Keywords: Bone/Skeletal, Bone
Motivation: (IR)-UTE MRI allows for (quantitative) assessment of bony tissue. However, imaging often suffers from poor SNR and time-consuming protocols.
Goal(s): To develop a reconstruction method that can transfer undersampled IR-UTE-based scans of the knee into high-quality images.
Approach: A physics-based reconstruction algorithm was implemented, which incorporates a denoising convolutional neural network (DnCNN) specifically trained for the presented application.
Results: Exploiting the tailored DnCNN within the iterative reconstruction algorithm leads to significant noise suppression and an overall improved image quality compared to incorporating a generic DnCNN or applying the networks alone.
Impact: The proposed reconstruction technique demonstrates potential for MR-based assessment of osseous tissue within clinically appealing scan time.
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