Focused navigation for respiratory–motion‐corrected free‐running radial 4D flow MRI

Abstract

Purpose To validate a respiratory motion correction method called focused navigation (fNAV) for free‐running radial whole‐heart 4D flow MRI. Methods Using fNAV, respiratory signals derived from radial readouts are converted into three orthogonal displacements, which are then used to correct respiratory motion in 4D flow datasets. Hundred 4D flow acquisitions were simulated with non‐rigid respiratory motion and used for validation. The difference between generated and fNAV displacement coefficients was calculated. Vessel area and flow measurements from 4D flow reconstructions with (fNAV) and without (uncorrected) motion correction were compared to the motion‐free ground‐truth. In 25 patients, the same measurements were compared between fNAV 4D flow, 2D flow, navigator‐gated Cartesian 4D flow, and uncorrected 4D flow datasets. Results For simulated data, the average difference between generated and fNAV displacement coefficients was 0.04 ±$$ \pm $$ 0.32 mm and 0.31 ±$$ \pm $$ 0.35 mm in the x and y directions, respectively. In the z direction, this difference was region‐dependent (0.02 ±$$ \pm $$ 0.51 mm up to 5.85 ±$$ \pm $$ 3.41 mm). For all measurements (vessel area, net volume, and peak flow), the average difference from ground truth was higher for uncorrected 4D flow datasets (0.32 ±$$ \pm $$ 0.11 cm2, 11.1 ±$$ \pm $$ 3.5 mL, and 22.3 ±$$ \pm $$ 6.0 mL/s) than for fNAV 4D flow datasets (0.10 ±$$ \pm $$ 0.03 cm2, 2.6 ±$$ \pm $$ 0.7 mL, and 5.1 ±0$$ \pm 0 $$.9 mL/s, p < 0.05). In vivo, average vessel area measurements were 4.92 ±$$ \pm $$ 2.95 cm2, 5.06 ±$$ \pm $$ 2.64 cm2, 4.87 ±$$ \pm $$ 2.57 cm2, 4.87 ±$$ \pm $$ 2.69 cm2, for 2D flow and fNAV, navigator‐gated and uncorrected 4D flow datasets, respectively. In the ascending aorta, all 4D flow datasets except for the fNAV reconstruction had significantly different vessel area measurements from 2D flow. Overall, 2D flow datasets demonstrated the strongest correlation to fNAV 4D flow for both net volume (r2 = 0.92) and peak flow (r2 = 0.94), followed by navigator‐gated 4D flow (r2 = 0.83 and r2 = 0.86, respectively), and uncorrected 4D flow (r2 = 0.69 and r2 = 0.86, respectively). Conclusion fNAV corrected respiratory motion in vitro and in vivo, resulting in fNAV 4D flow measurements that are comparable to those derived from 2D flow and navigator‐gated Cartesian 4D flow datasets, with improvements over those from uncorrected 4D flow.

Intra-bin correction and inter-bin compensation of respiratory motion in free-running five-dimensional whole-heart magnetic resonance imaging

“…3D translational displacement of the heart due to respiration is estimated within each bin using fNAV ( Fig. 1 b) adapted for use with free-running data as follows [22] , [28] . A respiratory self-gating signal (S) which spans each acquired time-point (t) coinciding with a given respiratory bin (r) is normalized and multiplied by three initially unknown fNAV coefficients A r = [ ] that describe the 3D amplitude of respiratory motion within each bin in millimeters.…”

Combined free-running four-dimensional anatomical and flow magnetic resonance imaging with native contrast using Synchronization of Neighboring Acquisitions by Physiological Signals

“…Fourth, respiratory curves are obtained using principal component analysis, used to reduce data complexity and to segregate the respiratory component [14] , followed by adaptive low-pass filtering that targets frequencies within physiologically plausible ranges for respiratory motion [14] and detrending to reduce potential signal offsets between the two sequences or signal drift [15] . The bulk translational motion of the heart due to respiration is then corrected for both free-running FISS and free-running PC-MRI datasets using focused navigation (fNAV) as previously described [23] , [24] . Briefly, the unitless respiratory curve derived from PT is multiplied by three initially unknown coefficients that describe the maximum displacement in millimeters of the heart over time along the three spatial dimensions.…”

“…All data were reconstructed on a workstation equipped with 2 Intel Xeon CPUs (Intel, Santa Clara, California, USA), 512 GB of RAM, and a NVIDIA Tesla GPU (Nvidia, Santa Clara, California, USA). For the k-t-sparse SENSE reconstruction [15] , [19] , [25] , after normalizing each acquisition to the maximum signal from a gridded image reconstruction, regularization parameters for reconstructing free-running FISS datasets were 0.03 for total variation applied along the cardiac dimension and 0.015 for total variation applied along the spatial dimension, while the regularization parameters for free-running PC-MRI were 0.0075 in the cardiac dimension and 0.015 in the spatial dimension [24] . The free-running FISS reconstructions resulted in 4D FISS datasets (x-y-z-cardiac); reconstructions of free-running PC-MRI data returned 4D flow datasets (x-y-z-cardiac-velocity encode).…”

Electric Potential Energy Optimized 3D Radial Sampling Trajectories for MRI

“…For cardiovascular imaging, data from different heart beats can be combined and reconstructed into a representative cine of the cardiac cycle. And all data can be reconstructed together for time-averaged imaging, coil sensitivity map estimation 8,9 , or motion estimation 10 . Certain motion-compensated cardiovascular MRI techniques rely on the use of all these types of reconstructions in a single reconstruction pipeline [11][12][13][14] .…”