Autonomous Velocity Encoding (VENC) Selection Improves Precision of Quantitative Flow Measurement

Area of Science:

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Cardiovascular Flow Measurement

Background:

• Optimal velocity encoding limit (VENC) in phase contrast MRI is crucial for accurate flow measurement but is subject-specific and often manually set.
• Current manual VENC setting relies on limited prior velocity data, potentially leading to suboptimal precision or extended scan times.

Purpose of the Study:

• To evaluate an automated workflow for selecting the optimal VENC without requiring technologist interaction.
• To assess the impact of autonomous VENC selection on flow measurement precision and scan duration.

Main Methods:

• A prospective and retrospective study utilizing a 30-second free-breathing calibration scan to autonomously estimate maximum velocity (Vmax).
• The workflow automatically calculates and applies the optimal VENC, targeting a VENC:Vmax ratio of 1.1-1.25.
• Statistical analysis included Shapiro-Wilk tests, bootstrap confidence intervals, and Wilcoxon signed-rank tests to compare VENC settings and measurement precision.

Main Results:

• Retrospective analysis revealed suboptimal VENC settings in 96.5% of examinations (VENC:Vmax ratio [2.03, 2.13]).
• Prospective autonomous VENC selection achieved a mean VENC:Vmax ratio of 1.19 ± 0.10, significantly lower than subject-invariant settings.
• Optimized VENC reduced velocity standard deviation by 41.9% ± 8.0% and enabled scan time reduction to approximately one-third compared to default settings.

Conclusions:

• Inline autonomous VENC selection significantly improves flow measurement precision.
• The automated workflow simplifies the MRI acquisition process.
• Autonomous VENC selection effectively reduces scan time at 0.55 T while maintaining velocity-to-noise performance.