Evidence map›Paper›PMID 39585354›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2025

A deep learning method for the recovery of standard-dose imaging quality from ultra-low-dose PET on wavelet domain.

Song Xue, Fanxuan Liu, Hanzhong Wang, Hong Zhu, Hasan Sari, Marco Viscione, Raphael Sznitman, Axel Rominger, Rui Guo, Biao Li and 1 more

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Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Song XueDepartment of Nuclear Medicine, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
Fanxuan LiuDepartment of Nuclear Medicine, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
Hanzhong WangDepartment of Nuclear Medicine, School of Medicine, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China.
Hong ZhuDepartment of Informatics, Technical University of Munich, Munich, Germany.
Hasan SariDepartment of Nuclear Medicine, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
Marco ViscioneDepartment of Nuclear Medicine, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
Raphael SznitmanARTORG Center, University of Bern, Bern, Switzerland.
Axel RomingerDepartment of Nuclear Medicine, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
Rui GuoDepartment of Nuclear Medicine, School of Medicine, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China. gr11734@rjh.com.cn.
Biao LiDepartment of Nuclear Medicine, School of Medicine, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China. lb10363@rjh.com.cn.ORCID 0000-0002-0493-2062
Kuangyu ShiDepartment of Nuclear Medicine, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.

Funding

Fondation Novartis Consumer Health Fondation Novartis Consumer HealthNational Natural Science Foundation of China 82171975Natural Science Foundation of Shanghai 23ZR1439600Shanghai Municipal Hospital Development Center SHDC22023201
6 · The paper itself

Abstract

purposeRecent development in positron emission tomography (PET) dramatically increased the effective sensitivity by increasing the geometric coverage leading to total-body PET imaging. This encouraging breakthrough brings the hope of ultra-low dose PET imaging equivalent to transatlantic flight with the assistance of deep learning (DL)-based methods. However, conventional DL approaches face limitations in addressing the heterogeneous domain of PET imaging. This study aims to develop a wavelet-based DL method capable of restoring high-quality imaging from ultra-low-dose PET scans. MATERIALS AND

methodsIn contrast to conventional DL techniques that denoise images in the spatial domain, we introduce WaveNet, a novel approach that inputs wavelet-decomposed frequency components of PET imaging to perform denoising in the frequency domain. A dataset comprising total-body

resultsOur proposed WaveNet consistently outperforms the baseline UNet model across all levels of dose reduction factors (DRF), with greater improvements observed as image quality decreases. Statistical analysis (p < 0.05) and visual inspection validated the superiority of WaveNet. Moreover, WaveNet demonstrated superior generalizability when applied to two cross-scanner datasets (p < 0.05).

conclusionWaveNet developed with total-body PET scanners may offer a computational-friendly and robust approach to recover image quality from ultra-low-dose PET imaging. Its adoption may enhance the reliability and clinical acceptance of DL-based dose reduction techniques.

Indexed as

Deep LearningImage Processing, Computer-AssistedPositron-Emission TomographyRadiation DosageWavelet AnalysisHumansDeep learningPETUltra-low-doseWavelet

Identifiers

PMID39585354

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.