Abstract
Computed tomography (CT) is a widely used non-invasive medical imaging technique for disease diagnosis. The diagnostic accuracy is often affected by image resolution, which can be insufficient in practice. For medical CT images, the through-plane resolution is often worse than the in-plane resolution and there can be overlap between slices, causing difficulties in diagnoses. Self-supervised methods for through-plane resolution enhancement, which train on in-plane images and infer on through-plane images, have shown promise for both CT and MRI imaging. However, existing self-supervised methods either neglect overlap or can only handle specific cases with fixed combinations of resolution and overlap. To address these limitations, we propose a self-supervised method called SR4ZCT. It employs the same off-axis training approach while being capable of handling arbitrary combinations of resolution and overlap. Our method explicitly models the relationship between resolutions and voxel spacings of different planes to accurately simulate training images that match the original through-plane images. We highlight the significance of accurate modeling in self-supervised off-axis training and demonstrate the effectiveness of SR4ZCT using a real-world dataset.
Abstract (translated)
计算断层扫描（CT）是一种广泛应用于疾病诊断的非侵入性医疗影像技术。诊断准确性通常受到图像分辨率的影响，在实际应用中可能不足以准确。对于医学CT图像，通过平面的分辨率通常比在平面分辨率更差，而且切片之间可能存在重叠，导致诊断困难。自监督方法通过在平面图像上训练并通过平面图像进行推断，对CT和MRI成像都显示出希望。然而，现有的自监督方法要么忽视重叠，要么只能处理具有固定组合分辨率和平面重叠的特定情况。为了克服这些限制，我们提出了一个名为SR4ZCT的自监督方法。它采用相同的离轴训练方法，同时能够处理任意组合的分辨率和重叠。我们的方法明确地建模了不同平面的分辨率和体素空间之间的关系，准确地模拟了与原始通过平面图像匹配的训练图像。我们强调了在自监督离轴训练中准确建模的重要性，并通过一个真实世界的数据集证明了SR4ZCT的有效性。
URL
https://arxiv.org/abs/2405.02515
