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Post-reconstruction...
Post-reconstruction enhancement of [18F]FDG PET images with a convolutional neural network
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- Ly, John (författare)
- Lund University,Lunds universitet,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,Nuclear medicine, Malmö,Lund University Research Groups,Central Hospital Kristianstad
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- Minarik, David (författare)
- Lund University,Lunds universitet,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,Nuclear medicine, Malmö,Lund University Research Groups,Skåne University Hospital
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- Jögi, Jonas (författare)
- Lund University,Lunds universitet,Klinisk fysiologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,Clinical Physiology (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Nuclear medicine, Malmö,Lund University Research Groups,Skåne University Hospital
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- Wollmer, Per (författare)
- Lund University,Lunds universitet,Klinisk fysiologi och nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,Clinical Physiology and Nuclear Medicine, Malmö,Lund University Research Groups
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- Trägårdh, Elin (författare)
- Lund University,Lunds universitet,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,WCMM- Wallenberg center för molekylär medicinsk forskning,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Nuclear medicine, Malmö,Lund University Research Groups,WCMM-Wallenberg Centre for Molecular Medicine,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital
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(creator_code:org_t)
- 2021-05-11
- 2021
- Engelska.
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Ingår i: EJNMMI Research. - : Springer Science and Business Media LLC. - 2191-219X. ; 11:1
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://ejnmmires.sp...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Background: The aim of the study was to develop and test an artificial intelligence (AI)-based method to improve the quality of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) images. Methods: A convolutional neural network (CNN) was trained by using pairs of excellent (acquisition time of 6 min/bed position) and standard (acquisition time of 1.5 min/bed position) or sub-standard (acquisition time of 1 min/bed position) images from 72 patients. A test group of 25 patients was used to validate the CNN qualitatively and quantitatively with 5 different image sets per patient: 4 min/bed position, 1.5 min/bed position with and without CNN, and 1 min/bed position with and without CNN. Results: Difference in hotspot maximum or peak standardized uptake value between the standard 1.5 min and 1.5 min CNN images fell short of significance. Coefficient of variation, the noise level, was lower in the CNN-enhanced images compared with standard 1 min and 1.5 min images. Physicians ranked the 1.5 min CNN and the 4 min images highest regarding image quality (noise and contrast) and the standard 1 min images lowest. Conclusions: AI can enhance [18F]FDG-PET images to reduce noise and increase contrast compared with standard images whilst keeping SUVmax/peak stability. There were significant differences in scoring between the 1.5 min and 1.5 min CNN image sets in all comparisons, the latter had higher scores in noise and contrast. Furthermore, difference in SUVmax and SUVpeak fell short of significance for that pair. The improved image quality can potentially be used either to provide better images to the nuclear medicine physicians or to reduce acquisition time/administered activity.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Nyckelord
- Artificial intelligence
- Cancer
- Image quality
- PET
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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