Mrudang Mathur

All Publications

• A generalizable deep learning system for cardiac MRI. Nature biomedical engineering Shad, R., Zakka, C., Kaur, D., Mathur, M., Fong, R., Cho, J., Filice, R. W., Mongan, J., Kallianos, K., Khandwala, N., Eng, D., Leipzig, M., Witschey, W. R., de Feria, A., Ferrari, V. A., Ashley, E. A., Acker, M. A., Langlotz, C., Hiesinger, W. 2026

  Abstract

  Cardiac MRI allows for a comprehensive assessment of myocardial structure, function and tissue characteristics. Here we describe a foundational vision system for cardiac MRI, capable of representing the breadth of human cardiovascular disease and health. Our deep-learning model is trained via self-supervised contrastive learning, in which visual concepts in cine-sequence cardiac MRI scans are learned from the raw text of the accompanying radiology reports. We train and evaluate our model on data from four large academic clinical institutions in the United States. We additionally showcase the performance of our models on the UK BioBank and two additional publicly available external datasets. We explore emergent capabilities of our system and demonstrate remarkable performance across a range of tasks, including the problem of left-ventricular ejection fraction regression and the diagnosis of 39 different conditions such as cardiac amyloidosis and hypertrophic cardiomyopathy. We show that our deep-learning system is capable of not only contextualizing the staggering complexity of human cardiovascular disease but can be directed towards clinical problems of interest, yielding impressive, clinical-grade diagnostic accuracy with a fraction of the training data typically required for such tasks.

  View details for DOI 10.1038/s41551-026-01637-3

  View details for PubMedID 41882174

• Sex disparities in deep learning estimation of ejection fraction from cardiac magnetic resonance imaging. NPJ digital medicine Kaur, D., Shad, R., Kumar, A., Mathur, M., Cho, J., Fong, R., Zakka, C., Phillips, C., Hiesinger, W. 2026

  Abstract

  The advent of artificial intelligence in cardiovascular imaging holds immense potential for earlier diagnoses, precision medicine, and improved disease management. However, the presence of sex-based disparities and strategies to mitigate biases in deep learning models for cardiac imaging remain understudied. In this study, we analyzed algorithmic bias in a foundation model that was pretrained on cardiac magnetic resonance imaging and radiology reports from multiple institutes and finetuned to estimate ejection fraction (EF) on the UK Biobank dataset. The model performed significantly worse in EF estimation for females than males in the diagnosis of reduced EF. Algorithmic fairness did not improve despite masking of protected attributes in radiology reports and data resampling, although explicit input of sex in model finetuning may improve EF estimation in some cases. The underdiagnosis of reduced EF among females holds critical implications for the exacerbation of existing sex-based disparities in cardiovascular health. We advise caution in the development of models for cardiovascular imaging to avoid such pitfalls.

  View details for DOI 10.1038/s41746-025-02330-6

  View details for PubMedID 41577988

• Tricuspid valve leaflet remodeling in sheep with biventricular heart failure: A comparison between leaflets. Acta biomaterialia Kostelnik, C. J., Meador, W. D., Lin, C., Mathur, M., Malinowski, M., Jazwiec, T., Malinowska, Z., Piekarska, M. L., Gaweda, B., Timek, T. A., Rausch, M. K. 2025

  Abstract

  Tricuspid valve leaflets are dynamic tissues that can respond to altered biomechanical and hemodynamic loads. Each leaflet has unique structural and mechanical properties, leading to differential in vivo strains. We hypothesized that these intrinsic differences drive heterogeneous, disease-induced remodeling between the leaflets. Although we previously reported significant remodeling changes in the anterior leaflet, the responses among the other two leaflets have not been reported. Using a sheep model of biventricular heart failure, we compared the remodeling responses between all tricuspid leaflets. Our results show that the anterior leaflet underwent the most significant remodeling, while the septal and posterior leaflets exhibited similar but less pronounced changes. We found several between-leaflet differences in key structural and mechanical metrics that have been shown to contribute to valvular dysfunction. Diseased animals exhibited significantly larger septal and anterior leaflets, thicker anterior and posterior leaflets, and stiffer anterior leaflets. Additionally, the septal leaflet's anisotropy index significantly decreased. Also, the septal and anterior leaflets showed increased collagen fiber dispersion near the atrial surface. As for remodeling markers, alpha-smooth muscle actin (alpha-SMA), Ki67, matrix-metalloprotease 13 (MMP13), and transforming growth factor beta-1 (TGF-beta1) were upregulated in spatially and leaflet-dependent patterns. That is, we observed increased expression of these markers within septal leaflets' near-annulus and belly regions, increased expression in anterior leaflets' belly region, and varied expression in posterior leaflets. These findings underscore the need to consider leaflet-specific remodeling to fully understand tricuspid valve dysfunction and to develop targeted therapies for its treatment and more accurate computational models. STATEMENT OF SIGNIFICANCE: Our study is significant as it advances our understanding of tricuspid valve remodeling by providing a comprehensive analysis of all three leaflets in a sheep model of biventricular heart failure. Unlike prior works that focused primarily on the anterior leaflet or generalized leaflet changes, we integrated morphological, histological, immunohistochemistry, biaxial mechanical testing, and two-photon microscopy to quantify differences between all three tricuspid valve leaflets (anterior, posterior, and septal) across multiple functional scales. This comprehensive approach highlights the unique remodeling response of each leaflet. Our findings offer critical insights for developing targeted therapeutic strategies and improving computational models of disease progression.

  View details for DOI 10.1016/j.actbio.2025.03.052

  View details for PubMedID 40180007

• Tricuspid valve edge-to-edge repair simulations are highly sensitive to annular boundary conditions. Journal of the mechanical behavior of biomedical materials Haese, C. E., Dubey, V., Mathur, M., Pouch, A. M., Timek, T. A., Rausch, M. K. 2024; 163: 106879

  Abstract

  Transcatheter edge-to-edge repair (TEER) simulations may provide insight into this novel therapeutic technology and help optimize its use. However, because of the relatively short history and technical complexity of TEER simulations, important questions remain unanswered. For example, there is no consensus on how to handle the annular boundary conditions in these simulations. In this short communication, we tested the sensitivity of such simulations to the choice of annular boundary conditions using a high-fidelity finite element model of a human tricuspid valve. Therein, we embedded the annulus among elastic springs to simulate the compliance of the perivalvular myocardium. Next, we varied the spring stiffness parametrically and explored the impact on two key measures of valve function: coaptation area and leaflet stress. Additionally, we compared our results to simulations with a pinned annulus. We found that a compliant annular boundary condition led to a TEER-induced "annuloplasty effect," i.e., annular remodeling, as observed clinically. Moreover, softer springs led to a larger coaptation area and smaller leaflet stresses. On the other hand, pinned annular boundary conditions led to unrealistically high stresses and no "annuloplasty effect." Furthermore, we found that the impact of the boundary conditions depended on the clip position. Our findings in this case study emphasize the importance of the annular boundary condition in tricuspid TEER simulations. Thus, we recommend that care be taken when choosing annular boundary conditions and that results from simulations using pinned boundaries should be interpreted with caution.

  View details for DOI 10.1016/j.jmbbm.2024.106879

  View details for PubMedID 39742687

• Leaflet remodeling reduces tricuspid valve function in a computational model JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS Mathur, M., Malinowski, M., Jazwiec, T., Timek, T. A., Rausch, M. K. 2024; 152: 106453

  Abstract

  Tricuspid valve leaflets have historically been considered "passive flaps". However, we have recently shown that tricuspid leaflets actively remodel in sheep with functional tricuspid regurgitation. We hypothesize that these remodeling-induced changes reduce leaflet coaptation and, therefore, contribute to valvular dysfunction. To test this, we simulated the impact of remodeling-induced changes on valve mechanics in a reverse-engineered computer model of the human tricuspid valve. To this end, we combined right-heart pressures and tricuspid annular dynamics recorded in an ex vivo beating heart, with subject-matched in vitro measurements of valve geometry and material properties, to build a subject-specific finite element model. Next, we modified the annular geometry and boundary conditions to mimic changes seen in patients with pulmonary hypertension. In this model, we then increased leaflet thickness and stiffness and reduced the stretch at which leaflets stiffen, which we call "transition-λ." Subsequently, we quantified mean leaflet stresses, leaflet systolic angles, and coaptation area as measures of valve function. We found that leaflet stresses, leaflet systolic angle, and coaptation area are sensitive to independent changes in stiffness, thickness, and transition-λ. When combining thickening, stiffening, and changes in transition-λ, we found that anterior and posterior leaflet stresses decreased by 26% and 28%, respectively. Furthermore, systolic angles increased by 43%, and coaptation area decreased by 66%; thereby impeding valve function. While only a computational study, we provide the first evidence that remodeling-induced leaflet thickening and stiffening may contribute to valvular dysfunction. Targeted suppression of such changes in diseased valves could restore normal valve mechanics and promote leaflet coaptation.

  View details for DOI 10.1016/j.jmbbm.2024.106453

  View details for Web of Science ID 001180594300001

  View details for PubMedID 38335648

  View details for PubMedCentralID PMC11048730

• Geometric data of commercially available tricuspid valve annuloplasty devices DATA IN BRIEF Haese, C. E., Mathur, M., Malinowski, M., Timek, T. A., Rausch, M. K. 2024; 52: 110051

  Abstract

  Tricuspid valve annuloplasty is the gold standard surgical treatment for functional tricuspid valve regurgitation. During this procedure, ring-like devices are implanted to reshape the diseased tricuspid valve annulus and to restore function. For the procedure, surgeons can choose from multiple available device options varying in shape and size. In this article, we provide the three-dimensional (3D) scanned geometry (*.stl) and reduced midline (*.vtk) of five different annuloplasty devices of all commercially available sizes. Three-dimensional images were captured using a 3D scanner. After extracting the surface geometry from these images, the images were converted to 3D point clouds and skeletonized to generate a 3D midline of each device. In total, we provide 30 data sets comprising the Edwards Classic, Edwards MC3, Edwards Physio, Medtronic TriAd, and Medtronic Contour 3D of sizes 26-36. This dataset can be used in computational models of tricuspid valve annuloplasty repair to inform accurate repair geometry and boundary conditions. Additionally, others can use these data to compare and inspire new device shapes and sizes.

  View details for DOI 10.1016/j.dib.2024.110051

  View details for Web of Science ID 001171308700001

  View details for PubMedID 38299102

  View details for PubMedCentralID PMC10828561