2 citations · 2 across the 10 of their papers we have counts for
11 papers
High-Resolution Quantitative MRI Using Contrast-Domain Multiscale Energy (MuSE) Regularization
Rushdi Rusho, Jyothi Rikhab Chand, Steve R. Kecskemeti +2
Quantitative MRI methods can offer tissue parameter maps (e.g. T1) that are comparable across scanners, sessions and subjects, together with multiple contrast-weighted images from…
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…
Accelerating Quantitative MRI using Subspace Multiscale Energy Model (SS-MuSE)
Yan Chen, Jyothi Rikhab Chand, Steven R. Kecskemeti +2
Multi-contrast MRI methods acquire multiple images with different contrast weightings, which are used for the differentiation of the tissue types or quantitative mapping. However,…
MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model
Jyothi Rikhab Chand, Mathews Jacob
We propose a multi-scale deep energy model that is strongly convex in the local neighbourhood around the data manifold to represent its probability density, with application in inv…
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)…
Fast multi-contrast MRI using joint multiscale energy model
Nima Yaghoobi, Jyothi Rikhab Chand, Yan Chen +3
The acquisition of 3D multicontrast MRI data with good isotropic spatial resolution is challenged by lengthy scan times. In this work, we introduce a CNN-based multiscale energy mo…
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…
Multi-Scale Energy (MuSE) plug and play framework for inverse problems
Jyothi Rikhab Chand, Mathews Jacob
We introduce multi-scale energy models to learn the prior distribution of images, which can be used in inverse problems to derive the Maximum A Posteriori (MAP) estimate and to sam…
Plug-and-Play Deep Energy Model for Inverse problems
Jyothi Rikabh Chand, Mathews Jacob
We introduce a novel energy formulation for Plug- and-Play (PnP) image recovery. Traditional PnP methods that use a convolutional neural network (CNN) do not have an energy based f…