Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects
Stanford University · Cardiovascular Institute of the South · +4 more institutions
Abstract
Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the…
Citation impact
- FWCI
- 38.73
- Percentile
- 100%
- References
- 100
Authors
9- LRLennart R. KoetzierCorresponding
Stanford University
- DMDomenico Mastrodicasa
Cardiovascular Institute of the South, Stanford University
- TPTimothy P. Szczykutowicz
University of Wisconsin–Madison, Stanford University
- NRNiels R. van der Werf
University of Wisconsin–Madison, Cardiovascular Institute of the South, Leiden University Medical Center, Erasmus MC, Philips (Netherlands), Stanford University
- AWAdam Wang
Stanford University
Topics & keywords
- Iterative reconstruction
- Image quality
- Artificial intelligence
- Deep learning
- Medicine
- Computer vision
- Projection (relational algebra)
- Noise reduction
- Sustainable cities and communities