Implementation of time-efficient adaptive sampling function design for improved undersampled MRI reconstruction.

Area of Science:

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Signal Processing

Background:

• Undersampled MRI accelerates scan times but often compromises image quality.
• Current methods for designing sampling functions can be suboptimal.
• Efficient data acquisition is crucial for advanced MRI applications.

Purpose of the Study:

• To develop a novel, implementable method for designing adaptive sampling functions to enhance undersampled MRI efficacy.
• To improve the performance of sampling functions in Fourier-transform (FT) MRI and compressed sensing (CS) MRI.
• To validate the computational efficiency and robustness of the proposed adaptive sampling method.

Main Methods:

• An approximation of image energy distribution (E-map) is estimated from undersampled k-space data.
• An adaptive probability density function (PDF) is generated by combining the E-map with a modeled PDF.
• The optimal sampling function is selected from candidates based on maximum energy, implemented online.

Main Results:

• The proposed adaptive sampling method consistently outperforms the conventional modeled PDF approach for undersampling ratios of 0.2 and higher.
• Robust performance was demonstrated in both FT-MRI and CS-MRI through simulations and phantom studies (cherry tomato).
• The method proved computationally efficient when implemented directly on an MR scanner.

Conclusions:

• The developed adaptive sampling function design method significantly improves the efficacy of undersampled MRI.
• Online implementation immediately before the main scan is recommended for maximal benefit.
• This approach offers a practical solution for enhancing undersampled MRI performance.