A Denoising Technique for Multi-Contrast Multi-Coil Magnetic Resonance Imaging (Case No. 2024-233)

Summary:

UCLA researchers in the Department of Radiological Sciences have developed a novel MRI denoising technique that enhances image clarity, sensitivity, and precision by significantly reducing distortion and noise.

Background:

Magnetic resonance imaging (MRI) is critical for medical diagnosis, but image quality depends on achieving  a high signal-to-noise ratio. Existing solutions, such as multiple scan averages, image filtering, constrained reconstruction, and deep learning, often increase scan time, blur details, or complicate interpretation. While image-domain random matrix theory (RMT)-based denoising offers improvements, it still introduces distortions and often requires pre-processing. Thus, there remains a need for a fast, reliable method to consistently produce high-quality, low-noise MRI images.

Innovation:

UCLA researchers have developed a novel denoising technique for MRI that significantly reduces noise and distortion, enhancing image clarity and diagnostic precision. The method achieves a 1.6-fold increase in signal-to-noise ratio compared to standard models and consistently lowers data variability and acquisition time. It is fully compatible with parallel imaging and extends seamlessly to 3D MRI, broadening its clinical and research applications. This advancement offers a powerful tool for improving MRI efficiency and accuracy, enabling earlier and more reliable detection of medical conditions.

Potential Applications:

●    MRI 
●    Biomedical sensing technology
●    Diagnostic tests 
●    Imaging in low-field or portable MRI systems
●    3D medical imaging

Advantages:
 
●    Improved image quality
●    Suppressed noise
●    Reduced image distortion
●    Shortened scan time 
●    Increased signal-to-noise ratio
●    Reduces need for repeat scans

State of Development:

The method has been successfully demonstrated to enhance signal-to-noise ratio and improve image clarity in MRI scans. The associated software for this technology is also publicly available via GitHub:  

Related Publications:

• Shih S-F, Zhang Z, Tasdelen B, Yagiz E, Cui SX, Zhong X, Nayak KS, Wu HH. Multi-Coil Multi-Contrast Random Matrix Theory-Based Denoising for Liver Fat and R2* Quantification at 0.55T. Proc ISMRM; 2023.
• Zhang, Zhaohuan & Aygun, Elif & Shih, Shu-Fu & Raman, Steven & Sung, Kyunghyun & Wu, Holden. (2024). High-resolution prostate diffusion MRI using eddy current-nulled convex optimized diffusion encoding and random matrix theory-based denoising. Magma (New York, N.Y.). 37. 10.1007/s10334-024-01147-w. 

Reference:

UCLA Case Nos. 2024-233 and 2024-234 (software tech)

Lead Inventor:

Professor Holden Wu