Purpose To demonstrate, for the first time, the feasibility of obtaining low‐latency 3D rigid‐body motion information from spherical Lissajous navigators acquired at extremely small k‐space radii, which has significant advantages compared with previous techniques. Theory and Methods A spherical navigator concept is proposed in which the surface of a k‐space sphere is sampled on a 3D Lissajous curve at a radius of 0.1/cm. The navigator only uses a single excitation and is acquired in less than 5 ms. Rotation estimations were calculated with an algorithm from computer vision that exploits a rotation theorem of the spherical harmonics transform and has minimal computational cost. The effectiveness of the concept was investigated with phantom and in vivo measurements on a commercial 3T MRI scanner. Results Scanner‐induced in vivo motion was measured with maximum absolute errors of 0.58° and 0.33 mm for rotations and translations, respectively. In the case of real, in vivo motion, the proposed method showed good agreement with motion information from FSL image registrations (mean/maximum deviations of 0.37°/1.24° and 0.44 mm/1.35 mm). In addition, phantom measurements indicated precisions of 0.014° and 0.013 mm. The computations for complete motion information took, on average, 24 ms on an ordinary laptop. Conclusions This work demonstrates a proof of concept for obtaining accurate motion information from small‐radius spherical navigators. The method has the potential to overcome several previously reported problems and could help increase the utility of navigator‐based motion correction both in research and in the clinic.

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Conventional diffusion‐weighted (DW) MRI suffers from free water contamination due to the finite voxel size. The most common case of free water contamination occurs with cerebrospinal fluid (CSF) in voxels located at the CSF‐tissue interface, such as at the ventricles in the human brain. Another case refers to intra‐tissue free water as in vasogenic oedema. In order to avoid the bias in diffusion metrics, several multi‐compartment methods have been introduced, which explicitly model the presence of a free water compartment. However, fitting multi‐compartment models in DW MRI represents a well known ill conditioned problem. Although during the last decade great effort has been devoted to mitigating this estimation problem, the research field remains active. The aim of this work is to introduce the design, characterise the NMR properties and demonstrate the use of two dedicated anisotropic diffusion fibre phantoms, useful for the study of free water elimination (FWE) and mapping models. In particular, we investigate the recently proposed FWE diffusion tensor imaging approach, which takes explicit account of differences in the transverse relaxation times between the free water and tissue compartments.

This study aims to integrate an ultra-high-strength gradient coil system on a clinical 3 T magnet and demonstrate its preclinical imaging capabilities. Dedicated phantoms were used to qualitatively and quantitatively assess the performance of the gradient system. Advanced MR imaging sequences, including diffusion tensor imaging (DTI) and quantitative susceptibility mapping (QSM), were implemented and executed on an ex vivo specimen as well as in vivo rats. The DTI and QSM results on the phantom agreed well with those in the literature. Furthermore, studies on ex vivo specimens have demonstrated the applicability of DTI and QSM on our system to probe microstructural changes in a mild traumatic brain injury rat model. The feasibility of in vivo rat DTI was also demonstrated. We showed that the inserted ultra-high-strength gradient coil was successfully integrated on a clinically used magnet. After careful tuning and calibration, we verified the accuracy and quantitative preclinical imaging capability of the integrated system in phantom and in vivo rat brain experiments. This study can be essential to establish dedicated animal MRI platform on clinical MRI scanners and facilitate translational studies at clinical settings.