Radiological Atlas for Patient Specific Model Generation

2014 ◽  
pp. 69-82 ◽  
Author(s):  
Jacek Kawa ◽  
Jan Juszczyk ◽  
Bartłomiej Pyciński ◽  
Paweł Badura ◽  
Ewa Pietka
Keyword(s):  
Specific Model ◽  
Patient Specific ◽  
Model Generation

scholarly journals Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Gaoyang Li ◽  
Haoran Wang ◽  
Mingzi Zhang ◽  
Simon Tupin ◽  
Aike Qiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Artery Bypass ◽  
Computational Cost ◽  
Specific Model ◽  
Arterial System ◽  
Patient Specific ◽  
Cardiovascular Hemodynamics ◽  
Before And After ◽  
Deep Learning Network ◽  
Cfd Method

AbstractThe clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

scholarly journals Evaluating Design of Abdominal Aortic Aneurysm Endografts in a Patient-Specific Model Using Computational Fluid Dynamics

2011 ◽  
Vol 5 (2) ◽  
Author(s):  
Polina A. Segalova ◽  
Guanglei Xiong ◽  
K. T. Rao ◽  
Christopher K. Zarins ◽  
Charles A. Taylor
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Specific Model ◽  
Patient Specific ◽  
Abdominal Aortic

Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation

2009 ◽  
Vol 87 (1-2) ◽  
pp. 156-169 ◽  
Author(s):  
Stefano Corazza ◽  
Lars Mündermann ◽  
Emiliano Gambaretto ◽  
Giancarlo Ferrigno ◽  
Thomas P. Andriacchi
Keyword(s):  
Motion Capture ◽  
Specific Model ◽  
Model Generation ◽  
Visual Hull ◽  
Markerless Motion Capture ◽  
Subject Specific

scholarly journals Determination of dominant loop in dual loop atrial tachycardia with direct graph mapping: an alternative for entrainment mapping?

EP Europace ◽  
2021 ◽  
Vol 23 (Supplement_3) ◽  
Author(s):  
L Lowie ◽  
E Van Nieuwenhuyse ◽  
J Sanchez ◽  
A Panfilov ◽  
S Knecht ◽  
...  
Keyword(s):  
Simulated Data ◽  
Anterior Wall ◽  
Specific Model ◽  
Patient Specific ◽  
Double Loop ◽  
Direct Graph ◽  
Funding Sources ◽  
Graph Mapping ◽  
Activation Times ◽  
Clinical Cases

Abstract Funding Acknowledgements Type of funding sources: None. Background Entrainment mapping (EM) is an important tool to determine the mechanism of complex reentrant atrial tachycardias (ATs), mostly to distinguish dominant from bystander reentrant loops. However, entrainment maneuvers are challenging, time consuming and risk to end the tachycardia.  Purpose Recently, we developed a novel method Directed Graph Mapping (DGM), using concepts of network theory, allowing to automatically determine AT reentry loops from the local activation times (LAT) of any clinical mapping system. DGM showed good performance: it correctly finds ablation target (100 % success rate) on simple AT cases and could automatically determine reentry loops confirmed by the expert electrophysiologist with EM in complex AT cases. Out of 32 single loop cases, 62.5 % was identified correctly with automated DGM and out of 6 true double loop cases, 83.3 %. Lower performance for single reentry complex cases compared to EM was mainly because DGM could not distinguish the dominant loop from additional bystander loops found by DGM. Hence, the purpose of this work was to develop additional algorithms which in case of multiple found DGM loops could automatically find the dominant loop and compare it with the results of EM. Methods We performed multiple  simulations of various types of double loop reentry on a patient specific model of the left atrium. Based on a clinical case, double loops were simulated around a scar at the anterior wall (localized reentry) and the mitral valve (MV). LAT maps were determined similar as in the clinic. By varying the size of the scar in multiple steps, we obtained a transition from a regime of a dominant loop around the scar (small scar), to a true double loop and further to a regime of a dominant loop around the MV (large scar). We developed a novel DGM algorithm to determine the dominant loop from the region of collision (ROC) found from the vector field of the wavefront graph.   The developed method was also tested on 8 clinical cases of double loop ATs with EM measurements. Results Our algorithm found the location of the ROC and determined the correct dominant loop in 100% of the simulated data.  We tested this on 8 clinical cases of AT, and accuracy of the method was 75 %. Conclusions Determining the ROC in case of multiple loops in AT could correctly determine the dominant versus bystander loop, leading to the correct ablation target, without the need for further EM.

scholarly journals Age-related pathological impairments in induced neurons derived from patients with idiopathic Parkinson’s disease

2021 ◽  
Author(s):  
Janelle Drouin-Ouellet ◽  
Karolina Pircs ◽  
Emilie M. Legault ◽  
Marcella Birtele ◽  
Fredrik Nilsson ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Alpha Synuclein ◽  
Specific Model ◽  
Dermal Fibroblasts ◽  
Patient Specific ◽  
Idiopathic Parkinson’S Disease ◽  
Age Related ◽  
Idiopathic Parkinson's Disease ◽  
Induced Neurons

AbstractUnderstanding the pathophysiology of Parkinson’s disease has been hampered by the lack of models that recapitulate all the critical factors underlying its development. Here, we generated functional induced dopaminergic neurons (iDANs) that were directly reprogrammed from adult human dermal fibroblasts of patients with idiopathic Parkinson’s disease to investigate diseaserelevant pathology. We show that iDANs derived from Parkinson’s disease patients exhibit lower basal chaperone-mediated autophagy as compared to iDANs of healthy donors. Furthermore, stress-induced autophagy resulted in an accumulation of macroautophagic structures in induced neurons (iNs) derived from Parkinson’s disease patients, independently of the specific neuronal subtype but dependent on the age of the donor. Finally, we found that these impairments in patient-derived iNs lead to an accumulation of phosphorylated alpha-synuclein, a hallmark of Parkinson’s disease pathology. Taken together, our results demonstrate that direct neural reprogramming provides a patient-specific model to study aged neuronal features relevant to idiopathic Parkinson’s disease.

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