CMRINet: Joint Groupwise Registration and Segmentation for Cardiac Function Quantification from Cine-MRI

arXiv (Cornell University)

Abstract

Accurate and efficient quantification of cardiac function is essential for the estimation of prognosis of cardiovascular diseases (CVDs). One of the most commonly used metrics for evaluating cardiac pumping performance is left ventricular ejection fraction (LVEF). However, LVEF can be affected by factors such as inter-observer variability and varying pre-load and after-load conditions, which can reduce its reproducibility. Additionally, cardiac dysfunction may not always manifest as alterations in LVEF, such as in heart failure and cardiotoxicity diseases. An alternative measure that can provide a relatively load-independent quantitative assessment of myocardial contractility is myocardial strain and strain rate. By using LVEF in combination with myocardial strain, it is possible to obtain a thorough description of cardiac function. Automated estimation of LVEF and other volumetric measures from cine-MRI sequences can be achieved through segmentation models, while strain calculation requires the estimation of tissue displacement between sequential frames, which can be accomplished using registration models. These tasks are often performed separately, potentially limiting the assessment of cardiac function. To address this issue, in this study we propose an end-to-end deep learning (DL) model that jointly estimates groupwise (GW) registration and segmentation for cardiac cine-MRI images. The proposed anatomically-guided Deep GW network was trained and validated on a large dataset of 4-chamber view cine-MRI image series of 374 subjects. A quantitative comparison with conventional GW registration using elastix and two DL-based methods showed that the proposed model improved performance and substantially reduced computation time.

Authors 10

1. Mohamed S. Elmahdy Aachen Division of Image Processing Department of Radiology, Leiden University Medical Center

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Division of Image Processing , Department of Radiology , Leiden University Medical Center , The Netherlands

2. Marius Staring Aachen Division of Image Processing Department of Radiology, Leiden University Medical Center

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Division of Image Processing , Department of Radiology , Leiden University Medical Center , The Netherlands

3. Patrick J. H. de Koning Aachen Division of Image Processing Department of Radiology, Leiden University Medical Center

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Division of Image Processing , Department of Radiology , Leiden University Medical Center , The Netherlands

4. Samer Alabed

   Sheffield Teaching Hospitals NHS Foundation Trust · University of Sheffield

   Affiliation as printed

   Department of Clinical Radiology , Sheffield Teaching Hospitals , Sheffield , UK

   Department of Infection, Immunity and Cardiovascular Disease , University of Sheffield , Sheffield , UK

5. Mahan Salehi

   University of Sheffield

   Affiliation as printed

   Department of Infection, Immunity and Cardiovascular Disease , University of Sheffield , Sheffield , UK

6. Faisal Alandejani

   University of Sheffield

   Affiliation as printed

   Department of Infection, Immunity and Cardiovascular Disease , University of Sheffield , Sheffield , UK

7. Michael J. Sharkey

   University of Sheffield

   Affiliation as printed

   Department of Infection, Immunity and Cardiovascular Disease , University of Sheffield , Sheffield , UK

8. Ziad Aldabbagh

   Insigneo · University of Sheffield

   Affiliation as printed

   INSIGNEO , Institute for In Silico Medicine , University of Sheffield , Sheffield , UK

9. Andrew J. Swift
10. Rob J. van der Geest Aachen Division of Image Processing Department of Radiology, Leiden University Medical Center

    Leiden University · Leiden University Medical Center

    Affiliation as printed

    Division of Image Processing , Department of Radiology , Leiden University Medical Center , The Netherlands