卷积神经网络在中耳和内耳植入物超高分辨率光子计数探测器CT中的金属伪影减少

文献信息

中文摘要

背景： 具有亚毫米结构、需要超高分辨率（UHR）成像的金属内耳和中耳植入物在CT中不可避免地会产生金属伪影，这些伪影可能掩盖邻近的微结构和细微的病理表现。当前的金属伪影减少技术对于小型金属物体或异质性背景解剖中的金属可能效果较差，并且有时会引入新的伪影或噪声。
目的： 开发并评估一种图像域卷积神经网络，用于减少UHR光子计数探测器（PCD）-CT中内耳和中耳植入物附近的金属伪影。
方法： 为了进行网络训练，将三个含有钛和铂的镫骨假体模型随机放置在头部大小的体模内，并使用UHR协议在PCD-CT上成像。通过减去体模背景并进行CT值阈值化，分离出金属和金属+伪影图像。通过乘以1到预先确定的伪影放大标量超参数之间的随机因子来增强分离的伪影信号。通过分别将无金属的患者颞骨图像与金属+伪影图像和金属图像相加，创建配对的训练输入（有伪影）和标签（无伪影）。使用9个患者CT体积和24个金属CT体积的组合创建了216个CT体积用于训练和验证，训练/验证比例为80%/20%。使用不同的放大标量（5、10、20、30、40）以及均方误差或平均绝对误差损失函数重新训练网络，以确定这些超参数的最佳组合。该网络在来自具有三种不同植入物类型的患者的7个CT体积上进行了测试：镫骨假体、人工耳蜗和横窦支架。通过网络性能通过伪影指数、噪声幅度、金属和骨的CT值保持以及金属尺寸（以CT值线剖面的半高全宽测量）进行量化。
结果： 使用均方误差训练的网络在伪影放大标量变化时表现出金属伪影减少与骨CT值保持之间的权衡。使用平均绝对误差时未观察到这种权衡。使用同时最佳保持骨和减少伪影的超参数（平均绝对误差，放大标量20）的网络，所有植入物类型的伪影指数改善了83.2%（p < 0.001），图像噪声减少了46.8%（p < 0.001）。镫骨假体病例的伪影减少显著优于其他植入物类型（p < 0.004），尽管所有植入物类型的伪影指数仍有很大改善（镫骨假体为91.1%，人工耳蜗为81.3%，横窦支架为72.9%）。网络输入和输出图像之间的金属植入物尺寸无显著差异（p = 0.67）。
结论： 具有任务特定超参数的图像域卷积神经网络减少了不同内耳和中耳植入物UHR PCD-CT中的伪影严重程度和图像噪声，而没有损失精细图像细节。

英文摘要

BACKGROUND: Metallic inner- and middle-ear implants with submillimeter structures requiring ultra-high resolution (UHR) imaging invariably result in metal artifacts at CT, which can obscure adjacent microstructures and subtle pathologic findings. Current metal artifact reduction techniques can be less effective for small metal objects or metal in heterogeneous background anatomy, and can sometimes introduce new artifacts or noise.
PURPOSE: To develop and evaluate an image-domain convolutional neural network to reduce metal artifacts near inner- and middle-ear implants in UHR photon-counting-detector (PCD)-CT.
METHODS: For network training, three stapes prosthesis models containing titanium and platinum were randomly oriented within a head-sized phantom and imaged on a PCD-CT with a UHR protocol. Metal and metal+artifact images were isolated by subtraction of the phantom background followed by CT number thresholding. Isolated artifact signal was enhanced via multiplication with a randomized factor ranging from one to a pre-determined artifact amplification scalar hyperparameter. Paired training inputs (with artifacts) and labels (without artifacts) were created by adding metal-free patient temporal bone images with metal+artifact and metal images, respectively. Combinations of 9 patient CT volumes and 24 metal CT volumes were used to create 216 CT volumes for training and validation with an 80%/20% training/validation split. The network was retrained with different amplification scalars (5, 10, 20, 30, 40) and with either mean-squared-error or mean-absolute-error loss functions to determine the optimal combination of these hyperparameters. The network was tested on 7 CT volumes from patients with three different implant types: stapes prostheses, cochlear implants, and a transverse sinus stent. Network performance was quantified through the artifact index, noise magnitude, CT number preservation of metal and bone, and dimensions of metal measured as the full-width at half-maximum of CT number line profiles.
RESULTS: Networks trained with mean-squared-error demonstrated a trade-off between metal artifact reduction and preservation of bone CT number as the artifact amplification scalar changed. The trade-off was not observed when mean-absolute error was used. Using the network with hyperparameters that best preserved bone and reduced artifacts simultaneously (mean-absolute-error, amplification scalar 20), the artifact index was improved by 83.2% (p < 0.001) across all implant types, and image noise was reduced by 46.8% (p < 0.001). Artifact reduction was significantly better for cases with stapes prostheses than the other implant types (p < 0.004), although artifact index was still greatly improved for all implant types (91.1% for the stapes prostheses, 81.3% for the CI, and 72.9% for the transverse sinus stent). Metal implant dimensions were not significantly different between the network input and output images (p = 0.67).
CONCLUSION: An image-domain convolutional neural network with task-specific hyperparameters reduced artifact severity and image noise in UHR PCD-CT of different inner- and middle-ear implants without loss of fine image detail.