Cao Wenchao, Shapira Nadav, Maidment Andrew, Daerr Heiner, Noël Peter B
University of Pennsylvania, Perelman School of Medicine, Department of Radiology, Philadelphia, Pennsylvania, United States.
Philips Research Europe, Hamburg, Germany.
J Med Imaging (Bellingham). 2022 Jan;9(1):014003. doi: 10.1117/1.JMI.9.1.014003. Epub 2022 Jan 31.
Dual-contrast protocols are a promising clinical multienergy computed tomography (CT) application for focal liver lesion detection and characterization. One avenue to enable multienergy CT is the introduction of photon-counting detectors (PCD). Although clinical translation is highly desired because of the diagnostic benefits of PCDs, it will still be a decade or more before they are broadly available. In our work, we investigated an alternative solution that can be implemented on widely used conventional CT systems (single source and integrating detector) to perform multimaterial spectral decomposition for dual-contrast imaging. We propose to slowly alternate the x-ray tube voltage between 3 kVp levels so each kVp level covers a few degrees of gantry rotation. This leads to the challenge of sparsely sampled projection data in each energy level. Performing the material decomposition (MD) in the sinogram domain is not directly possible as the projection images of the three energy levels are not angularly aligned. In order to overcome this challenge, we developed a convolutional neural network (CNN) framework for sparse sinogram completion (SC) and MD. To evaluate the feasibility of the slow kVp switching scheme, simulation studies of an abdominal phantom, which included liver lesions, were conducted. The line-integral SC network yielded sinograms with a pixel-wise of the line-integrals compared to the ground truth. This provided acceptable image quality up to a switching angle of 9 deg per kVp. The MD network we developed allowed us to differentiate iodine and gadolinium in the sinogram domain. The average relative quantification errors for iodine and gadolinium were below 10%. We developed a slow triple kVp switching data acquisition scheme and a CNN-based data processing pipeline. Results from a digital phantom validation illustrate the potential for future applications of dual-contrast agent protocols on practically available single-energy CT systems.
双对比协议是一种很有前景的临床多能量计算机断层扫描（CT）应用，用于检测和表征肝脏局灶性病变。实现多能量CT的一条途径是引入光子计数探测器（PCD）。尽管由于PCD的诊断优势，临床应用备受期待，但在其广泛应用之前仍需十年或更长时间。在我们的工作中，我们研究了一种替代方案，该方案可以在广泛使用的传统CT系统（单源和积分探测器）上实现，以进行双对比成像的多物质光谱分解。我们建议在3个千伏峰值（kVp）水平之间缓慢交替x射线管电压，以便每个kVp水平覆盖机架旋转的几度。这导致了每个能量水平上投影数据采样稀疏的挑战。由于三个能量水平的投影图像在角度上未对齐，因此无法直接在正弦图域中执行物质分解（MD）。为了克服这一挑战，我们开发了一种用于稀疏正弦图完成（SC）和MD的卷积神经网络（CNN）框架。为了评估慢kVp切换方案的可行性，我们对包含肝脏病变的腹部模型进行了模拟研究。与真实情况相比，线积分SC网络生成的正弦图在线积分方面具有逐像素的[此处原文缺失具体内容]。这在每个kVp的切换角度达到9°时提供了可接受的图像质量。我们开发的MD网络使我们能够在正弦图域中区分碘和钆。碘和钆的平均相对定量误差低于10%。我们开发了一种慢三kVp切换数据采集方案和基于CNN的数据处理管道。数字模型验证的结果说明了双对比剂协议在实际可用的单能量CT系统上未来应用的潜力。
