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首页> 外文期刊>Medical Physics >Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum‐likelihood polynomial fitting approach
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Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum‐likelihood polynomial fitting approach

机译：使用交错的深层学习分割和最大似然多项式拟合方法在乳房CT中遮蔽伪影校正

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Purpose 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. Methods 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. Results 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. Conclusion 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.
机译：目的这项工作的目的是双重的：（a）为专用乳房CT（BCT）体积数据集的图像分割提供一种坚固且准确的方法，并通过以下方式提高BCT中的Hounsfield单元（HU）精度。一种后处理方法，使用分段图像来校正重建图像中的低频阴影伪像。方法使用对BCT卷数据集的图像分割和低阶多项式拟合的顺序和迭代应用用于交织校正（IC）方法。通过具有修改的U-Net架构的深卷积神经网络（CNN）进行图像分割。共有来自111名患者体积数据集的45个冠状BCT图像（使用先前发布的分割算法），并用于神经网络训练，验证和测试。所有患者数据集选自在四种不同的原型乳房CT系统上执行的扫描。然后将使用所提出的CNN分段的每个患者体积数据集的脂肪素素被适用于三维低阶多项式。随后使用多项式配合，以校正通过散射和梁硬化引入的遮蔽伪像以“平面为”。重复了图像分割和平面场的交织利用，直到满足收敛标准。进行了数学和物理幻影研究以评估所提出的算法对乳腺大小和纤维族组织分布的依赖性。此外，使用患者扫描的子集（在CNN训练，测试或验证中使用）来研究不同扫描仪设计和光束质量的IC方法的准确性。结果IC方法导致不同组织类型的准确分类，平均骰子相似度系数？＆ 95％，精度？＆＆＆？95％和f1分数？所有组织类型的96％。 BCT图像的平坦场地校正导致数学和物理幻影研究中的拔罐或覆盖伪像显着减少，这是由整体不均匀度量测量的，平均减少为71％的拔罐，30％用于跨越不同的幻像尺寸。 ，平均降低的均匀性指数为拔罐的平均降低53％，用于封端的34％。结论验证研究表明，IC方法改善了Hounsfield单位（HU）精度，有效地校正了散射污染和光束硬化引起的遮光伪影。这里描述的后处理方法与BCT设备的广泛范围相关，并且不需要硬件或现有扫描协议的任何修改。培训的CNN参数和网络架构可供感兴趣的用户使用。

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