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CArtesian sampling with Variable density and Adjustable temporal resolution (CAVA).
Rich, Adam; Gregg, Michael; Jin, Ning; Liu, Yingmin; Potter, Lee; Simonetti, Orlando; Ahmad, Rizwan.
Afiliação
  • Rich A; Biomedical Engineering, The Ohio State University, Columbus, OH, USA.
  • Gregg M; Biomedical Engineering, The Ohio State University, Columbus, OH, USA.
  • Jin N; Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.
  • Liu Y; Cardiovascular MR R&D, Siemens Medical Solutions USA Inc., Columbus, OH, USA.
  • Potter L; Davis Heart & Lung Research Institute, The Ohio State University, Columbus, OH, USA.
  • Simonetti O; Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.
  • Ahmad R; Davis Heart & Lung Research Institute, The Ohio State University, Columbus, OH, USA.
Magn Reson Med ; 83(6): 2015-2025, 2020 06.
Article em En | MEDLINE | ID: mdl-31721303
ABSTRACT

PURPOSE:

To develop a variable density Cartesian sampling method that allows retrospective adjustment of temporal resolution for dynamic MRI applications and to validate it in real-time phase contrast MRI (PC-MRI). THEORY AND

METHODS:

The proposed method, called CArtesian sampling with Variable density and Adjustable temporal resolution (CAVA), begins by producing a sequence of phase encoding indices based on the golden ratio increment. Then, variable density is introduced by nonlinear stretching of the indices. Finally, the elements of the resulting sequence are rounded up to the nearest integer. The performance of CAVA is evaluated using PC-MRI data from a pulsatile flow phantom and real-time, free-breathing data from ten healthy volunteers.

RESULTS:

CAVA enabled image recovery at various temporal resolutions that were selected retrospectively. For the pulsatile flow phantom, image quality and flow quantification accuracy from CAVA were comparable to that from another pseudo-random sampling pattern with fixed temporal resolution. In addition, flow quantification results based on CAVA were in good agreement with a breath-held segmented acquisition.

CONCLUSIONS:

By allowing retrospective binning of the MRI data, CAVA provides an avenue to retrospectively adjust the temporal resolution of PC-MRI.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Interpretação de Imagem Assistida por Computador Tipo de estudo: Observational_studies Limite: Humans Idioma: En Revista: Magn Reson Med Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Interpretação de Imagem Assistida por Computador Tipo de estudo: Observational_studies Limite: Humans Idioma: En Revista: Magn Reson Med Ano de publicação: 2020 Tipo de documento: Article