Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
Sci Rep. 2019 Oct 29;9(1):15540. doi: 10.1038/s41598-019-51779-5.
Recent advancements in deep learning for automated image processing and classification have accelerated many new applications for medical image analysis. However, most deep learning algorithms have been developed using reconstructed, human-interpretable medical images. While image reconstruction from raw sensor data is required for the creation of medical images, the reconstruction process only uses a partial representation of all the data acquired. Here, we report the development of a system to directly process raw computed tomography (CT) data in sinogram-space, bypassing the intermediary step of image reconstruction. Two classification tasks were evaluated for their feasibility of sinogram-space machine learning: body region identification and intracranial hemorrhage (ICH) detection. Our proposed SinoNet, a convolutional neural network optimized for interpreting sinograms, performed favorably compared to conventional reconstructed image-space-based systems for both tasks, regardless of scanning geometries in terms of projections or detectors. Further, SinoNet performed significantly better when using sparsely sampled sinograms than conventional networks operating in image-space. As a result, sinogram-space algorithms could be used in field settings for triage (presence of ICH), especially where low radiation dose is desired. These findings also demonstrate another strength of deep learning where it can analyze and interpret sinograms that are virtually impossible for human experts.
深度学习在自动化图像处理和分类方面的最新进展加速了医学图像分析的许多新应用。然而，大多数深度学习算法都是使用重构的、可由人类解释的医学图像开发的。虽然从原始传感器数据重建医学图像是创建医学图像所必需的，但重建过程仅使用了所有采集数据的部分表示。在这里，我们报告了一种直接处理原始计算机断层扫描（CT）数据的系统的开发，该系统在正弦图空间中绕过了图像重建的中间步骤。我们评估了两种分类任务在正弦图空间机器学习中的可行性：身体区域识别和颅内出血（ICH）检测。我们提出的 SinoNet 是一种针对解释正弦图而优化的卷积神经网络，与传统的基于重建图像空间的系统相比，在两种任务中都表现出色，无论在投影或探测器方面的扫描几何形状如何。此外，与在图像空间中运行的传统网络相比，SinoNet 在使用稀疏采样正弦图时性能显著提高。因此，正弦图空间算法可以在现场环境中用于分诊（存在 ICH），特别是在需要低辐射剂量的情况下。这些发现还展示了深度学习的另一个优势，即它可以分析和解释对人类专家来说几乎不可能的正弦图。
