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IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information 电气和电子工程师学会辐射与等离子体医学科学杂志》(IEEE Transactions on Radiation and Plasma Medical Sciences)出版信息
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-05 DOI: 10.1109/TRPMS.2024.3475531
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引用次数: 0
IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors 电气和电子工程师学会《辐射与等离子体医学科学杂志》作者须知
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-05 DOI: 10.1109/TRPMS.2024.3475533
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引用次数: 0
Three-Gamma Imaging in Nuclear Medicine: A Review 核医学中的三伽马成像:综述
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-09-30 DOI: 10.1109/TRPMS.2024.3470836
Hideaki Tashima;Taiga Yamaya
Three-gamma imaging is attracting attention as a futuristic diagnostic imaging method that surpasses positron emission tomography (PET). Its conceptual key is using $beta ^{+}$ - $gamma $ nuclides that simultaneously emit a prompt gamma ray with the positron decay. In this review, we have categorized the utilizations of prompt gamma rays into three categories: 1) multiple positron emitter imaging; 2) reconstruction-less positron emission imaging; and 3) positronium lifetime imaging. Multiple positron emitter imaging utilizes the prompt gamma ray as a trigger to discriminate from signals of pure positron emitters to enable simultaneous injection and imaging of two different radioisotopes. Reconstruction-less positron emission imaging combines PET and Compton imaging technologies to estimate the source position as almost a point for each triple coincidence event. Positronium lifetime imaging utilizes the prompt gamma ray as a starting signal to measure the time difference between positronium formation and annihilation for each triple coincidence event as its lifetime. This is because the positronium lifetime is affected by the surrounding microenvironment of electrons, it is expected to provide new information regarding biological conditions, such as the hypoxia state. In this review we introduce the principles of the three categories of three-gamma imaging methods, prototype development, and demonstration experiments.
作为一种超越正电子发射断层扫描(PET)的未来诊断成像方法,三伽马成像技术备受关注。它的概念关键在于使用与正电子衰变同时发射瞬发伽马射线的$beta ^{+}$ - $gamma $核素。在本综述中,我们将瞬发伽马射线的利用分为三类:1) 多正电子发射器成像;2) 无重建正电子发射成像;3) 正电子寿命成像。多正电子发射器成像利用瞬发伽马射线作为触发器,以区分纯正电子发射器的信号,从而实现两种不同放射性同位素的同时注入和成像。无重建正电子发射成像技术结合了正电子发射计算机断层显像和康普顿成像技术,可将每个三重巧合事件的源位置估计为几乎一个点。正电子寿命成像利用瞬发伽马射线作为起始信号,测量每个三重巧合事件的正电子形成和湮灭之间的时间差,作为其寿命。这是因为正电子寿命受周围电子微环境的影响,因此有望提供有关缺氧状态等生物条件的新信息。在这篇综述中,我们将介绍三伽马成像方法的原理、原型开发和演示实验。
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引用次数: 0
IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information 电气和电子工程师学会辐射与等离子体医学科学杂志》(IEEE Transactions on Radiation and Plasma Medical Sciences)出版信息
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-09-06 DOI: 10.1109/TRPMS.2024.3449313
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引用次数: 0
IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors 电气和电子工程师学会《辐射与等离子体医学科学杂志》作者须知
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-09-06 DOI: 10.1109/TRPMS.2024.3449311
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引用次数: 0
Member Get-a-Member (MGM) Program 会员注册(MGM)计划
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-09-06 DOI: 10.1109/TRPMS.2024.3453689
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引用次数: 0
IEEE DataPort IEEE 数据端口
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-09-06 DOI: 10.1109/TRPMS.2024.3453691
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引用次数: 0
Deep Learning-Based Fast Volumetric Image Generation for Image-Guided Proton Radiotherapy 基于深度学习的图像引导质子放疗快速容积图像生成技术
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-08-06 DOI: 10.1109/TRPMS.2024.3439585
Chih-Wei Chang;Yang Lei;Tonghe Wang;Sibo Tian;Justin Roper;Liyong Lin;Jeffrey Bradley;Tian Liu;Jun Zhou;Xiaofeng Yang
Very fast imaging techniques can enhance the precision of image-guided radiation therapy, which can be useful for external beam radiation therapy. This work aims to develop a deep learning (DL)-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization for treating lung cancer patients with gating, and it is presented in the context of FLASH which leverages ultrahigh dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. The proposed framework comprises four modules, including orthogonal kV X-ray projection acquisition, DL-based volumetric image generation, image quality analyses, and proton water equivalent thickness (WET) evaluation. We investigated volumetric image reconstruction using kV projection pairs with four different source angles. Thirty patients with lung targets were identified from an institutional database, each patient having a 4-D computed tomography (CT) dataset with ten respiratory phases. Considering all evaluation metrics, the kV projections with source angles of 135° and 225° yielded the optimal volumetric images. The patient-averaged mean absolute error, peak signal-to-noise ratio, structural similarity index measure, and WET error were $75pm 22$ hounsfield unit, $19pm 3$ .7 dB, $0.938pm 0.044$ , and −1.3%±4.1%. The proposed framework can rapidly deliver volumetric images to potentially guide proton FLASH treatment delivery systems.
快速成像技术可以提高图像引导放射治疗的精确度,这对体外放射治疗非常有用。这项工作旨在开发一种基于深度学习(DL)的图像引导框架,以实现快速容积图像重建,从而为肺癌患者的门控治疗提供精确的靶点定位,该框架是在FLASH的背景下提出的,FLASH利用超高剂量率辐射,在不影响肿瘤控制概率的情况下加强了对危险器官的保护。拟议的框架由四个模块组成,包括正交 kV X 射线投影采集、基于 DL 的容积图像生成、图像质量分析和质子水当量厚度(WET)评估。我们研究了使用四种不同射线源角度的 kV 投影对进行容积图像重建的情况。我们从机构数据库中确定了 30 位肺部靶点患者,每位患者都有一个包含 10 个呼吸相位的 4-D 计算机断层扫描 (CT) 数据集。考虑到所有评价指标,135°和 225°源角的 kV 投影产生了最佳容积图像。患者平均绝对误差、峰值信噪比、结构相似性指数和WET误差分别为75/pm 22$ hounsfield unit、19/pm 3$ .7 dB、0.938/pm 0.044$和-1.3%±4.1%。所提出的框架可以快速提供容积图像,为质子FLASH治疗输送系统提供潜在指导。
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引用次数: 0
Experimental Measurement of Secondary Particle Count for Real-Time Proton Range Verification 用于实时质子范围验证的二次粒子计数实验测量
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-08-06 DOI: 10.1109/TRPMS.2024.3439517
Chuan Huang;Zhengguo Hu;Wei Lv;Yucong Chen;Xiuling Zhang;Zhiguo Xu;Faming Luo;Xinle Lang;Zulong Zhao;Ruishi Mao;Yongzhi Yin;Zhongming Wang;Di Wang;Guoqing Xiao
The real-time positioning of the particle beam range during treatment is a critical technology for improving the quality of the patient treatment. This article presents a scheme for the real-time proton range verification, and an experimental prototype is built at the Xi’an proton application facility (XiPAF) terminal. The experiment utilized a 150 MeV passive proton beam delivery mode to bombard the polymethyl methacrylate (PMMA) target for the real-time proton range verification. This scheme utilizes the secondary particle counts generated per monitor unit (MU) of primary particles and does not require identification of the secondary particle species, only its deposition energy in the cerium bromide (CeBr3) scintillator module exceeding 73.24 keV. The accuracy of range verification was evaluated at various acquisition periods by establishing the relationship between the secondary particle counts generated per MU of primary particles and the proton range. The range verification accuracy after one spill ( $sim ~1.67times 10$ 9 protons) delivery was measured at $0.01~pm ~0$ .29 mm. The accuracy of range verification within milliseconds is mainly affected by the statistical fluctuations in the secondary particle counts caused by the accumulation of activation products. Under constrained conditions, the range verification accuracy was measured at $0.16~pm ~0$ .69 mm within 110 ms acquisition time and $0.16~pm ~0$ .94 mm within 55 ms acquisition time. The experimental results confirm the feasibility of the scheme for the real-time range verification practice. The study hopes to provide a new reference scheme for reducing the impact of range uncertainty on the patient treatment quality.
治疗过程中粒子束射程的实时定位是提高患者治疗质量的关键技术。本文提出了一种质子射程实时验证方案,并在西安质子应用设施(XiPAF)终端搭建了实验样机。实验采用 150 MeV 被动质子束输送模式轰击聚甲基丙烯酸甲酯(PMMA)靶,进行实时质子射程验证。该方案利用每监测单位(MU)一次粒子产生的二次粒子计数,无需识别二次粒子种类,只需识别其在超过 73.24 keV 的溴化铈(CeBr3)闪烁器模块中的沉积能量。通过确定每 MU 一次粒子产生的二次粒子计数与质子量程之间的关系,评估了不同采集周期下量程验证的准确性。一次质子溢出(1.67乘以10的9次方倍)后的量程验证精度为0.01~/pm~0.29毫米。毫秒内的量程验证精度主要受活化产物积累引起的二次粒子数统计波动的影响。在受限条件下,在 110 毫秒采集时间内测得的量程验证精度为 0.16~pm ~0$ .69 毫米,在 55 毫秒采集时间内测得的量程验证精度为 0.16~pm ~0$ .94 毫米。实验结果证实了该方案在实时测距验证实践中的可行性。该研究希望为降低测距不确定性对患者治疗质量的影响提供一种新的参考方案。
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引用次数: 0
Deep Convolutional Backbone Comparison for Automated PET Image Quality Assessment 用于 PET 图像质量自动评估的深度卷积骨干比较。
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-08-01 DOI: 10.1109/TRPMS.2024.3436697
Jessica B. Hopson;Anthime Flaus;Colm J. McGinnity;Radhouene Neji;Andrew J. Reader;Alexander Hammers
Pretraining deep convolutional network mappings using natural images helps with medical imaging analysis tasks; this is important given the limited number of clinically annotated medical images. Many 2-D pretrained backbone networks, however, are currently available. This work compared 18 different backbones from 5 architecture groups (pretrained on ImageNet) for the task of assessing [18F]FDG brain positron emission tomography (PET) image quality (reconstructed at seven simulated doses), based on three clinical image quality metrics (global quality rating, pattern recognition, and diagnostic confidence). Using 2-D randomly sampled patches, up to eight patients (at three dose levels each) were used for training, with three separate patient datasets used for testing. Each backbone was trained five times with the same training and validation sets, and with six cross-folds. Training only the final fully connected layer (with ~6000–20000 trainable parameters) achieved a test mean-absolute-error (MAE) of ~0.5 (which was within the intrinsic uncertainty of clinical scoring). To compare “classical” and over-parameterized regimes, the pretrained weights of the last 40% of the network layers were then unfrozen. The MAE fell below 0.5 for 14 out of the 18 backbones assessed, including two that previously failed to train. Generally, backbones with residual units (e.g., DenseNets and ResNetV2s), were suited to this task, in terms of achieving the lowest MAE at test time (~0.45–0.5). This proof-of-concept study shows that over-parameterization may also be important for automated PET image quality assessments.
利用自然图像预训练深度卷积网络映射有助于医学影像分析任务;鉴于临床注释医学图像的数量有限,这一点非常重要。然而,目前有许多二维预训练骨干网络。这项研究比较了来自 5 个架构组(在 ImageNet 上经过预训练)的 18 种不同骨干网络,根据三种临床图像质量指标(全局质量评级、模式识别和诊断可信度),评估 [18F]FDG 脑正电子发射透射(PET)图像质量(按七种模拟剂量重建)。使用二维随机抽样斑块,对多达八名患者(每名患者三个剂量水平)进行训练,并使用三个独立的患者数据集进行测试。每个骨干层使用相同的训练集和验证集以及六个交叉褶皱训练五次。只训练最后的全连接层(可训练参数约为 6,000-20,000 个),测试平均绝对误差约为 0.5(在临床评分的内在不确定性范围内)。为了比较 "经典 "和过度参数化机制,对最后 40% 网络层的预训练权重进行了解冻。在接受评估的 18 个骨干网中,有 14 个骨干网的平均绝对误差低于 0.5,其中包括两个之前训练失败的骨干网。一般来说,具有残余单元的骨干网(如 DenseNets 和 ResNetV2)适合这项任务,在测试时可获得最低的平均绝对误差(~0.45 - 0.5)。这项概念验证研究表明,过度参数化对 PET 图像质量自动评估也很重要。
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IEEE Transactions on Radiation and Plasma Medical Sciences
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