Adityo Prakosa
Adityo Prakosa
Associate Research Scientist, ADVANCE, Johns Hopkins University
Verified email at
Cited by
Cited by
Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia
A Prakosa, HJ Arevalo, D Deng, PM Boyle, PP Nikolov, H Ashikaga, ...
Nature biomedical engineering 2 (10), 732-740, 2018
Computationally guided personalized targeted ablation of persistent atrial fibrillation
PM Boyle, T Zghaib, S Zahid, RL Ali, D Deng, WH Franceschi, JB Hakim, ...
Nature biomedical engineering 3 (11), 870-879, 2019
Benchmarking framework for myocardial tracking and deformation algorithms: An open access database
C Tobon-Gomez, M De Craene, K Mcleod, L Tautz, W Shi, A Hennemuth, ...
Medical image analysis 17 (6), 632-648, 2013
Feasibility of using patient-specific models and the “minimum cut” algorithm to predict optimal ablation targets for left atrial flutter
S Zahid, KN Whyte, EL Schwarz, RC Blake III, PM Boyle, J Chrispin, ...
Heart rhythm 13 (8), 1687-1698, 2016
3D strain assessment in ultrasound (straus): A synthetic comparison of five tracking methodologies
M De Craene, S Marchesseau, B Heyde, H Gao, M Alessandrini, ...
IEEE transactions on medical imaging 32 (9), 1632-1646, 2013
Generation of synthetic but visually realistic time series of cardiac images combining a biophysical model and clinical images
A Prakosa, M Sermesant, H Delingette, S Marchesseau, E Saloux, ...
IEEE transactions on medical imaging 32 (1), 99-109, 2012
Accuracy of prediction of infarct-related arrhythmic circuits from image-based models reconstructed from low and high resolution MRI
D Deng, H Arevalo, F Pashakhanloo, A Prakosa, H Ashikaga, E McVeigh, ...
Frontiers in physiology 6, 282, 2015
Lack of regional association between atrial late gadolinium enhancement on cardiac magnetic resonance and atrial fibrillation rotors
J Chrispin, EG Ipek, S Zahid, A Prakosa, M Habibi, D Spragg, JE Marine, ...
Heart rhythm 13 (3), 654-660, 2016
A feasibility study of arrhythmia risk prediction in patients with myocardial infarction and preserved ejection fraction
D Deng, HJ Arevalo, A Prakosa, DJ Callans, NA Trayanova
EP Europace 18 (suppl_4), iv60-iv66, 2016
Methodology for image-based reconstruction of ventricular geometry for patient-specific modeling of cardiac electrophysiology
A Prakosa, P Malamas, S Zhang, F Pashakhanloo, H Arevalo, DA Herzka, ...
Progress in biophysics and molecular biology 115 (2-3), 226-234, 2014
Image‐based reconstruction of three‐dimensional myocardial infarct geometry for patient‐specific modeling of cardiac electrophysiology
E Ukwatta, H Arevalo, M Rajchl, J White, F Pashakhanloo, A Prakosa, ...
Medical physics 42 (8), 4579-4590, 2015
An incompressible log-domain demons algorithm for tracking heart tissue
K McLeod, A Prakosa, T Mansi, M Sermesant, X Pennec
International Workshop on Statistical Atlases and Computational Models of …, 2011
Electromechanical modeling of human ventricles with ischemic cardiomyopathy: numerical simulations in sinus rhythm and under arrhythmia
M Salvador, M Fedele, PC Africa, E Sung, A Prakosa, J Chrispin, ...
Computers in Biology and Medicine 136, 104674, 2021
Substrate spatial complexity analysis for the prediction of ventricular arrhythmias in patients with ischemic cardiomyopathy
DR Okada, J Miller, J Chrispin, A Prakosa, N Trayanova, S Jones, ...
Circulation: Arrhythmia and Electrophysiology 13 (4), e007975, 2020
Ventricular arrhythmia risk prediction in repaired tetralogy of Fallot using personalized computational cardiac models
JK Shade, MJ Cartoski, P Nikolov, A Prakosa, A Doshi, E Binka, L Olivieri, ...
Heart Rhythm 17 (3), 408-414, 2020
Sensitivity of ablation targets prediction to electrophysiological parameter variability in image-based computational models of ventricular tachycardia in post-infarction patients
D Deng, A Prakosa, J Shade, P Nikolov, NA Trayanova
Frontiers in Physiology 10, 628, 2019
The fibrotic substrate in persistent atrial fibrillation patients: comparison between predictions from computational modeling and measurements from focal impulse and rotor mapping
PM Boyle, JB Hakim, S Zahid, WH Franceschi, MJ Murphy, A Prakosa, ...
Frontiers in Physiology, 1151, 2018
Predicting risk of sudden cardiac death in patients with cardiac sarcoidosis using multimodality imaging and personalized heart modeling in a multivariable classifier
JK Shade, A Prakosa, DM Popescu, R Yu, DR Okada, J Chrispin, ...
Science Advances 7 (31), eabi8020, 2021
Cardiac electrophysiological activation pattern estimation from images using a patient-specific database of synthetic image sequences
A Prakosa, M Sermesant, P Allain, N Villain, CA Rinaldi, K Rhode, ...
IEEE Transactions on Biomedical Engineering 61 (2), 235-245, 2013
How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post‐infarction patients
NA Trayanova, AN Doshi, A Prakosa
Wiley Interdisciplinary Reviews: Systems Biology and Medicine 12 (3), e1477, 2020
The system can't perform the operation now. Try again later.
Articles 1–20