IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"PET Detectors Based on Multi-Resolution SiPM Arrays","authors":"Jiahao Xie;Haibo Wang;Simon R. Cherry;Junwei Du","doi":"10.1109/TRPMS.2024.3381865","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3381865","url":null,"abstract":"Almost all high spatial resolution positron emission tomography (PET) detectors based on pixelated scintillator arrays utilize crystal arrays with smaller pitches than photodetector arrays, leading to challenges in resolving edge crystals. To address this issue, this article introduces a novel multi-resolution silicon photomultiplier (SiPM) array design aimed at decreasing the number of readout channels required while maintaining the crystal resolvability of the detector, especially for edge crystals. The performance of a pseudo \u0000<inline-formula> <tex-math>$9times9$ </tex-math></inline-formula>\u0000 multi-resolution SiPM array, consisting of \u0000<inline-formula> <tex-math>$6.47times6.47$ </tex-math></inline-formula>\u0000 mm 2, \u0000<inline-formula> <tex-math>$6.47times3.07$ </tex-math></inline-formula>\u0000 mm 2, and \u0000<inline-formula> <tex-math>$3.07times3.07$ </tex-math></inline-formula>\u0000 mm2 SiPMs, was compared to those of a pseudo \u0000<inline-formula> <tex-math>$8times8$ </tex-math></inline-formula>\u0000 SiPM array with a 6.8-mm pitch, and a \u0000<inline-formula> <tex-math>$16times16$ </tex-math></inline-formula>\u0000 SiPM array with a 3.4-mm pitch using a \u0000<inline-formula> <tex-math>$36times36$ </tex-math></inline-formula>\u0000 LYSO array with a pitch of 1.5 mm. The large-size pseudo SiPMs were implemented by digitally grouping multiple \u0000<inline-formula> <tex-math>$3.07times3.07$ </tex-math></inline-formula>\u0000 mm2 SiPMs. The flood histograms show that the edge crystal resolvability of the pseudo \u0000<inline-formula> <tex-math>$9times9$ </tex-math></inline-formula>\u0000 multi-resolution SiPM array is comparable to that of the \u0000<inline-formula> <tex-math>$16times16$ </tex-math></inline-formula>\u0000 SiPM array and is significantly better than that of the \u0000<inline-formula> <tex-math>$8times8$ </tex-math></inline-formula>\u0000 SiPM array.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"493-500"},"PeriodicalIF":4.4,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820283","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":"DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT","authors":"Md Ashequr Rahman;Zitong Yu;Richard Laforest;Craig K. Abbey;Barry A. Siegel;Abhinav K. Jha","doi":"10.1109/TRPMS.2024.3379215","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3379215","url":null,"abstract":"There is an important need for methods to process myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images acquired at lower-radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects compared to low-dose images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners (\u0000<inline-formula> <tex-math>$N,,=$ </tex-math></inline-formula>\u0000 338). The evaluation was performed at low-dose levels of 6.25%, 12.5%, and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic deep learning-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 4","pages":"439-450"},"PeriodicalIF":4.4,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10477616","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342755","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":"Emphasizing Cherenkov Photons From Bismuth Germanate by Single Photon Response Deconvolution","authors":"Ryosuke Ota;Kibo Ote","doi":"10.1109/TRPMS.2024.3403959","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3403959","url":null,"abstract":"Bismuth germanate (BGO) has been receiving attention again because it is a potential scintillator for future time-of-flight positron emission tomography. Owing to its optical properties, BGO emits a relatively large number of Cherenkov photons after 511-keV gamma-ray interactions, which can enable good coincidence time resolution (CTR). Nonetheless, optimally exploiting the Cherenkov emissions can be confounded by scintillation emissions. Thus, we propose a method efficiently emphasizing Cherenkov photon from a detector waveform by deconvolving a single photon response of photodetector. As a proof-of-concept, we perform the deconvolution, and a probability density function (PDF) of BGO was obtained, which is compared to a conventional time correlated single photon counting (TCSPC) method. Furthermore, we investigate if the proposed deconvolution can emphasize a faint Cherenkov signal. Consequently, the PDF obtained by the proposed deconvolution shows a good agreement with that obtained using a conventional TCSPC methods. A CTR obtained using the proposed deconvolution is improved by 12% and 43% in full width at half maximum compared to a voltage-based leading edge discriminator for with and without high-frequency readout electronics, respectively. Thus, the proposed deconvolution method can efficiently emphasize Cherenkov photon by lowering the threshold level and improve the timing performance of BGO-based detectors.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"595-606"},"PeriodicalIF":4.6,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500389","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":"Two-Stage Deep Denoising With Self-Guided Noise Attention for Multimodal Medical Images","authors":"S. M. A. Sharif;Rizwan Ali Naqvi;Woong-Kee Loh","doi":"10.1109/TRPMS.2024.3380090","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3380090","url":null,"abstract":"Medical image denoising is considered among the most challenging vision tasks. Despite the real-world implications, existing denoising methods have notable drawbacks as they often generate visual artifacts when applied to heterogeneous medical images. This study addresses the limitation of the contemporary denoising methods with an artificial intelligence (AI)-driven two-stage learning strategy. The proposed method learns to estimate the residual noise from the noisy images. Later, it incorporates a novel noise attention mechanism to correlate estimated residual noise with noisy inputs to perform denoising in a course-to-refine manner. This study also proposes to leverage a multimodal learning strategy to generalize the denoising among medical image modalities and multiple noise patterns for widespread applications. The practicability of the proposed method has been evaluated with dense experiments. The experimental results demonstrated that the proposed method achieved state-of-the-art performance by significantly outperforming the existing medical image denoising methods in quantitative and qualitative comparisons. Overall, it illustrates a performance gain of 7.64 in peak signal-to-noise ratio (PSNR), 0.1021 in structural similarity index (SSIM), 0.80 in DeltaE \u0000<inline-formula> <tex-math>$(Delta E)$ </tex-math></inline-formula>\u0000, 0.1855 in visual information fidelity pixelwise (VIFP), and 18.54 in mean squared error (MSE) metrics.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"521-531"},"PeriodicalIF":4.4,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820221","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":"Medical Multimodal Image Transformation With Modality Code Awareness","authors":"Zhihua Li;Yuxi Jin;Qingneng Li;Zhenxing Huang;Zixiang Chen;Chao Zhou;Na Zhang;Xu Zhang;Wei Fan;Jianmin Yuan;Qiang He;Weiguang Zhang;Dong Liang;Zhanli Hu","doi":"10.1109/TRPMS.2024.3379580","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3379580","url":null,"abstract":"In the planning phase of radiation therapy, positron emission tomography (PET) images are frequently integrated with computed tomography (CT) and MRI to accurately delineate the target region for treatment. However, obtaining additional CT or magnetic resonance (MR) images solely for localization purposes proves to be financially burdensome, time-intensive, and may increase patient radiation exposure. To alleviate these issues, a deep learning model with dynamic modality translation capabilities is introduced. This approach is achieved through the incorporation of adaptive modality translation layers within the decoder module. The adaptive modality translation layer effectively governs modality transformation by reshaping the data distribution of features extracted by the encoder using switch codes. The model’s performance is assessed on images with reference images using evaluation metrics, such as peak signal-to-noise ratio, structural similarity index measure, and normalized mean square error. For results without reference images, subjective assessments are provided by six nuclear medicine physicians based on clinical interpretations. The proposed model demonstrates impressive performance in transforming nonattenuation corrected PET images into user-specified modalities (attenuation corrected PET, MR, or CT), effectively streamlining the acquisition of supplemental modality images in radiation therapy scenarios.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"511-520"},"PeriodicalIF":4.4,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820224","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":"Implementing an Integrated Neural Network for Real-Time Position Reconstruction in Emission Tomography With Monolithic Scintillators","authors":"S. Di Giacomo;M. Ronchi;G. Borghi;D. R. Schaart;M. Carminati;C. Fiorini","doi":"10.1109/TRPMS.2024.3378421","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3378421","url":null,"abstract":"Embedding signal processing in the front-end of radiation detectors represents an approach to cope with the growing complexity of nuclear imaging scanners with increasing field of view (i.e., higher number of channels). Machine learning (ML) offers a good compromise between intrinsic image reconstruction performance and computational power. While most hardware accelerators for ML are based on digital circuits and, thus, require the analog-to-digital conversion of all individual signals from photodetectors, an analog approach allows to streamline the pipeline. We present the study of an analog accelerator implementing a neural network (NN) with 42 neurons in a 0.35-\u0000<inline-formula> <tex-math>${mu }$ </tex-math></inline-formula>\u0000m CMOS process node. The specific target is the reconstruction of the position of interaction of gamma-rays in the scintillator crystal of Anger cameras used for PET and SPECT. This chip can be used stand-alone or monolithically integrated within the application specific integrated circuit (ASIC) for the filtering of current signals from arrays of silicon photomultipliers (SiPMs). Computation is performed in charge domain by means of crossbar arrays of programmable capacitor. The architecture of the 64-input ASIC and the training of the NN are presented, discussing the impact of weight quantization on 5 bits. From MATLAB and circuit simulations, consistent with ASIC topology and operations, the NN capabilities were tested using two different datasets, obtained from both simulated data and experimental data, both based on PET detector composed by a monolithic scintillator crystal readout by an \u0000<inline-formula> <tex-math>$8times 8$ </tex-math></inline-formula>\u0000 array of SiPMs. Simulations show an achievable spatial resolution better than 2-mm full-width-at-half-maximum with a 10-mm thick crystal, a max. count rate of 200kHz and the energy efficiency per inference is estimated to be of 93.5GOP/J, i.e., competitive with digital counterparts, with an energy consumption of 38nJ per inference and area of 23mm2.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"501-510"},"PeriodicalIF":4.4,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820220","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":"Building a Kinetic Induced Voxel-Clustering Filter (KVCF) for Low-Dose Perfusion CT Imaging","authors":"Zixiang Chen;Yuxi Jin;Zhenxing Huang;Na Zhang;Kaiyi Liang;Guotao Quan;Dong Liang;Hairong Zheng;Zhanli Hu","doi":"10.1109/TRPMS.2024.3402272","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3402272","url":null,"abstract":"Dynamic cerebral perfusion CT (PCT) is an effective imaging technique for the clinical diagnosis and therapy guidance of many kinds of cerebrovascular diseases (CVDs), but the large radiation dose imposed on a patient during repeated CT scans greatly limits its clinical applications. Achieving low-dose PCT imaging with the help of advanced and satisfactory imaging methods is needed. A kinetic-induced voxel-clustering filter (KVCF) is proposed in this work to help process noisy and distorted PCT images acquired from the low-dose CT scan protocols. In this new method, the intrinsic kinetic information of objective PCT images is extracted and effectively utilized to construct an image filter for every PCT frame. The new method is validated using both the simulated and clinical low-dose PCT data, and the peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) are applied for quantitative evaluations of both the dynamic images and the calculated hemodynamic parametric maps. Compared to several existing methods, the proposed KVCF method produces the best qualitative and quantitative imaging effects. With satisfactory performance and high interpretability, KVCF is proven to be effective and implementable in the clinical low-dose PCT imaging tasks.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"762-773"},"PeriodicalIF":4.6,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143650","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":"Comparative Analysis of Data Acquisition Setups for Fast Timing in ToF-PET Applications","authors":"Riccardo Latella;Antonio J. Gonzalez;José M. Benlloch;Paul Lecoq;Georgios Konstantinou","doi":"10.1109/TRPMS.2024.3401391","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3401391","url":null,"abstract":"The signal-to-noise ratio in positron emission tomography (PET) improves with precise timing resolution. PET systems enabling the capability of time-of-flight (ToF) are nowadays available. This study assesses various data configurations, comparing the obtained timing performances applicable to time-of-flight positron emission tomography (ToF-PET) systems. Different readout configurations were evaluated together with silicon photomultipliers (SiPMs) photosensors from the Fondazione Bruno Kessler (FBK), with and without the so-called metal trench (MT) technology. The tests were carried out with scintillation crystals of \u0000<inline-formula> <tex-math>$3times 3times $ </tex-math></inline-formula>\u00005 mm\u0000³\u0000 (LYSO:Ce,Ca) from SIPAT. Two onboard FPGA-based systems, namely, the Felix time-to-digital converter (TDC) from Tediel S.r.l. and the ASIC-based FastIC from the University of Barcelona, along with custom-made high-frequency electronics (CM-HF), were compared. Considering only photopeak events, the best-coincidence timing resolution (CTR) results obtained were 71 ps with the MT SiPMs. This result worsened to 88 ps with the old version of the same device that does not include the MT technology (called HD). The results demonstrate substantial CTR improvements when MT SiPMs were used across the different scenarios, resulting in a timing improvement in the 10 to 45-ps range compared to HD SiPMs. Notably, the Felix TDC achieved sub-100-ps timing results, emphasizing the potential of FPGA technology in ToF-PET applications. Moreover, the fully passive version of the CM-HF connected to the MT SiPMs shows only a degradation of 8-ps difference compared to the version using amplifiers. The novel MT-type SiPMs promise superior timing performance, enhancing accuracy and efficiency in PET imaging systems.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"743-751"},"PeriodicalIF":4.6,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10531074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143677","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 High-Resolution Portable Gamma-Camera for Estimation of Absorbed Dose in Molecular Radiotherapy","authors":"T. Bossis;M.-A. Verdier;C. Trigila;L. Pinot;F. Bouvet;A. Blot;H. Ramarijaona;T. Beaumont;D. Broggio;O. Caselles;S. Zerdoud;L. Ménard","doi":"10.1109/TRPMS.2024.3376826","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3376826","url":null,"abstract":"Molecular radiotherapy is a treatment modality that requires personalized dosimetry for efficient treatment and reduced toxicity. Current clinical imaging systems and miniaturized gamma-cameras lack the necessary features for this task. In this article, we present the design and optimization of a mobile gamma-camera with a \u0000<inline-formula> <tex-math>$10times 10$ </tex-math></inline-formula>\u0000 cm2 field of view tailored for quantitative imaging during \u0000<inline-formula> <tex-math>$^{131}text{I}$ </tex-math></inline-formula>\u0000 therapy of thyroid diseases. The camera uses a monolithic \u0000<inline-formula> <tex-math>$10times 10times 1$ </tex-math></inline-formula>\u0000 cm3 CeBr3 scintillator coupled to a \u0000<inline-formula> <tex-math>$16times 16$ </tex-math></inline-formula>\u0000 SiPMs array and commercial electronics. It exhibits high imaging performance with an intrinsic spatial resolution (SR) of 1.15-mm FWHM, an energy resolution of 8% FWHM at 356 keV and negligible deadtime up to 150 kcps. Images are reconstructed in real time using a convolutional neural network. The manufacturing method of tungsten collimators and shielding was optimized using laser 3-D printing to achieve an effective density of 97% that of bulk tungsten. Their geometry was adjusted with Monte-Carlo simulations in order to reduce septal penetration and scattering and optimize the signal-to-noise ratio at short times after treatment administration. Two high-energy parallel-hole collimators with high sensitivity or very high SR were designed for treatment planning and post-treatment control. The fully operational gamma-camera will soon be clinically assessed.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"556-570"},"PeriodicalIF":4.4,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10472319","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820334","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 Parametric Physical Model-Based X-Ray Spectrum Estimation Approach for CT Imaging","authors":"Shaojie Chang;Chaoyang Zhang;Xuanqin Mou;Qiong Xu;Lijun He;Xi Chen","doi":"10.1109/TRPMS.2024.3374702","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3374702","url":null,"abstract":"X-ray spectrum plays an essential role in CT applications. Since it is difficult to measure X-ray spectrum directly in practice, X-ray spectrum is always indirectly obtained by using transmission measurements through a calibration phantom of known thickness and materials. These methods are independent of CT image reconstruction and bring extra cost. To solve this problem, we propose a parametric physical model-based X-ray spectrum estimation algorithm for CT imaging. First, an X-ray spectrum model with six parameters is proposed, which is derived from the X-ray imaging physics. Second, a template image containing different material components can be obtained by segmenting CT reconstructed images with a simple method. And the estimated projections can be calculated by reprojecting the template image with the proposed spectrum model. Finally, the six model parameters can be solved by iteratively minimizing the error between the estimated projection and real measurements. The effectiveness of the proposed method has been validated on both simulated and real data. Experimental results demonstrate that the proposed method can estimate the accurate spectra at different energies and provide a good reconstruction of characteristic radiations without extra cost.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"532-539"},"PeriodicalIF":4.4,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820222","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}