Spatial transformations of diffusion tensor magnetic resonance images

The authors address the problem of applying spatial transformations (or “image warps”) to diffusion tensor magnetic resonance images. The orientational information that these images contain must be handled appropriately when they are transformed spatially during image registration. The authors present solutions for global transformations of three-dimensional images up to 12-parameter affine complexity and indicate how their methods can be extended for higher order transformations. Several approaches are presented and tested using synthetic data. One method, the preservation of principal direction algorithm, which takes into account shearing, stretching and rigid rotation, is shown to be the most effective. Additional registration experiments are performed on human brain data obtained from a single subject, whose head was imaged in three different orientations within the scanner. All of the authors' methods improve the consistency between registered and target images over naive warping algorithms

Multiple-fiber reconstruction algorithms for diffusion MRI

This chapter reviews multiple-fiber reconstruction algorithms for diffusion magnetic resonance imaging (MRI) and provides some initial comparative results for two such algorithms, q-ball imaging and PASMRI, on data from a typical clinical diffusion MRI acquisition. The chapter highlights the problems with standard approaches, such as diffusion-tensor MRI, to motivate a recent set of alternative approaches. The review concentrates on the software implementation of the new techniques. Results of the preliminary comparison show that PASMRI recovers the principal directions of simple test functions more consistently than q-ball imaging and produces qualitatively better results on the test data set. Further simulations suggest that a moderate increase in data quality allows q-ball, which is much faster to run, to recover directions with consistency comparable to that of PASMRI on the test data

Los métodos de diagnóstico de la sarna sarcóptica en cerdos

Poster apresentado no II Congreso Ibérico de Epidemiologia Veterinária, que decorreu em Barcelona, na FVUAB de 2 a 5 de Fevereiro de 2010.El ácaro astigmatídeo Sarcoptes scabiei (Figura 1), que causa la sarna, es una especie adaptada a diferentes hospedadores y con especial importancia en el cerdo. La sarna es una enfermedad parasitaria de la piel comunes en los animales estabulados o explotados en virtud de las malas condiciones de higiene y por lo general se produce a finales de invierno o principios de primavera. La importancia económica de la enfermedad se asocia con disminución en la producción, con la devaluación de los canales en el matadero y el uso continuo de acaricidas en los animales infectados (Damriyasa et al., 2004)

Complete Set of Invariants of a 4th Order Tensor: The 12 Tasks of HARDI from Ternary Quartics

International audienceInvariants play a crucial role in Diffusion MRI. In DTI (2 nd order tensors), invariant scalars (FA, MD) have been successfully used in clinical applications. But DTI has limitations and HARDI models (e.g. 4 th order tensors) have been proposed instead. These, however, lack invariant features and computing them systematically is challenging. We present a simple and systematic method to compute a functionally complete set of invariants of a non-negative 3D 4 th order tensor with re-spect to SO3. Intuitively, this transforms the tensor's non-unique ternary quartic (TQ) decomposition (from Hilbert's theorem) to a unique canon-ical representation independent of orientation – the invariants. The method consists of two steps. In the first, we reduce the 18 degrees-of-freedom (DOF) of a TQ representation by 3-DOFs via an orthogonal transformation. This transformation is designed to enhance a rotation-invariant property of choice of the 3D 4 th order tensor. In the second, we further reduce 3-DOFs via a 3D rotation transformation of coordinates to arrive at a canonical set of invariants to SO3 of the tensor. The resulting invariants are, by construction, (i) functionally complete, (ii) functionally irreducible (if desired), (iii) computationally efficient and (iv) reversible (mappable to the TQ coefficients or shape); which is the novelty of our contribution in comparison to prior work. Results from synthetic and real data experiments validate the method and indicate its importance