Evidence map›Paper›PMID 35698006›Full record

ArticleEJNMMI physics2022

Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept.

Boyang Pan, Na Qi, Qingyuan Meng, Jiachen Wang, Siyue Peng, Chengxiao Qi, Nan-Jie Gong, Jun Zhao

Abstract read
In one paragraph

Article in EJNMMI physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

12 citing papers in PubMed.

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  11. Acquisition time reduction in pediatricJournal of applied clinical medical physics · 2023
    Article
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4 · The record

Corrections and comments

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

8 authors.

Boyang Pan *RadioDynamic Healthcare, Shanghai, China.
Na Qi *Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong New District, Shanghai, China.
Qingyuan Meng *Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong New District, Shanghai, China.
Jiachen WangRadioDynamic Healthcare, Shanghai, China.
Siyue PengRadioDynamic Healthcare, Shanghai, China.
Chengxiao QiRadioDynamic Healthcare, Shanghai, China.
Nan-Jie GongVector Lab for Intelligent Medical Imaging and Neural Engineering, International Innovation Center of Tsinghua University, No. 602 Tongpu Street, Putuo District, Shanghai, China. nanjie.gong@gmail.com.ORCID http://orcid.org/0000-0001-9249-413X
Jun ZhaoDepartment of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong New District, Shanghai, China. petcenter@126.com.

Funding

Project of Science and Technology Commission of Shanghai Municipality 19DZ1930703Shanghai Pujiang Program 2020PJD038
6 · The paper itself

Abstract

backgroundTo generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method. MATERIALS AND

methodsNormal-dose (925-1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U

resultsU

conclusionsOur proposed method can yield significant image quality improvement in the noise level, details of anatomical structure and SUV accuracy, which enabled applications of ultra fast SPECT bone imaging in real clinic settings.

Indexed as

BoneDeep learningImage quality enhancementSPECT

Identifiers

PMID35698006
PMCPMC9192886

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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.