The temporal resolution of x-ray computed tomography (CT) is limited by the scanner rotation speed and detector readout time. One way to reduce the detector readout time is to acquire fewer number of projections. However, reconstruction using sparse-view data could result in spatial resolution loss and reconstruction artifacts that may negatively affect the clinical diagnoses. Therefore, improving the spatial resolution of sparse-view CT (SVCT) is of great practical value. In this study, we proposed a deep learning-based approach for SVCT spatial resolution enhancement. The proposed method utilizes a densely connected convolutional neural network (CNN) that is further aided by a radial location map to recover the radially dependent blurring caused by the continuous rotation of an x-ray source. The proposed method was evaluated using sparse-view data synthesized from full-view projection data of real patients. The results showed that the proposed CNN was able to recover the resolution loss and improve the image quality. Compared with the network using the same main structure but without a radial location map, the proposed method achieved better image quality in terms of the mean absolute error and structure similarity.
Reducing radiation dose of computed tomography (CT) and thereby decreasing the potential risk to patients are desirable in CT imaging. Deep neural network has been proposed to reduce noise in low-dose CT images. However, the conventional way to train a neural network requires using high-dose CT images as the reference. Recently, a noise-tonoise (N2N) training method was proposed, which showed that a neural network could be trained with only noisy images. In this work, we applied the N2N training to low-dose CT denoising. Our results show that the N2N training works in both count and image domains without using any high-dose reference images.
Reducing the radiation dose of computed tomography (CT) and thereby decreasing the potential risk suffered by the patients is desirable in CT imaging. However, lower dose often results in additional noise and artifacts in reconstructed images that may negatively affect the clinical diagnoses. Recently, many image-domain denoising approaches based on deep learning have been proposed and obtained promising results. However, since reconstructed CT image values are not directly related to noise level, estimating noise level from CT images is not an easy task. In this work, we propose a count-domain denoising approach using a convolutional neural network (CNN) and a filter loss function. Compared with image-domain denoising methods, the proposed count-domain method can easily estimate the noise level in projections based on the measurement in each detector bin. Moreover, because each projection is ramp-filtered before being backprojected to the image-domain, we propose a filter loss function where the training loss is computed using the ramp filtered projection, rather than the original projection. Since the filter loss is closely related to the differences in the image-domain, it further improves the quality of reconstructed CT images.