Journal article
Artificial intelligence in image reconstruction: The change is here
Physica medica, Vol.79, pp.113-125
11/01/2020
DOI: 10.1016/j.ejmp.2020.11.012
PMID: 33246273
Abstract
Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and faster gantry rotation speeds. To tackle concerns over rising radiation doses from its increasing use and to improve image quality, CT reconstruction techniques evolved from filtered back projection to commercial release of iterative reconstruction techniques, and recently, of deep learning (DL)-based image reconstruction. These newer reconstruction techniques enable improved or retained image quality versus filtered back projection at lower radiation doses. DL can aid in image reconstruction with training data without total reliance on the physical model of the imaging process, unique artifacts of PCD-CT due to charge sharing, K-escape, fluorescence x-ray emission, and pulse pileups can be handled in the data-driven fashion. With sufficiently reconstructed images, a well-designed network can be trained to upgrade image quality over a practical/clinical threshold or define new/killer applications. Besides, the much smaller detector pixel for PCD-CT can lead to huge computational costs with traditional model-based iterative reconstruction methods whereas deep networks can be much faster with training and validation. In this review, we present techniques, applications, uses, and limitations of deep learning-based image reconstruction methods in CT.
Details
- Title: Subtitle
- Artificial intelligence in image reconstruction: The change is here
- Creators
- Ramandeep Singh - Massachusetts General HospitalWeiwen Wu - Rensselaer Polytechnic InstituteGe Wang - Rensselaer Polytechnic InstituteMannudeep K. Kalra - Massachusetts General Hospital
- Resource Type
- Journal article
- Publication Details
- Physica medica, Vol.79, pp.113-125
- DOI
- 10.1016/j.ejmp.2020.11.012
- PMID
- 33246273
- NLM abbreviation
- Phys Med
- ISSN
- 1120-1797
- eISSN
- 1724-191X
- Publisher
- Elsevier
- Number of pages
- 13
- Language
- English
- Date published
- 11/01/2020
- Academic Unit
- Radiology
- Record Identifier
- 9984697731102771
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