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6 results on '"Slesnick, Timothy"'

1. Design and implementation of multicenter pediatric and congenital studies with cardiovascular magnetic resonance: Big data in smaller bodies

Author

Cardiologie patientenzorg, Child Health, Circulatory Health, DiLorenzo, Michael P, Lee, Simon, Rathod, Rahul H, Raimondi, Francesca, Farooqi, Kanwal M, Jain, Supriya S, Samyn, Margaret M, Johnson, Tiffanie R, Olivieri, Laura J, Fogel, Mark A, Lai, Wyman W, Renella, Pierangelo, Powell, Andrew J, Buddhe, Sujatha, Stafford, Caitlin, Johnson, Jason N, Helbing, Willem A, Pushparajah, Kuberan, Voges, Inga, Muthurangu, Vivek, Miles, Kimberley G, Greil, Gerald, McMahon, Colin J, Slesnick, Timothy C, Fonseca, Brian M, Morris, Shaine A, Soslow, Jonathan H, Grosse-Wortmann, Lars, Beroukhim, Rebecca S, Grotenhuis, Heynric B, Cardiologie patientenzorg, Child Health, Circulatory Health, DiLorenzo, Michael P, Lee, Simon, Rathod, Rahul H, Raimondi, Francesca, Farooqi, Kanwal M, Jain, Supriya S, Samyn, Margaret M, Johnson, Tiffanie R, Olivieri, Laura J, Fogel, Mark A, Lai, Wyman W, Renella, Pierangelo, Powell, Andrew J, Buddhe, Sujatha, Stafford, Caitlin, Johnson, Jason N, Helbing, Willem A, Pushparajah, Kuberan, Voges, Inga, Muthurangu, Vivek, Miles, Kimberley G, Greil, Gerald, McMahon, Colin J, Slesnick, Timothy C, Fonseca, Brian M, Morris, Shaine A, Soslow, Jonathan H, Grosse-Wortmann, Lars, Beroukhim, Rebecca S, and Grotenhuis, Heynric B

Published
2024

2. Design and implementation of multicenter pediatric and congenital studies with cardiovascular magnetic resonance:Big data in smaller bodies

Author

DiLorenzo, Michael P., Lee, Simon, Rathod, Rahul H., Raimondi, Francesca, Farooqi, Kanwal M., Jain, Supriya S., Samyn, Margaret M., Johnson, Tiffanie R., Olivieri, Laura J., Fogel, Mark A., Lai, Wyman W., Renella, Pierangelo, Powell, Andrew J., Buddhe, Sujatha, Stafford, Caitlin, Johnson, Jason N., Helbing, Willem A., Pushparajah, Kuberan, Voges, Inga, Muthurangu, Vivek, Miles, Kimberley G., Greil, Gerald, McMahon, Colin J., Slesnick, Timothy C., Fonseca, Brian M., Morris, Shaine A., Soslow, Jonathan H., Grosse-Wortmann, Lars, Beroukhim, Rebecca S., Grotenhuis, Heynric B., DiLorenzo, Michael P., Lee, Simon, Rathod, Rahul H., Raimondi, Francesca, Farooqi, Kanwal M., Jain, Supriya S., Samyn, Margaret M., Johnson, Tiffanie R., Olivieri, Laura J., Fogel, Mark A., Lai, Wyman W., Renella, Pierangelo, Powell, Andrew J., Buddhe, Sujatha, Stafford, Caitlin, Johnson, Jason N., Helbing, Willem A., Pushparajah, Kuberan, Voges, Inga, Muthurangu, Vivek, Miles, Kimberley G., Greil, Gerald, McMahon, Colin J., Slesnick, Timothy C., Fonseca, Brian M., Morris, Shaine A., Soslow, Jonathan H., Grosse-Wortmann, Lars, Beroukhim, Rebecca S., and Grotenhuis, Heynric B.

Abstract

Cardiovascular magnetic resonance (CMR) has become the reference standard for quantitative and qualitative assessment of ventricular function, blood flow, and myocardial tissue characterization. There is a preponderance of large CMR studies and registries in adults; However, similarly powered studies are lacking for the pediatric and congenital heart disease (PCHD) population. To date, most CMR studies in children are limited to small single or multicenter studies, thereby limiting the conclusions that can be drawn. Within the PCHD CMR community, a collaborative effort has been successfully employed to recognize knowledge gaps with the aim to embolden the development and initiation of high-quality, large-scale multicenter research. In this publication, we highlight the underlying challenges and provide a practical guide toward the development of larger, multicenter initiatives focusing on PCHD populations, which can serve as a model for future multicenter efforts.

Published
2024

3. ARCollab: Towards Multi-User Interactive Cardiovascular Surgical Planning in Mobile Augmented Reality

Author

Mehta, Pratham, Karanth, Harsha, Yang, Haoyang, Slesnick, Timothy, Shaw, Fawwaz, Chau, Duen Horng, Mehta, Pratham, Karanth, Harsha, Yang, Haoyang, Slesnick, Timothy, Shaw, Fawwaz, and Chau, Duen Horng

Abstract

Surgical planning for congenital heart diseases requires a collaborative approach, traditionally involving the 3D-printing of physical heart models for inspection by surgeons and cardiologists. Recent advancements in mobile augmented reality (AR) technologies have offered a promising alternative, noted for their ease-of-use and portability. Despite this progress, there remains a gap in research exploring the use of multi-user mobile AR environments for facilitating collaborative cardiovascular surgical planning. We are developing ARCollab, an iOS AR application designed to allow multiple surgeons and cardiologists to interact with patient-specific 3D heart models in a shared environment. ARCollab allows surgeons and cardiologists to import heart models, perform gestures to manipulate the heart, and collaborate with other users without having to produce a physical heart model. We are excited by the potential for ARCollab to make long-term real-world impact, thanks to the ubiquity of iOS devices that will allow for ARCollab's easy distribution, deployment and adoption.

Published
2024

4. Deep Learning Pipeline for Preprocessing and Segmenting Cardiac Magnetic Resonance of Single Ventricle Patients from an Image Registry

Author

Yao, Tina, Clair, Nicole St., Miller, Gabriel F., Dorfman, Adam L., Fogel, Mark A., Ghelani, Sunil, Krishnamurthy, Rajesh, Lam, Christopher Z., Robinson, Joshua D., Schidlow, David, Slesnick, Timothy C., Weigand, Justin, Quail, Michael, Rathod, Rahul, Steeden, Jennifer A., Muthurangu, Vivek, Yao, Tina, Clair, Nicole St., Miller, Gabriel F., Dorfman, Adam L., Fogel, Mark A., Ghelani, Sunil, Krishnamurthy, Rajesh, Lam, Christopher Z., Robinson, Joshua D., Schidlow, David, Slesnick, Timothy C., Weigand, Justin, Quail, Michael, Rathod, Rahul, Steeden, Jennifer A., and Muthurangu, Vivek

Abstract

Purpose: To develop and evaluate an end-to-end deep learning pipeline for segmentation and analysis of cardiac magnetic resonance images to provide core-lab processing for a multi-centre registry of Fontan patients. Materials and Methods: This retrospective study used training (n = 175), validation (n = 25) and testing (n = 50) cardiac magnetic resonance image exams collected from 13 institutions in the UK, US and Canada. The data was used to train and evaluate a pipeline containing three deep-learning models. The pipeline's performance was assessed on the Dice and IoU score between the automated and reference standard manual segmentation. Cardiac function values were calculated from both the automated and manual segmentation and evaluated using Bland-Altman analysis and paired t-tests. The overall pipeline was further evaluated qualitatively on 475 unseen patient exams. Results: For the 50 testing dataset, the pipeline achieved a median Dice score of 0.91 (0.89-0.94) for end-diastolic volume, 0.86 (0.82-0.89) for end-systolic volume, and 0.74 (0.70-0.77) for myocardial mass. The deep learning-derived end-diastolic volume, end-systolic volume, myocardial mass, stroke volume and ejection fraction had no statistical difference compared to the same values derived from manual segmentation with p values all greater than 0.05. For the 475 unseen patient exams, the pipeline achieved 68% adequate segmentation in both systole and diastole, 26% needed minor adjustments in either systole or diastole, 5% needed major adjustments, and the cropping model only failed in 0.4%. Conclusion: Deep learning pipeline can provide standardised 'core-lab' segmentation for Fontan patients. This pipeline can now be applied to the >4500 cardiac magnetic resonance exams currently in the FORCE registry as well as any new patients that are recruited., Comment: 17 pages, 6 figures

Published
2023

5. Evaluating Cardiovascular Surgical Planning in Mobile Augmented Reality

Author

Yang, Haoyang, Mehta, Pratham Darrpan, Leo, Jonathan, Zhou, Zhiyan, Dass, Megan, Upadhayay, Anish, Slesnick, Timothy C., Shaw, Fawwaz, Randles, Amanda, Chau, Duen Horng, Yang, Haoyang, Mehta, Pratham Darrpan, Leo, Jonathan, Zhou, Zhiyan, Dass, Megan, Upadhayay, Anish, Slesnick, Timothy C., Shaw, Fawwaz, Randles, Amanda, and Chau, Duen Horng

Abstract

Advanced surgical procedures for congenital heart diseases (CHDs) require precise planning before the surgeries. The conventional approach utilizes 3D-printing and cutting physical heart models, which is a time and resource intensive process. While rapid advances in augmented reality (AR) technologies have the potential to streamline surgical planning, there is limited research that evaluates such AR approaches with medical experts. This paper presents an evaluation with 6 experts, 4 cardiothoracic surgeons, and 2 cardiologists, from Children's Healthcare of Atlanta (CHOA) Heart Center to validate the usability and technical innovations of CardiacAR, a prototype mobile AR surgical planning application. Potential future improvements based on user feedback are also proposed to further improve the design of CardiacAR and broaden its access., Comment: IEEE VIS 2022. 2 pages, 1 figure

Published
2022

6. Accuracy of Cardiac Magnetic Resonance Imaging in Diagnosing Pediatric Cardiac Masses: A Multicenter Study

Author

Cardiologie patientenzorg, Child Health, Circulatory Health, Beroukhim, Rebecca S, Ghelani, Sunil, Ashwath, Ravi, Balasubramanian, Sowmya, Biko, David M, Buddhe, Sujatha, Campbell, M Jay, Cross, Russell, Festa, Pierluigi, Griffin, Lindsay, Grotenhuis, Heynric, Hasbani, Keren, Hashemi, Sassan, Hegde, Sanjeet, Hussain, Tarique, Jain, Supriya, Kiaffas, Maria, Kutty, Shelby, Lam, Christopher Z, Liberato, Gabriela, Merlocco, Anthony, Misra, Nilanjana, Mowers, Katie L, Muniz, Juan Carlos, Nutting, Arni, Parra, David A, Patel, Jyoti K, Perez-Atayde, Antonio R, Prasad, Deepa, Rosental, Carlos F, Shah, Amee, Samyn, Margaret M, Sleeper, Lynn A, Slesnick, Timothy, Valsangiacomo, Emanuela, Geva, Tal, Cardiologie patientenzorg, Child Health, Circulatory Health, Beroukhim, Rebecca S, Ghelani, Sunil, Ashwath, Ravi, Balasubramanian, Sowmya, Biko, David M, Buddhe, Sujatha, Campbell, M Jay, Cross, Russell, Festa, Pierluigi, Griffin, Lindsay, Grotenhuis, Heynric, Hasbani, Keren, Hashemi, Sassan, Hegde, Sanjeet, Hussain, Tarique, Jain, Supriya, Kiaffas, Maria, Kutty, Shelby, Lam, Christopher Z, Liberato, Gabriela, Merlocco, Anthony, Misra, Nilanjana, Mowers, Katie L, Muniz, Juan Carlos, Nutting, Arni, Parra, David A, Patel, Jyoti K, Perez-Atayde, Antonio R, Prasad, Deepa, Rosental, Carlos F, Shah, Amee, Samyn, Margaret M, Sleeper, Lynn A, Slesnick, Timothy, Valsangiacomo, Emanuela, and Geva, Tal

Published
2022

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