Tiny golden angle stack-of-stars (tygaSoS) free-breathing functional lung imaging.

PURPOSE: MRI of the lung parenchyma is still challenging due to cardiac and respiratory motion, and the low proton density and short T2*. Clinical feasible MRI methods for functional lung assessment are of great interest. It was the objective of this study to evaluate the potential of combining the ultra-short echo-time stack-of-stars approach with tiny golden angle (tyGASoS) profile ordering for self-gated free-breathing lung imaging. METHODS: Free-breathing tyGASoS data were acquired in 10 healthy volunteers (3 smoker (S), 7 non-smoker (NS)). Images in different respiratory phases were reconstructed applying an image-based self-gating technique. Resulting image quality and sharpness, and parenchyma visibility were qualitatively scored by three blinded independent reader, and the signal-to-noise ratio (SNR), proton fraction (fP) and fractional ventilation (FV) quantified. RESULT: The imaging protocol was well tolerated by all volunteers. Image quality was sufficient for subsequent quantitative analysis in all cases with good to excellent inter-reader reliability. Between expiration (EX) and inspiration (IN) significant differences (p < 0.001) were observed in SNR (EX: 3.73 ± 0.89, IN: 3.14 ± 0.74) and fP (EX: 0.27 ± 0.09, IN: 0.25 ± 0.08). A significant (p < 0.05) higher fP (EX/IN: 0.22 ± 0.07/0.21 ± 0.07 (NS), 0.33 ± 0.07/0.30 ± 0.06 (S)) was observed in the smoker group. No significant FV differences resulted between S and NS. CONCLUSION: The study proves the feasibility of free-breathing tyGASoS for multiphase lung imaging. Changes in fP may indicate an initial response in the smoker group and as such proves the sensitivity of the proposed technique. A major limitation in FV quantification rises from the large inter-subject variability of breathing patterns and amplitudes, requiring further consideration.

Efficient 3D Low-Discrepancy k -Space Sampling Using Highly Adaptable Seiffert Spirals.

The overall duration of acquiring a Nyquist sampled 3D dataset can be significantly shortened by enhancing the efficiency of k -space sampling. This can be achieved by increasing the coverage of k -space for every trajectory interleave. Furthermore, acceleration is possible by making use of advantageous undersampling properties. In this paper, a versatile 3D center-out k -space trajectory based on Jacobian elliptic functions (Seiffert's spiral) is presented. The trajectory leads to a low-discrepancy coverage of k -space using a considerably reduced number of readouts compared with other approaches. Such a coverage is achieved for any number of interleaves, and, therefore, even single-shot trajectories can be constructed to be combined with, for example, hyperpolarized media. Furthermore, acceleration is achievable due to non-coherent undersampling properties of the trajectory in combination with non-linear reconstruction techniques like compressed sensing (CS). Simulations of point-spread functions and discrepancy evaluations compare Seiffert's spiral to the established 3D cones approach. Imaging capabilities are evaluated by comparison of in vivo knee images using Nyquist and undersampled datasets in combination with CS reconstructions.