Deep learning–based time-of-flight (ToF) image enhancement of non-ToF PET scans.
Purpose: To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF). Methods: A total of 273 [18F]-FDG PET scans were used, including data from 6 centres equipped with G...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 49; no. 11; pp. 3740 - 3750 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158671659&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158671659 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Sep2022 vid: 49 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 158671659 10.1007/s00259-022-05824-7 158671659 ppf: 3740 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning–based time-of-flight (ToF) image enhancement of non-ToF PET scans. aug: au: Mehranian, Abolfazl Wollenweber, Scott D. Walker, Matthew D. Bradley, Kevin M. Fielding, Patrick A. Huellner, Martin Kotasidis, Fotis Su, Kuan-Hao Johnsen, Robert Jansen, Floris P. McGowan, Daniel R. affil: GE Healthcare, Big Data Institute, University of Oxford, Oxford, UK sug: ab: Purpose: To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF). Methods: A total of 273 [18F]-FDG PET scans were used, including data from 6 centres equipped with GE Discovery MI ToF scanners. PET data were reconstructed using the block-sequential-regularised-expectation–maximisation (BSREM) algorithm with and without ToF. The images were then split into training (n = 208), validation (n = 15), and testing (n = 50) sets. Three DL-ToF models were trained to transform non-ToF BSREM images to their target ToF images with different levels of DL-ToF strength (low, medium, high). The models were objectively evaluated using the testing set based on standardised uptake value (SUV) in 139 identified lesions, and in normal regions of liver and lungs. Three radiologists subjectively rated the models using testing sets based on lesion detectability, diagnostic confidence, and image noise/quality. Results: The non-ToF, DL-ToF low, medium, and high methods resulted in − 28 ± 18, − 28 ± 19, − 8 ± 22, and 1.7 ± 24% differences (mean; SD) in the SUVmax for the lesions in testing set, compared to ToF-BSREM image. In background lung VOIs, the SUVmean differences were 7 ± 15, 0.6 ± 12, 1 ± 13, and 1 ± 11% respectively. In normal liver, SUVmean differences were 4 ± 5, 0.7 ± 4, 0.8 ± 4, and 0.1 ± 4%. Visual inspection showed that our DL-ToF improved feature sharpness and convergence towards ToF reconstruction. Blinded clinical readings of testing sets for diagnostic confidence (scale 0–5) showed that non-ToF, DL-ToF low, medium, and high, and ToF images scored 3.0, 3.0, 4.1, 3.8, and 3.5 respectively. For this set of images, DL-ToF medium therefore scored highest for diagnostic confidence. Conclusion: Deep learning–based image enhancement models may provide converged ToF-equivalent image quality without ToF reconstruction. In clinical scoring DL-ToF-enhanced non-ToF images (medium and high) on average scored as high as, or higher than, ToF images. The model is generalisable and hence, could be applied to non-ToF images from BGO-based PET/CT scanners. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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