Super-resolution reconstruction using cross-scale self-similarity in multi-slice MRI

Esben Plenge*, Dirk H.J. Poot, Wiro J. Niessen, Erik Meijering

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

32 Citations (Scopus)

Abstract

In MRI, the relatively thick slices of multi-slice acquisitions often hamper visualization and analysis of the underlying anatomy. A group of post-processing techniques referred to as super-resolution reconstruction (SRR) have been developed to address this issue. In this study, we present a novel approach to SRR in MRI, which exploits the high-resolution content usually available in the 2D slices of MRI slice stacks to reconstruct isotropic high-resolution 3D images. Relying on the assumption of local self-similarity of anatomical structures, the method can be applied both to a single slice stack and to the combination of multiple slice stacks that differ in the orientation of their field of view. We evaluate the method quantitatively on synthetic brain MRI and qualitatively on MRI of the lungs. The results show that the method outperforms state-of-the-art MRI super-resolution methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages123-130
Number of pages8
EditionPART 3
DOIs
Publication statusPublished - 2013
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: 22 Sept 201326 Sept 2013

Publication series

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8151 LNCS
ISSN0302-9743

Conference

Conference16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Country/TerritoryJapan
CityNagoya
Period22/09/1326/09/13

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