Intelligent ultrafast total-body PET for sedation-free pediatric [18F]FDG imaging.

Zhou, Xiang; Fu, Yu; Dong, Shunjie; Li, Lianghua; Xue, Song; Chen, Ruohua; Huang, Gang; Liu, Jianjun; Shi, Kuangyu (2024). Intelligent ultrafast total-body PET for sedation-free pediatric [18F]FDG imaging. European journal of nuclear medicine and molecular imaging, 51(8), pp. 2353-2366. Springer-Verlag 10.1007/s00259-024-06649-2

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PURPOSE

This study aims to develop deep learning techniques on total-body PET to bolster the feasibility of sedation-free pediatric PET imaging.

METHODS

A deformable 3D U-Net was developed based on 245 adult subjects with standard total-body PET imaging for the quality enhancement of simulated rapid imaging. The developed method was first tested on 16 children receiving total-body [18F]FDG PET scans with standard 300-s acquisition time with sedation. Sixteen rapid scans (acquisition time about 3 s, 6 s, 15 s, 30 s, and 75 s) were retrospectively simulated by selecting the reconstruction time window. In the end, the developed methodology was prospectively tested on five children without sedation to prove the routine feasibility.

RESULTS

The approach significantly improved the subjective image quality and lesion conspicuity in abdominal and pelvic regions of the generated 6-s data. In the first test set, the proposed method enhanced the objective image quality metrics of 6-s data, such as PSNR (from 29.13 to 37.09, p < 0.01) and SSIM (from 0.906 to 0.921, p < 0.01). Furthermore, the errors of mean standardized uptake values (SUVmean) for lesions between 300-s data and 6-s data were reduced from 12.9 to 4.1% (p < 0.01), and the errors of max SUV (SUVmax) were reduced from 17.4 to 6.2% (p < 0.01). In the prospective test, radiologists reached a high degree of consistency on the clinical feasibility of the enhanced PET images.

CONCLUSION

The proposed method can effectively enhance the image quality of total-body PET scanning with ultrafast acquisition time, leading to meeting clinical diagnostic requirements of lesion detectability and quantification in abdominal and pelvic regions. It has much potential to solve the dilemma of the use of sedation and long acquisition time that influence the health of pediatric patients.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Clinic of Nuclear Medicine

UniBE Contributor:

Shi, Kuangyu

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1619-7070

Publisher:

Springer-Verlag

Language:

English

Submitter:

Pubmed Import

Date Deposited:

22 Feb 2024 10:18

Last Modified:

15 Jun 2024 00:12

Publisher DOI:

10.1007/s00259-024-06649-2

PubMed ID:

38383744

Uncontrolled Keywords:

Deep learning Diagnostic performance Pediatrics Total-body PET Ultrafast [18F]FDG

BORIS DOI:

10.48350/193154

URI:

https://boris.unibe.ch/id/eprint/193154

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