HyperCMR: Enhanced Multi-contrast CMR Reconstruction with Eagle Loss

Ruru Xu; Caner Özer; Ilkay Oksuz

doi:10.1007/978-3-031-87756-8_15

HyperCMR: Enhanced Multi-contrast CMR Reconstruction with Eagle Loss

Ruru Xu^*
, Caner Özer
, Ilkay Oksuz

^*Bu çalışma için yazışmadan sorumlu yazar

Istanbul Technical University
University of Twente

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakemli

4 Atıf (Scopus)

Özet

Accelerating image acquisition for cardiac magnetic resonance imaging (CMRI) is a critical task. CMRxRecon2024 challenge aims to set the state of the art for multi-contrast CMR reconstruction. This paper presents HyperCMR, a novel framework designed to accelerate the reconstruction of multi-contrast cardiac magnetic resonance (CMR) images. HyperCMR enhances the existing PromptMR model by incorporating advanced loss functions, notably the innovative Eagle Loss, which is specifically designed to recover missing high-frequency information in undersampled k-space. Extensive experiments conducted on the CMRxRecon2024 challenge dataset demonstrate that HyperCMR consistently outperforms the baseline across multiple evaluation metrics, achieving superior SSIM and PSNR scores.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers. - 15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Revised Selected Papers
Editörler	Oscar Camara, Esther Puyol-Antón, Maxime Sermesant, Avan Suinesiaputra, Jichao Zhao, Chengyan Wang, Qian Tao, Alistair Young
Yayınlayan	Springer Science and Business Media Deutschland GmbH
Sayfalar	152-163
Sayfa sayısı	12
ISBN (Basılı)	9783031877551
DOI'lar	https://doi.org/10.1007/978-3-031-87756-8_15
Yayın durumu	Yayınlandı - 2025
Etkinlik	15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024 - Marrakesh, Morocco Süre: 10 Eki 2024 → 10 Eki 2024

Yayın serisi

Adı	Lecture Notes in Computer Science
Hacim	15448 LNCS
ISSN (Basılı)	0302-9743
ISSN (Elektronik)	1611-3349

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???event.eventtypes.event.conference???	15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024
Ülke/Bölge	Morocco
Şehir	Marrakesh
Periyot	10/10/24 → 10/10/24

Bibliyografik not

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Belgeye Erişim

10.1007/978-3-031-87756-8_15

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Xu, R., Özer, C., & Oksuz, I. (2025). HyperCMR: Enhanced Multi-contrast CMR Reconstruction with Eagle Loss. In O. Camara, E. Puyol-Antón, M. Sermesant, A. Suinesiaputra, J. Zhao, C. Wang, Q. Tao, & A. Young (Eds.), Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers. - 15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Revised Selected Papers (pp. 152-163). (Lecture Notes in Computer Science; Hac. 15448 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-87756-8_15

Xu, Ruru ; Özer, Caner ; Oksuz, Ilkay. / HyperCMR : Enhanced Multi-contrast CMR Reconstruction with Eagle Loss. Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers. - 15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Revised Selected Papers. editör / Oscar Camara ; Esther Puyol-Antón ; Maxime Sermesant ; Avan Suinesiaputra ; Jichao Zhao ; Chengyan Wang ; Qian Tao ; Alistair Young. Springer Science and Business Media Deutschland GmbH, 2025. pp. 152-163 (Lecture Notes in Computer Science).

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title = "HyperCMR: Enhanced Multi-contrast CMR Reconstruction with Eagle Loss",

abstract = "Accelerating image acquisition for cardiac magnetic resonance imaging (CMRI) is a critical task. CMRxRecon2024 challenge aims to set the state of the art for multi-contrast CMR reconstruction. This paper presents HyperCMR, a novel framework designed to accelerate the reconstruction of multi-contrast cardiac magnetic resonance (CMR) images. HyperCMR enhances the existing PromptMR model by incorporating advanced loss functions, notably the innovative Eagle Loss, which is specifically designed to recover missing high-frequency information in undersampled k-space. Extensive experiments conducted on the CMRxRecon2024 challenge dataset demonstrate that HyperCMR consistently outperforms the baseline across multiple evaluation metrics, achieving superior SSIM and PSNR scores.",

keywords = "Cardiac MRI, Deep Learning, Multi-Modality, Reconstruction",

author = "Ruru Xu and Caner {\"O}zer and Ilkay Oksuz",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024 ; Conference date: 10-10-2024 Through 10-10-2024",

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Xu, R, Özer, C & Oksuz, I 2025, HyperCMR: Enhanced Multi-contrast CMR Reconstruction with Eagle Loss. in O Camara, E Puyol-Antón, M Sermesant, A Suinesiaputra, J Zhao, C Wang, Q Tao & A Young (eds), Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers. - 15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Revised Selected Papers. Lecture Notes in Computer Science, hac. 15448 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 152-163, 15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, 10/10/24. https://doi.org/10.1007/978-3-031-87756-8_15

HyperCMR: Enhanced Multi-contrast CMR Reconstruction with Eagle Loss. / Xu, Ruru; Özer, Caner ; Oksuz, Ilkay.
Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers. - 15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Revised Selected Papers. ed. / Oscar Camara; Esther Puyol-Antón; Maxime Sermesant; Avan Suinesiaputra; Jichao Zhao; Chengyan Wang; Qian Tao; Alistair Young. Springer Science and Business Media Deutschland GmbH, 2025. p. 152-163 (Lecture Notes in Computer Science; Hac. 15448 LNCS).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakemli

TY - GEN

T1 - HyperCMR

T2 - 15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024

AU - Xu, Ruru

AU - Özer, Caner

AU - Oksuz, Ilkay

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - Accelerating image acquisition for cardiac magnetic resonance imaging (CMRI) is a critical task. CMRxRecon2024 challenge aims to set the state of the art for multi-contrast CMR reconstruction. This paper presents HyperCMR, a novel framework designed to accelerate the reconstruction of multi-contrast cardiac magnetic resonance (CMR) images. HyperCMR enhances the existing PromptMR model by incorporating advanced loss functions, notably the innovative Eagle Loss, which is specifically designed to recover missing high-frequency information in undersampled k-space. Extensive experiments conducted on the CMRxRecon2024 challenge dataset demonstrate that HyperCMR consistently outperforms the baseline across multiple evaluation metrics, achieving superior SSIM and PSNR scores.

AB - Accelerating image acquisition for cardiac magnetic resonance imaging (CMRI) is a critical task. CMRxRecon2024 challenge aims to set the state of the art for multi-contrast CMR reconstruction. This paper presents HyperCMR, a novel framework designed to accelerate the reconstruction of multi-contrast cardiac magnetic resonance (CMR) images. HyperCMR enhances the existing PromptMR model by incorporating advanced loss functions, notably the innovative Eagle Loss, which is specifically designed to recover missing high-frequency information in undersampled k-space. Extensive experiments conducted on the CMRxRecon2024 challenge dataset demonstrate that HyperCMR consistently outperforms the baseline across multiple evaluation metrics, achieving superior SSIM and PSNR scores.

KW - Cardiac MRI

KW - Deep Learning

KW - Multi-Modality

KW - Reconstruction

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DO - 10.1007/978-3-031-87756-8_15

M3 - Conference contribution

AN - SCOPUS:105004253586

SN - 9783031877551

T3 - Lecture Notes in Computer Science

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EP - 163

BT - Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers. - 15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Revised Selected Papers

A2 - Camara, Oscar

A2 - Puyol-Antón, Esther

A2 - Sermesant, Maxime

A2 - Suinesiaputra, Avan

A2 - Zhao, Jichao

A2 - Wang, Chengyan

A2 - Tao, Qian

A2 - Young, Alistair

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 10 October 2024 through 10 October 2024

ER -

Xu R, Özer C , Oksuz I. HyperCMR: Enhanced Multi-contrast CMR Reconstruction with Eagle Loss. In Camara O, Puyol-Antón E, Sermesant M, Suinesiaputra A, Zhao J, Wang C, Tao Q, Young A, editörler, Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers. - 15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2025. p. 152-163. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-87756-8_15