Autocalibrated wave-CAIPI reconstruction; Joint optimization of k-space trajectory and parallel imaging...

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Medical Physics
• Image Reconstruction

Background:

• Fast MRI acquisitions are susceptible to k-space trajectory deviations due to hardware limitations and physical effects.
• Characterizing these deviations typically requires additional measurements or generalized calibrations.

Purpose of the Study:

• To propose an autocalibrated technique for determining k-space trajectory discrepancies in fast MRI.
• To enable accurate parallel imaging reconstruction without supplementary calibration scans.

Main Methods:

• A joint optimization framework estimates the k-space trajectory concurrently with parallel imaging reconstruction.
• Model reduction is employed for computational efficiency and to maintain image quality.
• The approach is demonstrated on the wave-CAIPI (Controlled Aliasing in Parallel Imaging) method.

Main Results:

• The autocalibrated technique automatically calculates the 3D trajectory in under 30 seconds on standard hardware.
• Achieved accuracy is equivalent to that of full-gradient calibration scans.
• Demonstrated high-quality wave-CAIPI reconstructions across diverse imaging parameters (FOV, bandwidth, TE, resolution, etc.).

Conclusions:

• The proposed framework facilitates high-quality wave-CAIPI reconstruction across a wide range of protocol parameters.
• This autocalibration method is adaptable to various fast MRI techniques, offering clinically relevant timing.
• Enables accurate gradient trajectory determination for advanced MRI methods.