Accelerating Quantitative MRI using Subspace Multiscale Energy Model (SS-MuSE)
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, the scan time needed to acquire multiple contrasts is prohibitively long for 3D acquisition schemes, which can offer isotropic image resolution. While deep learning-based methods have been extensively used to accelerate 2D and 2D + time problems, the high memory demand, computation time, and need for large training data sets make them challenging for large-scale volumes. To address these challenges, we generalize the plug-and-play multi-scale energy-based model (MuSE) to a regularized subspace recovery setting, where we jointly regularize the 3D multi-contrast spatial factors in a subspace formulation. The explicit energy-based formulation allows us to use variable splitting optimization methods for computationally efficient recovery.
Code (0)
등록된 구현이 없습니다.
Tasks
Quantitative MRISimilar Papers 제목 키워드 기반
Machine Learning Multiscale Interactions
Realistic physical systems are characterised by emergent interactions across multiple length and time scales, posing a significant challenge for predictive machine learning (ML) models. Most scientific ML models focus on…
Multi-Scale Energy (MuSE) plug and play framework for inverse problems
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 sample from the posterior distribution. Com…
DenoisingSchedulingAccelerate Neural Subspace-Based Reduced-Order Solver of Deformable Simulation by Lipschitz Optimization
Reduced-order simulation is an emerging method for accelerating physical simulations with high DOFs, and recently developed neural-network-based methods with nonlinear subspaces have been proven effective in diverse appl…
Physical SimulationsMUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
In sequence to sequence learning, the self-attention mechanism proves to be highly effective, and achieves significant improvements in many tasks. However, the self-attention mechanism is not without its own flaws. Altho…
Machine TranslationRepresentation LearningTranslationMuSE-SVS: Multi-Singer Emotional Singing Voice Synthesizer that Controls Emotional Intensity
We propose a multi-singer emotional singing voice synthesizer, Muse-SVS, that expresses emotion at various intensity levels by controlling subtle changes in pitch, energy, and phoneme duration while accurately following …