MRI-Based Image Signal-to-Noise Ratio Enhancement with Different Receiving Gains in K-Space

Area of Science:

• Medical Imaging
• Signal Processing

Background:

• Magnetic resonance imaging (MRI) data exhibits varying echo signal amplitudes across k-space regions.
• Optimizing the signal-to-noise ratio (SNR) is crucial for diagnostic accuracy in MRI.
• Previous research has not fully explored k-space data splicing and gain normalization for SNR enhancement.

Purpose of the Study:

• To develop and validate a method for improving MRI SNR by differentially setting receiving gain (RG) in different k-space regions.
• To investigate the efficacy of k-space data splicing combined with gain and phase correction for SNR improvement.

Main Methods:

• Acquired three sets of k-space data using gradient echo sequences with distinct RG parameters for each scan.
• Developed a novel k-space data splicing technique incorporating gain and phase correction and compensation.
• Normalized the spliced k-space data to account for variations introduced by different RG settings.

Main Results:

• The developed k-space splicing and gain normalization method successfully improved the SNR by 5-13%.
• The method demonstrated applicability across different combinations of RG parameters.
• Experimental validation confirmed the effectiveness of the proposed SNR enhancement strategy.

Conclusions:

• The proposed method offers a practical approach to enhance MRI SNR through intelligent k-space data manipulation.
• This technique is robust and adaptable to various receiving gain configurations.
• The findings have significant implications for improving the quality and diagnostic potential of MRI scans.