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Nyquist ghost elimination for diffusion MRI by dual-polarity readout at low b-values. 低b值双极性读出弥散MRI奈奎斯特鬼影消除。
IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2025-01-21 DOI: 10.1088/2057-1976/ada8b0
Oscar Jalnefjord, Nicolas Geades, Guillaume Gilbert, Isabella M Björkman-Burtscher, Maria Ljungberg

Dual-polarity readout is a simple and robust way to mitigate Nyquist ghosting in diffusion-weighted echo-planar imaging but imposes doubled scan time. We here propose how dual-polarity readout can be implemented with little or no increase in scan time by exploiting an observed b-value dependence and signal averaging. The b-value dependence was confirmed in healthy volunteers with distinct ghosting at low b-values but of negligible magnitude atb= 1000 s/mm2. The usefulness of the suggested strategy was exemplified with a scan using tensor-valued diffusion encoding for estimation of parameter maps of mean diffusivity, and anisotropic and isotropic mean kurtosis, showing that ghosting propagated into all three parameter maps unless dual-polarity readout was applied. Results thus imply that extending the use of dual-polarity readout to low non-zero b-values provides effective ghost elimination and can be used without increased scan time for any diffusion MRI scan containing signal averaging at low b-values.

双极性读数是一种简单而稳健的方法,可以减轻扩散加权回波平面成像中的奈奎斯特重影,但会增加扫描时间。我们在这里提出如何双极性读出可以实现很少或不增加扫描时间利用观察到的b值依赖和信号平均。在健康志愿者中证实了b值依赖性,在低b值时有明显的鬼影,但在b = 1000 s/mm2时可忽略。通过使用张量值扩散编码来估计平均扩散率、各向异性和各向同性平均峰度的参数图的扫描,证明了所建议策略的有效性,表明除非应用双极性读数,否则重影会传播到所有三个参数图中。因此,结果表明,将双极性读出扩展到低非零b值可以有效地消除虚影,并且可以在不增加扫描时间的情况下用于任何包含低b值信号平均的弥散MRI扫描。
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引用次数: 0
Systematic application of saliency maps to explain the decisions of convolutional neural networks for glaucoma diagnosis based on disc and cup geometry. 系统应用显著性图来解释卷积神经网络在青光眼诊断中的决策。
IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2025-01-21 DOI: 10.1088/2057-1976/ada8ad
Francisco Fumero, Jose Sigut, José Estévez, Tinguaro Díaz-Alemán

This paper systematically evaluates saliency methods as explainability tools for convolutional neural networks trained to diagnose glaucoma using simplified eye fundus images that contain only disc and cup outlines. These simplified images, a methodological novelty, were used to relate features highlighted in the saliency maps to the geometrical clues that experts consider in glaucoma diagnosis. Despite their simplicity, these images retained sufficient information for accurate classification, with balanced accuracies ranging from 0.8331 to 0.8890, compared to 0.8090 to 0.9203 for networks trained on the original images. The study used a dataset of 606 images, along with RIM-ONE DL and REFUGE datasets, and explored nine saliency methods. A discretization algorithm was applied to reduce noise and compute normalized attribution values for standard eye fundus sectors. Consistent with other medical imaging studies, significant variability was found in the attribution maps, influenced by the method, model, or architecture, and often deviating from typical sectors experts examine. However, globally, the results were relatively stable, with a strong correlation of 0.9289 (p < 0.001) between relevant sectors in our dataset and RIM-ONE DL, and 0.7806 (p < 0.001) for REFUGE. The findings suggest caution when using saliency methods in critical fields like medicine. These methods may be more suitable for broad image relevance interpretation rather than assessing individual cases, where results are highly sensitive to methodological choices. Moreover, the regions identified by the networks do not consistently align with established medical criteria for disease severity.

本文系统地评估了显著性方法作为卷积神经网络的可解释性工具,训练卷积神经网络使用仅包含眼盘和眼杯轮廓的简化眼底图像诊断青光眼。这些简化的图像是一种新颖的方法,用于将显著性图中突出显示的特征与专家在青光眼诊断中考虑的几何线索联系起来。尽管这些图像很简单,但它们保留了足够的信息来进行准确分类,其平衡精度范围为0.8331至0.8890,而在原始图像上训练的网络的平衡精度为0.8090至0.9203。研究使用了606张图像的数据集,以及RIM-ONE DL和REFUGE数据集,并探索了9种显著性方法。采用离散化算法对标准眼底扇区进行降噪和归一化归因计算。与其他医学成像研究一致,在归因图中发现了显著的可变性,受方法、模型或架构的影响,并且经常偏离专家检查的典型部门。然而,在全球范围内,结果相对稳定,我们数据集中相关部门与RIM-ONE DL之间的相关性为0.9289 (p < 0.001), REFUGE的相关性为0.7806 (p < 0.001)。研究结果表明,在医学等关键领域使用显著性方法时要谨慎。这些方法可能更适合广泛的图像相关性解释,而不是评估个别情况,其中结果对方法选择高度敏感。此外,网络确定的区域并不始终符合既定的疾病严重程度医学标准。
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引用次数: 0
Hybrid Data Augmentation Strategies for Robust Deep Learning Classification of Corneal Topographic MapTopographic Map. 角膜地形图鲁棒深度学习分类的混合数据增强策略。
IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2025-01-20 DOI: 10.1088/2057-1976/adabea
Abir Chaari, Imen Fourati Kallel, Sonda Kammoun, Mondher Frikha

Deep learning has emerged as a powerful tool in medical imaging, particularly for corneal topographic map classification. However, the scarcity of labeled data poses a significant challenge to achieving robust performance. This study investigates the impact of various data augmentation strategies on enhancing the performance of a customized convolutional neural network model for corneal topographic map classification. We propose a hybrid data augmentation approach that combines traditional transformations, generative adversarial networks, and specific generative models. Experimental results demonstrate that the hybrid data augmentation method, achieves the highest accuracy of 99.54%, significantly outperforming individual data augmentation techniques. This hybrid approach not only improves model accuracy but also mitigates overfitting issues, making it a promising solution for medical image classification tasks with limited data availability.

深度学习已经成为医学成像,特别是角膜地形图分类的强大工具。然而,标记数据的稀缺性对实现稳健性能提出了重大挑战。本研究探讨了不同的数据增强策略对增强自定义卷积神经网络角膜地形图分类模型性能的影响。我们提出了一种混合数据增强方法,该方法结合了传统转换、生成对抗网络和特定生成模型。实验结果表明,混合数据增强方法的准确率高达99.54%,显著优于单个数据增强方法。这种混合方法不仅提高了模型精度,而且减轻了过拟合问题,使其成为数据可用性有限的医学图像分类任务的一个有希望的解决方案。
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引用次数: 0
Electroencephalogram Features Reflect Effort Corresponding to Graded Finger Extension: Implications for Hemiparetic Stroke. 脑电图特征反映了与手指伸展程度相对应的努力:对偏瘫中风的影响。
IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2025-01-20 DOI: 10.1088/2057-1976/adabeb
Chase Haddix, Madison Bates, Sarah Garcia Pava, Elizabeth Salmon Powell, Lumy Sawaki, Sridhar Sunderam

Brain-computer interfaces (BCIs) offer disabled individuals the means to interact with devices by decoding the electroencephalogram (EEG). However, decoding intent in fine motor tasks can be challenging, especially in stroke survivors with cortical lesions. Here, we attempt to decode graded finger extension from the EEG in stroke patients with left-hand paresis and healthy controls. Participants extended their fingers to one of four levels: low, medium, high, or "no-go" (none), while hand, muscle (electromyography: EMG), and brain (EEG) activity were monitored. Event-related desynchronization (ERD) was measured as the change in 8-30 Hz EEG power during movement. Classifiers were trained on the ERD, EMG power, or both (EEG+EMG) to decode finger extension, and accuracy assessed via four-fold cross-validation for each hand of each participant. Mean accuracy exceeded chance (25%) for controls (n=11) at 62% for EMG, 60% for EEG, and 71% for EEG+EMG on the left hand; and 67%, 60%, and 74%, respectively, on the right hand. Accuracies were similar on the unimpaired right hand for the stroke group (n=3): 61%, 68%, and 78%, respectively. But on the paretic left hand, EMG only discriminated no-go from movement above chance (41%); in contrast, EEG gave 65% accuracy (68% for EEG+EMG), comparable to the non-paretic hand. The median ERD was significant (p < 0.01) over the cortical hand area in both groups and increased with each level of finger extension. But while the ERD favored the hemisphere contralateral to the active hand as expected, it was ipsilateral for the left hand of stroke due to the lesion in the right hemisphere, which may explain its discriminative ability. Hence, the ERD captures effort in finger extension regardless of success or failure at the task; and harnessing residual EMG improves the correlation. This marker could be leveraged in rehabilitative protocols that focus on fine motor control.

脑机接口(bci)通过解码脑电图(EEG)为残疾人提供与设备交互的手段。然而,在精细运动任务中解码意图是具有挑战性的,特别是在有皮层病变的中风幸存者中。在这里,我们试图解码脑卒中患者左手麻痹和健康对照的分级手指延伸。参与者将手指伸到四个水平中的一个:低、中、高或“不”(没有),同时监测手、肌肉(肌电图)和大脑(脑电图)的活动。运动过程中8 ~ 30 Hz脑电功率的变化测量事件相关去同步(ERD)。分类器在ERD、肌电图功率或两者(EEG+EMG)上进行训练,以解码手指延伸,并通过对每个参与者的每只手进行四次交叉验证来评估准确性。对照组(n=11)的平均准确率超过机会(25%),肌电图为62%,脑电图为60%,脑电图+肌电图为71%;右边分别是67% 60% 74%中风组未受损右手的准确度相似(n=3):分别为61%、68%和78%。但在父母的左手,肌电图只区分不走和机会以上的运动(41%);相比之下,脑电图的准确率为65%(脑电图+肌电图为68%),与非双亲手相当。两组手部皮质区平均ERD值均显著(p < 0.01),且随手指伸度的增加而增加。但是,尽管ERD如预期的那样倾向于活动手的对侧半球,但由于右半球的病变,它对中风的左手是同侧的,这可能解释了它的辨别能力。因此,ERD捕捉手指伸展的努力,而不管任务的成功或失败;利用残馀肌电信号可以提高相关性。这种标记物可以用于专注于精细运动控制的康复方案。
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引用次数: 0
Quantification of Albumin and ɣ-Globulin Concentrations by Multivariate Regression Based on Admittance Relaxation Time Distribution (mrARTD). 基于导纳弛豫时间分布(mrARTD)的多元回归定量白蛋白和β -球蛋白浓度。
IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2025-01-20 DOI: 10.1088/2057-1976/adabec
Arbariyanto Mahmud Wicaksono, Daisuke Kawashima, Ryoma Ogawa, Shinsuke Akita, Masahiro Takei

Albumin and γ-globulin concentrations in subcutaneous adipose tissues (SAT) have been quantified by multivariate regression based on admittance relaxation time distribution (mraRTD) under the fluctuated background of sodium electrolyte concentration. The mraRTD formulates P = Ac + Ξ (P: peak matrix of distribution function magnitude ɣP and frequency τP, c: concentration matrix of albumin cAlb, γ-globulin Gloc, and sodium electrolyte Nac, A: coefficient matrix of a multivariate regression model, and Ξ: error matrix). The mraRTD is implemented by two processes which are: 1) the training process of A through the maximum likelihood estimation of P and 2) the quantification process of cAlb, Gloc, and Nac through the model prediction. In the training process, a positive correlation is present between cAlb, Gloc, and Nac to ɣP1 at τP1= 0.1 as well as ɣP2 at τP2= 1.40 µs as under a fixed concentration of proteins solution into a porcine SAT (cAlb = 0.800-2.400 g/dL, Gloc = 0.400-1.200 g/dL and Nac = 0.700-0.750 g/dL). The mraRTD method quantifies cAlb, Gloc, and Nac in SAT with an absolute error of 33.79%, 44.60%, and 2.18%, respectively.

在电解质钠浓度波动的背景下,采用基于导纳弛豫时间分布(mraRTD)的多元回归定量分析了皮下脂肪组织(SAT)中白蛋白和γ-球蛋白的浓度。mraRTD的公式为P = Ac + Ξ (P:分布函数幅度τP和频率τP的峰矩阵,c:白蛋白cAlb、γ-球蛋白Gloc和钠电解质Nac的浓度矩阵,A:多元回归模型的系数矩阵,Ξ:误差矩阵)。mraRTD通过两个过程实现:1)通过P的极大似然估计对A进行训练过程;2)通过模型预测对cAlb、Gloc、Nac进行量化过程。在猪SAT (cAlb = 0.800-2.400 g/dL, Gloc = 0.400-1.200 g/dL, Nac = 0.700-0.750 g/dL)中,在τP1= 0.1和τP2= 1.40µs时,cAlb、Gloc和Nac与α P1呈正相关。mraRTD方法定量SAT中cAlb、Gloc和Nac的绝对误差分别为33.79%、44.60%和2.18%。
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引用次数: 0
Automatic segmentation of MRI images for brain radiotherapy planning using deep ensemble learning. 基于深度集成学习的MRI图像自动分割用于脑放疗规划。
IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2025-01-17 DOI: 10.1088/2057-1976/ada6ba
S A Yoganathan, Tarraf Torfeh, Satheesh Paloor, Rabih Hammoud, Noora Al-Hammadi, Rui Zhang

Backgroundand Purpose:This study aimed to develop and evaluate an efficient method to automatically segment T1- and T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network (CNN) models against an ensemble approach to advance the accuracy of MRI-guided radiotherapy (RT) planning.Materials and Methods. The evaluation was conducted on a private clinical dataset and a publicly available dataset (HaN-Seg). Anonymized MRI data from 55 brain cancer patients, including T1-weighted, T1-weighted with contrast, and T2-weighted images, were used in the clinical dataset. We employed an EDL strategy that integrated five independently trained 2D neural networks, each tailored for precise segmentation of tumors and organs at risk (OARs) in the MRI scans. Class probabilities were obtained by averaging the final layer activations (Softmax outputs) from the five networks using a weighted-average method, which were then converted into discrete labels. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and Hausdorff distance at 95% (HD95). The EDL model was also tested on the HaN-Seg public dataset for comparison.Results. The EDL model demonstrated superior segmentation performance on both the clinical and public datasets. For the clinical dataset, the ensemble approach achieved an average DSC of 0.7 ± 0.2 and HD95 of 4.5 ± 2.5 mm across all segmentations, significantly outperforming individual networks which yielded DSC values ≤0.6 and HD95 values ≥14 mm. Similar improvements were observed in the HaN-Seg public dataset.Conclusions. Our study shows that the EDL model consistently outperforms individual CNN networks in both clinical and public datasets, demonstrating the potential of ensemble learning to enhance segmentation accuracy. These findings underscore the value of the EDL approach for clinical applications, particularly in MRI-guided RT planning.

背景与目的:本研究旨在开发和评估一种有效的脑磁共振成像(MRI)图像自动分割T1和t2加权的方法。我们特别比较了单个卷积神经网络(CNN)模型与集成方法的分割性能,以提高mri引导放射治疗(RT)计划的准确性。材料与方法。评估是在一个私人临床数据集和一个公开可用的数据集(HaN-Seg)上进行的。临床数据集中使用了55名脑癌患者的匿名MRI数据,包括t1加权、t1加权对比和t2加权图像。我们采用了一种EDL策略,该策略集成了五个独立训练的2D神经网络,每个神经网络都针对MRI扫描中的肿瘤和危险器官(OARs)进行精确分割。通过使用加权平均方法对五个网络的最终层激活(Softmax输出)进行平均,从而获得类概率,然后将其转换为离散标签。使用Dice相似系数(DSC)和95%的Hausdorff距离(HD95)来评估分割性能。EDL模型还在HaN-Seg公共数据集上进行了测试,以进行比较。EDL模型在临床和公共数据集上都表现出优异的分割性能。对于临床数据集,集成方法在所有分割上的平均DSC为0.7±0.2,HD95为4.5±2.5 mm,显著优于产生DSC值≤0.6和HD95值≥14 mm的单个网络。在HaN-Seg公共数据集中也观察到类似的改善。我们的研究表明,EDL模型在临床和公共数据集中始终优于单个CNN网络,证明了集成学习在提高分割精度方面的潜力。这些发现强调了EDL方法在临床应用中的价值,特别是在mri引导下的RT计划中。
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引用次数: 0
Enhanced AMD detection in OCT images using GLCM texture features with Machine Learning and CNN methods. 增强视网膜图像AMD检测的机器学习与GLCM分析。
IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2025-01-17 DOI: 10.1088/2057-1976/ada6bc
Loganathan R, Latha S

Global blindness is substantially influenced by age-related macular degeneration (AMD). It significantly shortens people's lives and severely impairs their visual acuity. AMD is becoming more common, requiring improved diagnostic and prognostic methods. Treatment efficacy and patient survival rates stand to benefit greatly from these upgrades. To improve AMD diagnosis in preprocessed retinal images, this study uses Grey Level Co-occurrence Matrix (GLCM) features for texture analysis. The selected GLCM features include contrast and dissimilarity. Notably, grayscale pixel values were also integrated into the analysis. Key factors such as contrast, correlation, energy, and homogeneity were identified as the primary focuses of the study. Various supervised machine learning (ML) and CNN techniques were employed on Optical Coherence Tomography (OCT) image datasets. The impact of feature selection on model performance is evaluated by comparing all GLCM features, selected GLCM features, and grayscale pixel features. Models using GSF features showed low accuracy, with OCTID at 23% and Kermany at 54% for BC, and 23% and 53% for CNN. In contrast, GLCM features achieved 98% for OCTID and 73% for Kermany in RF, and 83% and 77% in CNN. SFGLCM features performed the best, achieving 98% for OCTID across both RF and CNN, and 77% for Kermany. Overall, SFGLCM and GLCM features outperformed GSF, improving accuracy, generalization, and reducing overfitting for AMD detection. The Python-based research demonstrates ML's potential in ophthalmology to enhance patient outcomes.

全盲在很大程度上受年龄相关性黄斑变性(AMD)影响。它大大缩短了人们的寿命,并严重损害了他们的视力。AMD正变得越来越普遍,需要改进诊断和预后方法。这些升级将大大提高治疗效果和患者存活率。为了提高对预处理视网膜图像的AMD诊断,本研究采用灰度共生矩阵(GLCM)特征进行纹理分析。选择的GLCM特征包括对比和不相似。值得注意的是,灰度像素值也被整合到分析中。对比、相关性、能量和同质性等关键因素被确定为研究的主要焦点。各种监督机器学习(ML)和CNN技术被用于光学相干断层扫描(OCT)图像数据集。通过比较所有GLCM特征、选择的GLCM特征和灰度像素特征来评估特征选择对模型性能的影响。使用GSF特征的模型准确率较低,BC的OCTID为23%,Kermany为54%,CNN为23%和53%。相比之下,在RF中,GLCM特征在OCTID中达到98%,在Kermany中达到73%,在CNN中达到83%和77%。SFGLCM特征表现最好,在RF和CNN上的OCTID达到98%,在Kermany上达到77%。总体而言,SFGLCM和GLCM特征优于GSF,提高了AMD检测的准确性、泛化程度,并减少了过拟合。基于python的研究证明了机器学习在眼科中提高患者预后的潜力。
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引用次数: 0
Novel approach for quality control testing of medical displays using deep learning technology. 基于深度学习技术的医疗显示器质量控制测试新方法。
IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2025-01-15 DOI: 10.1088/2057-1976/ada6bd
Sho Maruyama, Fumiya Mizutani, Haruyuki Watanabe

Objectives:In digital image diagnosis using medical displays, it is crucial to rigorously manage display devices to ensure appropriate image quality and diagnostic safety. The aim of this study was to develop a model for the efficient quality control (QC) of medical displays, specifically addressing the measurement items of contrast response and maximum luminance as part of constancy testing, and to evaluate its performance. In addition, the study focused on whether these tasks could be addressed using a multitasking strategy.Methods:The model used in this study was constructed by fine-tuning a pretrained model and expanding it to a multioutput configuration that could perform both contrast response classification and maximum luminance regression. QC images displayed on a medical display were captured using a smartphone, and these images served as the input for the model. The performance was evaluated using the area under the receiver operating characteristic curve (AUC) for the classification task. For the regression task, correlation coefficients and Bland-Altman analysis were applied. We investigated the impact of different architectures and verified the performance of multi-task models against single-task models as a baseline.Results:Overall, the classification task achieved a high AUC of approximately 0.9. The correlation coefficients for the regression tasks ranged between 0.6 and 0.7 on average. Although the model tended to underestimate the maximum luminance values, the error margin was consistently within 5% for all conditions.Conclusion:These results demonstrate the feasibility of implementing an efficient QC system for medical displays and the usefulness of a multitask-based method. Thus, this study provides valuable insights into the potential to reduce the workload associated with medical-device management the development of QC systems for medical devices, highlighting the importance of future efforts to improve their accuracy and applicability.

目的:在使用医用显示器进行数字图像诊断时,严格管理显示设备以确保适当的图像质量和诊断安全至关重要。本研究的目的是建立一个医疗显示器的有效品质控制(QC)模型,特别是针对对比度响应和最大亮度的测量项目作为恒常性测试的一部分,并评估其性能。此外,研究重点是这些任务是否可以使用多任务策略来解决。方法:本研究中使用的模型是通过微调预训练模型并将其扩展到可以执行对比度响应分类和最大亮度回归的多输出配置来构建的。使用智能手机捕捉医疗显示器上显示的QC图像,并将这些图像作为模型的输入。使用分类任务的接收者工作特征曲线下面积(AUC)来评价分类任务的性能。对于回归任务,使用相关系数和Bland-Altman分析。我们研究了不同架构的影响,并验证了多任务模型与单任务模型作为基准的性能。结果:总体而言,分类任务实现了大约0.9的高AUC。回归任务的相关系数平均在0.6到0.7之间。尽管该模型倾向于低估最大亮度值,但在所有条件下误差范围始终在5%以内。结论:这些结果表明,在医疗显示器中实施高效QC系统是可行的,并且基于多任务的方法是有用的。因此,本研究提供了有价值的见解,以减少与医疗器械管理相关的工作量,以及医疗器械质量控制系统的发展,强调了未来努力提高其准确性和适用性的重要性。
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引用次数: 0
Noise reduction in abdominal acoustic recordings of maternal placental murmurs. 母体胎盘杂音的腹部声学记录降噪。
IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2025-01-15 DOI: 10.1088/2057-1976/ada6bb
Dagbjört Helga Eiríksdóttir, Gry Grønborg Hvass, Henrik Zimmermann, Johannes Jan Struijk, Samuel Emil Schmidt

Fetal phonocardiography is a well-known auscultation technique for evaluation of fetal health. However, murmurs that are synchronous with the maternal heartbeat can often be heard while listening to fetal heart sounds. Maternal placental murmurs (MPM) could be used to detect maternal cardiovascular and placental abnormalities, but the recorded MPMs are often contaminated by ambient interference and noise.Objective:The aim of this study was to compare noise reduction methods to reduce noise in the recorded MPMs. Approach:1) Bandpass filtering (BPF), 2) a multichannel noise reduction (MCh) using either Wiener filter (WF), Least-mean-square or Independent component analysis, 3) a combination of BPF with wavelet transient reduction (WTR) and 4) a combination of MCh and WTR. The methods were tested on signals recorded with two microphone units placed on the abdomen of pregnant women with an electrocardiogram (ECG) recorded simultaneously. The performance was evaluated using coherence and heart cycle duration error (HCDError) as compared with the ECG. Results: The mean of the absolute HCDErrorwas 32.7 ms for the BPF with all methods significantly lower (p < 0.05) than BPF. The lowest errors were obtained for WTR-WF where the HCDErrorranged 16.68-17.72 ms for seven different filter orders. All methods had significantly different coherence measure compared with BPF (p < 0.05). The lowest coherence was reached with WTR-WF (filter order 640) where the mean value decreased from 0.50 for BPF to 0.03.Significance:These results show how noise reduction techniques such as WF combined with wavelet denoising can greatly enhance the quality of MPM recordings.

胎儿心音图是一种众所周知的用于评估胎儿健康的听诊技术。然而,在听胎儿心音时,经常可以听到与母体心跳同步的杂音。母体胎盘杂音(MPM)可用于检测母体心血管和胎盘异常,但记录的MPM常受到环境干扰和噪声的污染。目的:比较不同降噪方法对声像图降噪效果的影响。方法:1)带通滤波(BPF), 2)使用维纳滤波器(WF),最小均方或独立分量分析进行多通道降噪(MCh), 3) BPF与小波瞬态降噪(WTR)的结合,4)MCh和WTR的结合。这些方法在放置在孕妇腹部的两个麦克风单元记录的信号上进行了测试,同时记录了心电图(ECG)。与ECG相比,使用相干性和心脏周期持续时间误差(HCDError)来评估其性能。结果:BPF的绝对hcdererror均值为32.7 ms,所有方法的hcdererror均显著降低。(p)意义:这些结果表明,WF与小波去噪相结合的降噪技术可以极大地提高MPM记录的质量。
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引用次数: 0
Quantitative Assessment of Delivered Dose in Carbon Ion Spatially Fractionated Radiotherapy (C-SFRT) and Biological Response to C-SFRT. 碳离子空间分割放疗(C-SFRT)给药剂量的定量评估及C-SFRT的生物学反应。
IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2025-01-14 DOI: 10.1088/2057-1976/ada964
Toshiro Tsubouchi, Misato Umemura, Kazumasa Minami, Noriaki Hamatani, Naoto Saruwatari, Masaaki Takashina, Masashi Yagi, Keith M Furutani, Shinichi Shimizu, Tatsuaki Kanai

Objective Applying carbon ion beams, which have high linear energy transfer and low scatter within the human body, to Spatially Fractionated Radiation Therapy (SFRT) could benefit the treatment of deep-seated or radioresistant tumors. This study aims to simulate the dose distributions of spatially fractionated beams (SFB) to accurately determine the delivered dose and model the cell survival rate following SFB irradiation. Approach Dose distributions of carbon ion beams are calculated using the Triple Gaussian Model. The sensitive volume of the detector used in measurements was also considered. If the measurements and simulations show good agreement, the dose distribution and absolute dose delivered by SFB can be accurately estimated. Three types of dose distributions were delivered to human salivary gland cells (HSGc-C5): uniform dose distribution (UDD), and one-dimensional (1D) grid-like dose distributions (GDD) with 6 mm and 8 mm spacing. These provided high (Peak-to-Valley Dose Ratio, PVDR=4.0) and low (PVDR=1.64) dose differences between peak and valley doses, respectively. Linear-Quadratic (LQ) model parameters for HSGc-C5 were derived from the UDD and cell survival fractions (SF) were simulated for 1D GDD using these values. Main results Good agreement was observed between measurements and simulations when accounting for detector volume. However, the TPS results overestimated dose in steep gradient region, likely due to the 2.0 mm calculation grid size. LQ parameters for HSGc-C5 were α = 0.34 and β = 0.057. The 1D GDD with 6 mm spacing showed good agreement between simulations and experiments, but the 8.0 mm spacing resulted in lower experimental cell survival. Significance We successfully simulated grid-like dose distributions and conducted SF simulations. The results suggest potential cell-killing effects due to high-dose differences in SFB. .

目的将碳离子束应用于空间分步放射治疗(SFRT)中,具有高线性能量传递和低体内散射的特点,有利于治疗深部或放射耐药肿瘤。本研究旨在模拟空间分异光束(SFB)的剂量分布,以准确确定空间分异光束照射后的剂量,并模拟SFB照射后的细胞存活率。方法 ;采用三重高斯模型计算碳离子束的剂量分布。还考虑了测量中所用探测器的灵敏体积。如果测量和模拟结果吻合良好,则可以准确地估计SFB的剂量分布和绝对剂量。对人唾液腺细胞(HSGc-C5)进行三种剂量分布:均匀剂量分布(UDD)和间隔6mm和8mm的一维网格状剂量分布(GDD)。它们分别提供了高(峰谷剂量比,PVDR=4.0)和低(PVDR=1.64)峰谷剂量差异。HSGc-C5的线性二次(LQ)模型参数由UDD导出,并使用这些值模拟1D GDD的细胞存活分数(SF)。主要结果 ;在考虑检测器体积时,观察到测量值与模拟值之间的良好一致性。然而,由于计算网格尺寸为2.0 mm, TPS结果在陡峭梯度区域高估了剂量。HSGc-C5的LQ参数α = 0.34, β = 0.057。模拟与实验结果吻合较好,但8.0 mm时实验细胞存活率较低。[#xD]意义我们成功模拟了栅格状剂量分布,并进行了SF模拟。结果表明,由于SFB的高剂量差异,潜在的细胞杀伤作用。
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Biomedical Physics & Engineering Express
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