Compressed Sensing Inspired Neural Decoder for Undersampled MRI with Self-Assessment

Filippo Martinini, Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


An important problem in magnetic resonance imaging (MRI) is the long time lapse required to acquire a fully sampled, high resolution scan. To speed up acquisition, Compressed Sensing (CS) has been used and recently coupled with Neural Networks (NN). In the latter setting, commonly CS has been split into two different problems: i) design of the encoder, or selection of the undersampling pattern, and ii) design of the decoder. A significant progress was recently introduced by a solution (called LOUPE) where encoding and decoding are simultaneously addressed. Here we propose an improvement of this model, called 'regularized-LOUPE' (r-LOUPE), which add measurement constraint into the picture, resulting in a ×8 speed-up in the MRI acquisition time. A further benefit of our methodology is that measurement constraint can be leveraged to implement a self-assessment tool able to predict the reconstruction error and to identify possible out-layers.
Original languageEnglish (US)
Title of host publicationBioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781728172040
StatePublished - Jan 1 2021
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-02-15


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