Directional sinogram interpolation for motion weighted 4D cone-beam CT reconstruction

A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy

Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

Limited-view CT Reconstruction Based on Autoencoder-like Generative Adversarial Networks with Joint Loss

Limiting the scan views of X-ray computed tomography (CT) can make radiation dose reduced efficiently and consequently weaken the damage of ionizing radiation. However, it will degrade the reconstructed CT images. In this paper, we proposed to predict the missing projections and improve the reconstructed CT images by constructing an autoencoder-like generative adversarial network (GAN) with joint loss function. In the generator network, we train an autoencoder-like convolutional neural network (CNN) to generate the missing projections given a sinogram of the limited-view CT projections. For the discriminator network, a CNN is used to classify an input sinogram as real or synthetic one. To produce more realistic images, the joint loss function which includes not only reconstruction loss, but the adversarial loss is employed. While reconstruction loss can capture the overall structure of the missing projections, the latter can pick a particular mode from the distribution and make the results much sharper. After the missing projections have been estimated, we reconstruct the CT images from the completed projections by utilizing conventional filtered back-projection (FBP) method. The experiments prove the capability of our method to achieve a considerable improvement in limited-view CT reconstruction.