Evidence map›Paper›PMID 41917000›Full record

ArticleNature communications2026

Single-breath-hold 3D abdominal metabolic MRI enables label-free diagnosis of liver cancer.

Chuyu Liu, Nan Gao, Haiqi Ren, Hao Liu, Juxiang Hou, Zhongsen Li, Benqi Zhao, Yibei Yu, Xiaowei He, Zhuozhao Zheng and 1 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Chuyu Liu *Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China.
Nan Gao *Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China.
Haiqi RenSchool of Information Sciences and Technology, Northwest University, Xi'an, Shaanxi, China.
Hao LiuSchool of Information Sciences and Technology, Northwest University, Xi'an, Shaanxi, China.
Juxiang HouCenter for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China.
Zhongsen LiCenter for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China.
Benqi ZhaoDepartment of Radiology, Beijing Tsinghua Changgung Hospital, Beijing, China.
Yibei YuDepartment of Radiology, Beijing Tsinghua Changgung Hospital, Beijing, China.
Xiaowei HeSchool of Information Sciences and Technology, Northwest University, Xi'an, Shaanxi, China.
Zhuozhao ZhengDepartment of Radiology, Beijing Tsinghua Changgung Hospital, Beijing, China.
Xiaolei SongCenter for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China. songxl@tsinghua.edu.cn.ORCID 0000-0002-0206-1574

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82071914
6 · The paper itself

Abstract

Chemical exchange saturation transfer (CEST) MRI could detect proteins/peptides, creatine, glucose, and glycogen by labeling their exchangeable amide, amine, and hydroxyl groups respectively, via frequency-selective RF pulses. Without the need for contrast agents or specialized hardware, CEST can be conveniently integrated into existing clinical MR protocols. However, its abdominal application is limited by long scan time (> 5 min) and susceptibility to respiratory motion (60-70% successful scan rate). We develop an ultra-fast 3D CEST MRI approach using spatial-spectral encoding (SSE), enabling a full spectral scan of whole-liver 3D images within a single breath-hold. SSE-CEST employs an efficient z-ω encoding pattern by applying a saturation gradient, followed by a data-driven spatial spectral reconstruction based on the low-rankness of CEST spectra. SSE-CEST is comprehensively evaluated in glycogen phantoms, ex vivo porcine liver, healthy volunteers and patients. Single breath-hold SSE-CEST largely improves successful rate, with a correlation of 0.95 between two repeated scans. SSE-CEST enables the detection of multi-metabolite changes in the liver and pancreas after an overnight fasting, and the dynamic mapping of hepatic glucose metabolism during an oral glucose test. For liver cancer patients, SSE could differentiate active lesions from post-treatment necrosis, featuring superior in-slice spatial resolution and motion-stabilized images. SSE-CEST MRI potentially could facilitate the diagnosis and patient management for liver and other abdominal diseases.

Indexed as

Breath HoldingImaging, Three-DimensionalLiver NeoplasmsMagnetic Resonance ImagingAbdomenAnimalsFemaleGlucoseGlycogenHumansLiverMalePhantoms, ImagingSwineGlucoseGlycogen

Identifiers

PMID41917000
PMCPMC13201843

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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.