Evidence map›Paper›PMID 38175218›Full record

ArticleEuropean radiology2024

Feasibility study on the clinical application of CT-based synthetic brain T1-weighted MRI: comparison with conventional T1-weighted MRI.

Zhaotong Li, Gan Cao, Li Zhang, Jichun Yuan, Sha Li, Zeru Zhang, Fengliang Wu, Song Gao, Jun Xia

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in European radiology, 2024. 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

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

1 citing paper in PubMed.

  1. Review
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

9 authors.

Zhaotong Li *Laboratory of Digital Medicine, Department of Medical Informatics, Medical School of Nantong University, Nantong, China.
Gan Cao *Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, China.
Li ZhangDepartment of Radiology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, People's Republic of China.
Jichun YuanDepartment of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China.
Sha LiInstitute of Medical Technology, Peking University Health Science Center, Beijing, China.
Zeru ZhangInstitute of Medical Technology, Peking University Health Science Center, Beijing, China.
Fengliang WuBeijing Key Laboratory of Spinal Disease Research, Engineering Research Center of Bone and Joint Precision Medicine, Department of Orthopedics, Peking University Third Hospital, Beijing, China.
Song GaoInstitute of Medical Technology, Peking University Health Science Center, Beijing, China. gaoss@bjmu.edu.cn.
Jun XiaDepartment of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China. xiajun@email.szu.edu.cn.

Funding

National Natural Science Foundation of China 12075011National Natural Science Foundation of China 82071280Natural Science Research of Jiangsu Higher Education Institutions of China 23KJB310019Science and Technology Plan Projects of Tibet Autonomous Region XZ202001ZY0005G
6 · The paper itself

Abstract

objectivesThis study aimed to examine the equivalence of computed tomography (CT)-based synthetic T1-weighted imaging (T1WI) to conventional T1WI for the quantitative assessment of brain morphology. MATERIALS AND

methodsThis prospective study examined 35 adult patients undergoing brain magnetic resonance imaging (MRI) and CT scans. An image synthesis method based on a deep learning model was used to generate synthetic T1WI (sT1WI) from CT data. Two senior radiologists used sT1WI and conventional T1WI on separate occasions to independently measure clinically relevant brain morphological parameters. The reliability and consistency between conventional and synthetic T1WI were assessed using statistical consistency checks, comprising intra-reader, inter-reader, and inter-method agreement.

resultsThe intra-reader, inter-reader, and inter-method reliability and variability mostly exhibited the desired performance, except for several poor agreements due to measurement differences between the radiologists. All the measurements of sT1WI were equivalent to that of T1WI at 5% equivalent intervals.

conclusionThis study demonstrated the equivalence of CT-based sT1WI to conventional T1WI for quantitatively assessing brain morphology, thereby providing more information on imaging diagnosis with a single CT scan. CLINICAL RELEVANCE STATEMENT: Real-time synthesis of MR images from CT scans reduces the time required to acquire MR signals, improving the efficiency of the treatment planning system and providing benefits in the clinical diagnosis of patients with contraindications such as presence of metal implants or claustrophobia. KEY POINTS: • Deep learning-based image synthesis methods generate synthetic T1-weighted imaging from CT scans. • The equivalence of synthetic T1-weighted imaging and conventional MRI for quantitative brain assessment was investigated. • Synthetic T1-weighted imaging can provide more information per scan and be used in preoperative diagnosis and radiotherapy.

Indexed as

BrainFeasibility StudiesMagnetic Resonance ImagingTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedProspective StudiesReproducibility of ResultsBrainComputed tomographyDeep learningMagnetic resonance imagingSynthetic T1-weighted imaging

Identifiers

PMID38175218

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