FR-Net: Joint Reconstruction and Segmentation in Compressed Sensing Cardiac MRI

Qiaoying Huang; Dong Yang; Jingru Yi; Leon Axel; Dimitris Metaxas

doi:10.1007/978-3-030-21949-9_38

FR-Net: Joint Reconstruction and Segmentation in Compressed Sensing Cardiac MRI

Qiaoying Huang
, Dong Yang
, Jingru Yi
, Leon Axel
, Dimitris Metaxas

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

21 Scopus citations

Abstract

We provide a novel solution to the inverse problem in medical imaging that takes as input the undersampled k-space data from Magnetic Resonance Imaging (MRI) scans and outputs both the reconstructed images and the segmented myocardium. Previously, the undersampled k-space data is first transformed into a reconstructed MRI image. From this image, the myocardium is contours are subsequently extracted using a segmentation method. However, this sequential approach is not optimal and requires manual intervention. In order to automate and improve the results of these approaches, we propose a new method to solve the reconstruction and segmentation problems simultaneously. Our method is based on a novel deep learning approach we term “Joint-FR-Net”, which consists of a reconstruction module derived from the fast iterative shrinkage-thresholding algorithm (FISTA) and a segmentation module. We test our approach on an undersampled short-axis (SAX) cardiac dataset and show the effectiveness of the Joint FR-Net in both image reconstruction and myocardium joint segmentation.

Original language	English (US)
Title of host publication	Functional Imaging and Modeling of the Heart - 10th International Conference, FIMH 2019, Proceedings
Editors	Valéry Ozenne, Edward Vigmond, Yves Coudière, Nejib Zemzemi
Publisher	Springer Verlag
Pages	352-360
Number of pages	9
ISBN (Print)	9783030219482
DOIs	https://doi.org/10.1007/978-3-030-21949-9_38
State	Published - 2019
Event	10th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2019 - Bordeaux, France Duration: Jun 6 2019 → Jun 8 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11504 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2019
Country/Territory	France
City	Bordeaux
Period	6/6/19 → 6/8/19

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-21949-9_38

Cite this

Huang, Q., Yang, D., Yi, J., Axel, L., & Metaxas, D. (2019). FR-Net: Joint Reconstruction and Segmentation in Compressed Sensing Cardiac MRI. In V. Ozenne, E. Vigmond, Y. Coudière, & N. Zemzemi (Eds.), Functional Imaging and Modeling of the Heart - 10th International Conference, FIMH 2019, Proceedings (pp. 352-360). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11504 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-21949-9_38

Huang, Qiaoying ; Yang, Dong ; Yi, Jingru et al. / FR-Net : Joint Reconstruction and Segmentation in Compressed Sensing Cardiac MRI. Functional Imaging and Modeling of the Heart - 10th International Conference, FIMH 2019, Proceedings. editor / Valéry Ozenne ; Edward Vigmond ; Yves Coudière ; Nejib Zemzemi. Springer Verlag, 2019. pp. 352-360 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f1046dde2f0a4403bac368e013e17be8,

title = "FR-Net: Joint Reconstruction and Segmentation in Compressed Sensing Cardiac MRI",

abstract = "We provide a novel solution to the inverse problem in medical imaging that takes as input the undersampled k-space data from Magnetic Resonance Imaging (MRI) scans and outputs both the reconstructed images and the segmented myocardium. Previously, the undersampled k-space data is first transformed into a reconstructed MRI image. From this image, the myocardium is contours are subsequently extracted using a segmentation method. However, this sequential approach is not optimal and requires manual intervention. In order to automate and improve the results of these approaches, we propose a new method to solve the reconstruction and segmentation problems simultaneously. Our method is based on a novel deep learning approach we term “Joint-FR-Net”, which consists of a reconstruction module derived from the fast iterative shrinkage-thresholding algorithm (FISTA) and a segmentation module. We test our approach on an undersampled short-axis (SAX) cardiac dataset and show the effectiveness of the Joint FR-Net in both image reconstruction and myocardium joint segmentation.",

author = "Qiaoying Huang and Dong Yang and Jingru Yi and Leon Axel and Dimitris Metaxas",

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year = "2019",

doi = "10.1007/978-3-030-21949-9\_38",

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Huang, Q, Yang, D, Yi, J, Axel, L & Metaxas, D 2019, FR-Net: Joint Reconstruction and Segmentation in Compressed Sensing Cardiac MRI. in V Ozenne, E Vigmond, Y Coudière & N Zemzemi (eds), Functional Imaging and Modeling of the Heart - 10th International Conference, FIMH 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11504 LNCS, Springer Verlag, pp. 352-360, 10th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2019, Bordeaux, France, 6/6/19. https://doi.org/10.1007/978-3-030-21949-9_38

FR-Net: Joint Reconstruction and Segmentation in Compressed Sensing Cardiac MRI. / Huang, Qiaoying; Yang, Dong; Yi, Jingru et al.
Functional Imaging and Modeling of the Heart - 10th International Conference, FIMH 2019, Proceedings. ed. / Valéry Ozenne; Edward Vigmond; Yves Coudière; Nejib Zemzemi. Springer Verlag, 2019. p. 352-360 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11504 LNCS).

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

TY - GEN

T1 - FR-Net

T2 - 10th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2019

AU - Huang, Qiaoying

AU - Yang, Dong

AU - Yi, Jingru

AU - Axel, Leon

AU - Metaxas, Dimitris

PY - 2019

Y1 - 2019

N2 - We provide a novel solution to the inverse problem in medical imaging that takes as input the undersampled k-space data from Magnetic Resonance Imaging (MRI) scans and outputs both the reconstructed images and the segmented myocardium. Previously, the undersampled k-space data is first transformed into a reconstructed MRI image. From this image, the myocardium is contours are subsequently extracted using a segmentation method. However, this sequential approach is not optimal and requires manual intervention. In order to automate and improve the results of these approaches, we propose a new method to solve the reconstruction and segmentation problems simultaneously. Our method is based on a novel deep learning approach we term “Joint-FR-Net”, which consists of a reconstruction module derived from the fast iterative shrinkage-thresholding algorithm (FISTA) and a segmentation module. We test our approach on an undersampled short-axis (SAX) cardiac dataset and show the effectiveness of the Joint FR-Net in both image reconstruction and myocardium joint segmentation.

AB - We provide a novel solution to the inverse problem in medical imaging that takes as input the undersampled k-space data from Magnetic Resonance Imaging (MRI) scans and outputs both the reconstructed images and the segmented myocardium. Previously, the undersampled k-space data is first transformed into a reconstructed MRI image. From this image, the myocardium is contours are subsequently extracted using a segmentation method. However, this sequential approach is not optimal and requires manual intervention. In order to automate and improve the results of these approaches, we propose a new method to solve the reconstruction and segmentation problems simultaneously. Our method is based on a novel deep learning approach we term “Joint-FR-Net”, which consists of a reconstruction module derived from the fast iterative shrinkage-thresholding algorithm (FISTA) and a segmentation module. We test our approach on an undersampled short-axis (SAX) cardiac dataset and show the effectiveness of the Joint FR-Net in both image reconstruction and myocardium joint segmentation.

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U2 - 10.1007/978-3-030-21949-9_38

DO - 10.1007/978-3-030-21949-9_38

M3 - Conference contribution

AN - SCOPUS:85067171834

SN - 9783030219482

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 352

EP - 360

BT - Functional Imaging and Modeling of the Heart - 10th International Conference, FIMH 2019, Proceedings

A2 - Ozenne, Valéry

A2 - Vigmond, Edward

A2 - Coudière, Yves

A2 - Zemzemi, Nejib

PB - Springer Verlag

Y2 - 6 June 2019 through 8 June 2019

ER -

Huang Q, Yang D, Yi J, Axel L, Metaxas D. FR-Net: Joint Reconstruction and Segmentation in Compressed Sensing Cardiac MRI. In Ozenne V, Vigmond E, Coudière Y, Zemzemi N, editors, Functional Imaging and Modeling of the Heart - 10th International Conference, FIMH 2019, Proceedings. Springer Verlag. 2019. p. 352-360. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-21949-9_38

FR-Net: Joint Reconstruction and Segmentation in Compressed Sensing Cardiac MRI

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