6 papers · 1 filter
Regularized joint reconstruction and slab combination for accelerated three-dimensional multi-slab diffusion-weighted imaging using multi-scale energy models
Reza Ghorbani, Jyothi Rikhab Chand, Chu-Yu Lee +2
This work presents Energy-based Profile Encoding, EPEN, a joint reconstruction framework for high-resolution diffusion-weighted MRI from undersampled 3D multi-slab k-space acquisit…
Annealed Langevin Posterior Sampling (ALPS): A Rapid Algorithm for Image Restoration with Multiscale Energy Models
Jyothi Rikhab Chand, Mathews Jacob
Solving inverse problems in imaging requires models that support efficient inference, uncertainty quantification, and principled probabilistic reasoning. Energy-Based Models (EBMs)…
Deep End-to-End Posterior ENergy (DEEPEN) for image recovery
Jyothi Rikhab Chand, Mathews Jacob
Current end-to-end (E2E) and plug-and-play (PnP) image reconstruction algorithms approximate the maximum a posteriori (MAP) estimate but cannot offer sampling from the posterior di…
Three-Dimensional Diffusion-Weighted Multi-Slab MRI With Slice Profile Compensation Using Deep Energy Model
Reza Ghorbani, Jyothi Rikhab Chand, Chu-Yu Lee +2
Three-dimensional (3D) multi-slab acquisition is a technique frequently employed in high-resolution diffusion-weighted MRI in order to achieve the best signal-to-noise ratio (SNR)…
Memory-efficient deep end-to-end posterior network (DEEPEN) for inverse problems
Jyothi Rikhab Chand, Mathews Jacob
End-to-End (E2E) unrolled optimization frameworks show promise for Magnetic Resonance (MR) image recovery, but suffer from high memory usage during training. In addition, these det…
Local monotone operator learning using non-monotone operators: MnM-MOL
Maneesh John, Jyothi Rikhab Chand, Mathews Jacob
The recovery of magnetic resonance (MR) images from undersampled measurements is a key problem that has seen extensive research in recent years. Unrolled approaches, which rely on…