A deep learning algorithm to generate synthetic computed tomography images for brain treatments from 0.35 T magnetic resonance imaging.

Background And Purpose: The development of Magnetic Resonance Imaging (MRI)-only Radiotherapy (RT) represents a significant advancement in the field. This study introduces a Deep Learning (DL) algorithm designed to quickly generate synthetic CT (sCT) images from low-field MR images in the brain, an area not yet explored.

Methods: Fifty-six patients were divided into training (32), validation (8), and test (16) groups.

A note on the Virasoro blocks at order 1 / .

We derive an explicit expression for the 1 /  contribution to the Virasoro blocks in 2D CFT in the limit of large with fixed values of the operators' dimensions. We follow the direct approach of orthonormalising, at order 1 / , the space of the Virasoro descendants to obtain the blocks as a series expansion around . For generic conformal weights this expansion can be summed in terms of hypergeometric functions and their first derivatives with respect to one parameter.

Unitary 4-point correlators from classical geometries.

We compute correlators of two heavy and two light operators in the strong coupling and large limit of the D1D5 CFT which is dual to weakly coupled gravity. The light operators have dimension two and are scalar descendants of the chiral primaries considered in arXiv:1705.09250, while the heavy operators belong to an ensemble of Ramond-Ramond ground states.