Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models

Fan Lam; Yahang Li; Xi Peng

doi:10.1109/TMI.2019.2930586

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Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models

Journal article

Open access

Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models

Fan Lam, Yahang Li and Xi Peng

IEEE transactions on medical imaging, Vol.39(3), pp.545-555

03/2020

DOI: 10.1109/TMI.2019.2930586

PMID: 31352337

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Abstract

Magnetic resonance spectroscopic imaging (MRSI) is a powerful molecular imaging modality but has very limited speed, resolution, and SNR tradeoffs. Construction of a low-dimensional model to effectively reduce the dimensionality of the imaging problem has recently shown great promise in improving these tradeoffs. This paper presents a new approach to model and reconstruct the spectroscopic signals by learning a nonlinear low-dimensional representation of the general MR spectra. Specifically, we trained a deep neural network to capture the low-dimensional manifold, where the high-dimensional spectroscopic signals reside. A regularization formulation is proposed to effectively integrate the learned model and physics-based data acquisition model for MRSI reconstruction with the capability to incorporate additional spatiospectral constraints. An efficient numerical algorithm was developed to solve the associated optimization problem involving back-propagating the trained network. Simulation and experimental results were obtained to demonstrate the representation power of the learned model and the ability of the proposed formulation in producing SNR-enhancing reconstruction from the practical MRSI data.

Spectroscopy

Data models

Feature extraction

Image reconstruction

Imaging

low-dimensionalmodels

manifold learning

Manifolds

MR spectroscopic imaging

neural network

Neural networks

Signal to noise ratio

spatiospectral constraint

Details

Title: Subtitle: Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models
Creators: Fan Lam - University of Illinois Urbana-Champaign
Yahang Li - University of Illinois Urbana-Champaign
Xi Peng - University of Illinois Urbana-Champaign
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.39(3), pp.545-555
DOI: 10.1109/TMI.2019.2930586
PMID: 31352337
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: IEEE
Language: English
Date published: 03/2020
Academic Unit: Radiology
Record Identifier: 9984446409702771

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