Adityo Prakosa1, Michael K Southworth2, Jennifer N Avari Silva3, Jonathan R Silva4, Natalia A Trayanova5. 1. Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA. Electronic address: adityo.prakosa@jhu.edu. 2. SentiAR, St. Louis, MO, USA. 3. Department of Pediatrics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA. 4. Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA. 5. Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Abstract
BACKGROUND: Recently, an augmented reality (AR) solution allows the physician to place the ablation catheter at the designated lesion site more accurately during cardiac electrophysiology studies. The improvement in navigation accuracy may positively affect ventricular tachycardia (VT) ablation termination, however assessment of this in the clinic would be difficult. Novel personalized virtual heart technology enables non-invasive identification of optimal lesion targets for infarct-related VT. This study aims to evaluate the potential impact of such catheter navigation accuracy improvement in virtual VT ablations. METHODS: 2 MRI-based virtual hearts with 2 in silico induced VTs (VT 1, VT 2) were included. VTs were terminated with virtual "ground truth" endocardial ablation lesions. 106 navigation error values that were previously assessed in a clinical study evaluating the improvement of ablation catheter navigation accuracy guided with AR (53 with, 53 without) were used to displace the "ground truth" ablation targets. The corresponding ablations were simulated based on these errors and VT termination for each simulation was assessed. RESULTS: In 54 VT 1 ablation simulations, smaller error with AR significantly resulted in more VT termination (25) compared to the error without AR (16) (P < 0.01). In 52 VT 2 ablation simulations, no significant difference was observed from error with (11) and without AR (13) (P = 0.58). The substrate characteristic may impact the effect of improved accuracy to an improved VT termination. CONCLUSION: Virtual heart shows that the increased catheter navigation accuracy provided by AR guidance can affect the VT termination.
BACKGROUND: Recently, an augmented reality (AR) solution allows the physician to place the ablation catheter at the designated lesion site more accurately during cardiac electrophysiology studies. The improvement in navigation accuracy may positively affect ventricular tachycardia (VT) ablation termination, however assessment of this in the clinic would be difficult. Novel personalized virtual heart technology enables non-invasive identification of optimal lesion targets for infarct-related VT. This study aims to evaluate the potential impact of such catheter navigation accuracy improvement in virtual VT ablations. METHODS: 2 MRI-based virtual hearts with 2 in silico induced VTs (VT 1, VT 2) were included. VTs were terminated with virtual "ground truth" endocardial ablation lesions. 106 navigation error values that were previously assessed in a clinical study evaluating the improvement of ablation catheter navigation accuracy guided with AR (53 with, 53 without) were used to displace the "ground truth" ablation targets. The corresponding ablations were simulated based on these errors and VT termination for each simulation was assessed. RESULTS: In 54 VT 1 ablation simulations, smaller error with AR significantly resulted in more VT termination (25) compared to the error without AR (16) (P < 0.01). In 52 VT 2 ablation simulations, no significant difference was observed from error with (11) and without AR (13) (P = 0.58). The substrate characteristic may impact the effect of improved accuracy to an improved VT termination. CONCLUSION: Virtual heart shows that the increased catheter navigation accuracy provided by AR guidance can affect the VT termination.
Authors: Jennifer N Avari Silva; Michael K Southworth; Walter M Blume; Christopher Andrews; George F Van Hare; Aarti S Dalal; Nathan Miller; Sandeep S Sodhi; Jonathan R Silva Journal: JACC Clin Electrophysiol Date: 2020-07-08
Authors: Adityo Prakosa; Hermenegild J Arevalo; Dongdong Deng; Patrick M Boyle; Plamen P Nikolov; Hiroshi Ashikaga; Joshua J E Blauer; Elyar Ghafoori; Carolyn J Park; Robert C Blake; Frederick T Han; Rob S MacLeod; Henry R Halperin; David J Callans; Ravi Ranjan; Jonathan Chrispin; Saman Nazarian; Natalia A Trayanova Journal: Nat Biomed Eng Date: 2018-09-03 Impact factor: 25.671