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MRI at low field: A review of software solutions for improving SNR
Reina Ayde1, Marc Vornehm2, Yujiao Zhao3
1Center for Adaptable MRI Technology, Institute of Medical Sciences, School of Medicine & Nutrition, University of Aberdeen, Aberdeen, UK.
Abstract:
Low magnetic field magnetic resonance imaging (MRI) ( < 1 T) is regaining interest in the magnetic resonance (MR) community as a complementary, more flexible, and cost-effective approach to MRI diagnosis. Yet, the impaired signal-to-noise ratio (SNR) per square root of time, or SNR efficiency, leading in turn to prolonged acquisition times, still challenges its relevance at the clinical level. To address this, researchers investigate various hardware and software solutions to improve SNR efficiency at low field, including the leveraging of latest advances in computing hardware. However, there may not be a single recipe for improving SNR at low field, and it is key to embrace the challenges and limitations of each proposed solution. In other words, suitable solutions depend on the final objective or application envisioned for a low-field scanner and, more importantly, on the characteristics of a specific low field. In this review, we aim to provide an overview on software solutions to improve SNR efficiency at low field. First, we cover techniques for efficient k-space sampling and reconstruction. Then, we present post-acquisition techniques that enhance MR images such as denoising and super-resolution. In addition, we summarize recently introduced electromagnetic interference cancellation approaches showing great promises when operating in shielding-free environments. Finally, we discuss the advantages and limitations of these approaches that could provide directions for future applications.
Insights
Low magnetic field magnetic resonance imaging (MRI) offers a cost-effective diagnostic alternative. Software solutions are crucial for enhancing signal-to-noise ratio (SNR) efficiency and reducing acquisition times in low-field MRI systems.
Area of Science:
- Medical Imaging
- Magnetic Resonance Imaging
- Biophysics
Background:
- Low magnetic field magnetic resonance imaging (< 1 Tesla) is gaining traction as a flexible and cost-effective diagnostic tool.
- Clinical adoption is hindered by low signal-to-noise ratio (SNR) efficiency, leading to extended scan durations.
- Advancements in computing hardware offer potential solutions for improving low-field MRI performance.
Purpose of the Study:
- To review software-based strategies for enhancing SNR efficiency in low-field MRI.
- To discuss the applicability and limitations of various software solutions tailored to specific low-field applications.
- To guide future research and development in low-field MRI technology.
Main Methods:
- Review of k-space sampling and reconstruction techniques for efficient data acquisition.
- Presentation of post-processing methods like denoising and super-resolution for image enhancement.
- Summary of novel electromagnetic interference cancellation approaches for shielding-free operation.
Main Results:
- Software solutions can significantly improve SNR efficiency in low-field MRI.
- Techniques range from optimized data acquisition to advanced image post-processing.
- Electromagnetic interference cancellation shows promise for improving performance in challenging environments.
Conclusions:
- No single solution fits all low-field MRI applications; tailored approaches are necessary.
- Software advancements are key to overcoming SNR limitations and enabling wider clinical use.
- Further research into specific software solutions will drive the future of low-field MRI.
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