Result with impact on practice detail

Content-aware low-rank plus sparse model for perfusion MRI reconstruction

DAŇKOVÁ, M.; RAJMIC, P.

Original Title

Content-aware low-rank plus sparse model for perfusion MRI reconstruction

English Title

Content-aware low-rank plus sparse model for perfusion MRI reconstruction

Type

Audiovisual work

Original Abstract

Perfusion magnetic resonance imaging is a promising diagnostic method in medicine. In order to perform perfusion analysis, leading to e.g. revealing a tumor, one starts with acquiring the anatomical image before injecting the contrast agent. Based on the inferred spatial distribution of organs, we develop a natural, content-aware, way of extension of the low-rank plus sparse model for reconstruction of undersampled perfusion MRI data.

English abstract

Perfusion magnetic resonance imaging is a promising diagnostic method in medicine. In order to perform perfusion analysis, leading to e.g. revealing a tumor, one starts with acquiring the anatomical image before injecting the contrast agent. Based on the inferred spatial distribution of organs, we develop a natural, content-aware, way of extension of the low-rank plus sparse model for reconstruction of undersampled perfusion MRI data.

Keywords

MRI, compressed sensing, perfusion, L+S model

Key words in English

MRI, compressed sensing, perfusion, L+S model

Authors

DAŇKOVÁ, M.; RAJMIC, P.

RIV year

2017

Released

06.06.2016

Location

Strobl, Rakousko

Pages from

1

Pages to

1

Pages count

1

BibTex

@misc{BUT128072,
  author="Marie {Mangová} and Pavel {Rajmic}",
  title="Content-aware low-rank plus sparse model for perfusion MRI reconstruction",
  year="2016",
  pages="1--1",
  address="Strobl, Rakousko",
  note="Audiovisual work"
}