Accelerated Acquisition of High-resolution Diffusion-weighted Imaging of the Brain with a Multi-shot Echo-planar Sequence: Deep-learning-based Denoising
Motohide Kawamura, Daiki Tamada, Satoshi Funayama, Marie‐Luise Kromrey, Shintaro Ichikawa, Hiroshi Onishi, Utaroh Motosugi
Abstract
To accelerate high-resolution diffusion-weighted imaging with a multi-shot echo-planar sequence, we propose an approach based on reduced averaging and deep learning. Denoising convolutional neural networks can reduce amplified noise without requiring extensive averaging, enabling shorter scan times and high image quality. The preliminary experimental results demonstrate the superior performance of the proposed denoising method over state-of-the-art methods such as the widely used block-matching and 3D filtering.
Topics & Concepts
Noise reductionMedicineArtificial intelligenceDeep learningSingle shotConvolutional neural networkEcho-planar imagingNoise (video)Block (permutation group theory)Diffusion MRIPlanarSequence (biology)DiffusionPattern recognition (psychology)Shot (pellet)Computer visionComputer scienceImage (mathematics)Magnetic resonance imagingRadiologyOpticsPhysicsGeneticsThermodynamicsComputer graphics (images)MathematicsBiologyOrganic chemistryGeometryChemistryAdvanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsMRI in cancer diagnosis