Optimized spectrally selective steady-state free precession sequences for cartilage imaging at ultra-high fields

OBJECT: Fat suppressed 3D steady-state free precession (SSFP) sequences are of special interest in cartilage imaging due to their short repetition time in combination with high signal-to-noise ratio. At low-to-high fields (1.5-3.0 T), spectral spatial (spsp) radio frequency (RF) pulses perform superiorly over conventional saturation of the fat signal (FATSAT pulses). However, ultra-high fields (7.0 T and more) may offer alternative fat suppression techniques as a result of the increased chemical shift. MATERIALS AND METHODS: Application of a single, frequency selective, RF pulse is compared to spsp excitation for water (or fat) selective imaging at 7.0 T. RESULTS: For SSFP, application of a single frequency selective RF pulse for selective water or fat excitation performs beneficially over the commonly applied spsp RF pulses. In addition to the overall improved fat suppression, the application of single RF pulses leads to decreased power depositions, still representing one of the major restrictions in the design and application of many pulse sequences at ultra-high fields. CONCLUSION: The ease of applicability and implementation of single frequency selective RF pulses at ultra-high-fields might be of great benefit for a vast number of applications where fat suppression is desirable or fat-water separation is needed for quantification purposes.

On degeneracy and invariances of random fields paths with applications in Gaussian process modelling

We study pathwise invariances and degeneracies of random fields with motivating applications in Gaussian process modelling. The key idea is that a number of structural properties one may wish to impose a priori on functions boil down to degeneracy properties under well-chosen linear operators. We first show in a second order set-up that almost sure degeneracy of random field paths under some class of linear operators defined in terms of signed measures can be controlled through the two first moments. A special focus is then put on the Gaussian case, where these results are revisited and extended to further linear operators thanks to state-of-the-art representations. Several degeneracy properties are tackled, including random fields with symmetric paths, centred paths, harmonic paths, or sparse paths. The proposed approach delivers a number of promising results and perspectives in Gaussian process modelling. In a first numerical experiment, it is shown that dedicated kernels can be used to infer an axis of symmetry. Our second numerical experiment deals with conditional simulations of a solution to the heat equation, and it is found that adapted kernels notably enable improved predictions of non-linear functionals of the field such as its maximum.