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Phase-change ultrasound contrast agents for proton range verification: towards anin vivoapplication. 用于质子范围验证的相变超声造影剂:向活体应用迈进。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-10 DOI: 10.1088/1361-6560/ad7e76
Bram Carlier, Sophie V Heymans, Gonzalo Collado-Lara, Luigi Musetta, Marcus Ingram, Yosra Toumia, Gaio Paradossi, Hendrik J Vos, Tania Roskams, Jan D'hooge, Koen Van Den Abeele, Edmond Sterpin, Uwe Himmelreich

Objective.In proton therapy, range uncertainties prevent optimal benefit from the superior depth-dose characteristics of proton beams over conventional photon-based radiotherapy. To reduce these uncertainties we recently proposed the use of phase-change ultrasound contrast agents as an affordable and effective range verification tool. In particular, superheated nanodroplets can convert into echogenic microbubbles upon proton irradiation, whereby the resulting ultrasound contrast relates to the proton range with high reproducibility. Here, we provide a firstin vivoproof-of-concept of this technology.Approach.First, thein vitrobiocompatibility of radiation-sensitive poly(vinyl alcohol) perfluorobutane nanodroplets was investigated using several colorimetric assays. Then,in vivoultrasound contrast was characterized using acoustic droplet vaporization (ADV) and later using proton beam irradiations at varying energies (49.7 MeV and 62 MeV) in healthy Sprague Dawley rats. A preliminary evaluation of thein vivobiocompatibility was performed using ADV and a combination of physiology monitoring and histology.Main results.Nanodroplets were non-toxic over a wide concentration range (<1 mM). In healthy rats, intravenously injected nanodroplets primarily accumulated in the organs of the reticuloendothelial system, where the lifetime of the generated ultrasound contrast (<30 min) was compatible with a typical radiotherapy fraction (<5 min). Spontaneous droplet vaporization did not result in significant background signals. Online ultrasound imaging of the liver of droplet-injected rats demonstrated an energy-dependent proton response, which can be tuned by varying the nanodroplet concentration. However, caution is warranted when deciding on the exact nanodroplet dose regimen as a mild physiological response (drop in cardiac rate, granuloma formation) was observed after ADV.Significance.These findings underline the potential of phase-change ultrasound contrast agents forin vivoproton range verification and provide the next step towards eventual clinical applications.

目的:在质子治疗中,质子束的深度剂量特性优于传统的光子放疗,但范围的不确定性阻碍了质子治疗的最佳效益。为了减少这些不确定性,我们最近提出使用相变超声造影剂作为一种经济有效的范围验证工具。特别是,过热的纳米液滴在质子照射时可转化为回声微气泡,由此产生的超声对比度与质子射程相关,具有很高的再现性。首先,使用几种比色法研究了对辐射敏感的聚乙烯醇全氟丁烷纳米液滴的体内生物相容性。然后,在健康的 Sprague Dawley 大鼠体内使用声学液滴汽化法和不同能量(49.7 MeV 和 62 MeV)的质子束辐照法对体内超声对比度进行了表征。利用声学液滴气化法以及生理学监测和组织学相结合的方法,对纳米液滴的生物相容性进行了初步评估。在健康大鼠体内,静脉注射的纳米微滴主要积聚在网状内皮系统器官中,在这些器官中产生的超声造影剂的寿命(< 30 分钟)与典型的放射治疗时间(< 5 分钟)相符。液滴自发汽化不会产生明显的背景信号。对注射液滴的大鼠肝脏进行的在线超声成像表明,质子响应与能量有关,可通过改变纳米液滴浓度来调节。不过,在决定纳米液滴的确切剂量方案时必须谨慎,因为在声学液滴汽化后观察到了轻微的生理反应(心率下降、肉芽肿形成) 意义。这些发现强调了相变超声造影剂在验证体内质子范围方面的潜力,并为最终的临床应用提供了下一步的依据 意义。
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引用次数: 0
Characterizing brain mechanics through 7 tesla magnetic resonance elastography. 通过 7 特斯拉磁共振弹性成像技术确定大脑力学特征。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-08 DOI: 10.1088/1361-6560/ad7fc9
Emily Triolo, Oleksandr Khegai, Matthew McGarry, Tyson Lam, Jelle Veraart, Akbar Alipour, Priti Balchandani, Mehmet Kurt

Magnetic resonance elastography (MRE) is a non-invasive method for determining the mechanical response of tissues using applied harmonic deformation and motion-sensitive MRI. MRE studies of the human brain are typically performed at conventional field strengths, with a few attempts at the ultra-high field strength, 7T, reporting increased spatial resolution with partial brain coverage. Achieving high-resolution human brain scans using 7T MRE presents unique challenges of decreased octahedral shear strain-based signal-to-noise ratio (OSS-SNR) and lower shear wave motion sensitivity. In this study, we establish high resolution MRE at 7T with a custom 2D multi-slice single-shot spin-echo echo-planar imaging sequence, using the Gadgetron advanced image reconstruction framework, applying Marchenko-Pastur Principal component analysis denoising, and using nonlinear viscoelastic inversion. These techniques allowed us to calculate the viscoelastic properties of the whole human brain at 1.1 mm isotropic imaging resolution with high OSS-SNR and repeatability. Using phantom models and 7T MRE data of eighteen healthy volunteers, we demonstrate the robustness and accuracy of our method at high-resolution while quantifying the feasible tradeoff between resolution, OSS-SNR, and scan time. Using these post-processing techniques, we significantly increased OSS-SNR at 1.1 mm resolution with whole-brain coverage by approximately 4-fold and generated elastograms with high anatomical detail. Performing high-resolution MRE at 7T on the human brain can provide information on different substructures within brain tissue based on their mechanical properties, which can then be used to diagnose pathologies (e.g. Alzheimer's disease), indicate disease progression, or better investigate neurodegeneration effects or other relevant brain disorders,in vivo.

磁共振弹性成像(MRE)是一种利用外加谐波形变和运动敏感磁共振成像确定组织机械响应的非侵入性方法。人脑的磁共振弹性成像研究通常在常规场强下进行,少数在超高场强(7T)下进行的尝试报告称,在部分脑部覆盖范围内提高了空间分辨率。使用 7T MRE 实现高分辨率人脑扫描面临着八面体剪切应变信噪比(OSS-SNR)降低和剪切波运动灵敏度降低的独特挑战。在这项研究中,我们利用定制的二维多切片单发自旋回波 EPI 序列,使用 Gadgetron 高级图像重建框架,应用马琴科-帕斯特尔主成分分析去噪,并使用非线性粘弹性反演,在 7T 下建立了高分辨率 MRE。这些技术使我们能够在 1.1 毫米各向同性成像分辨率下计算整个人脑的粘弹性特性,并具有较高的 OSS-SNR 和重复性。利用 18 名健康志愿者的模型和 7T MRE 数据,我们证明了我们的方法在高分辨率下的稳健性,同时量化了分辨率、OSS-SNR 和扫描时间之间的可行权衡。利用这些后处理技术,我们将全脑覆盖 1.1 毫米分辨率下的 OSS-SNR 大幅提高了约 4 倍,并生成了具有高度解剖细节的弹性图。在 7T 下对人脑进行高分辨率 MRE 可根据脑组织内不同亚结构的机械特性提供相关信息,这些信息可用于诊断病症(如阿尔茨海默病)、指示疾病进展或更好地研究神经变性效应或体内其他相关脑部疾病。
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引用次数: 0
Hybrid modality dual-energy imaging aggregating complementary advantages of kV-CT and MV-CBCT: concept proposal and clinical validation. 集合 kV-CT 和 MV-CBCT 互补优势的混合模式双能量成像:概念提案和临床验证。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-08 DOI: 10.1088/1361-6560/ad84b1
Junfeng Qi, Shutong Yu, Zhengkun Dong, Jiang Liu, Juan Deng, Guojian Mei, Chuou Yin, Qiao Li, Tian Li, Shi Wang, Yibao Zhang

Objective: Megavoltage cone-beam CT (MV-CBCT) is advantageous in metal artifact reduction during Image-Guided Radiotherapy (IGRT), although it is limited by poor soft tissue contrast. This study proposed and evaluated a novel hybrid modality dual-energy (DE) imaging method combining the complementary advantages of kV-CT and MV-CBCT. Approach: The kV-CT and MV-CBCT images were acquired on a planning CT scanner and a Halcyon linear accelerator respectively. After rigid registration, images of basis materials were generated using the iterative decomposition method in the volumetric images. The decomposition accuracy was quantitatively evaluated on a Gammex 1472 phantom. The performance of contrast enhancement and metal artifact reduction in virtual monochromatic images were evaluated on both phantom and patient studies. Main results: Using the proposed method, the mean percentage errors for RED and SPR were 0.90% and 0.81%, outperforming the clinical single-energy mapping method with mean errors of 1.28% and 1.07%, respectively. The contrasts of soft-tissue insets were enhanced by a factor of 2~3 at 40 keV compared to kV-CT. The standard deviation in the metal artifact area was reduced by ~67%, from 42 HU (kV-CT) to 14 HU (150 keV monochromatic). The head and neck patient test showed that the percent error of soft-tissue RED in the metal artifact area was reduced from 18.1% (HU-RED conversion) to less than 1.0% (the proposed method), which was equivalent to the maximum dosimetric difference of 28.7% based on the patient-specific plan. Significance: Without hardware modification or extra imaging dose, the proposed hybrid modality method enabled kV-MV DE imaging, providing improved accuracy of quantitative analysis, soft-tissue contrast and metal artifact suppression for more accurate IGRT. .

目的:巨电压锥束 CT(MV-CBCT)在图像引导放疗(IGRT)过程中具有减少金属伪影的优势,但它受到软组织对比度差的限制。本研究提出并评估了一种新型混合模式双能量(DE)成像方法,该方法结合了 kV-CT 和 MV-CBCT 的互补优势:分别在规划 CT 扫描仪和 Halcyon 直线加速器上获取 kV-CT 和 MV-CBCT 图像。经过刚性配准后,在容积图像中使用迭代分解法生成基础材料图像。在 Gammex 1472 模型上对分解的准确性进行了定量评估。在模型和患者研究中评估了虚拟单色图像中对比度增强和金属伪影减少的性能:使用建议的方法,RED 和 SPR 的平均百分比误差分别为 0.90% 和 0.81%,优于平均误差分别为 1.28% 和 1.07% 的临床单能量映射方法。与 kV-CT 相比,40 keV 下的软组织嵌入对比度提高了 2~3 倍。金属伪影区域的标准偏差降低了约 67%,从 42 HU(kV-CT)降至 14 HU(150 keV 单色)。头颈部患者测试表明,金属伪影区域的软组织 RED 百分比误差从 18.1%(HU-RED 转换)降低到 1.0%(建议方法)以下,这相当于根据特定患者计划的最大剂量学差异 28.7%:在不修改硬件或增加成像剂量的情况下,拟议的混合模式方法实现了 kV-MV DE 成像,提高了定量分析的准确性、软组织对比度和金属伪影抑制,从而实现了更精确的 IGRT。
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引用次数: 0
Enhancing timing performance of heterostructures with double-sided readout. 通过双面读取提高异质结构的计时性能。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-08 DOI: 10.1088/1361-6560/ad7fc8
Fiammetta Pagano, Nicolaus Kratochwil, Carsten Lowis, Woon-Seng Choong, Marco Paganoni, Marco Pizzichemi, Joshua W Cates, Etiennette Auffray

Objective.Heterostructured scintillators offer a promising solution to balance the sensitivity and timing in TOF-PET detectors. These scintillators utilize alternating layers of materials with complementary properties to optimize performance. However, the layering compromises time resolution due to light transport issues. This study explores double-sided readout-enabling improved light collection and Depth-of-Interaction (DOI) information retrieval-to mitigate this effect and enhance the timing capabilities of heterostructures.Approach.The time resolution and DOI performances of 3 × 3 × 20 mm3BGO&EJ232 heterostructures were assessed in a single and double-sided readout (SSR and DSR, respectively) configuration using high-frequency electronics.Main results.Selective analysis of photopeak events yielded a DOI resolution of 6.4 ± 0.04 mm. Notably, the Coincidence Time Resolution (CTR) improved from 262 ± 8 ps (SSR) to 174 ± 6 ps (DSR) when measured in coincidence with a fast reference detector. Additionally, symmetrical configuration of two identical heterostructures in coincidence was tested, yielding in DSR a CTR of 254 ± 8 ps for all photopeak events and 107 ± 5 ps for the fastest events.Significance.By using high-frequency double-sided readout, we could measure DOI resolution and improve the time resolution of heterostructures of up to 40%. The DOI information resulted intrinsically captured in the average between the timestamps of the two SiPMs, without requiring any further correction.

异质结构闪烁体为平衡 TOF-PET 探测器的灵敏度和定时提供了一种很有前景的解决方案。这些闪烁体利用具有互补特性的交替材料层来优化性能。然而,由于光传输问题,分层会影响时间分辨率。本研究探讨了双面读出(可改善光收集和相互作用深度(DOI)信息检索),以减轻这种影响并增强异质结构的计时能力。对光峰事件的选择性分析得出的 DOI 分辨率为 6.4x0.04mm。值得注意的是,在与快速参考探测器重合测量时,重合时间分辨率(CTR)从 262±8 ps(SSR)提高到 174±6 ps(DSR)。此外,我们还测试了两个完全相同的异质结构的对称配置,在 DSR 中,所有光峰事件的 CTR 为 254±8 ps,最快事件的 CTR 为 107±5 ps。DOI 信息从两个 SiPM 的时间戳平均值中获得,无需进一步校正。
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引用次数: 0
Exploring charge sharing compensation using inter-pixel coincidence counters for photon counting detectors by deep-learning from local information. 通过局部信息的深度学习,探索使用像素间重合计数器对光子计数探测器进行电荷共享补偿。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-07 DOI: 10.1088/1361-6560/ad841e
Shengzi Zhao, Le Shen, Katsuyuki Taguchi, Yuxiang Xing

Objective: Photon counting detectors (PCDs) have well-acknowledged advantages in computed tomography (CT) imaging. However, charge sharing and other problems prevent PCDs from fully realizing the anticipated potential in diagnostic CT. PCDs with multi-energy inter-pixel coincidence counters (MEICC) have been proposed to provide particular information about charge sharing, thereby achieving lower Cramér-Rao Lower Bound (CRLB) than conventional PCDs when assessing its performance by estimating material thickness or virtual monochromatic attenuation integrals (VMAIs). This work explores charge sharing compensation using local spatial coincidence counter information for MEICC detectors through a deep-learning method. Approach: By analyzing the impact of charge sharing on photon count detection, we designed our network with a focus on individual pixels. Employing MEICC data of patches centered on POIs as input, we utilized local information for effective charge sharing compensation. The output was VMAI at different energies to address real detector issues without knowledge of primary counts. To achieve data diversity, a fast and online data generation method was proposed to provide adequate training data. A new loss function was introduced to reduce bias for training with high-noise data. The proposed method was validated by Monte Carlo (MC) simulation data for MEICC detectors that were compared with conventional PCDs. Main-Results: For conventional data as a reference, networks trained on low-noise data yielded results with a minimal bias (about 0.7%) compared with > 3% for the polynomial fitting method. The results of networks trained on high-noise data exhibited a slightly increased bias (about 1.3%) but a significantly reduced standard deviation (STD) and normalized root mean square error (NRMSE). The simulation study of the MEICC detector demonstrated superior compared to the conventional detector across all the metrics. Specifically, for both networks trained on high-noise and low-noise data, their biases were reduced to about 1% and 0.6%, respectively. Meanwhile, the results from a MEICC detector were of about 10% lower noise than a conventional detector. Moreover, an ablation study showed that the additional loss function on bias was beneficial for training on high-noise data. Significance: We demonstrated that a network-based method could utilize local information in PCDs effectively by patch-based learning to reduce the impact of charge sharing. MEICC detectors provide very valuable local spatial information by additional coincidence counters. Compared with MEICC detectors, conventional PCDs only have limited local spatial information for charge sharing compensation, resulting in higher bias and standard deviation in VMAI estimation with the same patch strategy. .

目的:光子计数探测器(PCD)在计算机断层扫描(CT)成像中具有公认的优势。然而,电荷共享和其他问题阻碍了 PCD 充分发挥在 CT 诊断中的预期潜力。有人提出,带有多能量像素间重合计数器(MEICC)的 PCD 可提供电荷共享的特定信息,从而在通过估计材料厚度或虚拟单色衰减积分(VMAIs)来评估其性能时,实现比传统 PCD 更低的克拉梅尔-拉奥下限(CRLB)。这项工作通过一种深度学习方法,利用 MEICC 探测器的局部空间重合计数器信息探索电荷共享补偿:通过分析电荷共享对光子计数检测的影响,我们设计了以单个像素为重点的网络。我们使用以 POI 为中心的斑块 MEICC 数据作为输入,利用局部信息进行有效的电荷共享补偿。输出是不同能量下的 VMAI,以解决实际探测器问题,而无需了解原生计数。为了实现数据多样性,我们提出了一种快速在线数据生成方法,以提供充足的训练数据。还引入了一个新的损失函数,以减少使用高噪声数据进行训练时的偏差。针对 MEICC 探测器的蒙特卡罗(MC)模拟数据对所提出的方法进行了验证,并与传统的 PCD 进行了比较:以传统数据为参考,在低噪声数据上训练的网络得出的结果偏差极小(约 0.7%),而多项式拟合方法的偏差大于 3%。在高噪声数据上训练的网络结果显示偏差略有增加(约 1.3%),但标准偏差(STD)和归一化均方根误差(NRMSE)显著降低。MEICC 检测器的模拟研究表明,在所有指标上,MEICC 检测器都优于传统检测器。具体来说,对于在高噪声和低噪声数据上训练的两个网络,它们的偏差分别降低了约 1%和 0.6%。同时,MEICC 检测器的结果比传统检测器的噪声低约 10%。此外,一项消融研究表明,关于偏差的附加损失函数有利于在高噪声数据上进行训练:我们证明,基于网络的方法可以通过基于斑块的学习有效利用 PCD 中的局部信息,从而降低电荷共享的影响。MEICC 探测器通过额外的重合计数器提供了非常有价值的局部空间信息。与 MEICC 探测器相比,传统 PCD 在电荷共享补偿方面只能获得有限的局部空间信息,因此在采用相同补丁策略的情况下,VMAI 估计的偏差和标准偏差会更大。
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引用次数: 0
Joint segmentation of tumors in 3D PET-CT images with a network fusing multi-view and multi-modal information. 利用融合多视角和多模态信息的网络对三维 PET-CT 图像中的肿瘤进行联合分割。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-07 DOI: 10.1088/1361-6560/ad7f1b
HaoYang Zheng, Wei Zou, Nan Hu, Jiajun Wang

Objective. Joint segmentation of tumors in positron emission tomography-computed tomography (PET-CT) images is crucial for precise treatment planning. However, current segmentation methods often use addition or concatenation to fuse PET and CT images, which potentially overlooks the nuanced interplay between these modalities. Additionally, these methods often neglect multi-view information that is helpful for more accurately locating and segmenting the target structure. This study aims to address these disadvantages and develop a deep learning-based algorithm for joint segmentation of tumors in PET-CT images.Approach. To address these limitations, we propose the Multi-view Information Enhancement and Multi-modal Feature Fusion Network (MIEMFF-Net) for joint tumor segmentation in three-dimensional PET-CT images. Our model incorporates a dynamic multi-modal fusion strategy to effectively exploit the metabolic and anatomical information from PET and CT images and a multi-view information enhancement strategy to effectively recover the lost information during upsamping. A Multi-scale Spatial Perception Block is proposed to effectively extract information from different views and reduce redundancy interference in the multi-view feature extraction process.Main results. The proposed MIEMFF-Net achieved a Dice score of 83.93%, a Precision of 81.49%, a Sensitivity of 87.89% and an IOU of 69.27% on the Soft Tissue Sarcomas dataset and a Dice score of 76.83%, a Precision of 86.21%, a Sensitivity of 80.73% and an IOU of 65.15% on the AutoPET dataset.Significance. Experimental results demonstrate that MIEMFF-Net outperforms existing state-of-the-art models which implies potential applications of the proposed method in clinical practice.

目的:PET-CT 图像中肿瘤的联合分割对于精确的治疗计划至关重要。然而,目前的分割方法通常使用加法或并法来融合 PET 和 CT 图像,这可能会忽略这些模式之间微妙的相互作用。此外,这些方法往往忽略了多视角信息,而这些信息有助于更准确地定位和分割目标结构。本研究旨在解决这些缺点,并开发一种基于深度学习的算法,用于 PET-CT 图像中的肿瘤联合分割。针对这些局限性,我们提出了多视图信息增强和多模态特征融合网络(MIEMFF-Net),用于三维 PET-CT 图像中的联合肿瘤分割。我们的模型融合了动态多模态融合策略和多视图信息增强策略,前者可有效利用 PET 和 CT 图像中的代谢和解剖信息,后者可有效恢复上采样过程中丢失的信息。提出了多尺度空间感知块,以有效提取不同视图的信息,减少多视图特征提取过程中的冗余干扰。提出的 MIEMFF-Net 在 STS 数据集上的 Dice 得分为 83.93%,精确度为 81.49%,灵敏度为 87.89%,IOU 为 69.27%;在 AutoPET 数据集上的 Dice 得分为 76.83%,精确度为 86.21%,灵敏度为 80.73%,IOU 为 65.15%。实验结果表明,MIEMFF-Net 优于现有的最先进(SOTA)模型,这意味着所提出的方法有可能应用于临床实践。
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引用次数: 0
Global and local feature extraction based on convolutional neural network residual learning for MR image denoising. 基于卷积神经网络残差学习的全局和局部特征提取,用于磁共振图像去噪。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-04 DOI: 10.1088/1361-6560/ad7e78
Meng Li, Juntong Yun, Dingxi Liu, Daixiang Jiang, Hanlin Xiong, Du Jiang, Shunbo Hu, Rong Liu, Gongfa Li

Objective.Given the different noise distribution information of global and local magnetic resonance (MR) images, this study aims to extend the current work on convolutional neural networks that preserve global structure and local details in MR image denoising tasks.Approach.This study proposed a parallel and serial network for denoising 3D MR images, called 3D-PSNet. We use the residual depthwise separable convolution block to learn the local information of the feature map, reduce the network parameters, and thus improve the training speed and parameter efficiency. In addition, we consider the feature extraction of the global image and utilize residual dilated convolution to process the feature map to expand the receptive field of the network and avoid the loss of global information. Finally, we combine both of them to form a parallel network. What's more, we integrate reinforced residual convolution blocks with dense connections to form serial network branches, which can remove redundant information and refine features to further obtain accurate noise information.Main results.The peak signal-to-noise ratio, structural similarity index measure, and root mean square error metrics of 3D-PSNet are as high as 47.79%, 99.81%, and 0.40%, respectively, achieving competitive denoising effect on three public datasets. The ablation experiments demonstrated the effectiveness of all the designed modules regarding all the evaluated metrics in both datasets.Significance.The proposed 3D-PSNet takes advantage of multi-scale receptive fields, local feature extraction and residual dense connections to more effectively restore the global structure and local fine features in MR images, and is expected to help doctors quickly and accurately diagnose patients' conditions.

目的:鉴于全局和局部磁共振(MR)图像的噪声分布信息不同,本研究旨在扩展卷积神经网络的现有工作,在磁共振图像去噪任务中保留全局结构和局部细节:本研究提出了一种用于三维磁共振图像去噪的并行和串行网络,称为 3D-PSNet 。我们利用残差深度可分离卷积块来学习特征图的局部信息,减少网络参数,从而提高训练速度和参数效率。此外,我们还考虑了全局图像的特征提取,并利用残差扩张卷积来处理特征图,以扩大网络的感受野,避免全局信息的丢失。最后,我们将两者结合起来,形成一个并行网络。此外,我们还整合了具有密集连接的强化残差卷积块,形成串行网络分支,从而去除冗余信息,细化特征,进一步获取准确的噪声信息。3D-PSNet的峰值信噪比、结构相似度指标和均方根误差指标分别高达47.79%、99.81%和0.40%,在三个公开数据集上取得了具有竞争力的去噪效果。消融实验表明,在两个数据集的所有评估指标上,所有设计的模块都是有效的:所提出的 3D-PSNet 利用多尺度感受野、局部特征提取和残余密集连接的优势,更有效地还原了磁共振图像的全局结构和局部精细特征,有望帮助医生快速准确地诊断患者病情。
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引用次数: 0
Relative biological effectiveness of clinically relevant photon energies for the survival of human colorectal, cervical, and prostate cancer cell lines. 临床相关光子能量对人类结直肠癌、宫颈癌和前列腺癌细胞系存活的相对生物有效性。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-04 DOI: 10.1088/1361-6560/ad7d5a
Joanna Li, Naim Chabaytah, Joud Babik, Behnaz Behmand, Hamed Bekerat, Tanner Connell, Michael Evans, Russell Ruo, Te Vuong, Shirin Abbasinejad Enger

Objective.Relative biological effectiveness (RBE) differs between radiation qualities. However, an RBE of 1.0 has been established for photons regardless of the wide range of photon energies used clinically, the lack of reproducibility in radiobiological studies, and outdated reference energies used in the experimental literature. Moreover, due to intrinsic radiosensitivity, different cancer types have different responses to radiation. This study aimed to characterize the RBE of clinically relevant high and low photon energiesin vitrofor three human cancer cell lines: HCT116 (colon), HeLa (cervix), and PC3 (prostate).Approach.Experiments were conducted following dosimetry protocols provided by the American Association of Physicists in Medicine. Cells were irradiated with 6 MV x-rays, an192Ir brachytherapy source, 225 kVp and 50 kVp x-rays. Cell survival post-irradiation was assessed using the clonogenic assay. Survival fractions were fitted using the linear quadratic model, and survival curves were generated for RBE calculations.Main results.Cell killing was more efficient with decreasing photon energy. Using 225 kVp x-rays as the reference, the HCT116 RBESF0.1for 6 MV x-rays,192Ir, and 50 kVp x-rays were 0.89 ± 0.03, 0.95 ± 0.03, and 1.24 ± 0.04; the HeLa RBESF0.1were 0.95 ± 0.04, 0.97 ± 0.05, and 1.09 ± 0.03, and the PC3 RBESF0.1were 0.84 ± 0.01, 0.84 ± 0.01, and 1.13 ± 0.02, respectively. HeLa and PC3 cells had varying radiosensitivity when irradiated with 225 and 50 kVp x-rays.Significance.This difference supports the notion that RBE may not be 1.0 for all photons through experimental investigations that employed precise dosimetry. It highlights that different cancer types may not have identical responses to the same irradiation quality. Additionally, the RBE of clinically relevant photons was updated to the reference energy of 225 kVp x-rays.

目标:不同辐射质量的相对生物效应(RBE)是不同的。然而,尽管临床上使用的光子能量范围很广,放射生物学研究缺乏可重复性,而且实验文献中使用的参考能量已经过时,但光子的 RBE 仍被确定为 1.0。此外,由于固有的辐射敏感性,不同类型的癌症对辐射的反应也不尽相同。本研究旨在描述三种人类癌症细胞系在体外接受临床相关的高光子能量和低光子能量时的 RBE 特性:方法:实验按照美国医学物理学家协会提供的剂量测定方案进行。细胞接受 6 MV X 射线、192Ir 近距离放射源、225 kVp 和 50 kVp X 射线照射。细胞辐照后的存活率通过克隆生成试验进行评估。主要结果:光子能量越低,细胞杀伤效率越高:HCT116 RBESF0.1分别为 0.89 ± 0.03、0.95 ± 0.03 和 1.24 ± 0.04;HeLa RBESF0.1分别为 0.95 ± 0.04、0.97 ± 0.05 和 1.09 ± 0.03,PC3 RBESF0.1 分别为 0.84 ± 0.01、0.84 ± 0.01 和 1.13 ± 0.02。HeLa和PC3细胞在接受225 kVp和50 kVp X射线照射时具有不同的放射敏感性。它强调了不同类型的癌症对相同辐照质量的反应可能不尽相同。此外,临床相关光子的 RBE 已更新为 225 kVp X 射线的参考能量。
{"title":"Relative biological effectiveness of clinically relevant photon energies for the survival of human colorectal, cervical, and prostate cancer cell lines.","authors":"Joanna Li, Naim Chabaytah, Joud Babik, Behnaz Behmand, Hamed Bekerat, Tanner Connell, Michael Evans, Russell Ruo, Te Vuong, Shirin Abbasinejad Enger","doi":"10.1088/1361-6560/ad7d5a","DOIUrl":"10.1088/1361-6560/ad7d5a","url":null,"abstract":"<p><p><i>Objective.</i>Relative biological effectiveness (RBE) differs between radiation qualities. However, an RBE of 1.0 has been established for photons regardless of the wide range of photon energies used clinically, the lack of reproducibility in radiobiological studies, and outdated reference energies used in the experimental literature. Moreover, due to intrinsic radiosensitivity, different cancer types have different responses to radiation. This study aimed to characterize the RBE of clinically relevant high and low photon energies<i>in vitro</i>for three human cancer cell lines: HCT116 (colon), HeLa (cervix), and PC3 (prostate).<i>Approach.</i>Experiments were conducted following dosimetry protocols provided by the American Association of Physicists in Medicine. Cells were irradiated with 6 MV x-rays, an<sup>192</sup>Ir brachytherapy source, 225 kVp and 50 kVp x-rays. Cell survival post-irradiation was assessed using the clonogenic assay. Survival fractions were fitted using the linear quadratic model, and survival curves were generated for RBE calculations.<i>Main results.</i>Cell killing was more efficient with decreasing photon energy. Using 225 kVp x-rays as the reference, the HCT116 RBE<sub>SF0.1</sub>for 6 MV x-rays,<sup>192</sup>Ir, and 50 kVp x-rays were 0.89 ± 0.03, 0.95 ± 0.03, and 1.24 ± 0.04; the HeLa RBE<sub>SF0.1</sub>were 0.95 ± 0.04, 0.97 ± 0.05, and 1.09 ± 0.03, and the PC3 RBE<sub>SF0.1</sub>were 0.84 ± 0.01, 0.84 ± 0.01, and 1.13 ± 0.02, respectively. HeLa and PC3 cells had varying radiosensitivity when irradiated with 225 and 50 kVp x-rays.<i>Significance.</i>This difference supports the notion that RBE may not be 1.0 for all photons through experimental investigations that employed precise dosimetry. It highlights that different cancer types may not have identical responses to the same irradiation quality. Additionally, the RBE of clinically relevant photons was updated to the reference energy of 225 kVp x-rays.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Towards a fully automatic workflow for investigating the dynamics of lung cancer cachexia during radiotherapy using cone beam computed tomography. 利用锥形束计算机断层扫描研究放疗期间肺癌恶病质动态的全自动工作流程。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-04 DOI: 10.1088/1361-6560/ad7d5b
Lars H B A Daenen, Wouter R P H van de Worp, Behzad Rezaeifar, Joël de Bruijn, Peiyu Qiu, Justine M Webster, Stéphanie Peeters, Dirk De Ruysscher, Ramon C J Langen, Cecile J A Wolfs, Frank Verhaegen

Objective.Cachexia is a devastating condition, characterized by involuntary loss of muscle mass with or without loss of adipose tissue mass. It affects more than half of patients with lung cancer, diminishing treatment effects and increasing mortality. Cone-beam computed tomography (CBCT) images, routinely acquired during radiotherapy treatment, might contain valuable anatomical information for monitoring body composition changes associated with cachexia. For this purpose, we propose an automatic artificial intelligence (AI)-based workflow, consisting of CBCT to CT conversion, followed by segmentation of pectoralis muscles.Approach.Data from 140 stage III non-small cell lung cancer patients was used. Two deep learning models, cycle-consistent generative adversarial network (CycleGAN) and contrastive unpaired translation (CUT), were used for unpaired training of CBCT to CT conversion, to generate synthetic CT (sCT) images. The no-new U-Net (nnU-Net) model was used for automatic pectoralis muscle segmentation. To evaluate tissue segmentation performance in the absence of ground truth labels, an uncertainty metric (UM) based on Monte Carlo dropout was developed and validated.Main results.Both CycleGAN and CUT restored the Hounsfield unit fidelity of the CBCT images compared to the planning CT (pCT) images and visually reduced streaking artefacts. The nnU-Net model achieved a Dice similarity coefficient (DSC) of 0.93, 0.94, 0.92 for the CT, sCT and CBCT images, respectively, on an independent test set. The UM showed a high correlation with DSC with a correlation coefficient of -0.84 for the pCT dataset and -0.89 for the sCT dataset.Significance.This paper shows a proof-of-concept for automatic AI-based monitoring of the pectoralis muscle area of lung cancer patients during radiotherapy treatment based on CBCT images, which provides an unprecedented time resolution of muscle mass loss during cachexia progression. Ultimately, the proposed workflow could provide valuable information for early intervention of cachexia, ideally resulting in improved cancer treatment outcome.

目的:恶病质是一种破坏性疾病,其特征是肌肉质量不自主地减少,同时伴有或不伴有脂肪组织质量的减少。半数以上的肺癌患者都会出现这种症状,从而影响治疗效果并增加死亡率。在放疗过程中常规获取的锥形束计算机断层扫描(CBCT)图像可能包含有价值的解剖信息,可用于监测与恶病质相关的身体成分变化。为此,我们提出了一种基于人工智能(AI)的自动工作流程,包括将 CBCT 转换为 CT,然后对胸肌进行分割。我们使用了 140 名 III 期非小细胞肺癌患者的数据。两个深度学习模型,即循环一致性生成对抗网络(CycleGAN)和对比性无配对转换(CUT),被用于 CBCT 到 CT 转换的无配对训练,以生成合成 CT(sCT)图像。无新 U-Net (nnU-Net)模型用于自动胸肌分割。为了评估在没有地面实况标签的情况下的组织分割性能,我们开发并验证了一种基于蒙特卡洛剔除的不确定性度量(UM)。与规划 CT(pCT)图像相比,CycleGAN 和 CUT 都恢复了 CBCT 图像的 Hounsfield 单位保真度,并在视觉上减少了条纹伪影。在独立测试集上,nnU-Net 模型对 CT、sCT 和 CBCT 图像的 Dice 相似系数(DSC)分别达到了 0.93、0.94 和 0.92。UM 与 DSC 的相关性很高,pCT 数据集的相关系数为-0.84,sCT 数据集的相关系数为-0.89。本文展示了基于 CBCT 图像的人工智能自动监测肺癌患者放化疗期间胸肌面积的概念验证,它提供了前所未有的恶病质进展期间肌肉质量损失的时间分辨率。最终,所提出的工作流程可为恶病质的早期干预提供有价值的信息,从而改善癌症治疗效果。
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引用次数: 0
Macrophage uptake rate of Sonazoid in breast lymphosonography is highly conserved in healthy controls. 乳腺淋巴造影中巨噬细胞对 Sonazoid 的摄取率在健康对照组中高度一致。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-04 DOI: 10.1088/1361-6560/ad7f1c
Kenneth M Tichauer, Priscilla Machado, Ji-Bin Liu, A S Chalmika Sarathchandra, Maria Stanczak, Walter K Kraft, Flemming Forsberg

Subcutaneous microbubble administration in connection with contrast enhanced ultrasound (CEUS) imaging is showing promise as a noninvasive and sensitive way to detect tumor draining sentinel lymph nodes (SLNs) in patients with breast cancer. Moreover, there is potential to harness the results from these approaches to directly estimate cancer burden, since some microbubble formulas, such as the Sonazoid used in this study, are rapidly phagocytosed by macrophages, and the macrophage concentration in a lymph node is inversely related to the cancer burden. This work presents a mathematical model that can approximate a rate constant governing macrophage uptake of Sonazoid,ki, given dynamic CEUS Sonazoid imaging data. Twelve healthy women were injected with 1.0 ml of Sonazoid in an upper-outer quadrant of one of their breasts and SLNs were imaged in each patient immediately after injection, and then at 0.25, 0.5, 1, 2, 4, 6, and 24 h after injection. The mathematical model developed was fit to the dynamic CEUS data from each subject resulting in a mean ± sd of 0.006 ± 0.005 h-1and 0.4 ± 0.1 h-1for relative lymphatic flow (EFl) andki, respectively. Furthermore, the roughly 25% sd of thekimeasurement was similar to the sd that would be expected from realistic noise simulations for a stable 0.4 h-1value ofki, suggesting that macrophage concentration is highly consistent among cancer-free SLNs. These results, along with the significantly smaller variance inkimeasurement observed compared to relative lymphatic flow suggest thatkimay be a more precise and promising approach of estimating macrophage abundance, and inversely cancer burden. Future studies comparing tumor-free to tumor-bearing nodes are planned to verify this hypothesis.

皮下注射微泡并结合造影剂增强超声(CEUS)成像技术,是检测乳腺癌患者肿瘤引流前哨淋巴结(SLN)的一种无创、灵敏的方法。此外,利用这些方法得出的结果直接估算癌症负荷也很有潜力,因为一些微泡配方(如本研究中使用的 Sonazoid)会被巨噬细胞快速吞噬,而淋巴结中的巨噬细胞浓度与癌症负荷成反比。本研究提出了一个数学模型,在给出动态 CEUS Sonazoid 成像数据的情况下,可以近似推算出巨噬细胞摄取 Sonazoid 的速率常数 ki。对 12 名健康女性的一侧乳房外上象限注射 1.0 毫升 Sonazoid,注射后立即对每位患者的 SLN 进行成像,然后在注射后 0.25、0.5、1、2、4、6 和 24 小时分别进行成像。将所建立的数学模型与每个受试者的动态 CEUS 数据进行拟合,得出相对淋巴流量 (EFl) 和 ki 的平均值(± sd)分别为 0.006 ± 0.005 h-1 和 0.4 ± 0.1 h-1。此外,ki 测量值约 25% 的 sd 与现实噪声模拟对稳定的 0.4 h-1 ki 值的预期 sd 相似,这表明巨噬细胞浓度在无癌症的 SLN 中高度一致。这些结果以及与相对淋巴流量相比观察到的 ki 测量方差明显较小的情况表明,ki 可能是一种更精确、更有前途的估算巨噬细胞丰度和反向癌症负担的方法,适用于未来建立无创 CEUS SLN 活检的工作。
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