MLS: Self-learned Joint Manifold Geometry and Sparsity aware Framework for Highly Accelerated Cardiac Cine Imaging

Ukash Nakarmi; Kostantinos Slavakis; Hongyu Li; Chaoyi Zhang; Peizhou Huang; Sunil Gaire

MLS: Self-learned Joint Manifold Geometry and Sparsity aware Framework for Highly Accelerated Cardiac Cine Imaging

Ukash Nakarmi, Kostantinos Slavakis, Hongyu Li, Chaoyi Zhang, Peizhou Huang, Sunil Gaire

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this work, we propose a novel joint manifold learning and sparsity aware framework for highly accelerated cardiac cine imaging. The proposed method efficiently captures the intrinsic low dimensional nonlinear manifold geometry and inherent periodicity of cardiac data, and outperforms the current state-of-the-art accelerated MRI methods.

Original language	English
Title of host publication	Unknown book
State	Published - 2018

Cite this

@inproceedings{7eeb8c59e43b487a8a6579a66b8ad743,

title = "MLS: Self-learned Joint Manifold Geometry and Sparsity aware Framework for Highly Accelerated Cardiac Cine Imaging",

abstract = "In this work, we propose a novel joint manifold learning and sparsity aware framework for highly accelerated cardiac cine imaging. The proposed method efficiently captures the intrinsic low dimensional nonlinear manifold geometry and inherent periodicity of cardiac data, and outperforms the current state-of-the-art accelerated MRI methods.",

author = "Ukash Nakarmi and Kostantinos Slavakis and Hongyu Li and Chaoyi Zhang and Peizhou Huang and Sunil Gaire",

year = "2018",

language = "English",

booktitle = "Unknown book",

}

TY - GEN

T1 - MLS: Self-learned Joint Manifold Geometry and Sparsity aware Framework for Highly Accelerated Cardiac Cine Imaging

AU - Nakarmi, Ukash

AU - Slavakis, Kostantinos

AU - Li, Hongyu

AU - Zhang, Chaoyi

AU - Huang, Peizhou

AU - Gaire, Sunil

PY - 2018

Y1 - 2018

N2 - In this work, we propose a novel joint manifold learning and sparsity aware framework for highly accelerated cardiac cine imaging. The proposed method efficiently captures the intrinsic low dimensional nonlinear manifold geometry and inherent periodicity of cardiac data, and outperforms the current state-of-the-art accelerated MRI methods.

AB - In this work, we propose a novel joint manifold learning and sparsity aware framework for highly accelerated cardiac cine imaging. The proposed method efficiently captures the intrinsic low dimensional nonlinear manifold geometry and inherent periodicity of cardiac data, and outperforms the current state-of-the-art accelerated MRI methods.

M3 - Conference contribution

BT - Unknown book

ER -

MLS: Self-learned Joint Manifold Geometry and Sparsity aware Framework for Highly Accelerated Cardiac Cine Imaging

Abstract

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