Malcova LLC, Baltimore, MD, 21211, USA.
Department of Radiology, University of California Davis, Sacramento, CA, 95817, USA.
Med Phys. 2019 Aug;46(8):3414-3430. doi: 10.1002/mp.13599. Epub 2019 Jun 23.
The purpose of this work was twofold: (a) To provide a robust and accurate method for image segmentation of dedicated breast CT (bCT) volume data sets, and (b) to improve Hounsfield unit (HU) accuracy in bCT by means of a postprocessing method that uses the segmented images to correct for the low-frequency shading artifacts in reconstructed images.
A sequential and iterative application of image segmentation and low-order polynomial fitting to bCT volume data sets was used in the interleaved correction (IC) method. Image segmentation was performed through a deep convolutional neural network (CNN) with a modified U-Net architecture. A total of 45 621 coronal bCT images from 111 patient volume data sets were segmented (using a previously published segmentation algorithm) and used for neural network training, validation, and testing. All patient data sets were selected from scans performed on four different prototype breast CT systems. The adipose voxels for each patient volume data set, segmented using the proposed CNN, were then fit to a three-dimensional low-order polynomial. The polynomial fit was subsequently used to correct for the shading artifacts introduced by scatter and beam hardening in a method termed "flat fielding." An interleaved utilization of image segmentation and flat fielding was repeated until a convergence criterion was satisfied. Mathematical and physical phantom studies were conducted to evaluate the dependence of the proposed algorithm on breast size and the distribution of fibroglandular tissue. In addition, a subset of patient scans (not used in the CNN training, testing or validation) were used to investigate the accuracy of the IC method across different scanner designs and beam qualities.
The IC method resulted in an accurate classification of different tissue types with an average Dice similarity coefficient > 95%, precision > 97%, recall > 95%, and F1-score > 96% across all tissue types. The flat fielding correction of bCT images resulted in a significant reduction in either cupping or capping artifacts in both mathematical and physical phantom studies as measured by the integral nonuniformity metric with an average reduction of 71% for cupping and 30% for capping across different phantom sizes, and the Uniformity Index with an average reduction of 53% for cupping and 34% for capping.
The validation studies demonstrated that the IC method improves Hounsfield Units (HU) accuracy and effectively corrects for shading artifacts caused by scatter contamination and beam hardening. The postprocessing approach described herein is relevant to the broad scope of bCT devices and does not require any modification in hardware or existing scan protocols. The trained CNN parameters and network architecture are available for interested users.
本研究旨在实现以下两个目标：(a) 提供一种稳健且精确的方法，用于对专用乳腺 CT(bCT)容积数据集进行图像分割；(b) 通过后处理方法提高 bCT 中的体素值(Hounsfield 单位，HU)准确性，该方法利用分割后的图像来校正重建图像中的低频阴影伪影。
在交叠校正(IC)方法中，采用了图像分割和低阶多项式拟合的顺序迭代应用。bCT 容积数据集的图像分割是通过具有改进的 U-Net 架构的深度卷积神经网络(CNN)实现的。总共对来自 111 个患者容积数据集的 45621 个冠状位 bCT 图像进行了分割(使用先前发布的分割算法)，并将其用于神经网络的训练、验证和测试。所有患者数据集均选自在四个不同的原型乳腺 CT 系统上进行的扫描。使用所提出的 CNN 对每个患者容积数据集的脂肪体素进行分割，然后拟合到三维低阶多项式中。随后，使用多项式拟合来校正散射和束硬化引起的阴影伪影，这种方法称为“平坦场校正”。重复进行图像分割和平坦场校正的交叠利用，直到满足收敛准则。还进行了数学和物理体模研究，以评估所提出算法对乳房大小和纤维腺体组织分布的依赖性。此外，使用一部分患者扫描(未用于 CNN 的训练、测试或验证)来研究 IC 方法在不同的扫描仪设计和射线质条件下的准确性。
IC 方法能够对不同的组织类型进行准确分类，平均 Dice 相似系数>95%，精度>97%，召回率>95%，F1 分数>96%，所有组织类型的分类结果均如此。bCT 图像的平坦场校正显著减少了数学和物理体模研究中的杯状或帽状伪影，使用积分不均匀性指标测量时，不同体模尺寸的杯状伪影的平均校正率为 71%，帽状伪影的平均校正率为 30%；使用均匀性指数测量时，杯状伪影的平均校正率为 53%，帽状伪影的平均校正率为 34%。
验证研究表明，IC 方法提高了 Hounsfield 单位(HU)的准确性，并有效校正了散射污染和束硬化引起的阴影伪影。本文描述的后处理方法与广泛的乳腺 CT 设备相关，不需要对硬件或现有的扫描协议进行任何修改。感兴趣的用户可以获得经过训练的 CNN 参数和网络架构。
