The Large Observatory For x-ray Timing (LOFT) is a mission concept which was proposed to ESA as M3 and M4 candidate in the framework of the Cosmic Vision 2015-2025 program. Thanks to the unprecedented combination of effective area and spectral resolution of its main instrument and the uniquely large field of view of its wide field monitor, LOFT will be able to study the behaviour of matter in extreme conditions such as the strong gravitational field in the innermost regions close to black holes and neutron stars and the supra-nuclear densities in the interiors of neutron stars. The science payload is based on a Large Area Detector (LAD, >8m2 effective area, 2-30 keV, 240 eV spectral resolution, 1 degree collimated field of view) and a Wide Field Monitor (WFM, 2-50 keV, 4 steradian field of view, 1 arcmin source location accuracy, 300 eV spectral resolution). The WFM is equipped with an on-board system for bright events (e.g., GRB) localization. The trigger time and position of these events are broadcast to the ground within 30 s from discovery. In this paper we present the current technical and programmatic status of the mission.
Various approaches have been proposed for segmentation of cardiac MRI. An accurate segmentation of the
myocardium and ventricles is essential to determine parameters of interest for the function of the heart, such as
the ejection fraction. One problem with MRI is the poor resolution in one dimension.
A 3D registration algorithm will typically use a trilinear interpolation of intensities to determine the intensity
of a deformed template image. Due to the poor resolution across slices, such linear approximation is highly
inaccurate since the assumption of smooth underlying intensities is violated. Registration-based interpolation
is based on 2D registrations between adjacent slices and is independent of segmentations. Hence, rather than
assuming smoothness in intensity, the assumption is that the anatomy is consistent across slices. The basis for
the proposed approach is the set of 2D registrations between each pair of slices, both ways. The intensity of a
new slice is then weighted by (i) the deformation functions and (ii) the intensities in the warped images. Unlike
the approach by Penney et al. 2004, this approach takes into account deformation both ways, which gives more
robustness where correspondence between slices is poor.
We demonstrate the approach on a toy example and on a set of cardiac CINE MRI. Qualitative inspection reveals
that the proposed approach provides a more convincing transition between slices than images obtained by linear
interpolation. A quantitative validation reveals significantly lower reconstruction errors than both linear and
registration-based interpolation based on one-way registrations.
We present a new statistical deformation model suited for parameterized grids with different resolutions. Our method models the covariances between multiple grid levels explicitly, and allows for very efficient fitting of the model to data on multiple scales.
The model is validated on a data set consisting of 62 annotated MR images of Corpus Callosum. One fifth of the data set was used as a training set, which was non-rigidly registered to each other without a shape prior. From the non-rigidly registered training set a shape prior was constructed by performing principal component analysis on each grid level and using the results to construct a conditional shape model, conditioning the finer parameters with the coarser grid levels. The remaining shapes were registered with the constructed shape prior. The dice measures for the registration without prior and the registration with a prior were 0.875 ± 0.042 and 0.8615 ± 0.051, respectively.
This paper proposes an extension to the standard iterative closest point method (ICP). In contrast to ICP,
our approach (ICP-M) uses the Mahalanobis distance to align a set of shapes thus assigning an anisotropic
independent Gaussian noise to each point in the reference shape.
The paper introduces the notion of a mahalanobis distance map upon a point set with associated covariance
matrices which in addition to providing correlation weighted distance implicitly provides a method for assigning
correspondence during alignment. This distance map provides an easy formulation of the ICP problem that
permits a fast optimization.
Initially, the covariance matrices are set to the identity matrix, and all shapes are aligned to a randomly selected
shape (equivalent to standard ICP). From this point the algorithm iterates between the steps: (a) obtain mean
shape and new estimates of the covariance matrices from the aligned shapes, (b) align shapes to the mean shape.
Three different methods for estimating the mean shape with associated covariance matrices are explored in the
The proposed methods are validated experimentally on two separate datasets (IMM face dataset and femur-bones).
The superiority of ICP-M compared with ICP in recovering the underlying correspondences in the face
dataset is demonstrated.