12 citations · 17 across the 5 of their papers we have counts for
5 papers
Ultrafast Deep Learning-Based Scatter Estimation in Cone-Beam Computed Tomography
Harshit Agrawal, Ari Hietanen, Simo Särkkä
Purpose: Scatter artifacts drastically degrade the image quality of cone-beam computed tomography (CBCT) scans. Although deep learning-based methods show promise in estimating scat…
Utilizing U-Net Architectures with Auxiliary Information for Scatter Correction in CBCT Across Different Field-of-View Settings
Harshit Agrawal, Ari Hietanen, Simo Särkkä
Cone-beam computed tomography (CBCT) has become a vital imaging technique in various medical fields but scatter artifacts are a major limitation in CBCT scanning. This challenge is…
Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset
Jorma Järnstedt, Jaakko Sahlsten, Joel Jaskari +8
Preoperative radiological identification of mandibular canals is essential for maxillofacial surgery. This study demonstrates the reproducibility of a deep learning system (DLS) by…
Deep learning based projection domain metal segmentation for metal artifact reduction in cone beam computed tomography
Harshit Agrawal, Ari Hietanen, Simo Särkkä
Metal artifact correction is a challenging problem in cone beam computed tomography (CBCT) scanning. Metal implants inserted into the anatomy cause severe artifacts in reconstructe…
Comparison of Deep Learning Segmentation and Multigrader-annotated Mandibular Canals of Multicenter CBCT scans
Jorma Järnstedt, Jaakko Sahlsten, Joel Jaskari +9
Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validati…