Optimization methods for magnetic resonance image reconstruction: Key models and optimization algorithms
JA Fessler - IEEE signal processing magazine, 2020 - ieeexplore.ieee.org
The development of compressed-sensing (CS) methods for magnetic resonance (MR)
image reconstruction led to an explosion of research on models and optimization algorithms …
image reconstruction led to an explosion of research on models and optimization algorithms …
Model-based image reconstruction for MRI
JA Fessler - IEEE signal processing magazine, 2010 - ieeexplore.ieee.org
Magnetic resonance imaging (MRI) is a sophisticated and versatile medical imaging
modality. The inverse FFT has served the MR community very well as the conventional …
modality. The inverse FFT has served the MR community very well as the conventional …
ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA
Purpose Parallel imaging allows the reconstruction of images from undersampled multicoil
data. The two main approaches are: SENSE, which explicitly uses coil sensitivities, and …
data. The two main approaches are: SENSE, which explicitly uses coil sensitivities, and …
Plug-and-play methods for magnetic resonance imaging: Using denoisers for image recovery
Magnetic resonance imaging (MRI) is a noninvasive diagnostic tool that provides excellent
soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging …
soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging …
Low-cost high-performance MRI
M Sarracanie, CD LaPierre, N Salameh… - Scientific reports, 2015 - nature.com
Abstract Magnetic Resonance Imaging (MRI) is unparalleled in its ability to visualize
anatomical structure and function non-invasively with high spatial and temporal resolution …
anatomical structure and function non-invasively with high spatial and temporal resolution …
Compressed sensing: From research to clinical practice with deep neural networks: Shortening scan times for magnetic resonance imaging
Compressed sensing (CS) reconstruction methods leverage sparse structure in underlying
signals to recover high-resolution images from highly undersampled measurements. When …
signals to recover high-resolution images from highly undersampled measurements. When …
Calibrationless parallel imaging reconstruction based on structured low‐rank matrix completion
PJ Shin, PEZ Larson, MA Ohliger… - Magnetic resonance …, 2014 - Wiley Online Library
Purpose A calibrationless parallel imaging reconstruction method, termed simultaneous
autocalibrating and k‐space estimation (SAKE), is presented. It is a data‐driven, coil‐by‐coil …
autocalibrating and k‐space estimation (SAKE), is presented. It is a data‐driven, coil‐by‐coil …
Wave‐CAIPI for highly accelerated 3D imaging
Purpose To introduce the wave‐CAIPI (controlled aliasing in parallel imaging) acquisition
and reconstruction technique for highly accelerated 3D imaging with negligible g‐factor and …
and reconstruction technique for highly accelerated 3D imaging with negligible g‐factor and …
Accelerating SENSE using compressed sensing
Both parallel MRI and compressed sensing (CS) are emerging techniques to accelerate
conventional MRI by reducing the number of acquired data. The combination of parallel MRI …
conventional MRI by reducing the number of acquired data. The combination of parallel MRI …
Fast -SPIRiT Compressed Sensing Parallel Imaging MRI: Scalable Parallel Implementation and Clinically Feasible Runtime
We present \ell_1-SPIRiT, a simple algorithm for auto calibrating parallel imaging (acPI) and
compressed sensing (CS) that permits an efficient implementation with clinically-feasible …
compressed sensing (CS) that permits an efficient implementation with clinically-feasible …