicit Diffusion Prior for Low-Dose CT Sinogram Recovery 低剂量CT正弦图恢复中噪声产生机制驱动的隐式扩散先验

IF 4.6

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-12-11 DOI: 10.1109/TRPMS.2024.3515036

Xing Li;Yan Yang;Qingyong Zhu;Jianhua Ma;Hairong Zheng;Zongben Xu

{"title":"Noise-Generating Mechanism-Driven Implicit Diffusion Prior for Low-Dose CT Sinogram Recovery","authors":"Xing Li;Yan Yang;Qingyong Zhu;Jianhua Ma;Hairong Zheng;Zongben Xu","doi":"10.1109/TRPMS.2024.3515036","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3515036","url":null,"abstract":"Low-dose computed tomography (CT) images often suffer from noise and artifacts from photon starvation and electronic noise. Recent advancements in deep learning (DL) techniques have significantly improved outcomes in low-dose CT (LDCT) imaging. However, many existing methods require costly low-dose/high-dose CT image pairs for supervised training, which is difficult to obtain in clinical. In this article, we propose a novel unsupervised approach for LDCT sinogram recovery based on the noise generation mechanism within the Bayes framework. Specifically, we introduce a novel formulation of sinogram recovery model based on the noise-generating mechanism without additional regularization terms. Then, we design an efficient algorithm that utilizes Bayes rules to solve the sinogram recovery model, offering approximate and analytical solutions for all decomposed score functions. Instead of relying on deep network priors, we adopt an implicit diffusion model to characterize the common latent prior of sinogram data and enable the iterative algorithm more efficient and interpretable. Extensive experiments conducted on two datasets demonstrate the superiority of our proposed method over competing techniques in both denoising and generalization performance.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"586-597"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900517","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 ConvNeXt-2U：一种基于三维深度学习的分割模型，用于Y-90放射栓塞剂量学中肺、正常肝脏和肿瘤的统一自动分割

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 从CT到[¹⁸F]-FDG PET图像的交叉模态综合增强胃肠道间质瘤的风险分层

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 通过短链脂肪酸代谢的无监督聚类动态PET成像表征原发性脑胶质瘤的基因型

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 稀疏视图CT重建的逐步快速引导模型

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}

引用次数: 0