A magnetic liquid deformable mirror for high stroke and low order axially symmetrical aberrations

We present a new class of magnetically shaped deformable liquid mirrors made of a magnetic liquid (ferrofluid). Deformable liquid mirrors offer advantages with respect to deformable solid mirrors: large deformations, low costs and the possibility of very large mirrors with added aberration control. They have some disadvantages (e.g. slower response time). We made and tested a deformable mirror, producing axially symmetrical wavefront aberrations by applying electric currents to 5 concentric coils made of copper wire wound on aluminum cylinders. Each of these coils generates a magnetic field which combines to deform the surface of a ferrofluid to the desired shape. We have carried out laboratory tests on a 5 cm diameter prototype mirror and demonstrated defocus as well as Seidel and Zernike spherical aberrations having amplitudes up to 20 microns, which was the limiting measurable amplitude of our equipmentComment: To appear in Optics Expres

A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration

Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for \textit{unsupervised} affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.Comment: Accepted: Medical Image Analysis - Elsevie

Optical Design and Active Optics Methods in Astronomy

Optical designs for astronomy involve implementation of active optics and adaptive optics from X-ray to the infrared. Developments and results of active optics methods for telescopes, spectrographs and coronagraph planet finders are presented. The high accuracy and remarkable smoothness of surfaces generated by active optics methods also allow elaborating new optical design types with high aspheric and/or non-axisymmetric surfaces. Depending on the goal and performance requested for a deformable optical surface analytical investigations are carried out with one of the various facets of elasticity theory: small deformation thin plate theory, large deformation thin plate theory, shallow spherical shell theory, weakly conical shell theory. The resulting thickness distribution and associated bending force boundaries can be refined further with finite element analysis. Keywords: active optics, optical design, elasticity theory, astronomical optics, diffractive optics, X-ray optic