Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence
Motivation: The U-net is widely used in deep learning-based MRI reconstruction. Its encoding-decoding component enlarges receptive field while the pooling and interpolation operation limits the ability to recover sparsely sampled MR signals.
Goal(s): The wavelet transform and inverse wavelet transform are introduced to replace pooling and interpolation operations in order to maintain the spatial information of images during the encoding-decoding process within the neural network.
Approach: A cascaded multi-level wavelet neural network with data consistency, termed as CAMERA-Net, is presented for under-sampled MRI reconstruction.
Results: CAMERA-Net demonstrates significant enhancement in reconstructing quality with public fastMRI knee dataset.
Impact: The improved reconstruction capabilities of CAMERA-Net have the potential to enhance precision and reliability when reconstructing under-sampled MRI data. This could result in more efficient clinical scans.
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