Magnetic resonance fingerprinting using recurrent neural networks

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

28 Citations (Scopus)

Abstract

Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time-efficient acquisition. Standard MRF reconstructs parametric maps using dictionary matching and requires high computational time. We propose to perform MRF map reconstruction using a recurrent neural network, which exploits the time-dependent information of the MRF signal evolution. We evaluate our method on multiparametric synthetic signals and compare it to existing MRF map reconstruction approaches, including those based on neural networks. Our method achieves state-of-the-art estimates of T1 and T2 values. In addition, the reconstruction time is reduced compared to dictionary-matching based approach.

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages1537-1540
Number of pages4
Volume2019-April
ISBN (Electronic)9781538636411
DOIs
Publication statusPublished - 1 Apr 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 8 Apr 201911 Apr 2019

Conference

Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Country/TerritoryItaly
CityVenice
Period8/04/201911/04/2019

Keywords

  • Gru
  • Lstm
  • Magnetic resonance fingerprinting
  • Parameter mapping
  • Recurrent neural networks

Fingerprint

Dive into the research topics of 'Magnetic resonance fingerprinting using recurrent neural networks'. Together they form a unique fingerprint.

Cite this