Patient-based 4D digital breast phantom for perfusion contrast-enhanced breast CT imaging.
Patient-based 4D digital breast phantom for perfusion contrast-enhanced breast CT imaging.
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DOI：
10.1002/mp.13156
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发表时间：
2018-10
期刊：
影响因子：
3.8
通讯作者：
Sechopoulos I
中科院分区：
文献类型：
--
作者：
Caballo M;Mann R;Sechopoulos I
本研究的目的是开发一种逼真的基于患者的4D数字乳腺体模，包括用于模拟专用乳腺CT灌注成像的时变对比度增强。首先通过将来自健康患者的乳房CT图像分割成皮肤、纤维腺体组织、脂肪组织和脉管系统来创建3D静态体模。为了创建异常病例，开发了乳腺病变模型，并可将其添加到体模中。在为每个组织定义必要的灌注参数（例如，血管系统的动脉输入函数、其他正常组织的血容量和血流量），根据对比增强的动态乳腺MRI数据，根据组织类型计算体模中每个体素的相应时间增强曲线。这些曲线通过动脉输入函数和移位指数函数之间的卷积来计算。该指数取决于与每个组织体素相关的灌注参数，并且为了结合正常的生物学可变性，使用均匀随机分布来基于体素改变灌注参数。最后，通过以期望的采样率对连续时间增强曲线进行采样来产生4D阵列。除了根据给定的输入灌注参数对不同的增强动态进行建模之外，体模还包括考虑供应乳房的动脉源来真实地模拟乳房实质的不同空间增强模式的可能性。最后，还可以针对肿瘤模型模拟造影剂摄取的不同模式（均匀和边缘增强）。例如，典型的4D体模尺寸为426 × 421 × 260 × 559（x，y，z，t），体素尺寸为273 μm，采样时间为1 s。肿瘤模型的特征可以随意修改，以评估不同类型乳腺病变的灌注。结果显示预期的组织增强，与给定的输入参数一致。此外，在这项工作中评估的肿瘤模型根据肿瘤类型（由不同的输入灌注参数定义）显示出不同的增强动力学，并且与其他健康组织相比也呈现出更高的增强，正如预期的那样。所提出的数字体模可以在4D乳房CT图像采集期间对乳房组织灌注进行建模，显示可以在真实的患者乳房中发现的不同增强动态。该体模可在动态对比增强专用乳腺CT成像的开发过程中使用，用于优化图像采集、图像重建和图像分析。这种模式可以提供乳房的功能信息，从而提高乳腺癌的检测、诊断和治疗水平。
The purpose of this study was to develop a realistic patient‐based 4D digital breast phantom including time‐varying contrast enhancement for simulation of dedicated breast CT perfusion imaging. A 3D static phantom is first created by segmenting a breast CT image from a healthy patient into skin, fibroglandular tissue, adipose tissue, and vasculature. For the creation of abnormal cases, a breast lesion model was developed and can be added to the phantom. After defining the necessary perfusion parameters for each tissue (e.g., arterial input function for vasculature, blood volume and blood flow for the other normal tissues) based on contrast‐enhanced dynamic breast MRI data, the corresponding time‐enhancement curves are computed for each voxel in the phantom, according to tissue type. These curves are calculated by convolution between the arterial input function and a shifted exponential function. This exponential depends on the perfusion parameters associated with each tissue voxel, and, to incorporate normal biological variability, a uniform random distribution is used to vary the perfusion parameters on a voxel‐basis. Finally, a 4D array is produced by sampling the continuous time‐enhancement curves at the desired sampling rate. Beside modeling different enhancement dynamics according to the given input perfusion parameters, the phantom also includes the possibility to realistically simulate different spatial enhancement patterns for the breast parenchyma, taking into account the arterial sources supplying the breast. Finally, different patterns of contrast medium uptake can also be simulated for the tumor models (homogeneous and rim enhancement). As an example, a typical 4D phantom has dimensions of 426 × 421 × 260 × 559 (x, y, z, t), with a voxel size of 273 μm and a sampling time of 1 s. The characteristics of the tumor model can be modified at will to evaluate perfusion in different types of breast lesions. Results show the expected enhancement of tissues, consistent with the given input parameters. Moreover, the tumor models evaluated in this work show different enhancement dynamics according to the tumor type (defined by different input perfusion parameters), and also present a higher enhancement compared to the other healthy tissues, as expected. The proposed digital phantom can model the breast tissue perfusion during 4D breast CT image acquisition, displaying the different enhancement dynamics that could be found in a real patient breast. This phantom can be used during the development of dynamic contrast‐enhanced dedicated breast CT imaging, for optimization of image acquisition, image reconstruction, and image analysis. This modality could provide functional information of the breast, resulting in detection, diagnosis, and treatment improvements of breast cancer with breast CT.
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影响因子：
10.6
作者：
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通讯作者：
Sechopoulos I
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