Exploiting local low-rank structure in higher-dimensional MRI applications

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

13 Citations (Scopus)

Abstract

In many clinical MRI applications, not one but a series of images is acquired. Techniques that promote intra-and inter-image sparsity have recently emerged as powerful strategies for accelerating MRI applications; however, sparsity alone cannot always describe the complex relationships that exist between images in these series. In this paper, we will discuss the modeling of higher-dimensional MRI signals as matrices and tensors, and why promoting these signals to be low-rank (and, specifically, locally low-rank) can effectively identify and exploit these complex relationships. Example applications including training-free dynamic and calibrationless parallel MRI will be demonstrated.

Original languageEnglish (US)
Title of host publicationWavelets and Sparsity XV
Volume8858
DOIs
StatePublished - 2013
EventWavelets and Sparsity XV - San Diego, CA, United States
Duration: Aug 26 2013Aug 29 2013

Other

OtherWavelets and Sparsity XV
CountryUnited States
CitySan Diego, CA
Period8/26/138/29/13

Fingerprint

Magnetic resonance imaging
High-dimensional
Sparsity
Series
education
Tensor
tensors
Tensors
matrices
Modeling
Relationships
Training
Strategy

Keywords

  • Image Reconstruction
  • Low-Rank
  • MRI
  • Sparsity

ASJC Scopus subject areas

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

Cite this

Exploiting local low-rank structure in higher-dimensional MRI applications. / Trazasko, Joshua D.

Wavelets and Sparsity XV. Vol. 8858 2013. 885821.

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

Trazasko, JD 2013, Exploiting local low-rank structure in higher-dimensional MRI applications. in Wavelets and Sparsity XV. vol. 8858, 885821, Wavelets and Sparsity XV, San Diego, CA, United States, 8/26/13. https://doi.org/10.1117/12.2027059
@inproceedings{f22ece94f0864d9f967663d0501c6166,
title = "Exploiting local low-rank structure in higher-dimensional MRI applications",
abstract = "In many clinical MRI applications, not one but a series of images is acquired. Techniques that promote intra-and inter-image sparsity have recently emerged as powerful strategies for accelerating MRI applications; however, sparsity alone cannot always describe the complex relationships that exist between images in these series. In this paper, we will discuss the modeling of higher-dimensional MRI signals as matrices and tensors, and why promoting these signals to be low-rank (and, specifically, locally low-rank) can effectively identify and exploit these complex relationships. Example applications including training-free dynamic and calibrationless parallel MRI will be demonstrated.",
keywords = "Image Reconstruction, Low-Rank, MRI, Sparsity",
author = "Trazasko, {Joshua D}",
year = "2013",
doi = "10.1117/12.2027059",
language = "English (US)",
isbn = "9780819497086",
volume = "8858",
booktitle = "Wavelets and Sparsity XV",

}

TY - GEN

T1 - Exploiting local low-rank structure in higher-dimensional MRI applications

AU - Trazasko, Joshua D

PY - 2013

Y1 - 2013

N2 - In many clinical MRI applications, not one but a series of images is acquired. Techniques that promote intra-and inter-image sparsity have recently emerged as powerful strategies for accelerating MRI applications; however, sparsity alone cannot always describe the complex relationships that exist between images in these series. In this paper, we will discuss the modeling of higher-dimensional MRI signals as matrices and tensors, and why promoting these signals to be low-rank (and, specifically, locally low-rank) can effectively identify and exploit these complex relationships. Example applications including training-free dynamic and calibrationless parallel MRI will be demonstrated.

AB - In many clinical MRI applications, not one but a series of images is acquired. Techniques that promote intra-and inter-image sparsity have recently emerged as powerful strategies for accelerating MRI applications; however, sparsity alone cannot always describe the complex relationships that exist between images in these series. In this paper, we will discuss the modeling of higher-dimensional MRI signals as matrices and tensors, and why promoting these signals to be low-rank (and, specifically, locally low-rank) can effectively identify and exploit these complex relationships. Example applications including training-free dynamic and calibrationless parallel MRI will be demonstrated.

KW - Image Reconstruction

KW - Low-Rank

KW - MRI

KW - Sparsity

UR - http://www.scopus.com/inward/record.url?scp=84889073395&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84889073395&partnerID=8YFLogxK

U2 - 10.1117/12.2027059

DO - 10.1117/12.2027059

M3 - Conference contribution

SN - 9780819497086

VL - 8858

BT - Wavelets and Sparsity XV

ER -