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Brain MRI reconstruction with cascade of CNNs and a learnable regularization

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dc.contributor.author Tryfonopoulos, Dimitris en
dc.contributor.author Τρυφωνόπουλος, Δημήτρης el
dc.date.accessioned 2023-04-06T07:53:01Z
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/57496
dc.identifier.uri http://dx.doi.org/10.26240/heal.ntua.25193
dc.rights Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/gr/ *
dc.subject MRI ανακατασκευή εικόνας el
dc.subject Μηχανική Μάθηση el
dc.subject Επιστήμη Δεδομένων el
dc.subject Απεικονιστική Ιατρική el
dc.subject Βιοιατρική Απεικόνιση el
dc.subject Biomedical Engineering en
dc.subject MRI Reconstruction en
dc.subject Deep Learning en
dc.subject CNN Networks en
dc.subject Compressed Sensing en
dc.subject Biomedical Imaging en
dc.title Brain MRI reconstruction with cascade of CNNs and a learnable regularization en
heal.type masterThesis
heal.classification Machine Learning el
heal.classification MRI Reconstruction el
heal.classification Compressed Sensing en
heal.classification Regularization Learning en
heal.dateAvailable 2024-04-05T21:00:00Z
heal.language en
heal.access embargo
heal.recordProvider ntua el
heal.publicationDate 2023-02-01
heal.abstract In the field of Medical Imaging, the recent years special attention has been given to Magnetic Resonance Imaging (MRI). MR imaging is a non-invasive imaging modality capable of producing cross-sectional images with high spatial resolution. Unlike Computed tomography (CT) the nature of the acquisition does not employ ionising radiation. However, MRI acquisitions are characterized by long scan time (acquisition time) which is directly related to the number of samples acquired in the k-space. Being able to reduce the acquisition time will help increase patient satisfaction, reduce several artifacts (mainly motion), and finally reduce in overall the medical costs. The main initiative on reducing the scan time from a software perspective, is Compressed Sensing (CS) optimization [1]-[3] according to which given some constraints that are fulfilled we can reconstruct highly undersampled images. Following the success of deep learning (DL) in a wide range of applications, neural networks based on CS optimization have received significant interest for accelerating MR acquisitions and reconstruction strategies. In CS signal reconstruction, the iterative algorithm unrolled over a deep neural network. The basic strategy is to train the network to learn the weights (CNN kernels) for dealising undersampled MR images from a large dataset containing pairs of aliased and dealiased images. In this work, we are motivated from the 2D Deep Cascaded CNN-Network (DCNN) [4]. DCNN mainly consists of two blocks the CNN and the Data Consistency block, operating on the image and k-space (sampling domain) respectively, linked by a regularization term which adjusts the data fidelity based on the noise level of the acquired measurements. In this work, we introduce an improved regularization for the data consistency term in two novel settings, a learnable regularization parameter per k-space slice and a spatially learnable regularization parameter. We show that the introduced regularization is independent of the CNN block architecture used and can be incorporated in any DL-CS based optimization network setting. We show that the employment of the proposed regularization in any DL-CS based network highly improves the reconstruction performance without adding any computational burden to the network. el
heal.advisorName Karantzalos, Konstantinos en
heal.committeeMemberName Vakalopoulou, Maria en
heal.committeeMemberName Voulodimos, Athanasios en
heal.committeeMemberName Douskos, Makis en
heal.academicPublisher Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών el
heal.academicPublisherID ntua
heal.numberOfPages 65 σ. el
heal.fullTextAvailability false


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Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα Except where otherwise noted, this item's license is described as Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα