Prisca Eser^1,*, Thomas Gruber¹, Thimo Marcin¹, Claudia Boeni^1,2, Kerstin Wustmann³, Christina DeLuigi¹, Matthias Greutmann⁴, Daniel Tobler⁵, Markus Schwerzmann³, Matthias Wilhelm¹

Congenital Heart Disease, Vol.16, No.1, pp. 73-84, 2021, DOI:10.32604/CHD.2021.013051 - 23 December 2020

Abstract Background: The purpose of this study was to investigate whether patients with adult congenital heart disease (ACHD) benefit from exercise-based cardiac rehabilitation (CR) short- and long-term with regard to improvement of cardiorespiratory fitness. Methods: Cardiopulmonary exercise tests (CPET) completed by ACHD patients between January 2000 and October 2019 were analysed retrospectively. Linear mixed models were performed for peak oxygen consumption (VO₂) with patients as random effect and age, sex, disease classification, preceding surgery (≤3 months) and preceding CR (≤4 weeks for short term and >4 weeks for long term) as fixed effects. Results: 1056 CPETs of 311… More >

Yongqiang Li^1,#, Changxin Lai^1,#, Chengchen Zhang², Alexa Singer¹, Suhao Qiu¹, Boming Sun², Michael S. Sacks³, Yuan Feng^1,*

Molecular & Cellular Biomechanics, Vol.16, Suppl.2, pp. 111-111, 2019, DOI:10.32604/mcb.2019.07098

Abstract Surgeries such as implantation of deep brain stimulation devices require accurate placement of devices within the brain. Because placement affects performance, image guidance and robotic assistance techniques have been widely adopted. These methods require accurate prediction of brain deformation during and following implantation. In this study, a magnetic resonance (MR) image-based finite element (FE) model was proposed by using a coupled Eulerian-Lagrangian method. Anatomical accuracy was achieved by mapping image voxels directly to the volumetric mesh space. The potential utility was demonstrated by evaluating the effect of different surgical approaches on the deformation of the… More >

Yongqiang Li^1,#, Changxin Lai^1,#, Chengchen Zhang², Alexa Singer¹, Suhao Qiu¹, Boming Sun², Michael S. Sacks³, Yuan Feng^1,*

Molecular & Cellular Biomechanics, Vol.16, No.4, pp. 245-251, 2019, DOI:10.32604/mcb.2019.07441

Abstract Surgeries such as implantation of deep brain stimulation devices require accurate placement of devices within the brain. Because placement affects performance, image guidance and robotic assistance techniques have been widely adopted. These methods require accurate prediction of brain deformation during and following implantation. In this study, a magnetic resonance (MR) image-based finite element (FE) model was proposed by using a coupled Eulerian-Lagrangian method. Anatomical accuracy was achieved by mapping image voxels directly to the volumetric mesh space. The potential utility was demonstrated by evaluating the effect of different surgical approaches on the deformation of the… More >