Background Radial self-navigated (RSN) whole-heart coronary cardiovascular magnetic resonance angiography (CCMRA) is a free-breathing technique that estimates and corrects for respiratory motion. However, RSN has been limited to a 1D rigid correction which is often insufficient for patients with complex respiratory patterns. The goal of this work is therefore to improve the robustness and quality of 3D radial CCMRA by incorporating both 3D motion information and nonrigid intra-acquisition correction of the data into a framework called focused navigation (fNAV). Methods We applied fNAV to 500 data sets from a numerical simulation, 22 healthy subjects, and 549 cardiac patients. In each of these cohorts we compared fNAV to RSN and respiratory resolved extradimensional golden-angle radial sparse parallel (XD-GRASP) reconstructions of the same data. Reconstruction times for each method were recorded. Motion estimate accuracy was measured as the correlation between fNAV and ground truth for simulations, and fNAV and image registration for in vivo data. Percent vessel sharpness was measured in all simulated data sets and healthy subjects, and a subset of patients. Finally, subjective image quality analysis was performed by a blinded expert reviewer who chose the best image for each in vivo data set and scored on a Likert scale 0–4 in a subset of patients by two reviewers in consensus. Results The reconstruction time for fNAV images was significantly higher than RSN (6.1 ± 2.1 min vs 1.4 ± 0.3, min, p < 0.025) but significantly lower than XD-GRASP (25.6 ± 7.1, min, p < 0.025). Overall, there is high correlation between the fNAV and reference displacement estimates across all data sets (0.73 ± 0.29). For simulated data, healthy subjects, and patients, fNAV lead to significantly sharper coronary arteries than all other reconstruction methods (p < 0.01). Finally, in a blinded evaluation by an expert reviewer fNAV was chosen as the best image in 444 out of 571 data sets (78%; p < 0.001) and consensus grades of fNAV images (2.6 ± 0.6) were significantly higher (p < 0.05) than uncorrected (1.7 ± 0.7), RSN (1.9 ± 0.6), and XD-GRASP (1.8 ± 0.8). Conclusion fNAV is a promising technique for improving the quality of RSN free-breathing 3D whole-heart CCMRA. This novel approach to respiratory self-navigation can derive 3D nonrigid motion estimations from an acquired 1D signal yielding statistically significant improvement in image sharpness relative to 1D translational correction as well as XD-GRASP reconstructions. Further study of the diagnostic impact of this technique is therefore warranted to evaluate its full clinical utility.
PurposeTo develop SPARCQ (Signal Profile Asymmetries for Rapid Compartment Quantification), a novel approach to quantify fat fraction (FF) using asymmetries in the phase‐cycled balanced SSFP (bSSFP) profile.MethodsSPARCQ uses phase‐cycling to obtain bSSFP frequency profiles, which display asymmetries in the presence of fat and water at certain TRs. For each voxel, the measured signal profile is decomposed into a weighted sum of simulated profiles via multi‐compartment dictionary matching. Each dictionary entry represents a single‐compartment bSSFP profile with a specific off‐resonance frequency and relaxation time ratio. Using the results of dictionary matching, the fractions of the different off‐resonance components are extracted for each voxel, generating quantitative maps of water and FF and banding‐artifact‐free images for the entire image volume. SPARCQ was validated using simulations, experiments in a water‐fat phantom and in knees of healthy volunteers. Experimental results were compared with reference proton density FFs obtained with 1H‐MRS (phantoms) and with multiecho gradient‐echo MRI (phantoms and volunteers). SPARCQ repeatability was evaluated in six scan‐rescan experiments.ResultsSimulations showed that FF quantification is accurate and robust for SNRs greater than 20. Phantom experiments demonstrated good agreement between SPARCQ and gold standard FFs. In volunteers, banding‐artifact‐free quantitative maps and water‐fat‐separated images obtained with SPARCQ and ME‐GRE demonstrated the expected contrast between fatty and non‐fatty tissues. The coefficient of repeatability of SPARCQ FF was 0.0512.ConclusionSPARCQ demonstrates potential for fat quantification using asymmetries in bSSFP profiles and may be a promising alternative to conventional FF quantification techniques.
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.
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