1357768 - “Free” respiratory navigation for stack-of-spiral trajectories

Free-breathing acquisitions are ideal for robustness and patient comfort but are dependent on navigation techniques such as sequence-based acquisitions, bellows, and RF-sensing [1-5]. For 3D trajectories, the DC signal sampled each TR as the trajectory crosses k-space center (k₀) can be used to determine physiological motion [3]. However, with “stack” 3D trajectories, the DC signal is measured less frequently, and may not have sufficient sampling frequency to resolve respiratory motion.

In this study, we investigate a potentially “free” self-navigator by measuring the free induction decay (FID) signal immediately following imaging RF-excitation. This navigator is implementable with minimal penalty in acquisition efficiency. We demonstrate this self-navigator for respiratory binning of a 3D stack-of-spiral acquisition.

Methods:

Institutional review board approval and informed consent was obtained for imaging studies. Healthy volunteers (n=5) were imaged at 0.55T (prototype MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany). Free-breathing 2.0mm isotropic 3D stack-of-spirals acquisitions were acquired in a short-axis orientation (TE/TR = 1.1/4.5 ms, flip angle = 50, 80 partitions, total acquisition time ~3 min).

The proposed navigator is acquired by sampling during the slice-select ramp down following RF-excitation pulse (Fig. 1a). For the purposes of characterizing and comparing this navigator signal, we designed a purpose-built pulse sequence that enabled the sampling of multiple navigator signals within the same acquisition.

We compared:

1. SS_nav: sampling the slice-select ramp-down.


2. FID_nav: sampling 100us post un-refocused slab-encoding.


3. DC_nav: rewinding to k₀, before acquisition slice-encoding, included for reference purposes only.  


4. Respiratory bellows.

Respiratory navigator signals (all 22 Hz) were filtered to remove non-respiratory temporal variations (Fig. 1b). Signals were binned in to 8 respiratory phases and analyzed to compare the number of peaks and average respiratory intervals during the 3 minute acquisition. Respiratory-resolved images were reconstructed using the Gadgetron framework using compressed-sensing with temporal and spatial constraints [1].

Results:

Good agreement was found between the “free” navigation signals acquired each TR (SS_nav, FID_nav) and the reference signals (DC_nav, bellows) (Fig. 1c). Images reconstructed from the “free” navigation signal could resolve respiratory phases (Fig. 2). No significant differences were found in the number of detected respiratory phases (p > 0.25, ANOVA), and estimated mean respiratory interval (p > 0.15, ANOVA), between the navigation signals, though the bellows signal may be unreliable (Fig. 3).

Conclusion:

We demonstrated that “free” SS_nav and FID_nav navigators can resolve respiratory motion, with minimal penalty to acquisition efficiency. Further work will characterize excitation pulse, imaging resolution, and contrast on this technique’s efficacy.