1 citations · 2 across the 4 of their papers we have counts for
4 papers
A unified deeplearning framework for contrast-phase-specific virtual monochromatic imaging
Antony Jerald, Hemant K Aggarwal, Brian Nett +4
Dual-energy CT (DECT) enables virtual monochromatic imaging (VMI) and improved contrast resolution, but its clinical adoption is limited by hardware complexity and cost. In this wo…
Physics-Guided Dual-Domain Plug-and-Play ADMM for Low-Dose CT Reconstruction
Sayantan Dutta, Sudhanya Chatterjee, Ashwini Galande +2
Ultra-low-dose CT (ULDCT) imaging can greatly reduce patient radiation exposure, but the resulting scans suffer from severe structured and random noise that degrades image quality.…
Display Field-Of-View Agnostic Robust CT Kernel Synthesis Using Model-Based Deep Learning
Hemant Kumar Aggarwal, Antony Jerald, Phaneendra K. Yalavarthy +2
In X-ray computed tomography (CT) imaging, the choice of reconstruction kernel is crucial as it significantly impacts the quality of clinical images. Different kernels influence sp…
Label Sharing Incremental Learning Framework for Independent Multi-Label Segmentation Tasks
Deepa Anand, Bipul Das, Vyshnav Dangeti +5
In a setting where segmentation models have to be built for multiple datasets, each with its own corresponding label set, a straightforward way is to learn one model for every data…