Skip to main content
Top

Hint

Swipe to navigate through the chapters of this book

Published in:
Cover of the book

2018 | OriginalPaper | Chapter

Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging

Authors : Akshay Chaudhari, Zhongnan Fang, Jin Hyung Lee, Garry Gold, Brian Hargreaves

Published in: Machine Learning for Medical Image Reconstruction

Publisher: Springer International Publishing

share
SHARE

Abstract

Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and research applications. However, acquiring quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at odds with high-resolution in MRI, especially in a single rapid sequence. In this paper, we demonstrate how super-resolution (SR) can be utilized to maintain adequate SNR for accurate quantification of the T\(_2\) relaxation time biomarker, while simultaneously generating high-resolution images. We compare the efficacy of resolution enhancement using metrics such as peak SNR and structural similarity. We assess accuracy of cartilage T\(_2\) relaxation times by comparing against a standard reference method. Our evaluation suggests that SR can successfully maintain high-resolution and generate accurate biomarkers for accelerating MRI scans and enhancing the value of clinical and research MRI.
Literature
1.
go back to reference Chaudhari, A.S., et al.: Five-minute knee MRI for simultaneous morphometry and T \(_2\) relaxometry of cartilage and meniscus and for semiquantitative radiological assessment using double-echo in steady-state at 3T. J. Magn. Reson. Imaging 47, 1328–1341 (2017) CrossRef Chaudhari, A.S., et al.: Five-minute knee MRI for simultaneous morphometry and T \(_2\) relaxometry of cartilage and meniscus and for semiquantitative radiological assessment using double-echo in steady-state at 3T. J. Magn. Reson. Imaging 47, 1328–1341 (2017) CrossRef
2.
go back to reference Mosher, T.J., Dardzinski, B.J.: Cartilage MRI T \(_2\) relaxation time mapping: overview and applications. Semin. Musculoskelet. Radiol. 8, 355–368 (2004) CrossRef Mosher, T.J., Dardzinski, B.J.: Cartilage MRI T \(_2\) relaxation time mapping: overview and applications. Semin. Musculoskelet. Radiol. 8, 355–368 (2004) CrossRef
3.
go back to reference Peterfy, C.G., Schneider, E., Nevitt, M.: The osteoarthritis initiative: report on the design rationale for the magnetic resonance imaging protocol for the knee. Osteoarthritis Cartilage 16(12), 1433–1441 (2008) CrossRef Peterfy, C.G., Schneider, E., Nevitt, M.: The osteoarthritis initiative: report on the design rationale for the magnetic resonance imaging protocol for the knee. Osteoarthritis Cartilage 16(12), 1433–1441 (2008) CrossRef
4.
go back to reference Sveinsson, B., Chaudhari, A., Gold, G., Hargreaves, B.: A simple analytic method for estimating T2 in the knee from DESS. Magn. Reson. Imaging 38, 63–70 (2017) CrossRef Sveinsson, B., Chaudhari, A., Gold, G., Hargreaves, B.: A simple analytic method for estimating T2 in the knee from DESS. Magn. Reson. Imaging 38, 63–70 (2017) CrossRef
5.
go back to reference Monu, U.D., Jordan, C.D., Samuelson, B.L., Hargreaves, B.A., Gold, G.E., McWalter, E.J.: Cluster analysis of quantitative MRI T \(_2\) and T \(_{1\rho }\) relaxation times of cartilage identifies differences between healthy and ACL-injured individuals at 3T. Osteoarthritis Cartilage 25(October), 1–8 (2016) Monu, U.D., Jordan, C.D., Samuelson, B.L., Hargreaves, B.A., Gold, G.E., McWalter, E.J.: Cluster analysis of quantitative MRI T \(_2\) and T \(_{1\rho }\) relaxation times of cartilage identifies differences between healthy and ACL-injured individuals at 3T. Osteoarthritis Cartilage 25(October), 1–8 (2016)
6.
go back to reference Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016) Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)
7.
go back to reference Wang, Y.H., Qiao, J., Li, J.B., Fu, P., Chu, S.C., Roddick, J.F.: Sparse representation-based MRI super-resolution reconstruction. Measurement 47, 946–953 (2014) CrossRef Wang, Y.H., Qiao, J., Li, J.B., Fu, P., Chu, S.C., Roddick, J.F.: Sparse representation-based MRI super-resolution reconstruction. Measurement 47, 946–953 (2014) CrossRef
8.
go back to reference Chaudhari, A.S., et al.: Super-resolution musculoskeletal MRI using deep learning. Magn. Reson. Med. (2018) Chaudhari, A.S., et al.: Super-resolution musculoskeletal MRI using deep learning. Magn. Reson. Med. (2018)
9.
go back to reference Baum, T., Joseph, G.B., Karampinos, D.C., Jungmann, P.M., Link, T.M., Bauer, J.S.: Cartilage and meniscal T2 relaxation time as non-invasive biomarker for knee osteoarthritis and cartilage repair procedures. Osteoarthritis Cartilage/OARS 21(10), 1474–84 (2013) CrossRef Baum, T., Joseph, G.B., Karampinos, D.C., Jungmann, P.M., Link, T.M., Bauer, J.S.: Cartilage and meniscal T2 relaxation time as non-invasive biomarker for knee osteoarthritis and cartilage repair procedures. Osteoarthritis Cartilage/OARS 21(10), 1474–84 (2013) CrossRef
Metadata
Title
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging
Authors
Akshay Chaudhari
Zhongnan Fang
Jin Hyung Lee
Garry Gold
Brian Hargreaves
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-00129-2_1

Premium Partner