Electronic tuneability of a structurally rigid surface intermetallic and Kondo lattice: CePt5_5 / Pt(111)

We present an extensive study of structure, composition, electronic and magnetic properties of Ce--Pt surface intermetallic phases on Pt(111) as a function of their thickness. The sequence of structural phases appearing in low energy electron diffraction (LEED) may invariably be attributed to a single underlying intermetallic atomic lattice. Findings from both microscopic and spectroscopic methods, respectively, prove compatible with CePt$_5$ formation when their characteristic probing depth is adequately taken into account. The intermetallic film thickness serves as an effective tuning parameter which brings about characteristic variations of the Cerium valence and related properties. Soft x-ray absorption (XAS) and magnetic circular dichroism (XMCD) prove well suited to trace the changing Ce valence and to assess relevant aspects of Kondo physics in the CePt$_5$ surface intermetallic. We find characteristic Kondo scales of the order of 10$^2$ K and evidence for considerable magnetic Kondo screening of the local Ce $4f$ moments. CePt$_5$/Pt(111) and related systems therefore appear to be promising candidates for further studies of low-dimensional Kondo lattices at surfaces.Comment: 14 pages, 11 figure

DIRBoost : an algorithm for boosting deformable image registration

We introduce a novel boosting algorithm to boost - i.e. improve on - existing methods for deformable image registration. The proposed DIRBoost algorithm is inspired by the theory on hypothesis boosting, well-known in the field of machine learning. DIRBoost involves a classifier for landmark-based Registration Error Detection (RED). Based on these RED predictions a Voronoi tessellation is generated to obtain a dense estimate of local image registration quality. All areas predicted as erroneous registration are subjected to boosting, i.e. undergo iterative registrations by employing boosting masks on both the fixed and moving image. We evaluated the DIRBoost algorithm on five CT pulmonary breathhold inspiration and expiration scan pairs, employing the NiftyReg registration algorithm. DIRBoost could boost about 50% of the wrongly registered areas which in turn also improved the average landmark registration error by 24%. © 2012 IEEE

On combining algorithms for deformable image registration

We propose a meta-algorithm for registration improvement by combining deformable image registrations (MetaReg). It is inspired by a well-established method from machine learning, the combination of classifiers. MetaReg consists of two main components: (1) A strategy for composing an improved registration by combining deformation fields from different registration algorithms. (2) A method for regularization of deformation fields post registration (UnfoldReg). In order to compare and combine different registrations, MetaReg utilizes a landmark-based classifier for assessment of local registration quality. We present preliminary results of MetaReg, evaluated on five CT pulmonary breathhold inspiration and expiration scan pairs, employing a set of three registration algorithms (NiftyReg, Demons, Elastix). MetaReg generated for each scan pair a registration that is better than any registration obtained by each registration algorithm separately. On average, 10% improvement is achieved, with a reduction of 30% of regions with misalignments larger than 5mm, compared to the best single registration algorithm. © 2012 Springer-Verlag

DIRBoost-An algorithm for boosting deformable image registration: Application to lung CT intra-subject registration

Item does not contain fulltextWe introduce a boosting algorithm to improve on existing methods for deformable image registration (DIR). The proposed DIRBoost algorithm is inspired by the theory on hypothesis boosting, well known in the field of machine learning. DIRBoost utilizes a method for automatic registration error detection to obtain estimates of local registration quality. All areas detected as erroneously registered are subjected to boosting, i.e. undergo iterative registrations by employing boosting masks on both the fixed and moving image. We validated the DIRBoost algorithm on three different DIR methods (ANTS gSyn, NiftyReg, and DROP) on three independent reference datasets of pulmonary image scan pairs. DIRBoost reduced registration errors significantly and consistently on all reference datasets for each DIR algorithm, yielding an improvement of the registration accuracy by 5-34\% depending on the dataset and the registration algorithm employed

Supervised Quality Assessment Of Medical Image Registration: Application to intra-patient CT lung registration

Item does not contain fulltextA novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal multi-feature model, classifies local alignments into three quality categories: correct, poor or wrong alignment. We establish a reference registration error set as basis for training and testing of the method. It consists of image registrations obtained from different nonrigid registration algorithms and manually established point correspondences of automatically determined landmarks. We employ a set of different classifiers and evaluate the performance of the proposed image features based on the classification performance of corresponding single-feature classifiers. Feature selection is conducted to find an optimal subset of image features and the resulting multi-feature model is validated against the set of single-feature classifiers. We consider the setup generic, however, its application is demonstrated on 51 CT follow-up scan pairs of the lung. On this data, the proposed method performs with an overall classification accuracy of 90%