Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Denoising
Motivation: Denoising of 3-dimensional MR images with 2D CNNs introduces unwanted inter-slice fluctuations. High-dimensional CNNs do not introduce these artifacts but place high demands on the underlying computer hardware, which might not be met on today’s preclinical systems.
Goal(s): Investigating a method for consistent denoising of high-dimensional datasets using 2-dimensional convolutional neural networks.
Approach: Denoising of a high-dimensional dataset is divided into multiple 2D processing steps. Data resulting from these steps is then weighted and combined in k-space.
Results: A large 3-dimensional dataset was denoised on a desktop workstation using 2D convolutional neural networks, avoiding inter-slice fluctuations in the final dataset.
Impact: The presented method enables consistent high-dimensional denoising using 2-dimensional convolutional networks. Thus, processing of large MRI datasets becomes possible on standard workstations without the need of expensive computer hardware or connection to a remote server.
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