Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation

Qing Zou; Abdul Haseeb Ahmed; Sanja Dzelebdzic; Tarique Hussain

doi:10.3390/app13042281

Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation

Qing Zou, Abdul Haseeb Ahmed, Sanja Dzelebdzic, Tarique Hussain

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Free-breathing and ungated cardiac MRI is a challenging problem due to the cardiac motion and respiration motion, which are not tracked. In this work, we propose an unsupervised deep kernel method for reconstructing real-time free-breathing and ungated cardiac MRI from highly undersampled k-t space measurements. We propose implementing the feature map and kernel function in the kernel method using CNNs. The parameters of the CNNs are learned from specific-subject data directly. Comparisons with state-of-the-art kernel methods show improved performance of the proposed deep kernel method.

Original language	English (US)
Article number	2281
Journal	Applied Sciences (Switzerland)
Volume	13
Issue number	4
DOIs	https://doi.org/10.3390/app13042281
State	Published - Feb 2023

Keywords

convolutional neural networks
image reconstruction
kernel method
real-time cardiac MRI

ASJC Scopus subject areas

General Materials Science
Instrumentation
General Engineering
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes

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10.3390/app13042281

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title = "Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation",

abstract = "Free-breathing and ungated cardiac MRI is a challenging problem due to the cardiac motion and respiration motion, which are not tracked. In this work, we propose an unsupervised deep kernel method for reconstructing real-time free-breathing and ungated cardiac MRI from highly undersampled k-t space measurements. We propose implementing the feature map and kernel function in the kernel method using CNNs. The parameters of the CNNs are learned from specific-subject data directly. Comparisons with state-of-the-art kernel methods show improved performance of the proposed deep kernel method.",

keywords = "convolutional neural networks, image reconstruction, kernel method, real-time cardiac MRI",

author = "Qing Zou and Ahmed, {Abdul Haseeb} and Sanja Dzelebdzic and Tarique Hussain",

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doi = "10.3390/app13042281",

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T1 - Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation

AU - Zou, Qing

AU - Ahmed, Abdul Haseeb

AU - Dzelebdzic, Sanja

AU - Hussain, Tarique

PY - 2023/2

Y1 - 2023/2

N2 - Free-breathing and ungated cardiac MRI is a challenging problem due to the cardiac motion and respiration motion, which are not tracked. In this work, we propose an unsupervised deep kernel method for reconstructing real-time free-breathing and ungated cardiac MRI from highly undersampled k-t space measurements. We propose implementing the feature map and kernel function in the kernel method using CNNs. The parameters of the CNNs are learned from specific-subject data directly. Comparisons with state-of-the-art kernel methods show improved performance of the proposed deep kernel method.

AB - Free-breathing and ungated cardiac MRI is a challenging problem due to the cardiac motion and respiration motion, which are not tracked. In this work, we propose an unsupervised deep kernel method for reconstructing real-time free-breathing and ungated cardiac MRI from highly undersampled k-t space measurements. We propose implementing the feature map and kernel function in the kernel method using CNNs. The parameters of the CNNs are learned from specific-subject data directly. Comparisons with state-of-the-art kernel methods show improved performance of the proposed deep kernel method.

KW - convolutional neural networks

KW - image reconstruction

KW - kernel method

KW - real-time cardiac MRI

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Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation

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Keywords

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