Kumrular Raziye Kubra, Blumensath Thomas
Institute of Sound and Vibration Research, Department of Engineering and the Environment, University of Southampton, University Rd., Southampton SO17 1BJ, UK.
Sensors (Basel). 2024 Oct 16;24(20):6654. doi: 10.3390/s24206654.
Spectral Computed Tomography (CT) is a versatile imaging technique widely utilized in industry, medicine, and scientific research. This technique allows us to observe the energy-dependent X-ray attenuation throughout an object by using Photon Counting Detector (PCD) technology. However, a major drawback of spectral CT is the increase in noise due to a lower achievable photon count when using more energy channels. This challenge often complicates quantitative material identification, which is a major application of the technology. In this study, we investigate the Noise2Inverse image denoising approach for noise removal in spectral computed tomography. Our unsupervised deep learning-based model uses a multi-dimensional U-Net paired with a block-based training approach modified for additional energy-channel regularization. We conducted experiments using two simulated spectral CT phantoms, each with a unique shape and material composition, and a real scan of a biological sample containing a characteristic K-edge. Measuring the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) for the simulated data and the contrast-to-noise ratio (CNR) for the real-world data, our approach not only outperforms previously used methods-namely the unsupervised Low2High method and the total variation-constrained iterative reconstruction method-but also does not require complex parameter tuning.
光谱计算机断层扫描（CT）是一种在工业、医学和科学研究中广泛应用的多功能成像技术。该技术通过使用光子计数探测器（PCD）技术，使我们能够观察物体内与能量相关的X射线衰减情况。然而，光谱CT的一个主要缺点是，在使用更多能量通道时，由于可实现的光子计数较低，噪声会增加。这一挑战常常使定量材料识别变得复杂，而定量材料识别是该技术的一项主要应用。在本研究中，我们研究了用于光谱计算机断层扫描噪声去除的Noise2Inverse图像去噪方法。我们基于无监督深度学习的模型使用了一个多维U-Net，并结合了一种基于块的训练方法，该方法针对额外的能量通道正则化进行了修改。我们使用了两个模拟光谱CT体模进行实验，每个体模都有独特的形状和材料组成，还对一个含有特征K边的生物样本进行了实际扫描。通过测量模拟数据的峰值信噪比（PSNR）和结构相似性指数（SSIM）以及实际数据的对比噪声比（CNR），我们的方法不仅优于先前使用的方法——即无监督的Low2High方法和总变差约束迭代重建方法——而且不需要复杂的参数调整。
