Journal
PROCEEDINGS OF THE IEEE
Volume 108, Issue 1, Pages 86-109Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPROC.2019.2936204
Keywords
Image reconstruction; Computed tomography; Mathematical model; Magnetic resonance imaging; Machine learning; X-ray imaging; Data models; Compressed sensing (CS); deep learning; dictionary learning (DL); efficient algorithms; image reconstruction; machine learning; magnetic resonance imaging (MRI); multilayer models; nonconvex optimization; positron emission tomography (PET); single-photon emission computed tomography (SPECT); sparse and low-rank models; structured models; transform learning; X-ray computed tomography (CT)
Categories
Funding
- National Research Foundation of Korea [2016R1A2B3008104]
- National Institutes of Health (NIH) [R01 CA214981, R01 EB023618, U01 EB018753, U01 EB026977]
- National Research Foundation of Korea [2016R1A2B3008104] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Ask authors/readers for more resources
The field of medical image reconstruction has seen roughly four types of methods. The first type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the imaging systems. These methods are typically fast, but have suboptimal properties such as poor resolution-noise tradeoff for CT. A second type is iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics. These iterative methods improved image quality by reducing noise and artifacts. The U.S. Food and Drug Administration (FDA)-approved methods among these have been based on relatively simple regularization models. A third type of methods has been designed to accommodate modified data acquisition methods, such as reduced sampling in MRI and CT to reduce scan time or radiation dose. These methods typically involve mathematical image models involving assumptions such as sparsity or low rank. A fourth type of methods replaces mathematically designed models of signals and systems with data-driven or adaptive models inspired by the field of machine learning. This article focuses on the two most recent trends in medical image reconstruction: methods based on sparsity or low-rank models and data-driven methods based on machine learning techniques.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available