Abstract #4692

Playing with k-space lines: downsampling through deep learning to improve clinical sensitivity of diffusion imaging

Marta Gaviraghi¹, Baris Kanber^2,3, Antonio Ricciardi², Fulvia Palesi^1,4, Francesco Grussu^2,5, Carmen Tur^2,6, Alberto Calvi², Sara Collorone², Rebecca S. Samson², and Claudia A.M. Gandini Wheeler-Kingshott^1,2,4

¹Department of Brain & Behavioral Sciences, University of Pavia, Pavia, Italy, ²NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, ³Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom, ⁴Digital Neuroscience Centre, IRCCS Mondino Foundation, Pavia, Italy, ⁵Radiomics Group, Vall d’Hebron Institute of Oncology, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain, ⁶Neurology-Neuroimmunology Department Multiple Sclerosis Centre of Catalonia (Cemcat), Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain

Synopsis

Keywords: AI/ML Image Reconstruction, Neuro, k-space, clinical sensitivity

Motivation: The long acquisition time of diffusion-weighted (DW) imaging hinders its adoption in the clinic for studying pathological microstructural changes in vivo.

Goal(s): The goal of this study was to reduce these long acquisition times by performing downsampling in k-space while maintaining clinical sensitivity.

Approach: Deep learning was used to transform k-space data to DW images. Experiments were performed eliminating 30% of k-space lines using different methods.

Results: DW images obtained with k-space down-sampling showed a reduction in artefacts, while fractional anisotropy images fitted from the network output appeared to have increased clinical sensitivity.

Impact: This work paves the way for the design of acquisition strategies for fast diffusion-imaging. Through deep learning, it was possible to downsample k-space data in several ways, while obtaining a reduction in artefacts, with a potential increase in clinical sensitivity.

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