Image-based Artefact Removal in Laser Scanning Microscopy
Advances in laser scanning microscopy (LSM) have greatly extended its applicability in cancer imaging not only to observe dynamic biological processes, but also to quantitatively measure them. The fast acquisition with increased spatial resolution and field of view enables scanning of larger areas of the specimen. However in practice, image quality is compromised by the motion of specimen and the motion of the microscope laser. In this paper, we present a framework for efficient removal of jaggedness artefacts caused by the varying speeds of the laser. Our framework compensates for the local displacements and reduces the level of noise, demonstrating substantial improvement over other state-of-the-art acquisition methods.
Predicting The Influence of Microvascular Structure On Tumour Response to Radiotherapy
Tumour response to radiotherapy depends on oxygen availability and thus the structure of the supplying microvessel network. We combine these networks with a hybrid multiscale model that couples a cellular automaton model of tumour growth with a model for oxygen transport from blood vessels. We compare predicted viable fractions of cells following one week of simulated radiotherapy in a real network and a collection of artificial networks.