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O.8.2 CUSTOM-MADE PLA FILAMENT DOPED WITH THERMOLUMINESCENCE POWDER, FOR CONSTRUCTING 3D PRINTED RADIATION DETECTORS: PRELIMINARY RESULTS o.8.2 用于制造 3d 打印辐射探测器的掺有热释光粉末的定制 pla 长丝:初步结果
IF 3.3 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 DOI: 10.1016/j.ejmp.2024.104570
G. Giakoumettis, N. Okkalidis, F. Okkalidis, C. Chatsigeorgiou, H. Yordanov, M. Gelev, A. Siountas, E. Papanastasiou
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引用次数: 0
IL.3 DUAL-MODALITY HIGH-FLOW IMAGING SCHEME FOR CELL DISCRIMINATION COMBINING NEUROMORPHIC 2D CAMERA AND NIR TIME-STRETCH IMAGER il.3 结合神经形态 2d 相机和 nir 时间拉伸成像仪的用于细胞分辨的双模态高流量成像方案
IF 3.3 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 DOI: 10.1016/j.ejmp.2024.104520
C. Mesaritakis, I. Tsilikas, S. Deligiannidis, G.A. Karydis, D. Syvridis, A. Bogris
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引用次数: 0
IL.4 ARTIFICIAL INTELLIGENCE IN RADIONUCLIDE THERAPY APPLICATIONS IL.4 人工智能在放射性核素治疗中的应用
IF 3.3 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 DOI: 10.1016/j.ejmp.2024.104521
P. Papadimitroulas
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引用次数: 0
Dosimetric study of bevel factors in IOERT with mobile linacs: Towards a unified code of practice 利用移动式直列加速器对 IOERT 中的斜面因素进行剂量学研究:制定统一的操作规范
IF 3.3 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 DOI: 10.1016/j.ejmp.2024.104836
Rafael Ayala , Rocío García , Gema Ruiz , María Jesús García , Álvaro Soza , Susana Gómez , José Manuel Udías , Paula Ibáñez

Background:

Dosimetry in intraoperative electron radiotherapy (IOERT) poses distinct challenges, especially with inclined applicators deviating from international protocols. Ion recombination in ionization chambers, electron beam degradation due to scattering in cylindrical applicators, coupled with a lack of a well-defined beam quality surrogate, complicate output factor determination with ionization chambers. Synthetic diamond-based detectors, offer potential solutions; however, their suitability requires further exploration.

Purpose:

This study addresses output factor determination for beveled applicators. Objectives include assessing the suitability of PTW microDiamond detectors and determining correction factors for ionization chamber measurements based on energy variations at the depth of maximum dose (zmax) for beveled applicators. Experimental data are compared against results obtained from Monte Carlo simulations.

Methods:

We conducted measurements using both PTW microDiamond and IBA CC01 detectors. In addition to benchmarking bevel factors with penEasy, we employed Monte Carlo simulations to determine angular response correction factors for microDiamond detectors and to evaluate energy variations at zmax for beveled applicators.

Results:

The findings indicate that angular response correction factors are unnecessary for microDiamond detectors with beveled applicators. However, variations in mean energy at zmax potentially impact absorbed dose calculations with ionization chambers, particularly with the most inclined applicators.

Conclusions:

Based on our results, the study recommends using microDiamond detectors over cylindrical ionization chambers for output factor determination in IOERT with inclined applicators. Addressing energy variations at zmax is crucial to improve accuracy in ionization chamber measurements. These findings have implications for dosimetry protocols in IOERT, contributing to enhanced delivery in clinical practice.
背景:术中电子放射治疗(IOERT)的剂量测定面临着独特的挑战,尤其是使用偏离国际协议的倾斜涂药器时。电离室中的离子重组、圆柱形涂抹器中散射导致的电子束衰减,再加上缺乏定义明确的光束质量替代物,使得电离室的输出因子测定变得复杂。合成金刚石探测器提供了潜在的解决方案,但其适用性还需要进一步探讨。目标包括评估 PTW 微型金刚石探测器的适用性,并根据斜面施药器最大剂量深度(zmax)的能量变化,确定电离室测量的校正系数。方法:我们使用 PTW microDiamond 和 IBA CC01 探测器进行了测量。结果:研究结果表明,采用斜面涂抹器的微钻石探测器不需要角响应修正系数。结论:根据我们的结果,该研究建议在使用倾斜施药器的 IOERT 中使用微钻石探测器而不是圆柱形电离室来确定输出因子。处理 zmax 处的能量变化对于提高电离室测量的准确性至关重要。这些发现对 IOERT 的剂量测定方案具有重要意义,有助于提高临床实践中的放射治疗效果。
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引用次数: 0
Enhancing MRI brain tumor classification: A comprehensive approach integrating real-life scenario simulation and augmentation techniques 增强磁共振成像脑肿瘤分类:整合真实场景模拟和增强技术的综合方法。
IF 3.3 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 DOI: 10.1016/j.ejmp.2024.104841
Mohamad Abou Ali , Fadi Dornaika , Ignacio Arganda-Carreras , Rejdi Chmouri , Hussien Shayeh
Brain cancer poses a significant global health challenge, with mortality rates showing a concerning surge over recent decades. The incidence of brain cancer-related mortality has risen from 140,000 to 250,000, accompanied by a doubling in new diagnoses from 175,000 to 350,000. In response, magnetic resonance imaging (MRI) has emerged as a pivotal diagnostic tool, facilitating early detection and treatment planning. However, the translation of deep learning approaches to brain cancer diagnosis faces a critical obstacle: the scarcity of public clinical datasets reflecting real-world complexities. This study aims to bridge this gap through a comprehensive exploration and augmentation of training data. Initially, a battery of pre-trained deep models undergoes evaluation on a main brain cancer MRI “BT-MRI” dataset, yielding remarkable performance metrics, including 100% accuracy, precision, recall, and F1-Score, substantiated by the Score-CAM methodology. This initial success underscores the potential of deep learning in brain cancer diagnosis. Subsequently, the model’s efficacy undergoes further scrutiny using a supplementary brain cancer MRI “BCD-MRI” dataset, affirming its robustness and applicability across diverse datasets. However, the ultimate litmus test lies in confronting the model with synthetic testing datasets crafted to emulate real-world scenarios. The synthetic testing datasets, a BCD-MRI testing sub-dataset enriched with noise, blur, and simulated patient motion, reveal a sobering reality: the model’s performance plummets, exposing inherent limitations in generalization. To address this issue, a diverse set of optimization strategies and augmentation techniques, ranging from diverse optimizers to sophisticated data augmentation methods, are exhaustively explored. Despite these efforts, the problem of generalization persists. The breakthrough emerges with the integration of noise and blur as augmentation techniques during the training process. Leveraging Gaussian noise and Gaussian blur kernels, the model undergoes a transformative evolution, exhibiting newfound robustness and resilience. Retesting the refined model against the challenging synthetic datasets reveals a remarkable transformation, with performance metrics witnessing a notable ascent. This achievement underscores the important role of correct selection of data augmentation in fortifying the generalization of deep learning models for brain cancer diagnosis. This study not only advances the frontiers of diagnostic precision in brain cancer but also underscores the paramount importance of methodological rigor and innovation in confronting the complexities of real-world clinical scenarios.
脑癌对全球健康构成了重大挑战,近几十年来,死亡率急剧上升,令人担忧。与脑癌相关的死亡率已从 14 万上升到 25 万,新诊断病例也从 17.5 万增加到 35 万,翻了一番。为此,磁共振成像(MRI)已成为一种关键的诊断工具,有助于早期检测和治疗规划。然而,将深度学习方法应用于脑癌诊断面临着一个关键障碍:反映真实世界复杂性的公共临床数据集非常稀缺。本研究旨在通过全面探索和增强训练数据来弥补这一差距。最初,一组预先训练好的深度模型在一个主要的脑癌磁共振成像(MRI)"BT-MRI "数据集上进行了评估,获得了显著的性能指标,包括 100% 的准确率、精确度、召回率和 F1 分数,并通过 Score-CAM 方法得到证实。这一初步成功彰显了深度学习在脑癌诊断中的潜力。随后,利用补充脑癌 MRI "BCD-MRI "数据集对该模型的功效进行了进一步检查,肯定了其在不同数据集上的鲁棒性和适用性。不过,最终的试金石还在于用模拟真实世界场景的合成测试数据集来检验该模型。合成测试数据集是一个 BCD-MRI 测试子数据集,富含噪声、模糊和模拟患者运动,它揭示了一个令人警醒的现实:模型的性能急剧下降,暴露了泛化的固有局限性。为了解决这个问题,人们对各种优化策略和增强技术进行了详尽的探索,从多样化的优化器到复杂的数据增强方法,不一而足。尽管做出了这些努力,但通用化问题依然存在。在训练过程中,将噪声和模糊作为增强技术进行整合,就出现了突破。利用高斯噪声和高斯模糊核,模型发生了变革性的演变,表现出新发现的鲁棒性和弹性。针对具有挑战性的合成数据集对改进后的模型进行重新测试,结果显示模型发生了显著的转变,性能指标明显上升。这一成就强调了正确选择数据增强在强化深度学习模型在脑癌诊断中的泛化方面的重要作用。这项研究不仅推动了脑癌精准诊断的前沿发展,还强调了方法论的严谨性和创新性在面对现实世界复杂临床场景时的极端重要性。
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引用次数: 0
PP.7.1 MULTIFREQUENCY TYMPANOMETRY: FROM MIDDLE EAR MECHANICS TO DECISION MAKING IN EAR SURGERY 第 7.1 页 多频鼓室测量:从中耳力学到耳科手术决策
IF 3.3 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 DOI: 10.1016/j.ejmp.2024.104612
C. Tsilivigkos, A. Warnecke, E. Ferekidis
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引用次数: 0
O.6.2 COMPARISON OF MACHINE LEARNING ALGORITHMS TO PREDICT CLINICALLY SIGNIFICANT PROSTATE CANCER WITH T2WEIGHTED-MRI DERIVED RADIOMIC FEATURES O.6.2 用机器学习算法预测具有临床意义的前列腺癌与 T2 加权成像得出的放射学特征的比较
IF 3.3 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 DOI: 10.1016/j.ejmp.2024.104560
E. Bekou, C. Loukas, D. Thanasas, A. Tsochatzis, A. Moulita, I. Seimenis, E. Karavasilis
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引用次数: 0
O.4.2 CANCER RISK FACTORS FOR TYPICAL SPACE MISSION SCENARIOS USING A MICRODOSIMETRIC APPROACH o.4.2 利用微剂量测定方法计算典型空间飞行任务情况下的癌症风险系数
IF 3.3 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 DOI: 10.1016/j.ejmp.2024.104546
A. Papadopoulos, I. Kyriakou, S. Incerti, G. Santin, P. Nieminen, I.A. Daglis, W. Li, D. Emfietzoglou
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引用次数: 0
O.1.10 OPTIMIZING SINGLE-ISOCENTER STEREOTACTIC TECHNIQUE FOR MULTIPLE BRAIN METASTASES o.1.10 优化治疗多发性脑转移瘤的单中心立体定向技术
IF 3.3 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 DOI: 10.1016/j.ejmp.2024.104536
D. Fortatos, M. Psarras, D. Stasinou, T. Stroubinis, A. Zygogianni, V. Kouloulias, M. Protopapa, K. Platoni
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引用次数: 0
PP.1.2 PATIENT-SPECIFIC QUALITY ASSURANCE FOR LUNG CANCER PATIENTS: A COMPARATIVE STUDY BETWEEN FIXED (DMLC) AND ROTATING GANTRY (VMAT) pp.1.2 针对肺癌患者的质量保证:固定式(DMLC)和旋转龙门式(VMAT)的比较研究
IF 3.3 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 DOI: 10.1016/j.ejmp.2024.104579
A. Koukogiorgos, G. Giakoumettis, E. Papanastasiou
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引用次数: 0
期刊
Physica Medica-European Journal of Medical Physics
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