In this work, we propose LOUPE-ST, which extends the previously introduced optimal k-space sampling pattern learning framework called LOUPE by employing a straight-through estimator to better handle the gradient back-propagation in the binary sampling layer and incorporating an unrolled optimization network (MoDL) to reconstruct T2w images from under-sampled k-space data with high fidelity. Our results indicate that, compared with the variable density under-sampling pattern at the same under-sampling ratio (10%), superior reconstruction performance can be achieved with LOUPE-ST optimized under-sampling pattern. This was observed for all reconstruction methods that we experimented with.
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