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 Physics in medicine and biology Pub Date : 2024-10-07 DOI:10.1088/1361-6560/ad841e
Shengzi Zhao, Le Shen, Katsuyuki Taguchi, Yuxiang Xing
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引用次数: 0

摘要

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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Exploring charge sharing compensation using inter-pixel coincidence counters for photon counting detectors by deep-learning from local information.

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. .

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来源期刊
Physics in medicine and biology
Physics in medicine and biology 医学-工程:生物医学
CiteScore
6.50
自引率
14.30%
发文量
409
审稿时长
2 months
期刊介绍: The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry
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