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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.09746 |
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| _version_ | 1866916737350369280 |
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| author | Morales, Xabier Elsayed, Ayah Zhao, Debbie Loncaric, Filip Aguado, Ainhoa Masias, Mireia Quill, Gina Ramos, Marc Doltra, Ada Garcia, Ana Sitges, Marta Marlevi, David Young, Alistair Nash, Martyn Bijnens, Bart Camara, Oscar |
| author_facet | Morales, Xabier Elsayed, Ayah Zhao, Debbie Loncaric, Filip Aguado, Ainhoa Masias, Mireia Quill, Gina Ramos, Marc Doltra, Ada Garcia, Ana Sitges, Marta Marlevi, David Young, Alistair Nash, Martyn Bijnens, Bart Camara, Oscar |
| contents | The left atrium (LA) plays a pivotal role in modulating left ventricular filling, but our comprehension of its hemodynamics is significantly limited by the constraints of conventional ultrasound analysis. 4D flow magnetic resonance imaging (4D Flow MRI) holds promise for enhancing our understanding of atrial hemodynamics. However, the low velocities within the LA and the limited spatial resolution of 4D Flow MRI make analyzing this chamber challenging. Furthermore, the absence of dedicated computational frameworks, combined with diverse acquisition protocols and vendors, complicates gathering large cohorts for studying the prognostic value of hemodynamic parameters provided by 4D Flow MRI. In this study, we introduce the first open-source computational framework tailored for the analysis of 4D Flow MRI in the LA, enabling comprehensive qualitative and quantitative analysis of advanced hemodynamic parameters. Our framework proves robust to data from different centers of varying quality, producing high-accuracy automated segmentations (Dice $>$ 0.9 and Hausdorff 95 $<$ 3 mm), even with limited training data. Additionally, we conducted the first comprehensive assessment of energy, vorticity, and pressure parameters in the LA across a spectrum of disorders to investigate their potential as prognostic biomarkers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09746 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Computational Pipeline for Advanced Analysis of 4D Flow MRI in the Left Atrium Morales, Xabier Elsayed, Ayah Zhao, Debbie Loncaric, Filip Aguado, Ainhoa Masias, Mireia Quill, Gina Ramos, Marc Doltra, Ada Garcia, Ana Sitges, Marta Marlevi, David Young, Alistair Nash, Martyn Bijnens, Bart Camara, Oscar Computer Vision and Pattern Recognition The left atrium (LA) plays a pivotal role in modulating left ventricular filling, but our comprehension of its hemodynamics is significantly limited by the constraints of conventional ultrasound analysis. 4D flow magnetic resonance imaging (4D Flow MRI) holds promise for enhancing our understanding of atrial hemodynamics. However, the low velocities within the LA and the limited spatial resolution of 4D Flow MRI make analyzing this chamber challenging. Furthermore, the absence of dedicated computational frameworks, combined with diverse acquisition protocols and vendors, complicates gathering large cohorts for studying the prognostic value of hemodynamic parameters provided by 4D Flow MRI. In this study, we introduce the first open-source computational framework tailored for the analysis of 4D Flow MRI in the LA, enabling comprehensive qualitative and quantitative analysis of advanced hemodynamic parameters. Our framework proves robust to data from different centers of varying quality, producing high-accuracy automated segmentations (Dice $>$ 0.9 and Hausdorff 95 $<$ 3 mm), even with limited training data. Additionally, we conducted the first comprehensive assessment of energy, vorticity, and pressure parameters in the LA across a spectrum of disorders to investigate their potential as prognostic biomarkers. |
| title | A Computational Pipeline for Advanced Analysis of 4D Flow MRI in the Left Atrium |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.09746 |