Evidence map›Paper›PMID 40893509›Full record

ArticleQuantitative imaging in medicine and surgery2025

Comparing respiratory-triggered T2WI MRI with an artificial intelligence-assisted technique and motion-suppressed respiratory-triggered T2WI in abdominal imaging.

Nan Wang, Yuhui Liu, Jiangnan Ran, Qi An, Lihua Chen, Ying Zhao, Dan Yu, Ailian Liu, Lina Zhuang, Qingwei Song

Abstract read
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Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Nan WangDepartment of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Yuhui LiuSchool of Medical Imaging, Dalian Medical University, Dalian, China.
Jiangnan RanDepartment of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Qi AnDepartment of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Lihua ChenDepartment of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Ying ZhaoDepartment of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Dan YuUnited Imaging Research Institute of Intelligent Imaging, United Imaging Healthcare Technology, Beijing, China.
Ailian LiuDepartment of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Lina ZhuangDepartment of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Qingwei SongDepartment of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of abdominal conditions. A comprehensive assessment, especially of the liver, requires multi-planar T2-weighted sequences. To mitigate the effect of respiratory motion on image quality, the combination of acquisition and reconstruction with motion suppression (ARMS) and respiratory triggering (RT) is commonly employed. While this method maintains image quality, it does so at the expense of longer acquisition times. We evaluated the effectiveness of free-breathing, artificial intelligence-assisted compressed-sensing respiratory-triggered T2-weighted imaging (ACS-RT T2WI) compared to conventional acquisition and reconstruction with motion-suppression respiratory-triggered T2-weighted imaging (ARMS-RT T2WI) in abdominal MRI, assessing both qualitative and quantitative measures of image quality and lesion detection. Methods: In this retrospective study, 334 patients with upper abdominal discomfort were examined on a 3.0T MRI system. Each patient underwent both ARMS-RT T2WI and ACS-RT T2WI. Image quality was analyzed by two independent readers using a five-point Likert scale. The quantitative measurements included the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and sharpness. Lesion detection rates and contrast ratios (CRs) were also evaluated for liver, biliary system, and pancreatic lesions. Results: There ACS-RT T2WI protocol had a significantly reduced median scanning time compared to the ARMS-RT T2WI protocol (148.22±38.37 Conclusions: ACS-RT T2WI ensures clinical reliability with a substantial scan time reduction (>80%). Despite minor losses in detail and SNR reduction, ACS-RT T2WI does not impair lesion detection, marking its efficacy in abdominal imaging.

Indexed as

Artificial intelligencecompressed sensinglesion assessmentmagnetic resonance imaging (MRI)

Identifiers

PMID40893509
PMCPMC12397657

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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.