Enhancement of the low resolution image quality using randomly sampled data for multi-slice MR imaging

Area of Science:

• Medical Imaging
• Magnetic Resonance Imaging
• Image Reconstruction

Background:

• Low-resolution images are common in in vivo MRI for large field-of-view (FOV) and high-speed applications like whole-body screening and functional MRI.
• Enhancing image quality in these scenarios without increasing scan time is a significant challenge.

Purpose of the Study:

• To investigate a novel multi-slice imaging strategy using compressed sensing (CS) MRI to improve low-resolution image quality.
• To achieve enhanced image quality without prolonging the MRI acquisition duration.

Main Methods:

• A multi-slice imaging sequence acquires low-resolution images across all slices.
• Compressed sensing (CS) acquires additional random data in a center slice.
• Weighting functions derived from low-resolution k-space images of adjacent slices are used to interpolate CS data into other slices.

Main Results:

• The proposed method was investigated using in vivo human brain MRI.
• Quantitative comparisons demonstrated the advantage of the proposed method over conventional low-resolution imaging.
• The strategy successfully enhanced the quality of low-resolution MR images.

Conclusions:

• The developed multi-slice CS-MRI strategy effectively enhances image quality for low-resolution acquisitions.
• This technique offers a viable solution for improving MR imaging in time-constrained and large FOV applications.
• The method shows promise for applications such as whole-body MRI screening and functional MRI.