A deep learning approach to gold nanoparticle quantification in computed tomography.
| Title: | A deep learning approach to gold nanoparticle quantification in computed tomography. |
|---|---|
| Authors: | Oumano M; Medical Physics Program, Department of Physics and Applied Physics, University of Massachusetts Lowell, Lowell, MA 01854, United States; Landauer Medical Physics, 2 Science Road, Glenwood, IL 60425, United States; Department of Medical Physics and Radiation Safety, Rhode Island Hospital, Providence, RI 02903, United States. Electronic address: moumano@landauermp.com.; Yu H; Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, United States. |
| Source: | Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) [Phys Med] 2021 Jul; Vol. 87, pp. 83-89. Date of Electronic Publication: 2021 Jun 11. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Istituti Editoriali e Poligrafici Internazionali Country of Publication: Italy NLM ID: 9302888 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1724-191X (Electronic) Linking ISSN: 11201797 NLM ISO Abbreviation: Phys Med Subsets: MEDLINE |
| Imprint Name(s): | Publication: Pisa : Istituti Editoriali e Poligrafici Internazionali; Original Publication: Lugano, Switzerland : Giardini editori S.A. |
| MeSH Terms: | Deep Learning* ; Metal Nanoparticles*; Gold ; Humans ; Phantoms, Imaging ; Tomography, X-Ray Computed |
| Abstract: | Introduction: Deep learning (DL) is used to classify, detect, and quantify gold nanoparticles (AuNPs) in a human-sized phantom with a clinical MDCT scanner.; Methods: AuNPs were imaged at concentrations between 0.0274 and 200 mgAu/mL in a 33 cm phantom. 1 mm-thick CT image slices were acquired at 120 kVp with a CTDIvol of 23.6 mGy. A convolutional neural network (CNN) was trained on 544 images to classify 17 different tissue types and AuNP concentrations. A second set of 544 images was then used for testing.; Results: AuNPs were classified with 95% accuracy at 0.1095 mgAu/mL and 97% accuracy at 0.2189 mgAu/mL. Both these concentrations are lower than what humans can visually perceive (0.3-1.4 mgAu/mL). AuNP concentrations were also classified with 95% accuracy at 150 and 200 mgAu/mL. These high concentrations result in CT numbers that are at or above the 12-bit limit for CT's dynamic range where extended Hounsfield scales are otherwise required for measuring differences in contrast.; Conclusions: We have shown that DL can be used to detect AuNPs at concentrations lower than what humans can visually perceive and can also quantify very high AuNP concentrations that exceed the typical 12-bit dynamic range of clinical MDCT scanners. This second finding is possible due to inhomogeneous AuNP distributions and characteristic streak artifacts. It may even be possible to extend this approach beyond AuNP imaging in CT for quantifying high density objects without extended Hounsfield scales.; (Copyright © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.) |
| Contributed Indexing: | Keywords: AuNPs; Deep learning; Gold; Machine learning; Nanoparticles |
| Substance Nomenclature: | 7440-57-5 (Gold) |
| Entry Date(s): | Date Created: 20210613 Date Completed: 20210629 Latest Revision: 20210629 |
| Update Code: | 20260130 |
| DOI: | 10.1016/j.ejmp.2021.05.036 |
| PMID: | 34120072 |
| Database: | MEDLINE |
Journal Article