MODEL-BASED IMAGE RECONSTRUCTION FOR MRI
By Jeffrey A. Fessler. IEEE Signal Processing Magazine, 2010
339 citations recorded by the source corpus.
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 image reconstruction method for k-space data with full Cartesian sampling. And for well sampled non-Cartesian data, the gridding method with appropriate density compensation factors is fast and effective. But when only under-sampled data is available, or when non-Fourier physical effects like field inhomogeneity are important, then gridding/FFT methods for image reconstruction are suboptimal, and iterative algorithms based on appropriate models can improve image quality, rat the price of increased computation. This article reviews the use of iterative algorithms for model-based MR image reconstruction.
Subfields: Radiation, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/19f9e0ddc1cc8daa55c950c938a1c8d086461e71
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Radiation
- Richard Baraniuk
- Munawar Hayat
- Salman Hameed Khan
- Huan Fu
- Muhammad Haris Khan
- Fahad Shahbaz Khan
- Fahad Shamshad
- Syed Waqas Zamir
In Radiology, Nuclear Medicine and Imaging
- Richard Baraniuk
- Mathews Jacob
- Munawar Hayat
- Rebecca M. Willett
- Salman Hameed Khan
- A. Dimakis
- Huan Fu
- Ajil Jalal
Citations in the corpus
- Deep Learning Techniques for Inverse Problems in Imaging (2020, background): “resolution, and image inpainting [2], to a wide variety of tomographic imaging applications, including magnetic resonance imaging [3], X-ray computed tomography [4], and radar imaging [5].”
- MoDL: Model-Based Deep Learning Architecture for Inverse Problems (2017, background): “MODEL-BASED recovery of images from noisy and sparse multichannel measurements is now a mature area with success in several application areas such as MRI [1], CT [2], PET [3], microscopy [4].”
- Transformers in Medical Imaging: A Survey (2022, methodology): “From left to right (top row): Fourier method (ZF) [287], GANDIP (unsupervised CNN), GANPrior [288] (unsupervised CNN with pre-training), Self-Attention GANDIP [46] (unsupervised CNN), Self-Attention GAN [288] (unsupervised CNN with pre-training), SLATERDIP [38] (unsupervised transformer), SLATER [38”