12 citations · 16 across the 3 of their papers we have counts for
3 papers
eess.SP2025
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…
eess.SP2024★ 4 cited
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…
eess.IV2022★ 12 cited
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…