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

Abstract

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 .

CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy

“…Second, here we use a segmentation map as a representation of the anatomy so that the generative model can focus on learning the variations of anatomy, instead of intensity image styles. Future explorations could be extended to the generation of intensity images for the heart [ 1 ] or using mesh as a representation for the anatomy [ 33 ], which may be computationally more efficient. Third, we use a cross-sectional imaging dataset of mainly healthy volunteers for training the generative model, due to the challenge of curating large-scale longitudinal datasets with high spatial resolution.…”

“…Duchateau et al [ 17 ] built a scheme for synthesizing pathological cardiac sequences from real healthy sequences. Amirrajab et al [ 1 ] developed a framework for simulating cardiac MRI with variable anatomical and imaging characteristics. For cardiac temporal modeling scheme, some work [ 57 ], [ 60 ], [ 61 ] showed dynamic cardiac data could be described by low-dimensional latent representations, i.e.…”

CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy

“…Second, here we use segmentation map as a representation for the anatomy, so that the generative model can focus on learning the variations of anatomy, instead of intensity image styles. Future explorations could be extended to generation of intensity images for the heart [30] or using mesh as a representation for the anatomy [52], which may be computationally more efficient. Third, we use a crosssectional imaging dataset of mainly healthy volunteers for training the generative model, due to the challenge in curating large-scale longitudinal dataset with high spatial resolution.…”

“…Duchateau et al [29] built a scheme for synthesizing pathological cardiac sequences from real healthy sequences. Amirrajab et al [30] developed a framework for simulating cardiac MRI with variable anatomical and imaging characteristics. These works provide useful insights for conditional medical image generation.…”