Keywords: Lung, Lung
Motivation: Spiral-UTE MRI has been proposed for more efficient lung imaging to permit breath-hold ultra-short echo time acquisition of the lung. It is more valuable to further accelerate the acquisition of the spiral-UTE MRI of lung images, thus enabling shorter breath-holds and higher spatial resolutions.
Goal(s): This work presents a deep learning based method to enable the reconstruction of spiral-UTE MRI of lung images from accelerated spiral k-space.
Approach: An unrolled network was developed for reconstructing images from the accelerated non-cartesian k-space.
Results: The unrolled network allows for higher reconstruction quality for spiral-UTE MRI of lungs compared to a standard U-Net.
Impact: The proposed unrolled network tailored for spiral MRI reconstruction enables reconstruction of accelerated spiral-UTE of lung images to allow shorter breath-holds and higher spatial resolutions. This reconstruction technique can also extended to other multi-coil non-cartesian accelerated MRI reconstructions.
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