POSE: POSition Encoding for accelerated quantitative MRI.

Area of Science:

• Medical Imaging
• Biophysics
• Signal Processing

Background:

• Quantitative MRI (qMRI) requires multiple scans, leading to long acquisition times.
• Existing acceleration methods often compromise image quality or increase noise.
• There is a need for faster, high-resolution quantitative imaging techniques.

Purpose of the Study:

• To introduce and validate a new general strategy for accelerating MRI called Position Encoding (POSE).
• To demonstrate POSE's ability to generate accelerated and enhanced resolution maps of biophysical parameters.
• To assess POSE's performance against conventional methods and its potential for further acceleration.

Main Methods:

• Developed the POSE framework utilizing subvoxel shifts as an additional encoding source.
• Validated the method using numerical Bloch equation simulations (numerical and digital brain phantoms).
• Conducted phantom (NIST) and in vivo experiments, including Monte Carlo simulations for noise analysis.

Main Results:

• POSE demonstrated excellent agreement with reference methods in numerical simulations and phantom experiments across various T1 values.
• In vivo results showed good agreement with reference methods and superior g-factors compared to conventional parallel imaging at identical acceleration.
• Combining POSE with parallel imaging allowed further acceleration with better noise performance than standard parallel imaging.

Conclusions:

• POSE is a validated and effective strategy for accelerating quantitative MRI.
• The method offers significant improvements in scan time and noise performance over existing techniques.
• POSE holds promise for enhancing the efficiency and quality of advanced MRI applications.