Search Results for author:
Found 5 papers, 0 papers with code
Neural Representation-Based Method for Metal-induced Artifact Reduction in Dental CBCT Imaging
This study introduces a novel reconstruction method for dental cone-beam computed tomography (CBCT), focusing on effectively reducing metal-induced artifacts commonly encountered in the presence of prevalent metallic implants.
Automatic 3D Registration of Dental CBCT and Face Scan Data using 2D Projection Images
A main contribution of this study is that the proposed method does not require annotated training data of facial landmarks because it uses a pre-trained facial landmark detection algorithm that is known to be robust and generalized to various 2D face image models.
A robust multi-domain network for short-scanning amyloid PET reconstruction
Notably, for external validation datasets from unseen domains, the proposed method achieved comparable or superior results relative to methods trained with these datasets, in terms of quantitative metrics such as normalized root mean-square error and structure similarity index measure.
Machine Learning-based Signal Quality Assessment for Cardiac Volume Monitoring in Electrical Impedance Tomography
In clinical applications, however, a cardiac volume signal is often of low quality, mainly because of the patient's deliberate movements or inevitable motions during clinical interventions.
Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks
The purpose of this study was to evaluate the accuracy of our newly-developed system using a deep learning algorithm for automatic 3D cephalometric annotation.
