A Framework for Simulating Cardiac MR Images with Varying Anatomy and Contrast

Sina Amirrajab (Corresponding author), Yasmina Al Khalil, Cristian Lorenz, Jürgen Weese, Josien Pluim, Marcel Breeuwer

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
70 Downloads (Pure)


One of the limiting factors for the development and adoption of novel deep-learning (DL) based medical image analysis methods is the scarcity of labeled medical images. Medical image simulation and synthesis can provide solutions by generating ample training data with corresponding ground truth labels. Despite recent advances, generated images demonstrate limited realism and diversity. In this work, we develop a flexible framework for simulating cardiac magnetic resonance (MR) images with variable anatomical and imaging characteristics for the purpose of creating a diversified virtual population. We advance previous works on both cardiac MR image simulation and anatomical modeling to increase the realism in terms of both image appearance and underlying anatomy. To diversify the generated images, we define parameters: 1) to alter the anatomy, 2) to assign MR tissue properties to various tissue types, and 3) to manipulate the image contrast via acquisition parameters. The proposed framework is optimized to generate a substantial number of cardiac MR images with ground truth labels suitable for downstream supervised tasks. A database of virtual subjects is simulated and its usefulness for aiding a DL segmentation method is evaluated. Our experiments show that training completely with simulated images can perform comparable with a model trained with real images for heart cavity segmentation in mid-ventricular slices. Moreover, such data can be used in addition to classical augmentation for boosting the performance when training data is limited, particularly by increasing the contrast and anatomical variation, leading to better regularization and generalization. The database is publicly available at https://osf.io/ bkzhm/ and the simulation code will be available at https: //github.com/sinaamirrajab/CMRI_Simulation.

Original languageEnglish
Article number9924194
Pages (from-to)726-738
Number of pages13
JournalIEEE Transactions on Medical Imaging
Issue number3
Publication statusPublished - Mar 2023


FundersFunder number
European Union 's Horizon 2020 - Research and Innovation Framework Programme764465
European Commission


    • Biological system modeling
    • cardiac MRI
    • Data models
    • database generation
    • Databases
    • deep-learning
    • Deformable models
    • Heart
    • image simulation
    • image synthesis
    • Mathematical models
    • physics-based
    • Solid modeling
    • Cardiac MRI
    • Humans
    • Heart/diagnostic imaging
    • Magnetic Resonance Imaging
    • Computer Simulation


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