H. Abbasi

H. Abbasi, M. H. Jenab, H. Hakimi Pajouh

A method of solution of the collisionless Vlasov equation, by following collisionless phase point trajectories in phase space, is presented. It is shown that by increasing the number of phase points, without enhancing the resolution of phase space grid, the accuracy of simulation will be improved. Besides, the phase points spacing introduces a smaller scale than grid spacing on which fine structures might be more conveniently handled. In order to perform simulation with a large population of phase points, an effective interpolation scheme is introduced that reduces the number of operations. It is shown that by randomizing initial position of the phase points along velocity axis, the recurrence effect does not happen. Finally, the standard problem of linear Landau damping will be examined.

Firoozeh Shomal Zadeh, Sevda Molani, Maysam Orouskhani et al.

In medical imaging, image synthesis is the estimation process of one image (sequence, modality) from another image (sequence, modality). Since images with different modalities provide diverse biomarkers and capture various features, multi-modality imaging is crucial in medicine. While multi-screening is expensive, costly, and time-consuming to report by radiologists, image synthesis methods are capable of artificially generating missing modalities. Deep learning models can automatically capture and extract the high dimensional features. Especially, generative adversarial network (GAN) as one of the most popular generative-based deep learning methods, uses convolutional networks as generators, and estimated images are discriminated as true or false based on a discriminator network. This review provides brain image synthesis via GANs. We summarized the recent developments of GANs for cross-modality brain image synthesis including CT to PET, CT to MRI, MRI to PET, and vice versa.