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18 results on '"Karmakar, Abhishek"'

2. In Vitro and In Silico Characterization of the Aggregation of Thrombi on Ventricular Assist Device Cannula

Author

He, Wenxuan, Karmakar, Abhishek, Rowlands, Grant, Schirmacher, Samuel, Méndez-Rojano, Rodrigo, and Antaki, James F.

Subjects
Physics - Fluid Dynamics
Abstract

The unacceptably high stroke rate of HeartMate III VAD without signs of adherent pump thrombosis is hypothesized to be the result of the thrombi originating on the inflow cannula, ingesting and ejecting emboli from the VAD. Therefore, inflow cannula thrombosis has been an emerging focus. The inflow cannula of contemporary VADs, which incorporate both polished and rough regions serve as useful benchmarks to study the effects of roughness and shear on thrombogenesis. An in vitro study was conducted to emulate the micro-hemodynamic condition on a sintered inflow cannula, and to observe the deposition and detachment patterns. Together with a computational fluid dynamic tool, this study aimed to provide insight into the optimization of inflow cannula and potentially reducing adverse neurological events due to upstream thrombus.

Published
2023

3. Morphology-based non-rigid registration of coronary computed tomography and intravascular images through virtual catheter path optimization

Author

Kadry, Karim, Karmakar, Abhishek, Schuh, Andreas, Peterson, Kersten, Schaap, Michiel, Marlevi, David, Taylor, Charles, Edelman, Elazer, and Nezami, Farhad

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Coronary computed tomography angiography (CCTA) provides 3D information on obstructive coronary artery disease, but cannot fully visualize high-resolution features within the vessel wall. Intravascular imaging, in contrast, can spatially resolve atherosclerotic in cross sectional slices, but is limited in capturing 3D relationships between each slice. Co-registering CCTA and intravascular images enables a variety of clinical research applications but is time consuming and user-dependent. This is due to intravascular images suffering from non-rigid distortions arising from irregularities in the imaging catheter path. To address these issues, we present a morphology-based framework for the rigid and non-rigid matching of intravascular images to CCTA images. To do this, we find the optimal virtual catheter path that samples the coronary artery in CCTA image space to recapitulate the coronary artery morphology observed in the intravascular image. We validate our framework on a multi-center cohort of 40 patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our registration approach significantly outperforms other approaches for bifurcation alignment. By providing a differentiable framework for multi-modal vascular co-registration, our framework reduces the manual effort required to conduct large-scale multi-modal clinical studies and enables the development of machine learning-based co-registration approaches., Comment: Accepted to IEEE Transactions in Medical Imaging

Published
2022

4. Diabetes Prediction Using Classification Methods

Author

Karmakar, Abhishek, Gazi, Sharik, Singh, Varsha, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Joshi, Amit, editor, Mahmud, Mufti, editor, Ragel, Roshan G., editor, and Kartik, S., editor

Published
2024
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7. Household Power Consumption Analysis and Prediction Using LSTM

Author

Singh, Varsha, Karmakar, Abhishek, Gazi, Sharik, Mishra, Shashwat, Kumar, Shubhendra, Tiwary, Uma Shanker, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Bhateja, Vikrant, editor, Yang, Xin-She, editor, Ferreira, Marta Campos, editor, Sengar, Sandeep Singh, editor, and Travieso-Gonzalez, Carlos M., editor

Published
2023
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9. Computational Fluid Dynamic Optimization of Micropatterned Surfaces: Towards Biofunctionalization of Artificial Organs.

Author

He, Wenxuan, Ibrahim, Aminat M., Karmakar, Abhishek, Tuli, Shivani, Butcher, Jonathan T., and Antaki, James F.

Subjects
ARTIFICIAL organs, SURFACE topography, SHEARING force, ENDOTHELIAL cells, HEART assist devices
Abstract

Modifying surface topography to prevent surface-induced thrombosis in cardiovascular implants allows endothelialization, which is the natural thrombo-resistance of blood-contacting surfaces, and is deemed to be the only long-term solution for hemocompatible materials. We adapted a simulation framework to predict platelet deposition on a modified surface and developed an optimization strategy to promote endothelial retention and limit platelet deposition. Under supraphysiological bulk shear stress, a maximum of 79% linear coverage was achieved. This study concludes that the addition of microtrenches promotes endothelial retention and can be improved through the optimal selection of geometric parameters. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Analysis of Lakes Over the Period of Time Through Image Processing

Author

Ghosal, Sattam, Karmakar, Abhishek, Sahay, Pushkar, Das, Uma, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Mandal, Jyotsna Kumar, editor, Mukherjee, Imon, editor, Bakshi, Sambit, editor, Chatterji, Sanjay, editor, and Sa, Pankaj K., editor

Published
2021
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14. Morphology-based non-rigid registration of coronary computed tomography and intravascular images through virtual catheter path optimization

Author

Kadry, Karim, Karmakar, Abhishek, Schuh, Andreas, Peterson, Kersten, Schaap, Michiel, Marlevi, David, Taylor, Charles, Edelman, Elazer, and Nezami, Farhad

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Coronary Computed Tomography Angiography (CCTA) provides information on the presence, extent, and severity of obstructive coronary artery disease. Large-scale clinical studies analyzing CCTA-derived metrics typically require ground-truth validation in the form of high-fidelity 3D intravascular imaging. However, manual rigid alignment of intravascular images to corresponding CCTA images is both time consuming and user-dependent. Moreover, intravascular modalities suffer from several non-rigid motion-induced distortions arising from distortions in the imaging catheter path. To address these issues, we here present a semi-automatic segmentation-based framework for both rigid and non-rigid matching of intravascular images to CCTA images. We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image. We validate our co-registration framework on a cohort of $n=40$ patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our results indicate that our non-rigid registration significantly outperforms other co-registration approaches for luminal bifurcation alignment in both longitudinal (mean mismatch: 3.3 frames) and rotational directions (mean mismatch: 28.6 degrees). By providing a differentiable framework for automatic multi-modal intravascular data fusion, our developed co-registration modules significantly reduces the manual effort required to conduct large-scale multi-modal clinical studies while also providing a solid foundation for the development of machine learning-based co-registration approaches.

Published
2023
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18. Morphology-based non-rigid registration of coronary computed tomography and intravascular images through virtual catheter path optimization.

Author

Kadry K, Olender ML, Schuh A, Karmakar A, Petersen K, Schaap M, Marlevi D, UpdePac A, Mizukami T, Taylor C, Edelman ER, and Nezami FR

Abstract

Coronary computed tomography angiography (CCTA) provides 3D information on obstructive coronary artery disease, but cannot fully visualize high-resolution features within the vessel wall. Intravascular imaging, in contrast, can spatially resolve atherosclerotic in cross sectional slices, but is limited in capturing 3D relationships between each slice. Co-registering CCTA and intravascular images enables a variety of clinical research applications but is time consuming and user-dependent. This is due to intravascular images suffering from non-rigid distortions arising from irregularities in the imaging catheter path. To address these issues, we present a morphology-based framework for the rigid and non-rigid matching of intravascular images to CCTA images. To do this, we find the optimal virtual catheter path that samples the coronary artery in CCTA image space to recapitulate the coronary artery morphology observed in the intravascular image. We validate our framework on a multi-center cohort of 40 patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our registration approach significantly outperforms other approaches for bifurcation alignment. By providing a differentiable framework for multi-modal vascular co-registration, our framework reduces the manual effort required to conduct large-scale multi-modal clinical studies and enables the development of machine learning-based co-registration approaches.

Published
2024
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