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Reducing Navigators in Free-Breathing Abdominal MRI via Temporal Interpolation Using Convolutional Neural Networks


Karani, Neerav; Tanner, Christine; Kozerke, Sebastian; Konukoglu, Ender (2018). Reducing Navigators in Free-Breathing Abdominal MRI via Temporal Interpolation Using Convolutional Neural Networks. IEEE Transactions on Medical Imaging, 37(10):2333-2343.

Abstract

Navigated 2-D multi-slice dynamic magnetic resonance imaging (MRI) acquisitions are essential for MR guided therapies. This technique yields time-resolved volumetric images during free-breathing, which are ideal for visualizing and quantifying breathing induced motion. To achieve this, navigated dynamic imaging requires acquiring multiple navigator slices. Reducing the number of navigator slices would allow for acquiring more data slices in the same time, and hence, increasing through-plane resolution or alternatively the overall acquisition time can be reduced while keeping resolution unchanged. To this end, we propose temporal interpolation of navigator slices using convolutional neural networks (CNNs). Our goal is to acquire fewer navigators and replace the missing ones with interpolation. We evaluate the proposed method on abdominal navigated dynamic MRI sequences acquired from 14 subjects. Investigations with several CNN architectures and training loss functions show favorable results for L2 cost and a simple feed-forward network with no skip connections. When compared with interpolation by nonlinear registration, the proposed method achieves higher interpolation accuracy on average as quantified in terms of root mean square error and residual motion. Analysis of the differences shows that the better performance is due to more accurate interpolation at peak exhalation and inhalation positions. Furthermore, the CNN-based approach requires substantially lower execution times than that of the registration-based method. At last, experiments on dynamic volume reconstruction reveal minimal differences between reconstructions with acquired and interpolated navigator slices

Abstract

Navigated 2-D multi-slice dynamic magnetic resonance imaging (MRI) acquisitions are essential for MR guided therapies. This technique yields time-resolved volumetric images during free-breathing, which are ideal for visualizing and quantifying breathing induced motion. To achieve this, navigated dynamic imaging requires acquiring multiple navigator slices. Reducing the number of navigator slices would allow for acquiring more data slices in the same time, and hence, increasing through-plane resolution or alternatively the overall acquisition time can be reduced while keeping resolution unchanged. To this end, we propose temporal interpolation of navigator slices using convolutional neural networks (CNNs). Our goal is to acquire fewer navigators and replace the missing ones with interpolation. We evaluate the proposed method on abdominal navigated dynamic MRI sequences acquired from 14 subjects. Investigations with several CNN architectures and training loss functions show favorable results for L2 cost and a simple feed-forward network with no skip connections. When compared with interpolation by nonlinear registration, the proposed method achieves higher interpolation accuracy on average as quantified in terms of root mean square error and residual motion. Analysis of the differences shows that the better performance is due to more accurate interpolation at peak exhalation and inhalation positions. Furthermore, the CNN-based approach requires substantially lower execution times than that of the registration-based method. At last, experiments on dynamic volume reconstruction reveal minimal differences between reconstructions with acquired and interpolated navigator slices

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Scopus Subject Areas:Physical Sciences > Software
Health Sciences > Radiological and Ultrasound Technology
Physical Sciences > Computer Science Applications
Physical Sciences > Electrical and Electronic Engineering
Uncontrolled Keywords:Electrical and Electronic Engineering, Radiological and Ultrasound Technology, Software, Computer Science Applications
Language:English
Date:1 October 2018
Deposited On:06 Mar 2019 13:31
Last Modified:29 Jul 2020 10:03
Publisher:Institute of Electrical and Electronics Engineers
ISSN:0278-0062
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/tmi.2018.2831442
PubMed ID:29994024

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