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Open-source 3D-printed portable MRI machine built for under $70,000

🔄 Updated 1d ago
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Key points

  • OSI2 ONE MRI scanner costs under $70,000, compared to $1.1M+ for standard units.
  • The portable device has a 3D-printed core and 50mT field strength.
  • AI can improve image quality despite lower resolution and signal-to-noise ratio.
  • The open-source nature allows for physics-informed AI model training.

Affordable MRI Technology Developed

The Open Source Imaging Initiative (OSI2) has created an open-source, 3D-printed portable MRI scanner called the OSI2 ONE. This device can be built for under $70,000, which is less than 7% of the starting price for a full-sized MRI machine, typically over $1.1 million.

Technical Specifications and Limitations

The OSI2 ONE MRI scanner has a limited field strength of 50mT, significantly lower than the 1.5T to 3T of full-sized units. This results in lower spatial resolution and a reduced signal-to-noise ratio in the images produced by the portable device.

AI for Image Enhancement

Tech analyst Brian Roemmele suggests that AI can address the limitations of low-field MRI. Specialized AI models, trained on high-field MRI data or physics-informed models, can denoise, correct for inhomogeneity, and improve resolution beyond raw acquisition limits. This makes the images usable for medical diagnoses.

The open-source nature of the OSI2 ONE scanner allows researchers to use its publicly available information to build physics models for AI training, even without access to anonymized patient data.

Impact and Accessibility

While regulatory challenges exist in some countries, this low-cost, portable MRI machine is particularly relevant for regions with limited access to expensive medical technologies. It offers a more accessible diagnostic tool for various critical conditions.

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Reporting from

The Open Source Imaging Initiative developed an open-source, 3D-printed portable MRI scanner, the OSI2 ONE, costing under $70,000, significantly less than standard MRI machines. AI is proposed to overcome the device's limitations in spatial resolution and signal-to-noise ratio, making it viable for medical diagnoses, particularly in regions with limited access to expensive medical equipment.