HULFSynth : An INR based Super-Resolution and Ultra Low-Field MRI Synthesis via Contrast factor estimation
arXiv:2511.14897v2 Announce Type: replace-cross Abstract: We present an unsupervised single image bidirectional Magnetic Resonance Image (MRI) synthesizer that synthesizes an Ultra-Low Field (ULF) like image from a High-Field (HF) magnitude image and vice-versa. Unlike existing MRI synthesis models, our approach is inspired by the physics that drives contrast changes between HF and ULF MRIs. Our forward model simulates a HF to ULF transformation by estimating the tissue-type Signal-to-Noise ratio (SNR) values based on target contrast values. For the Super-Resolution task, we used an Implicit N
The continuous advancements in AI and specifically in medical imaging are driving innovations like HULFSynth to improve diagnostic capabilities and accessibility. Research in unsupervised learning and implicit neural representations is maturing to enable these sophisticated synthesis models.
This development can significantly enhance the utility of Ultra-Low Field MRI by providing clearer images and enabling super-resolution, potentially democratizing access to MRI diagnostics in resource-limited settings and improving early disease detection. It offers a low-cost, high-impact pathway for medical imaging.
MRI diagnostics could become more accessible and affordable globally, allowing for wider deployment of less powerful, lower-cost ULF MRI machines without sacrificing image quality. The ability to synthesize and super-resolve images based on physical principles marks a departure from purely data-driven approaches.
- · Healthcare providers in low-resource settings
- · Patients needing MRI diagnostics
- · Medical AI companies focused on imaging
- · Manufacturers of Ultra-Low Field MRI machines
- · High-Field MRI manufacturers (potential pressure on market differentiation)
- · Traditional MRI image enhancement software providers
Improved diagnostic accuracy and accessibility for conditions requiring MRI, particularly in underserved regions.
Reduced healthcare costs associated with MRI scans due to increased adoption of more affordable ULF systems.
Accelerated medical research and drug discovery facilitated by broader and more detailed MRI data from diverse populations.
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Read at arXiv cs.LG