Pub Date : 2024-06-06DOI: 10.1109/TRPMS.2024.3408870
Mikiko Ito;Dahea Han;Tae-Hyung Kim;Young-Tae Kim;Sungeun Lee;Jeongtae Soh;Young-Jun Jung;Byungkee Lee
Digital tomosynthesis (DTS) can enhance diagnostic accuracy by providing 3-D volume images with a remarkably low-X-ray dose. The aim of this study is to provide an initial assessment of the image quality and the X-ray dose for a mobile DTS system employing a moving carbon-nanotube (CNT)-based digital X-ray source array and a fixed detector for extremity scans. This design allows to reduce the source-to-detector distance (SDD) to only 400 mm, thereby enabling a compact and highly mobile system. We first measured the entrance surface dose (ESD), which is the sum of the X-ray dose irradiated from individual projections using a dosimeter placed at the center of the X-ray detector. The ESDs obtained for hand, foot, and knee scan configurations were 0.15, 0.22, and 0.43 mGy, respectively, which were comparable to those obtained from 2-D radiography exposures. For the evaluation of its reconstructed image quality, the in-plane modulation transfer function (MTF), Z -resolution, geometry distortion, and image homogeneity were assessed by utilizing a wire-phantom, sphere-phantom, and PMMA phantoms. The reconstructed images of hand, ankle and knee phantoms were evaluated qualitatively. The results of the evaluation demonstrate the successful development of the mobile DTS system proposed in this article.
数字断层扫描（DTS）能以极低的 X 射线剂量提供三维容积图像，从而提高诊断的准确性。本研究的目的是对采用移动碳纳米管（CNT）数字 X 射线源阵列和固定探测器进行四肢扫描的移动 DTS 系统的图像质量和 X 射线剂量进行初步评估。这种设计可将光源到探测器的距离（SDD）缩短到仅 400 毫米，从而实现了系统的紧凑性和高度移动性。我们首先测量了入口表面剂量（ESD），即使用放置在 X 射线探测器中心的剂量计测量从单个投影照射的 X 射线剂量的总和。手部、足部和膝部扫描配置获得的 ESD 分别为 0.15、0.22 和 0.43 mGy，与二维放射摄影曝光获得的 ESD 相当。为了评估其重建图像的质量，利用线状模型、球状模型和 PMMA 模型对平面内调制传递函数（MTF）、Z 分辨率、几何失真和图像均匀性进行了评估。对手部、踝关节和膝关节模型的重建图像进行了定性评估。评估结果表明，本文提出的移动 DTS 系统开发成功。
{"title":"Performance Evaluation of a Mobile Digital Tomosynthesis System Using a Moving CNT-Based Tube Array for Extremity Scans","authors":"Mikiko Ito;Dahea Han;Tae-Hyung Kim;Young-Tae Kim;Sungeun Lee;Jeongtae Soh;Young-Jun Jung;Byungkee Lee","doi":"10.1109/TRPMS.2024.3408870","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3408870","url":null,"abstract":"Digital tomosynthesis (DTS) can enhance diagnostic accuracy by providing 3-D volume images with a remarkably low-X-ray dose. The aim of this study is to provide an initial assessment of the image quality and the X-ray dose for a mobile DTS system employing a moving carbon-nanotube (CNT)-based digital X-ray source array and a fixed detector for extremity scans. This design allows to reduce the source-to-detector distance (SDD) to only 400 mm, thereby enabling a compact and highly mobile system. We first measured the entrance surface dose (ESD), which is the sum of the X-ray dose irradiated from individual projections using a dosimeter placed at the center of the X-ray detector. The ESDs obtained for hand, foot, and knee scan configurations were 0.15, 0.22, and 0.43 mGy, respectively, which were comparable to those obtained from 2-D radiography exposures. For the evaluation of its reconstructed image quality, the in-plane modulation transfer function (MTF), \u0000<italic>Z</i>\u0000-resolution, geometry distortion, and image homogeneity were assessed by utilizing a wire-phantom, sphere-phantom, and PMMA phantoms. The reconstructed images of hand, ankle and knee phantoms were evaluated qualitatively. The results of the evaluation demonstrate the successful development of the mobile DTS system proposed in this article.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"826-838"},"PeriodicalIF":4.6,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143750","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}
Pub Date : 2024-06-05DOI: 10.1109/TRPMS.2024.3410021
Jiayi Lu;Renchao Jin;Enmin Song
Objective: Serial medical image registration plays an important role in radiation therapy treatment planning. However, current deep learning-based deformable registration models suffer from excessive resource consumption and suboptimal precision issues. Moreover, the global regularization term may result in unrealistic deformations due to displacement field noise and intertissue sliding motion omission. Methods: This article proposes a patch-based pyramid convolutional recurrent neural network (pyramid CRNet) for serial medical image registration. Patch-wise training is employed to alleviate resource constraints. Incorporating spatiotemporal features across multiple scales is beneficial for focusing on more details to improve accuracy. Moreover, two motion adaptive techniques are introduced to provide anatomically plausible displacement fields. The first uses a guided filter to reduce noise and maintain motion continuity within organs. The second involves a pixel-wise weight regularization term within the loss function to provide a tailored solution for distinctive tissue characteristics, especially for sliding motion at organ boundaries. Results: Experiments were conducted on lung 4DCT images and cardiac cine MR images. Quantitative and qualitative results have demonstrated that our method can align anatomical structures across multiple images in a physiologically sensible manner. Conclusion: The significance of this work lies in its potential to address pressing challenges in clinical applications, and further investigations could be extended to explore different modalities and dimensions.
{"title":"Pyramid Convolutional Recurrent Network for Serial Medical Image Registration With Adaptive Motion Regularizations","authors":"Jiayi Lu;Renchao Jin;Enmin Song","doi":"10.1109/TRPMS.2024.3410021","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3410021","url":null,"abstract":"<italic>Objective:</i>\u0000 Serial medical image registration plays an important role in radiation therapy treatment planning. However, current deep learning-based deformable registration models suffer from excessive resource consumption and suboptimal precision issues. Moreover, the global regularization term may result in unrealistic deformations due to displacement field noise and intertissue sliding motion omission. \u0000<italic>Methods:</i>\u0000 This article proposes a patch-based pyramid convolutional recurrent neural network (pyramid CRNet) for serial medical image registration. Patch-wise training is employed to alleviate resource constraints. Incorporating spatiotemporal features across multiple scales is beneficial for focusing on more details to improve accuracy. Moreover, two motion adaptive techniques are introduced to provide anatomically plausible displacement fields. The first uses a guided filter to reduce noise and maintain motion continuity within organs. The second involves a pixel-wise weight regularization term within the loss function to provide a tailored solution for distinctive tissue characteristics, especially for sliding motion at organ boundaries. \u0000<italic>Results:</i>\u0000 Experiments were conducted on lung 4DCT images and cardiac cine MR images. Quantitative and qualitative results have demonstrated that our method can align anatomical structures across multiple images in a physiologically sensible manner. \u0000<italic>Conclusion:</i>\u0000 The significance of this work lies in its potential to address pressing challenges in clinical applications, and further investigations could be extended to explore different modalities and dimensions.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"800-813"},"PeriodicalIF":4.6,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143653","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}
Pub Date : 2024-06-03DOI: 10.1109/TRPMS.2024.3408818
Yuya Kuriyama;Mitsuhiro Nakamura;Megumi Nakao
The field of medical machine learning has encountered the challenge of constructing a large-scale image database that includes both the anatomical variability and teaching labels because there are often not sufficient cases of a specific disease. Adversarial learning has been studied for nonlinear data augmentation. However, deep learning models may produce anatomically unrealistic structures or inaccurate pixel values when applied to small sets of computed tomography (CT) images. To overcome this issue, we propose a data augmentation method that uses the hierarchical encoding of deformation fields between the CT images. This allows for the generation of synthetic CT images with shape variability while preserving the patient-specific CT values. Our framework encodes the spatial features of deformation fields into hierarchical latent variables, and generates the synthetic deformation fields by updating the values in specific layers. To implement this concept, we applied the StyleGAN2 and its encoder pixel2style2pixel to the deformation fields and added the ability to control the level of detail in the deformation through the Style Mixing. Our experiments demonstrated that our framework produced high-quality synthetic CT images compared with a conventional framework. Additionally, we applied the augmented datasets with teaching labels to semantic segmentation tasks targeting the liver and stomach, and found that accuracy improved by 1.3% and 7.9%, respectively, which surpassed the results obtained by the existing data augmentation methods.
{"title":"Data Augmentation Using the Hierarchical Encoding of Deformation Fields Between CT Images","authors":"Yuya Kuriyama;Mitsuhiro Nakamura;Megumi Nakao","doi":"10.1109/TRPMS.2024.3408818","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3408818","url":null,"abstract":"The field of medical machine learning has encountered the challenge of constructing a large-scale image database that includes both the anatomical variability and teaching labels because there are often not sufficient cases of a specific disease. Adversarial learning has been studied for nonlinear data augmentation. However, deep learning models may produce anatomically unrealistic structures or inaccurate pixel values when applied to small sets of computed tomography (CT) images. To overcome this issue, we propose a data augmentation method that uses the hierarchical encoding of deformation fields between the CT images. This allows for the generation of synthetic CT images with shape variability while preserving the patient-specific CT values. Our framework encodes the spatial features of deformation fields into hierarchical latent variables, and generates the synthetic deformation fields by updating the values in specific layers. To implement this concept, we applied the StyleGAN2 and its encoder pixel2style2pixel to the deformation fields and added the ability to control the level of detail in the deformation through the Style Mixing. Our experiments demonstrated that our framework produced high-quality synthetic CT images compared with a conventional framework. Additionally, we applied the augmented datasets with teaching labels to semantic segmentation tasks targeting the liver and stomach, and found that accuracy improved by 1.3% and 7.9%, respectively, which surpassed the results obtained by the existing data augmentation methods.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"939-949"},"PeriodicalIF":4.6,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587626","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}
High-performance radiation detectors often employ crystal arrays where light can leak between them, a phenomenon called intercrystal crosstalk, which demands mitigation for optimal detector performance. The complexity of measuring optical crosstalk in conventional detector geometries makes optical Monte Carlo simulation essential to study and reduce crosstalk through better designs. Addressing the absence of validated transmission models in Monte Carlo toolkits, we developed and integrated a new simulation model into the look-up table Davis Model, aiming at simulating optical photon refraction at the crystal interfaces using GATE. For the first time, we validated the intercrystal optical crosstalk model with experiments in two optically coupled Lutetium-yttrium oxyorthosilicate crystals read by two SiPMs, testing three thicknesses and four interfaces (air, glue, Teflon, and ESR). Simulated and experimental crosstalk agreed within one FWHM for all configurations. These results show the possibility of predicting optical photon transmission in detector designs with multiple crystal elements. Indeed, although validated using only two crystals, the model can be used in more complex geometries. The model, available to GATE users upon request, provides a valuable resource for researchers when optimizing detector geometry where optical crosstalk needs to be considered, i.e., ensuring optical isolation between the photodetector’s responses.
{"title":"Intercrystal Optical Crosstalk in Radiation Detectors: Monte Carlo Modeling and Experimental Validation","authors":"Carlotta Trigila;N. Kratochwil;B. Mehadji;G. Ariño-Estrada;E. Roncali","doi":"10.1109/TRPMS.2024.3395131","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3395131","url":null,"abstract":"High-performance radiation detectors often employ crystal arrays where light can leak between them, a phenomenon called intercrystal crosstalk, which demands mitigation for optimal detector performance. The complexity of measuring optical crosstalk in conventional detector geometries makes optical Monte Carlo simulation essential to study and reduce crosstalk through better designs. Addressing the absence of validated transmission models in Monte Carlo toolkits, we developed and integrated a new simulation model into the look-up table Davis Model, aiming at simulating optical photon refraction at the crystal interfaces using GATE. For the first time, we validated the intercrystal optical crosstalk model with experiments in two optically coupled Lutetium-yttrium oxyorthosilicate crystals read by two SiPMs, testing three thicknesses and four interfaces (air, glue, Teflon, and ESR). Simulated and experimental crosstalk agreed within one FWHM for all configurations. These results show the possibility of predicting optical photon transmission in detector designs with multiple crystal elements. Indeed, although validated using only two crystals, the model can be used in more complex geometries. The model, available to GATE users upon request, provides a valuable resource for researchers when optimizing detector geometry where optical crosstalk needs to be considered, i.e., ensuring optical isolation between the photodetector’s responses.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"734-742"},"PeriodicalIF":4.6,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10510415","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143679","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}
Pub Date : 2024-04-24DOI: 10.1109/TRPMS.2024.3393235
Ran Cheng;Mingchen Sun;Fei Wang;Dengyun Mu;Yu Liu;Qingguo Xie;Bensheng Qiu;Xun Chen;Peng Xiao
To minimize parallax errors and achieve high spatial resolution positron emission tomography (PET) systems, developing depth-of-Interaction (DOI) encoding detectors has become a significant research topic. In this article, we investigated a dual-ended readout PET detector based on the multivoltage threshold (MVT) sampling method combined with a convolutional neural network (CNN) to calculate the pulse’s energy (MVT-CNN method). The MVT sampling method was used to acquire time-threshold samples and digitize scintillation pulses. The CNN model was employed to establish an accurate mapping between MVT sampling points and energy information. The dual-ended readout detector’s energy, DOI, and timing performance were evaluated with two irradiation configurations. The results demonstrated that the performance of the MVT-CNN method was close to that of the integration method based on oscilloscope sampling. Using the MVT-CNN method, the average energy resolution of the tested crystals over all depths was $14.5 , pm , 1.2$ %, and the average DOI resolution was $2.81 , pm , 0$ .70 mm. In the side irradiation configuration, the average coincidence timing resolution of the tested crystals at 2 mm depth was 435 ps. The performance of the dual-ended readout DOI-PET detector basedon the MVT-CNN method suggested that it could develop small animal and organ-dedicated PET systems with high sensitivity and uniform spatial resolutionxs.
{"title":"Dual-Ended Readout PET Detector Based on Multivoltage Threshold Sampling Combined With Convolutional Neural Network for Energy Calculation","authors":"Ran Cheng;Mingchen Sun;Fei Wang;Dengyun Mu;Yu Liu;Qingguo Xie;Bensheng Qiu;Xun Chen;Peng Xiao","doi":"10.1109/TRPMS.2024.3393235","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3393235","url":null,"abstract":"To minimize parallax errors and achieve high spatial resolution positron emission tomography (PET) systems, developing depth-of-Interaction (DOI) encoding detectors has become a significant research topic. In this article, we investigated a dual-ended readout PET detector based on the multivoltage threshold (MVT) sampling method combined with a convolutional neural network (CNN) to calculate the pulse’s energy (MVT-CNN method). The MVT sampling method was used to acquire time-threshold samples and digitize scintillation pulses. The CNN model was employed to establish an accurate mapping between MVT sampling points and energy information. The dual-ended readout detector’s energy, DOI, and timing performance were evaluated with two irradiation configurations. The results demonstrated that the performance of the MVT-CNN method was close to that of the integration method based on oscilloscope sampling. Using the MVT-CNN method, the average energy resolution of the tested crystals over all depths was \u0000<inline-formula> <tex-math>$14.5 , pm , 1.2$ </tex-math></inline-formula>\u0000%, and the average DOI resolution was \u0000<inline-formula> <tex-math>$2.81 , pm , 0$ </tex-math></inline-formula>\u0000.70 mm. In the side irradiation configuration, the average coincidence timing resolution of the tested crystals at 2 mm depth was 435 ps. The performance of the dual-ended readout DOI-PET detector basedon the MVT-CNN method suggested that it could develop small animal and organ-dedicated PET systems with high sensitivity and uniform spatial resolutionxs.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"709-717"},"PeriodicalIF":4.6,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143651","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}
Pub Date : 2024-04-22DOI: 10.1109/TRPMS.2024.3391944
Go Kawata;M. Teshigawara
A time-of-flight (ToF) spectrum model has been developed to quantitatively understand the emission sequences in realistic scintillation detectors. This model is used to carefully investigate the Cherenkov and scintillation photon emission processes. To construct this model, we initially identified several primary physical processes occurring within scintillators and selected those that significantly contribute to the spectrum. The characteristics of each process were statistically incorporated into the model. Importantly, the model also takes into account the variance in the interaction point between the incident gamma photon and the electron, which serves as a contributing factor. To confirm the model’s validity, an experiment was conducted. A pair of 20-mm long bismuth germanate oxide detectors, paired with a silicon photomultiplier, used for this purpose. Experimental results provided the number of scintillation photons and the scintillation decay time constants. The time constant for Cherenkov emission was derived from the existing literature, and approximately one Cherenkov photon was used to fit the ToF spectrum obtained by the experiment. The model successfully reproduced the experimental ToF spectra with validity using the parameter values obtained in the experiment. However, the estimated number of scintillation photons in our experiment was about half of the yield number reported in literatures, while the number of Cherenkov photons utilized in the validation process was in line with those reported by other groups. Our results suggest that a combined analysis of the phenomenological model that accepts the behavior of the real system and particle-based Monte-Carlo simulation that treats the ideal system deductively is a meaningful approach for detector development based on an accurate understanding of the real system.
{"title":"Analyze Methodology of ToF Spectrum on Cherenkov and Scintillation Emission in BGO Scintillator","authors":"Go Kawata;M. Teshigawara","doi":"10.1109/TRPMS.2024.3391944","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3391944","url":null,"abstract":"A time-of-flight (ToF) spectrum model has been developed to quantitatively understand the emission sequences in realistic scintillation detectors. This model is used to carefully investigate the Cherenkov and scintillation photon emission processes. To construct this model, we initially identified several primary physical processes occurring within scintillators and selected those that significantly contribute to the spectrum. The characteristics of each process were statistically incorporated into the model. Importantly, the model also takes into account the variance in the interaction point between the incident gamma photon and the electron, which serves as a contributing factor. To confirm the model’s validity, an experiment was conducted. A pair of 20-mm long bismuth germanate oxide detectors, paired with a silicon photomultiplier, used for this purpose. Experimental results provided the number of scintillation photons and the scintillation decay time constants. The time constant for Cherenkov emission was derived from the existing literature, and approximately one Cherenkov photon was used to fit the ToF spectrum obtained by the experiment. The model successfully reproduced the experimental ToF spectra with validity using the parameter values obtained in the experiment. However, the estimated number of scintillation photons in our experiment was about half of the yield number reported in literatures, while the number of Cherenkov photons utilized in the validation process was in line with those reported by other groups. Our results suggest that a combined analysis of the phenomenological model that accepts the behavior of the real system and particle-based Monte-Carlo simulation that treats the ideal system deductively is a meaningful approach for detector development based on an accurate understanding of the real system.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"867-875"},"PeriodicalIF":4.6,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10506219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587513","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}
Pub Date : 2024-04-22DOI: 10.1109/TRPMS.2024.3392248
Bin Huang;Shiyu Lu;Liu Zhang;Boyu Lin;Weiwen Wu;Qiegen Liu
Low-dose computed tomography (CT) is crucial in clinical applications for reducing radiation risks. However, lowering the radiation dose will significantly degrade the image quality. In the meanwhile, common deep learning methods require large data, which are short for privacy leaking, expensive, and time-consuming. Therefore, we propose a fully unsupervised one-sample diffusion modeling (OSDM) in projection domain for low-dose CT reconstruction. To extract sufficient prior information from a single sample, the Hankel matrix formulation is employed. Besides, the penalized weighted least-squares and total variation are introduced to achieve superior image quality. First, we train a score-based diffusion model on one sinogram to capture the prior distribution with input tensors extracted from the structural-Hankel matrix. Then, at inference, we perform iterative stochastic differential equation solver and data-consistency steps to obtain sinogram data, followed by the filtered back-projection algorithm for image reconstruction. The results approach normal-dose counterparts, validating OSDM as an effective and practical model to reduce artifacts while preserving image quality.
{"title":"One-Sample Diffusion Modeling in Projection Domain for Low-Dose CT Imaging","authors":"Bin Huang;Shiyu Lu;Liu Zhang;Boyu Lin;Weiwen Wu;Qiegen Liu","doi":"10.1109/TRPMS.2024.3392248","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3392248","url":null,"abstract":"Low-dose computed tomography (CT) is crucial in clinical applications for reducing radiation risks. However, lowering the radiation dose will significantly degrade the image quality. In the meanwhile, common deep learning methods require large data, which are short for privacy leaking, expensive, and time-consuming. Therefore, we propose a fully unsupervised one-sample diffusion modeling (OSDM) in projection domain for low-dose CT reconstruction. To extract sufficient prior information from a single sample, the Hankel matrix formulation is employed. Besides, the penalized weighted least-squares and total variation are introduced to achieve superior image quality. First, we train a score-based diffusion model on one sinogram to capture the prior distribution with input tensors extracted from the structural-Hankel matrix. Then, at inference, we perform iterative stochastic differential equation solver and data-consistency steps to obtain sinogram data, followed by the filtered back-projection algorithm for image reconstruction. The results approach normal-dose counterparts, validating OSDM as an effective and practical model to reduce artifacts while preserving image quality.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"902-915"},"PeriodicalIF":4.6,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10506793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587522","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}
Early and accurate identification of intracranial hemorrhage (ICH) is crucial for treatment, but the inherently low-contrast resolution of computed tomography (CT) imaging poses challenges in identification of specific cerebral regions, impacting effective and timely clinical decision-making. We propose brain structure-enhanced domain adaptation (BraSEDA), a CT-based unsupervised domain adaptation (UDA) model designed to assist in the identification of brain regions. BraSEDA framework utilizes a cross-modal instance normalization (CMIN) module for enhancing CT image structural features and creating high-quality pseudo magnetic resonance (MR) images. A multilevel CMIN architecture is also introduced for further improvement. The BraSEDA framework improved the quality of pseudo MR images in head CT to MR domain adaptation task, as reflected by the lowest-Fréchet inception distance scores $95.0pm 12.1$ (p-value < 0.001) with and highest-BC scores $0.915pm 0.396$ (p-value <0.01),>https://github.com/YixinChen-AI/BraSEDA .
{"title":"Structure-Enhanced Unsupervised Domain Adaptation for CT Whole-Brain Segmentation","authors":"Yixin Chen;Yajun Gao;Lei Zhu;Jianan Li;Yan Wang;Jiakui Hu;Hongbin Han;Yanye Lu;Zhaoheng Xie","doi":"10.1109/TRPMS.2024.3391285","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3391285","url":null,"abstract":"Early and accurate identification of intracranial hemorrhage (ICH) is crucial for treatment, but the inherently low-contrast resolution of computed tomography (CT) imaging poses challenges in identification of specific cerebral regions, impacting effective and timely clinical decision-making. We propose brain structure-enhanced domain adaptation (BraSEDA), a CT-based unsupervised domain adaptation (UDA) model designed to assist in the identification of brain regions. BraSEDA framework utilizes a cross-modal instance normalization (CMIN) module for enhancing CT image structural features and creating high-quality pseudo magnetic resonance (MR) images. A multilevel CMIN architecture is also introduced for further improvement. The BraSEDA framework improved the quality of pseudo MR images in head CT to MR domain adaptation task, as reflected by the lowest-Fréchet inception distance scores \u0000<inline-formula> <tex-math>$95.0pm 12.1$ </tex-math></inline-formula>\u0000 (p-value < 0.001) with and highest-BC scores \u0000<inline-formula> <tex-math>$0.915pm 0.396$ </tex-math></inline-formula>\u0000 (p-value <0.01),>https://github.com/YixinChen-AI/BraSEDA</uri>\u0000.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"926-938"},"PeriodicalIF":4.6,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587584","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}
In this article, we investigate quantum entanglement (QE) filtering to address the challenges in multi-isotope positron emission tomography (PET) or in PET studies utilizing radiotracers with dual-positron and prompt gamma emissions. Via GATE simulation, we demonstrate the efficacy of QE filtering using a one-of-a-kind cadmium–zinc–telluride (CZT) PET system—establishing its viability as a multimodal scanner and ability to perform QE filtering. We show the high Compton scattering probability in this CZT-based scanner with 44.2% of gammas undergoing a single scatter prior to absorption. Additionally, the overall system sensitivity as a standard PET scanner (11.29%), QE-PET scanner (6.81%), and Compton camera (10.05%) is quantified. Further, we find a 23% decrease in the double Compton scatter (DCSc) frequency needed for QE filtering for each mm decrease in crystal resolution and an increase in mean absolute error (MAE) of their $Delta phi $ s from 6.8° for 1 mm resolution to 9.5°, 12.2°, and 15.3° for 2, 4, and 8 mm resolution, respectively. These results reinforce the potential of CZT detectors to lead next-generation PET systems by fully leveraging QE information of positron annihilation photons.
在这篇文章中，我们研究了量子纠缠（QE）滤波，以解决多同位素正电子发射断层扫描（PET）或利用具有双正电子和瞬时伽马射线发射的放射性同位素进行 PET 研究时所面临的挑战。通过 GATE 仿真，我们利用独一无二的碲锌镉（CZT）正电子发射计算机断层成像系统展示了 QE 滤波的功效，证明了该系统作为多模态扫描仪的可行性以及执行 QE 滤波的能力。我们展示了这种基于 CZT 的扫描仪的高康普顿散射概率，44.2% 的伽马在吸收前发生一次散射。此外，我们还量化了标准 PET 扫描仪（11.29%）、QE-PET 扫描仪（6.81%）和康普顿相机（10.05%）的整体系统灵敏度。此外，我们还发现晶体分辨率每降低一毫米，QE 滤波所需的双康普顿散射 (DCSc) 频率就会降低 23%，其 $Delta phi $ s 的平均绝对误差 (MAE) 也会从 1 毫米分辨率的 6.8° 分别增加到 2、4 和 8 毫米分辨率的 9.5°、12.2° 和 15.3°。这些结果加强了 CZT 探测器通过充分利用正电子湮灭光子的 QE 信息引领下一代 PET 系统的潜力。
{"title":"Quantum Entanglement Filtering: A PET Feasibility Study in Imaging Dual-Positron and Prompt Gamma Emission via Monte Carlo Simulation","authors":"Gregory Romanchek;Greyson Shoop;Kimia Gholami;Emily Enlow;Shiva Abbaszadeh","doi":"10.1109/TRPMS.2024.3388872","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3388872","url":null,"abstract":"In this article, we investigate quantum entanglement (QE) filtering to address the challenges in multi-isotope positron emission tomography (PET) or in PET studies utilizing radiotracers with dual-positron and prompt gamma emissions. Via GATE simulation, we demonstrate the efficacy of QE filtering using a one-of-a-kind cadmium–zinc–telluride (CZT) PET system—establishing its viability as a multimodal scanner and ability to perform QE filtering. We show the high Compton scattering probability in this CZT-based scanner with 44.2% of gammas undergoing a single scatter prior to absorption. Additionally, the overall system sensitivity as a standard PET scanner (11.29%), QE-PET scanner (6.81%), and Compton camera (10.05%) is quantified. Further, we find a 23% decrease in the double Compton scatter (DCSc) frequency needed for QE filtering for each mm decrease in crystal resolution and an increase in mean absolute error (MAE) of their \u0000<inline-formula> <tex-math>$Delta phi $ </tex-math></inline-formula>\u0000s from 6.8° for 1 mm resolution to 9.5°, 12.2°, and 15.3° for 2, 4, and 8 mm resolution, respectively. These results reinforce the potential of CZT detectors to lead next-generation PET systems by fully leveraging QE information of positron annihilation photons.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"916-925"},"PeriodicalIF":4.6,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10499999","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587523","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}
Pub Date : 2024-04-02DOI: 10.1109/TRPMS.2024.3380109
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information","authors":"","doi":"10.1109/TRPMS.2024.3380109","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3380109","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 4","pages":"C2-C2"},"PeriodicalIF":4.4,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488732","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342665","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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