Magnetic resonance imaging with gradient sound respiration guide

Abstract

Respiratory motion management is crucial for high-resolution MRI of the heart, lung, liver and kidney. In this article, respiration guide using acoustic sound generated by pulsed gradient waveforms was introduced in the pulmonary ultrashort echo time (UTE) sequence and validated by comparing with retrospective respiratory gating techniques. The validated sound-guided respiration was implemented in non-contrast enhanced renal angiography. In the sound-guided respiration, breathe−in and–out instruction sounds were generated with sinusoidal gradient waveforms with two different frequencies (602 and 321 Hz). Performance of the sound-guided respiration was evaluated by measuring sharpness of the lung-liver interface with a 10–90% rise distance, w10-90, and compared with three respiratory motion managements in a free-breathing UTE scan: without respiratory gating (w/o gating), 0-dimensional k-space navigator (k-point navigator), and image-based self-gating (Img-SG). The sound-guided respiration was implemented in stack-of-stars balanced steady-state free precession with inversion recovery preparation for renal angiography. No subjects reported any discomfort or inconvenience with the sound-guided respiration in pulmonary or renal MRI scans. The lung-liver interface of the UTE images for sound-guided respiration (w10-90 = 6.99 ± 2.90 mm), k-point navigator (8.51 ± 2.71 mm), and Img-SG (7.01 ± 2.06 mm) was significantly sharper than that for w/o gating (17.13 ± 2.91 mm; p < 0.0001 for all of sound-guided respiration, k-point navigator and Img-SG). Sharpness of the lung-liver interface was comparable between sound-guided respiration and Img-SG (p = 0.99), but sound-guided respiration achieved better visualization of pulmonary vasculature. Renal angiography with the sound-guided respiration clearly delineated renal, segmental and interlobar arteries. In conclusion, the gradient sound guided respiration can facilitate a consistent diaphragm position in every breath and achieve performance of respiratory motion management comparable to image-based self-gating.

Dynamic imaging using motion-compensated smoothness regularization on manifolds (MoCo-SToRM)

“…• Signal-to-noise ratio (SNR) (Dietrich et al 2007, Gutberlet et al 2018, Kobayashi 2021): The SNR is computed as…”