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An adaptive parameter decoupling algorithm-based image reconstruction model (ADAIR) for rapid golden-angle radial DCE-MRI. 基于自适应参数解耦算法的图像重建模型 (ADAIR),用于快速黄金角径向 DCE-MRI。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-18 DOI: 10.1088/1361-6560/ad8545
Zhifeng Chen, Zhenguo Yuan, Junying Cheng, Jinhai Liu, Feng Liu, Zhaolin Chen

Objective. The acceleration of magnetic resonance imaging (MRI) acquisition is crucial for both clinical and research applications, particularly in dynamic MRI. Existing compressed sensing (CS) methods, despite being effective for fast imaging, face limitations such as the need for incoherent sampling and residual noise, which restrict their practical use for rapid MRI.Approach. To overcome these challenges, we propose a novel image reconstruction framework that integrates the MRI physical model with a flexible, self-adjusting, decoupling data-driven model. We validated this method through experiments using both simulated andin vivodynamic contrast-enhanced MRI datasets.Main results. The experimental results demonstrate that the proposed framework achieves high spatial and temporal resolution reconstructions. Additionally, when compared to state-of-the-art image reconstruction approaches, our method significantly enhances acceleration capabilities, enabling sparse and rapid imaging with high resolution.Significance. Our proposed framework offers a promising solution for real-time imaging and image-guided radiation therapy applications by providing superior performance in achieving high spatial and temporal resolution reconstructions, thus addressing the limitations of existing CS schemes.

目的:加速磁共振成像(MRI)采集对于临床和研究应用至关重要,尤其是动态磁共振成像。现有的压缩传感方法尽管对快速成像很有效,但面临着一些限制,如需要不连贯采样和残留噪声,这限制了它们在快速磁共振成像中的实际应用:为了克服这些挑战,我们提出了一种新型图像重建框架,它将磁共振成像物理模型与灵活、自调整、解耦数据驱动模型相结合。我们利用模拟和体内动态对比增强 MRI 数据集进行实验,验证了这一方法:实验结果表明,所提出的框架可实现高空间和时间分辨率的重建。此外,与最先进的图像重建方法相比,我们的方法大大提高了加速能力,实现了高分辨率的稀疏和快速成像:我们提出的框架为实时成像和图像引导放射治疗应用提供了一个前景广阔的解决方案,它在实现高空间和时间分辨率重建方面具有卓越的性能,从而解决了现有压缩传感方案的局限性。
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引用次数: 0
Dual convolution-transformer UNet (DCT-UNet) for organs at risk and clinical target volume segmentation in MRI for cervical cancer brachytherapy. 用于宫颈癌近距离放射治疗磁共振成像中危险器官和临床目标体积分割的双卷积-变换器 UNet (DCT-UNet)。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-18 DOI: 10.1088/1361-6560/ad84b2
Gayoung Kim, Akila N Viswanathan, Rohini Bhatia, Yosef Landman, Michael Roumeliotis, Beth Erickson, Ehud J Schmidt, Junghoon Lee

Objective. MRI is the standard imaging modality for high-dose-rate brachytherapy of cervical cancer. Precise contouring of organs at risk (OARs) and high-risk clinical target volume (HR-CTV) from MRI is a crucial step for radiotherapy planning and treatment. However, conventional manual contouring has limitations in terms of accuracy as well as procedural time. To overcome these, we propose a deep learning approach to automatically segment OARs (bladder, rectum, and sigmoid colon) and HR-CTV from female pelvic MRI.Approach. In the proposed pipeline, a coarse multi-organ segmentation model first segments all structures, from which a region of interest is computed for each structure. Then, each organ is segmented using an organ-specific fine segmentation model separately trained for each organ. To account for variable sizes of HR-CTV, a size-adaptive multi-model approach was employed. For coarse and fine segmentations, we designed a dual convolution-transformer UNet (DCT-UNet) which uses dual-path encoder consisting of convolution and transformer blocks. To evaluate our model, OAR segmentations were compared to the clinical contours drawn by the attending radiation oncologist. For HR-CTV, four sets of contours (clinical + three additional sets) were obtained to produce a consensus ground truth as well as for inter/intra-observer variability analysis.Main results. DCT-UNet achieved dice similarity coefficient (mean ± SD) of 0.932 ± 0.032 (bladder), 0.786 ± 0.090 (rectum), 0.663 ± 0.180 (sigmoid colon), and 0.741 ± 0.076 (HR-CTV), outperforming other state-of-the-art models. Notably, the size-adaptive multi-model significantly improved HR-CTV segmentation compared to a single-model. Furthermore, significant inter/intra-observer variability was observed, and our model showed comparable performance to all observers. Computation time for the entire pipeline per subject was 12.59 ± 0.79 s, which is significantly shorter than the typical manual contouring time of >15 min.Significance. These experimental results demonstrate that our model has great utility in cervical cancer brachytherapy by enabling fast and accurate automatic segmentation, and has potential in improving consistency in contouring. DCT-UNet source code is available athttps://github.com/JHU-MICA/DCT-UNet.

目的:核磁共振成像是宫颈癌高剂量率近距离放射治疗的标准成像方式。通过核磁共振成像精确勾画高危器官(OAR)和高危临床靶体积(HR-CTV)是放疗计划和治疗的关键步骤。然而,传统的手动轮廓绘制在准确性和程序时间方面存在局限性。为了克服这些问题,我们提出了一种深度学习方法,用于自动分割女性盆腔 MRI 中的 OAR(膀胱、直肠和乙状结肠)和 HR-CTV:在所提出的管道中,一个粗略的多器官分割模型首先分割所有结构,然后计算每个结构的感兴趣区。然后,使用针对每个器官单独训练的特定器官精细分割模型对每个器官进行分割。为了考虑 HR-CTV 的不同尺寸,我们采用了尺寸自适应多模型方法。对于粗细分割,我们设计了一个双卷积-变换器 UNet(DCT-UNet),它使用由卷积块和变换器块组成的双路径编码器。为了评估我们的模型,我们将 OAR 分割结果与放射肿瘤主治医生绘制的临床轮廓进行了比较。对于 HR-CTV,我们获得了四组轮廓(临床轮廓+三组附加轮廓),以生成一致的基本真相,并进行观察者间/观察者内的差异分析:主要结果:DCT-UNet 的骰子相似系数(平均值±SD)分别为 0.932±0.032(膀胱)、0.786±0.090(直肠)、0.663±0.180(乙状结肠)和 0.741±0.076(HR-CTV),优于其他先进模型。值得注意的是,与单一模型相比,尺寸自适应多模型明显改善了 HR-CTV 的分割。此外,还观察到观察者之间/观察者内部存在明显的差异,而我们的模型与所有观察者的表现相当。每个受试者整个管道的计算时间为(12.59±0.79)秒,明显短于一般的人工轮廓绘制时间(>15 分钟):这些实验结果表明,我们的模型可以实现快速、准确的自动分割,在宫颈癌近距离治疗中大有用武之地,并有望提高轮廓绘制的一致性。
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引用次数: 0
Monte Carlo damage models of different complexity levels predict similar trends in radiation induced DNA damage. 不同复杂程度的蒙特卡洛损伤模型预测了辐射诱导 DNA 损伤的相似趋势。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-18 DOI: 10.1088/1361-6560/ad88d0
Shannon J Thompson, Kevin M Prise, Stephen J McMahon

Introduction: Ion therapies have an increased relative biological effectiveness (RBE) compared to X-rays, but this remains poorly quantified across different radiation qualities. Mechanistic models that simulate DNA damage and repair after irradiation could be used to help better quantify RBE. However, there is large variation in model design with the simulation detail and number of parameters required to accurately predict key biological endpoints remaining unclear. This work investigated damage models with varying detail to determine how different model features impact the predicted DNA damage. Methods: Damage models of reducing detail were designed in TOPAS-nBio and Medras investigating the inclusion of chemistry, realistic nuclear geometries, single strand break damage, and track structure. The nucleus models were irradiated with 1 Gy of protons across a range of linear energy transfers (LETs). Damage parameters in the models with reduced levels of simulation detail were fit to proton double strand break (DSB) yield predicted by the most detailed model. Irradiation of the optimised models with a range of radiation qualities was then simulated, before undergoing repair in the Medras biological response model. Results: Simplified damage models optimised to proton exposures predicted similar trends in DNA damage across radiation qualities. On average across radiation qualities, the simplified models experienced an 8% variation in double strand break (DSB) yield but a larger 28% variation in chromosome aberrations. Aberration differences became more prominent at higher LETs, with model features having an increasing impact on the distribution and therefore misrepair of DSBs. However, overall trends remained similar with better agreement likely achievable through repair model optimisation. Conclusion: Several model simplifications could be made without compromising key damage yield predictions, although changes in damage complexity and distribution were observed. This suggests simpler, more efficient models may be sufficient for initial radiation damage comparisons, if validated against experimental data. .

导言:与 X 射线相比,离子疗法具有更高的相对生物有效性(RBE),但对不同辐射质量的量化程度仍然很低。模拟 DNA 损伤和辐照后修复的机理模型可用于帮助更好地量化 RBE。然而,模型设计存在很大差异,准确预测关键生物终点所需的模拟细节和参数数量仍不清楚。这项工作研究了不同细节的损伤模型,以确定不同的模型特征如何影响预测的 DNA 损伤:在 TOPAS-nBio 和 Medras 中设计了细节更少的损伤模型,研究了包含化学、现实核几何、单链断裂损伤和轨道结构的损伤模型。核模型在一定的线性能量传递(LET)范围内受到 1 Gy 质子辐照。降低了模拟详细程度的模型中的损伤参数与最详细模型预测的质子双股断裂(DSB)产量相匹配。然后模拟用一系列辐射质量对优化模型进行辐照,然后在 Medras 生物反应模型中进行修复:针对质子照射进行优化的简化损伤模型预测了不同辐射强度下 DNA 损伤的相似趋势。平均而言,在不同辐射强度下,简化模型的双链断裂(DSB)率变化为 8%,但染色体畸变的变化更大,为 28%。畸变差异在较高的 LET 值下变得更加突出,模型特征对 DSB 的分布和错误修复的影响越来越大。不过,总体趋势仍然相似,通过优化修复模型可能会获得更好的一致性:虽然观察到了损伤复杂性和分布的变化,但可以对几个模型进行简化,而不影响关键的损伤产量预测。这表明,如果根据实验数据进行验证,更简单、更有效的模型可能足以进行初步的辐射损伤比较。
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引用次数: 0
Performance evaluation of an inorganic optical fibre dosimeter for use in external beam radiotherapy with pulsed beams. 用于脉冲束外放射治疗的无机光纤剂量计的性能评估。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-18 DOI: 10.1088/1361-6560/ad84b7
C McDonnell, O McLaughlin, C K McGarry, A R Hounsell, S O'Keeffe, E Lewis, K M Prise

Objective. Optical fibre dosimeters (OFDs) offer great promise for real-timein vivodose measurement in radiation-based treatment modalities such as radiotherapy and brachytherapy. This is attributed to their many useful qualities such as high spatial resolution and sensitivity. However, there are several requirements that an optical fibre dosimeter must meet to be acceptable for dose measurement in a specified treatment modality. In this work, the dosimetric performance of a novel optical fibre dosimeter for use in external beam radiotherapy is presented.Approach. The dosimeter was characterised for photon beam energies between 6-15 MV using a Varian TrueBeam Linac at dose rates between 100-2400 MU/min and assessed based on its repeatability, dose dependence, dose rate dependence, energy dependence and dose-per-pulse dependence.Main Results. The results demonstrated excellent short-term repeatability of 0.3%, good linearity in response (R2>0.9997), and minor dose rate dependence between 0.53%-2.49% for all beam qualities investigated. As the scintillator of the OFD is non-water equivalent, Monte-Carlo-TOPAS simulations were used to calculate the absorbed dose energy dependence. A dose-per-pulse dependence was also investigated and compared with dosimetry measurements made with an ionisation chamber and simulated from the treatment planning system. An over-response of 20%was found at the lowest investigated dose-per-pulse, and an under-response of 34%was found at the highest investigated dose-per-pulse. This is believed to be due to an intrinsic energy dependence making this type of OFD unsuitable for external beam radiotherapy dosimetry.Significance. The OFD evaluated in this work was primarily designed for high-dose-rate brachytherapy whereas this study includes the first measurements made in external beam radiotherapy and highlights the challenges of transferability of the dosimeter to a different radiation source.

目的。光纤剂量计为放疗和近距离放射治疗等基于辐射的治疗模式中的实时活体剂量测量提供了巨大的前景。这要归功于光纤剂量计的许多有用特性,如空间分辨率高和灵敏度高。然而,光纤剂量计要想在特定治疗模式中进行可接受的剂量测量,必须满足多项要求。在这项工作中,介绍了用于体外放射治疗的新型光纤剂量计的剂量测定性能。使用瓦里安 TrueBeam 直列加速器,在剂量率为 100-2400 MU/min 的条件下,对光子束能量为 6-15 MV 的剂量计进行了表征,并根据其可重复性、剂量依赖性、剂量率依赖性、能量依赖性和每脉冲剂量依赖性进行了评估。结果表明,在所研究的所有光束质量中,短期重复性极佳,为 0.3 $/%$,响应线性度良好(R$^{2} >$ 0.9997),剂量率依赖性较小,为 0.53-2.49 $/%$。由于光纤剂量计的闪烁体是非水当量的,因此使用蒙特卡洛-TOPAS 模拟来计算吸收剂量的能量依赖性。此外,还研究了每脉冲剂量的相关性,并与电离室和治疗计划系统模拟的剂量测定结果进行了比较。在研究的最低剂量-脉冲下,发现了 20 $/%$的过响应,而在研究的最高剂量-脉冲下,发现了 34 $/%$的欠响应。这被认为是由于内在的能量依赖性,使得这种光纤剂量计不适合外照射剂量测定。在这项工作中评估的光纤剂量计主要是为高剂量率近距离放射治疗而设计的,而这项研究包括首次在体外射束放射治疗中进行的测量,并强调了将剂量计转移到不同放射源所面临的挑战。
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引用次数: 0
Research and application of omics and artificial intelligence in cancer. 癌症中的全息图学和人工智能研究与应用。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-18 DOI: 10.1088/1361-6560/ad6951
Ye Zhang, Wenwen Ma, Zhiqiang Huang, Kun Liu, Zhaoyi Feng, Lei Zhang, Dezhi Li, Tianlu Mo, Qing Liu

Cancer has a high incidence and lethality rate, which is a significant threat to human health. With the development of high-throughput technologies, different types of cancer genomics data have been accumulated, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. A comprehensive analysis of various omics data is needed to understand the underlying mechanisms of tumor development. However, integrating such a massive amount of data is one of the main challenges today. Artificial intelligence (AI) techniques such as machine learning are now becoming practical tools for analyzing and understanding multi-omics data on diseases. Enabling great optimization of existing research paradigms for cancer screening, diagnosis, and treatment. In addition, intelligent healthcare has received widespread attention with the development of healthcare informatization. As an essential part of innovative healthcare, practical, intelligent prognosis analysis and personalized treatment for cancer patients are also necessary. This paper introduces the advanced multi-omics data analysis technology in recent years, presents the cases and advantages of the combination of both omics data and AI applied to cancer diseases, and finally briefly describes the challenges faced by multi-omics analysis and AI at the current stage, aiming to provide new perspectives for oncology research and the possibility of personalized cancer treatment.

癌症的发病率和致死率都很高,是人类健康的重大威胁。随着高通量技术的发展,已经积累了不同类型的癌症基因组学数据,包括基因组学、表观基因组学、转录组学、蛋白质组学和代谢组学。要了解肿瘤发生发展的内在机制,需要对各种 omics 数据进行综合分析。然而,整合如此海量的数据是当今面临的主要挑战之一。机器学习等人工智能技术正在成为分析和理解疾病多组学数据的实用工具。这极大地优化了癌症筛查、诊断和治疗的现有研究范式。此外,随着医疗信息化的发展,智能医疗也受到了广泛关注。作为创新医疗的重要组成部分,对癌症患者进行实用的智能预后分析和个性化治疗也十分必要。本文介绍了近年来先进的多组学数据分析技术,介绍了omics数据与人工智能结合应用于肿瘤疾病的案例和优势,最后简要阐述了现阶段多组学分析和人工智能面临的挑战,旨在为肿瘤学研究提供新的视角,为肿瘤的个性化治疗提供可能。
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引用次数: 0
Magneto-acousto-electrical tomography based on frequency response compensated linearly frequency-modulated signal stimulation. 基于频率响应补偿线性频率调制信号刺激的磁声电断层成像。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-18 DOI: 10.1088/1361-6560/ad8296
Meijia Li, Jianfei Wang, Kebin Jia, Zhishen Sun

Objective. In magneto-acousto-electrical tomography (MAET), linearly frequency-modulated (LFM) signal stimulation uses much lower peak voltage than the spike pulse stimulation, lengthening the operation life of the transducer. However, due to the uneven frequency responses of the transducer, the low-noise amplifier (LNA), and the bandpass filter (BPF), MAET using LFM signal stimulation suffers from longitudinal resolution loss. In this paper, frequency response compensated linearly frequency-modulated (FRC-LFM) signal stimulation is investigated to resolve the problem.Approach. The physical model of measurement of the frequency responses of the transducer and the cascading module of the detection electrodes, the LNA, and the BPF is constructed. The frequency responses are approximated by fitting a curve to the measurement data. The frequency response compensation function is set to the reciprocal of the product of the frequency responses. The digital FRC-LFM signal is generated in MATLAB and converted to analog signal through an arbitrary waveform generator. Two groups of MAET experiments are designed to confirm the performance of the FRC-LFM signal stimulation. Pure agar phantom with rectangular through-holes and agar phantom with pork tissue inclusion serve as the samples.Main results. The pulse-compressed magneto-acousto-electrical signal obtained using FRC-LFM stimulation has narrower main-lobe than that obtained using LFM excitation, although the signal to noise pulse interference ratio of the former is little lower than that of the latter, which is due to the limitation of the power amplifier. FRC-LFM also proves to be an effective method to utilize the frequency outside the working band of the transducer in MAET.Significance. The method in this study compensates for the longitudinal resolution loss due to the uneven frequency responses. Combining with high-capability power amplifier and high-performance LNA, the MAET using FRC-LFM signal stimulation can potentially achieve high longitudinal resolution and high sensitivity, advancing MAET toward the clinical application.

目的:在磁声电子断层成像(MAET)中,线性频率调制(LFM)信号刺激使用的峰值电压比尖峰脉冲刺激低得多,从而延长了换能器的工作寿命。然而,由于传感器、低噪声放大器(LNA)和带通滤波器(BPF)的频率响应不均匀,使用 LFM 信号刺激的 MAET 存在纵向分辨率损失。本文研究了频率响应补偿线性频率调制(FRC-LFM)信号激励来解决这一问题:方法:构建了测量换能器频率响应的物理模型,以及由检测电极、低噪声放大器和 BPF 组成的级联模块。通过对测量数据进行曲线拟合来近似频率响应。频率响应补偿函数设置为频率响应乘积的倒数。数字 FRC-LFM 信号在 MATLAB 中生成,并通过任意波形发生器转换为模拟信号。为证实 FRC-LFM 信号刺激的性能,设计了两组 MAET 实验。主要结果:主要结果:使用 FRC-LFM 激励获得的脉冲压缩磁声学(MAE)信号比使用 LFM 激励获得的信号主叶更窄,但前者的信噪比脉冲干扰比略低于后者,这是由于功率放大器的限制造成的。事实证明,FRC-LFM 也是在 MAET 中利用传感器工作频带以外频率的有效方法:本研究中的方法弥补了因频率响应不均匀而造成的纵向分辨率损失。结合高性能功率放大器和高性能低噪声放大器,使用 FRC-LFM 信号刺激的 MAET 有可能实现高纵向分辨率和高灵敏度,从而推动 MAET 走向临床应用。
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引用次数: 0
An in-silico study of conventional and FLASH radiotherapy iso-effectiveness: potential impact of radiolytic oxygen depletion on tumor growth curves and tumor control probability. 传统放疗和FLASH放疗等效性模拟研究:放射性氧耗竭对肿瘤生长曲线和肿瘤控制概率的潜在影响。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-18 DOI: 10.1088/1361-6560/ad8291
I González-Crespo, F Gómez, Ó López Pouso, J Pardo-Montero

Objective. This work aims to investigate the iso-effectiveness of conventional and FLASH radiotherapy on tumors through in-silico mathematical models. We focused on the role of radiolytic oxygen depletion (ROD), which has been argued as a possible factor to explain the FLASH effect.Approach. We used a spatiotemporal reaction-diffusion model, including ROD, to simulate tumor oxygenation and response. From those oxygen distributions we obtained surviving fractions (SFs) using the linear-quadratic (LQ) model with the oxygen enhancement ratios (OERs). We then employed the calculated SFs to describe the evolution of preclinical tumor volumes through a mathematical model of tumor response, and we also extrapolated those results to calculate tumor control probabilities (TCPs) using the Poisson-LQ approach.Main results. Our study suggests that the ROD effect may cause differences in SF between FLASH and conventional radiotherapy, especially in lowα/βandpoorly oxygenatedcells. However, a statistical analysis showed that these changes in SF generally do not result in significant differences in the evolution of preclinical tumor growth curves when the sample size is small, because such differences in SF may not be noticeable in the heterogeneity of the population of animals. Nonetheless, when extrapolating this effect to TCP curves, we observed important differences between both techniques (TCP is lower in FLASH radiotherapy). When analyzing the response of tumors with heterogeneous oxygenations, differences in TCP are more important forwell oxygenatedtumors. This apparent contradiction with the results obtained for homogeneously oxygenated cells is explained by the complex interplay between the heterogeneity of tumor oxygenation, the OER effect, and the ROD effect.Significance. This study supports the experimentally observed iso-effectiveness of FLASH and conventional radiotherapy when analyzing the volume evolution of preclinical tumors (that are far from control). However, this study also hints that tumor growth curves may be less sensitive to small variations in SF than tumor control probability: ROD may lead to increased SF in FLASH radiotherapy, which while not large enough to cause significant differences in tumor growth curves, could lead to important differences in clinical TCPs. Nonetheless, it cannot be discarded that other effects not modeled in this work, like radiation-induced immune effects, can contribute to tumor control and maintain the iso-effectiveness of FLASH radiotherapy. The study of tumor growth curves may not be the ideal experiment to test the iso-effectiveness of FLASH, and experiments reporting TCP orD50may be preferred.

目的:这项工作旨在通过室内数学模型研究传统放疗和FLASH放疗对肿瘤的等效性。我们重点研究了放射性氧耗竭(ROD)的作用,ROD 被认为是解释 FLASH 效果的一个可能因素:我们使用时空反应-扩散模型(包括 ROD)来模拟肿瘤氧合和反应。根据这些氧分布,我们利用线性二次方(LQ)模型和氧增强比(OER)获得了存活分数(SFs)。然后,我们利用计算出的 SFs,通过肿瘤反应数学模型来描述临床前肿瘤体积的演变,并利用泊松-LQ 方法推断这些结果,计算肿瘤控制概率 (TCP):我们的研究表明,ROD效应可能会导致FLASH与传统放疗之间的SF差异,尤其是在α/β值低和氧合作用差的细胞中。然而,统计分析显示,当样本量较小时,SF 的这些变化一般不会导致临床前肿瘤生长曲线演变的显著差异,因为在动物群体的异质性中,SF 的这种差异可能并不明显}。然而,当把这种效应推断到 TCP 曲线时,我们观察到两种技术之间存在着重要差异(FLASH 放射疗法的 TCP 较低)。在分析氧合不均匀肿瘤的反应时,氧合良好肿瘤的 TCP 差异更为重要。这与均匀氧合细胞的结果明显矛盾,原因在于肿瘤氧合的异质性、OER效应和ROD效应之间复杂的相互作用:在分析临床前肿瘤(远离对照)的体积演变时,本研究支持实验观察到的 FLASH 和传统放疗的等效性。不过,这项研究也暗示,与肿瘤控制概率相比,肿瘤生长曲线可能对 SF 的微小变化不那么敏感:ROD可能会导致FLASH放疗中SF的增加,虽然不足以导致肿瘤生长曲线的显著差异,但可能会导致临床TCP的重要差异。尽管如此,也不能排除本研究未模拟的其他效应,如辐射诱导的免疫效应,也会促进肿瘤控制并保持 FLASH 放射治疗的等效性。肿瘤生长曲线研究可能不是测试 FLASH 等效性的理想实验,报告 TCP 或 D50 的实验可能更可取。
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引用次数: 0
Sparse-Laplace hybrid graph manifold method for fluorescence molecular tomography. 用于荧光分子断层成像的稀疏-拉普拉斯混合图流形方法。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-17 DOI: 10.1088/1361-6560/ad84b8
Beilei Wang, Shuangchen Li, Heng Zhang, Lizhi Zhang, Jintao Li, Jingjing Yu, Xiaowei He, Hongbo Guo

Objective.Fluorescence molecular tomography (FMT) holds promise for early tumor detection by mapping fluorescent agents in three dimensions non-invasively with low cost. However, since ill-posedness and ill-condition due to strong scattering effects in biotissues and limited measurable data, current FMT reconstruction is still up against unsatisfactory accuracy, including location prediction and morphological preservation.Approach.To strike the above challenges, we propose a novel Sparse-Laplace hybrid graph manifold (SLHGM) model. This model integrates a hybrid Laplace norm-based graph manifold learning term, facilitating a trade-off between sparsity and preservation of morphological features. To address the non-convexity of the hybrid objective function, a fixed-point equation is designed, which employs two successive resolvent operators and a forward operator to find a converged solution.Main results.Through numerical simulations andin vivoexperiments, we demonstrate that the SLHGM model achieves an improved performance in providing accurate spatial localization while preserving morphological details.Significance.Our findings suggest that the SLHGM model has the potential to advance the application of FMT in biological research, not only in simulation but also inin vivostudies.

目的:荧光分子断层成像(FMT)能以低成本、非侵入性的方式绘制三维荧光制剂图,有望用于早期肿瘤检测。然而,由于生物组织中的强散射效应和可测量数据的有限性,目前的荧光分子断层重构在位置预测和形态保存等方面的准确性仍不尽人意。该模型集成了基于拉普拉斯规范的混合图流形学习项,有助于在稀疏性和形态特征保留之间进行权衡。主要结果:通过数值模拟和活体实验,我们证明了稀疏-拉普拉斯混合图流形模型在提供精确空间定位的同时保留了形态细节,性能得到了改善。
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引用次数: 0
Study of modulation in complex refractive indices induced by ultrafast relativistic electrons using infrared and THz probe pulses. 利用红外线和太赫兹探针脉冲研究超快相对论电子诱导的复折射率调制。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-17 DOI: 10.1088/1361-6560/ad8832
Diana Jeong, Hyeon Sang Bark, Yushin Kim, Junho Shin, Hyun Woo Kim, Key Young Oang, Kyuha Jang, Kitae Lee, Young Uk Jeong, In Hyung Baek, Craig S Levin

Objective Achieving ultra-precise temporal resolution in ionizing radiation detection is essential, particularly in positron emission tomography, where precise timing enhances signal-to-noise ratios and may enable reconstruction-less imaging. A promising approach involves utilizing ultrafast modulation of the complex refractive index, where sending probe pulses to the detection crystals will result in changes in picoseconds (ps), and thus a sub - 10 ps coincidence time resolution can be realized. Towards this goal, here, we aim to first measure the ps changes in probe pulses using an ionizing radiation source with high time resolution. Approach We used relativistic, ultrafast electrons to induce complex refractive index and use probe pulses in the near-infrared (800 nm) and terahertz (THz, 300 µm) regimes to test the hypothesized wavelength-squared increase in absorption coefficient in the Drude free-carrier absorption model. We measured BGO, ZnSe, BaF2, ZnS, PBG, and PWO with 1 mm thickness to control the deposited energy of the 3 MeV electrons, simulating ionization energy of the 511 keV photons. Main results Both with the 800 nm and THz probe pulses, transmission decreased across most samples, indicating the free carrier absorption, with an induced signal change of 11% in BaF2, but without the predicted Drude modulation increase. To understand this discrepancy, we simulated ionization tracks and examined the geometry of the free carrier distribution, attributing the mismatch in THz modulations to the sub-wavelength diameter of trajectories, despite the lengths reaching 500 µm to 1 mm. Additionally, thin samples truncated the final segments of the ionization tracks, and the measured initial segments have larger inter-inelastic collision distances due to lower stopping power (dE/dx) for high-energy electrons, exacerbating diffraction-limited resolution. Significance Our work offers insights into ultrafast radiation detection using complex refractive index modulation and highlights critical considerations in sample preparation, probe wavelength, and probe-charge carrier coupling scenarios.

目标 在电离辐射探测中实现超精确的时间分辨率至关重要,特别是在正电子发射断层扫描中,精确的时间可提高信噪比,并可实现无重建成像。一种很有前景的方法是利用复折射率的超快调制,向探测晶体发送探测脉冲将导致皮秒级(ps)的变化,从而实现低于 10 ps 的重合时间分辨率。为了实现这一目标,我们在此首先利用电离辐射源测量探针脉冲的皮秒级变化,以获得高时间分辨率。我们利用相对论超快电子诱导复折射率,并使用近红外(800 纳米)和太赫兹(THz,300 微米)探针脉冲来测试 Drude 自由载波吸收模型中假设的吸收系数波长平方的增加。我们测量了厚度为 1 毫米的 BGO、ZnSe、BaF2、ZnS、PBG 和 PWO,以控制 3 MeV 电子的沉积能量,模拟 511 keV 光子的电离能量。 主要结果 在 800 纳米和太赫兹探针脉冲下,大多数样品的透射率都有所下降,表明存在自由载流子吸收,BaF2 的诱导信号变化率为 11%,但没有出现预测的 Drude 调制增加。为了理解这一差异,我们模拟了电离轨迹,并检查了自由载流子分布的几何形状,将太赫兹调制的不匹配归因于轨迹的亚波长直径,尽管其长度达到 500 微米至 1 毫米。此外,薄样品截断了电离轨迹的末段,由于高能电子的停止功率(dE/dx)较低,测量的初始段具有较大的非弹性碰撞间距,从而加剧了衍射限制分辨率。
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引用次数: 0
MFCA-MICNN: a convolutional neural network with multiscale fast channel attention and multibranch irregular convolution for noise removal in dMRI. MFCA-MICNN:采用多尺度快速通道关注和多分支不规则卷积的卷积神经网络,用于 dMRI 中的噪声去除。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-16 DOI: 10.1088/1361-6560/ad8294
Lingmei Ai, Yunfan Shi, Ruoxia Yao, Liangfu Li

Diffusion magnetic resonance imaging (dMRI) currently stands as the foremost noninvasive method for quantifying brain tissue microstructure and reconstructing white matter fiber pathways. However, the inherent free diffusion motion of water molecules in dMRI results in signal decay, diminishing the signal-to-noise ratio (SNR) and adversely affecting the accuracy and precision of microstructural data. In response to this challenge, we propose a novel method known as the Multiscale Fast Attention-Multibranch Irregular Convolutional Neural Network for dMRI image denoising. In this work, we introduce Multiscale Fast Channel Attention, a novel approach for efficient multiscale feature extraction with attention weight computation across feature channels. This enhances the model's capability to capture complex features and improves overall performance. Furthermore, we propose a multi-branch irregular convolutional architecture that effectively disrupts spatial noise correlation and captures noise features, thereby further enhancing the denoising performance of the model. Lastly, we design a novel loss function, which ensures excellent performance in both edge and flat regions. Experimental results demonstrate that the proposed method outperforms other state-of-the-art deep learning denoising methods in both quantitative and qualitative aspects for dMRI image denoising with fewer parameters and faster operational speed.

扩散磁共振成像(dMRI)是目前量化脑组织微观结构和重建白质纤维通路的最重要的无创方法。然而,dMRI 中水分子固有的自由扩散运动会导致信号衰减,从而降低信噪比(SNR),并对微观结构数据的准确性和精确性产生不利影响。为了应对这一挑战,我们提出了一种用于 dMRI 图像去噪的新方法,即多尺度快速注意-多分支不规则卷积神经网络。在这项工作中,我们引入了多尺度快速通道注意力,这是一种高效多尺度特征提取的新方法,可跨特征通道计算注意力权重。这增强了模型捕捉复杂特征的能力,并提高了整体性能。此外,我们还提出了一种多分支不规则卷积架构,可有效破坏空间噪声相关性并捕捉噪声特征,从而进一步提高模型的去噪性能。最后,我们设计了一种新颖的损失函数,它能确保在边缘和平坦区域都有出色的表现。实验结果表明,在 dMRI 图像去噪方面,所提出的方法在定量和定性方面都优于其他最先进的深度学习去噪方法,而且参数更少,运行速度更快。
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引用次数: 0
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Physics in medicine and biology
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