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AI4MEDIMAGING and Intel: Accelerating Cardiac MRI Analysis with OpenVINO

At AI4MEDIMAGING, we harness artificial intelligence to enhance medical imaging, aiming to deliver faster and more accurate diagnostics. Our recent collaboration with Intel has significantly optimized the processing time of our AI models for cardiac MRI analysis, maintaining high clinical standards while improving performance.

The Challenge: Processing Cardiac MRIs

The analysis of Cardiac MRI cine sequences involves analyzing large 4D datasets (3D plus time), often exceeding 300 images. These complex and voluminous datasets require substantial computational power, traditionally handled in robust cloud environments. The AI solution includes image segmentation and volume quantification of ventricles, at a significantly faster speed then manual segmentation. Despite having powerful infrastructure, the processing times were considerable, impacting efficiency and increasing operational costs.

The Solution: Partnership with Intel and OpenVINO

To address these challenges, AI4MEDIMAGING partnered with Intel to utilize their OpenVINO toolkit. OpenVINO optimizes deep learning model inference on Intel hardware, including CPUs and iGPUs, aiming to reduce latency and improve throughput without compromising clinical performance. Testing was completed on the Intel Core i5-1240P CPU.

Implementation and Results

Key steps in the integration process included:

  • Model Optimization: Converting our models to the Intermediate Representation (IR) format supported by OpenVINO and applying optimization techniques.
  • Hardware Utilization: Deploying optimized models on Intel CPUs and iGPUs to maximize hardware capabilities.
  • Enhanced Docker Images: Improving the startup time of our docker images, resulting in a more responsive system.

The acceleration of our AI models brings several benefits:

  • Faster Diagnostics: Reduced processing times allow for quicker access to diagnostic results, resulting in a better user experience for clinicians.
  • Improved Efficiency: Enhanced model performance enables handling larger data volumes, streamlining workflows.
  • Cost-Effectiveness: Efficient model operation on Intel hardware reduces the need for costly cloud infrastructure.

Conclusion

The collaboration between AI4MEDIMAGING and Intel demonstrates the potential of integrating advanced AI with hardware optimization tools like OpenVINO. This partnership has not only improved the speed and efficiency of our cardiac MRI analysis but has also ensured high clinical performance standards. As we continue to innovate, such partnerships will be crucial in delivering superior diagnostic tools to healthcare providers worldwide.

For more information, please visit our website or contact us directly.