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Can Super-Resolution be used to accelerate MRI acquisition?
Hi fellow computer scientists,
​
After some research I came to the conclusion that both Super-Resolution (SR) and Compressed Sensing (CS) are used for MRI reconstruction. However, I'm questioning whether super-resolution is suited for MRI reconstruction when the goal is to accelerate image acquisition or reduce scan time while maintaining image quality?
I've seen some really good papers that support super-resolution as a technique for MRI acceleration, but I have not yet found a general opinion about this. Is it possible/practical to leverage MRI images reconstructed from undersampled k-space data, and then use super-resolution algorithms to enhance the spatial resolution and generate higher-resolution MRI images?
Won't that imply acquisition acceleration, since we deliberately collect fewer data points in k-space than what would be required for a fully sampled image?
​
Thank you!
Hi fellow computer scientists,
​
After some research I came to the conclusion that both Super-Resolution (SR) and Compressed Sensing (CS) are used for MRI reconstruction. However, I'm questioning whether super-resolution is suited for MRI reconstruction when the goal is to accelerate image acquisition or reduce scan time while maintaining image quality?
I've seen some really good papers that support super-resolution as a technique for MRI acceleration, but I have not yet found a general opinion about this. Is it possible/practical to leverage MRI images reconstructed from undersampled k-space data, and then use super-resolution algorithms to enhance the spatial resolution and generate higher-resolution MRI images?
Won't that imply acquisition acceleration, since we deliberately collect fewer data points in k-space than what would be required for a fully sampled image?
​
Thank you!
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