MRI Reconstruction with Separate Magnitude and Phase Priors Based on Dual-Tree Complex Wavelet Transform

Area of Science:

• Medical Imaging
• Signal Processing
• Applied Mathematics

Background:

• Compressed Sensing Magnetic Resonance Imaging (CS-MRI) methods are crucial for accelerating image acquisition.
• Existing CS-MRI techniques often struggle with artifacts caused by phase jumps, impacting image quality.
• CS-MRI methods can be broadly categorized by their approach to reconstructing complex-valued images.

Purpose of the Study:

• To propose a novel CS-MRI method utilizing dual-tree complex wavelet (DT CWT) sparsity.
• To reduce the impact of phase jumps on magnitude reconstruction.
• To enhance the reconstruction of detailed information in both magnitude and phase MRI images.

Main Methods:

• Developed a new CS-MRI method employing DT CWT sparsity within a separate regularization framework.
• Implemented separate penalties for magnitude and phase reconstruction to mitigate phase jump interference.
• Leveraged the unique properties of DT CWT, including non-oscillating coefficients and multidirectional selectivity.

Main Results:

• The proposed method effectively reduces artifacts in magnitude reconstructions caused by phase jumps.
• Experimental results demonstrate superior recovery of image contour and edge information.
• The DT CWT-based approach captures finer details in both magnitude and phase MRI images compared to conventional methods.

Conclusions:

• The novel CS-MRI method based on DT CWT sparsity offers improved image reconstruction quality.
• This approach effectively addresses the challenge of phase jumps in CS-MRI.
• The method shows significant potential for enhancing diagnostic accuracy in MRI through detailed and artifact-free image recovery.