High through-plane resolution CT imaging with self-supervised deep learning.

CT images for radiotherapy planning are usually acquired in thick slice to reduce imaging dose, especially for pediatric patients, and to lessen the need for contouring and treatment planning on more slices. However, low through-plane resolution may degrade the accuracy of dose calculations. In this paper, a self-supervised deep learning workflow is proposed to synthesize high through-plane resolution CT images by learning from their high in-plane resolution features. The proposed workflow was designed to facilitate the neural networks to learn the mapping from low resolution (LR) to high resolution (HR) images in the axial plane. During the inference step, the HR sagittal and coronal images were generated by feeding two parallelly trained neural networks with the respective LR sagittal and coronal images to the trained neural networks. The CT simulation images of a cohort of 75 head and neck (HN) cancer patients (1 mm slice thickness) and 200 CT images of a cohort of 20 lung cancer patients (3 mm slice thickness) were retrospectively investigated with a cross validation manner. The generated HR images with the proposed method were qualitatively (visual quality, image intensity profiles and preliminary observer study) and quantitatively (Mean Absolute Error (MAE), Edge Keeping Index (EKI), Structural Similarity Index Measurement (SSIM), Information Fidelity Criterion (IFC) and Visual Information Fidelity in Pixel domain (VIFP)) inspected, while taking the original HN and lung cancer patients' CT images as the reference. The qualitative results have shown the capability of the proposed method for generating high through-plane resolution CT images with data of the HN and lung cancer patients. All the improvements of the measure metrics are confirmed to be statistically significant with paired two-sample t-test analysis. The innovative point of the work is that the proposed deep learning workflow for CT image generation with high through-plane resolution in radiotherapy is self-supervised, which means it does not rely on ground truth CT images to train the network. In addition, the assumption that the in-plane HR information can supervise the through-plane HR generation is confirmed and anticipated to potentially inspire more researches on this topic to further improve the through-plane resolution of medical images.