Nai, Y-H;
Schaefferkoetter, J;
Fakhry-Darian, D;
O'Doherty, S;
Totman, JJ;
Conti, M;
Townsend, DW;
... Reilhac, A; + view all
(2020)
Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning.
Physica Medica
, 81
pp. 285-294.
10.1016/j.ejmp.2020.11.027.
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Abstract
PURPOSE: To conduct a simplified lesion-detection task of a low-dose (LD) PET-CT protocol for frequent lung screening using 30% of the effective PETCT dose and to investigate the feasibility of increasing clinical value of low-statistics scans using machine learning. METHODS: We acquired 33 SD PET images, of which 13 had actual LD (ALD) PET, and simulated LD (SLD) PET images at seven different count levels from the SD PET scans. We employed image quality transfer (IQT), a machine learning algorithm that performs patch-regression to map parameters from low-quality to high-quality images. At each count level, patches extracted from 23 pairs of SD/SLD PET images were used to train three IQT models - global linear, single tree, and random forest regressions with cubic patch sizes of 3 and 5 voxels. The models were then used to estimate SD images from LD images at each count level for 10 unseen subjects. Lesion-detection task was carried out on matched lesion-present and lesion-absent images. RESULTS: LD PET-CT protocol yielded lesion detectability with sensitivity of 0.98 and specificity of 1. Random forest algorithm with cubic patch size of 5 allowed further 11.7% reduction in the effective PETCT dose without compromising lesion detectability, but underestimated SUV by 30%. CONCLUSION: LD PET-CT protocol was validated for lesion detection using ALD PET scans. Substantial image quality improvement or additional dose reduction while preserving clinical values can be achieved using machine learning methods though SUV quantification may be biased and adjustment of our research protocol is required for clinical use.
| Type: | Article |
|---|---|
| Title: | Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning |
| Location: | Italy |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ejmp.2020.11.027 |
| Publisher version: | https://doi.org/10.1016/j.ejmp.2020.11.027 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Lesion detection, Lung cancer, Machine learning, Positron emission tomography (PET) |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10122909 |
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