Purpose: The aim of this paper is to propose a transoesophageal echocardiography (TOE) image acquisition protocol which provides a systematic manner of acquiring a minimal number of overlapping 3D TOE datasets allowing the reconstruction of a wide 3D view of the left atrium (LA) with anatomical landmarks that are important for atrial fibrillation catheter ablation.
Methods: In eight cardiac surgical patients, 3D TOE datasets were acquired with a six-step protocol. In the protocol, step 1 aims to acquire the central view of the mitral valve (MV), aortic valve (AV) and left atrial appendage (LAA). Step 2 was developed to acquire the left pulmonary veins (PVs) and step 3 to acquire the right PVs. Steps 4, 5 and 6 were developed to create a sufficient overlap between different datasets. 3D TOE datasets were registered and fused manually in end diastole.
Results: The image acquisition protocol was feasible in all patients. In the fused 3D dataset, a wide 3D view of the LA is shown, and left and right PVs could be seen simultaneously. The LAA, MV, AV and fossa ovalis (FO) were visualised clearly in the 3D TOE datasets. The PV ostia, which are located at the edges of the 3D datasets, suffered more from the artefact of echo loss. The volume overlaps between neighbouring TOE datasets were 50-75 %.
Conclusion: The major part of the LA anatomy incorporating the PVs, LAA, MV, AV and FO as important anatomical landmarks can be reconstructed by registering and fusing 3D datasets acquired with the six-step TOE image acquisition protocol.
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http://dx.doi.org/10.1007/s10840-012-9757-3 | DOI Listing |
Mol Phylogenet Evol
January 2025
Department of Biology, University of Florida, Gainesville, FL 32611, USA. Electronic address:
EuroIntervention
October 2024
Department of Cardiology, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
Background: Transoesophageal echocardiography (TOE) provides accurate evaluation of mitral valve (MV) function following mitral transcatheter edge-to-edge repair (M-TEER) and may better detect complications in case of suboptimal result.
Aims: We aimed to evaluate midterm anatomical changes and structural complications after M-TEER using TOE and investigate their association with clinical outcomes at 2 years.
Methods: A follow-up TOE at 6 months was systematically recommended to all patients included in our institutional prospective M-TEER registry until December 2021.
J Neuroeng Rehabil
September 2024
University Rehabilitation Institute Republic of Slovenia, Linhartova 51, SI-1000, Ljubljana, Slovenia.
Background: Gait event detection is crucial for assessment, evaluation and provision of biofeedback during rehabilitation of walking. Existing online gait event detection algorithms mostly rely on add-on sensors, limiting their practicality. Instrumented treadmills offer a promising alternative by utilizing the Center of Pressure (CoP) signal for real-time gait event detection.
View Article and Find Full Text PDFPLoS One
May 2024
School of Kinesiology, University of British Columbia, Vancouver, BC, Canada.
Capturing human locomotion in nearly any environment or context is becoming increasingly feasible with wearable sensors, giving access to commonly encountered walking conditions. While important in expanding our understanding of locomotor biomechanics, these more variable environments present challenges to identify changes in data due to person-level factors among the varying environment-level factors. Our study examined foot-specific biomechanics while walking on terrain commonly encountered with the goal of understanding the extent to which these variables change due to terrain.
View Article and Find Full Text PDFSensors (Basel)
November 2023
Neuroscience Program, Beckman Institute, College of Liberal Arts & Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
Wearable sensors provide a tool for at-home monitoring of motor impairment progression in neurological conditions such as Parkinson's disease (PD). This study examined the ability of deep learning approaches to grade the motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. We hypothesized that expanding training datasets with motion data from healthy older adults (HOAs) and initializing classifiers with weights learned from unsupervised pre-training would lead to an improvement in performance when classifying lower vs.
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