Share:
Share this content in WeChat
X
Technical Article
Comparison of respiratory triggering and navigator triggering on image quality and scan efficiency of upper-abdominal T2-weighted imaging with fat saturation under the BioMatrix system
HAN Linmei  DU Taoming 

Cite this article as: HAN L M, DU T M. Comparison of respiratory triggering and navigator triggering on image quality and scan efficiency of upper-abdominal T2-weighted imaging with fat saturation under the BioMatrix system[J]. Chin J Magn Reson Imaging, 2026, 17(4): 95-100, 148. DOI:10.12015/issn.1674-8034.2026.04.013.


[Abstract] Objective To compare the impact of respiratory triggering under BioMatrix (RT-BM) versus navigator triggering (NT) on image quality and scan efficiency of upper-abdominal T2-weighted fat-suppressed (T2WI FS) magnetic resonance imaging (MRI) under the BioMatrix platform, and to provide evidence for protocol optimization.Materials and Methods One hundred consecutive patients (52 males, 48 females) scheduled for upper-abdominal MRI between June and November 2025 were prospectively enrolled. All examinations were performed on a Siemens 3.0 T Magnetom VIDA system; T2WI FS was acquired first with NT and then repeated with RT-BM. Scan duration, standard deviation (SD), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were recorded. Two senior radiologists independently graded image quality using a 5-point Likert scale evaluating hepatic edge sharpness, intrahepatic vessel depiction, gallbladder and extrahepatic bile duct definition, pancreatic duct visibility, motion artifacts and overall image quality. Detection for lesions ≤ 1 cm were documented. Statistical analysis of SD, SNR and CNR was performed using the Mann-Whitney U test. Subjective scoring consistency was assessed using intra-class correlation coefficient (ICC) consistency tests, scanning time and score differences between the two sequences were analyzed using paired t-tests, the detection and classification of minor lesions were performed using Bowker's matched chi-square test, and interlayer dislocation control comparison using McNemar test.Results RT-BM shortened scan time by 31.32% compared with NT (average shortened 94.3 s, P < 0.001). No significant differences were observed between groups for SD, SNR or CNR (P > 0.05). NT achieved higher subjective scores than RT-BM for hepatic edge sharpness, vessel depiction, motion-artifact control and overall image quality (P < 0.05), whereas gallbladder, extrahepatic bile duct and pancreatic duct visualisation were equivalent (P > 0.05). Both techniques fulfilled diagnostic requirements in every dimension. The detection rate of microlesions (NT 99.5% vs. RT-BM 96.4%) and the consistency rate of signal intensity four-category classification (undetectable/low/slightly high/high signal) (95.9%) showed no statistically significant differences between the two groups (Bowker test, χ2 = 5.333, P = 0.502). Inter-slice misregistration was comparable between groups (χ2 = 0.000, P > 0.999).Conclusions RT-BM can significantly reduce scanning time while maintaining comparable objective image quality and diagnostic efficacy to NT. Although the subjective score is slightly lower than NT, it still fully meets diagnostic requirements. For patients with poor breath-holding cooperation or those requiring shortened examination time, RT-BM can serve as an alternative solution while ensuring objective image quality and diagnostic efficacy.
[Keywords] magnetic resonance imaging;T2 weighted imaging;respiratory triggering;navigator triggering;upper-abdominal;fat saturation

HAN Linmei   DU Taoming*  

Department of Radiology, NO.7 People′s Hospital of Chengdu, Chengdu 610000, China

Corresponding author: DU T M, E-mail: dtm0528@sina.com

Conflicts of interest   None.

Received  2025-12-16
Accepted  2026-03-05
DOI: 10.12015/issn.1674-8034.2026.04.013
Cite this article as: HAN L M, DU T M. Comparison of respiratory triggering and navigator triggering on image quality and scan efficiency of upper-abdominal T2-weighted imaging with fat saturation under the BioMatrix system[J]. Chin J Magn Reson Imaging, 2026, 17(4): 95-100, 148. DOI:10.12015/issn.1674-8034.2026.04.013.

[1]
ZOU J, JIANG Y L, FAN F X, et al. Evaluation of liver function and prediction of first decompensation event in patients with chronic hepatitis b by MRI functional liver imaging score and spontaneous portosystemic shunt[J]. Chin J Magn Reson Imaging, 2025, 16(7): 30-38. DOI: 10.12015/issn.1674-8034.2025.07.005.
[2]
BAO Y Y, PAN Y Q, MAI X L. Progress on the role of magnetic resonance imaging techniques in the staged diagnosis of hepatic fibrosis[J]. Chin J Magn Reson Imaging, 2025, 16(3): 196-200. DOI: 10.12015/ISSN.1674-8034.2025.03.033.
[3]
LONG D, HUA L, SHANG W Y, et al. Advances in radiomics for predicting the efficacy of local treatments in liver malignancies[J]. Chin J Magn Reson Imaging, 2025, 16(7): 166-172. DOI: 10.12015/ISSN.1674-8034.2025.07.027.
[4]
DONATO H, FRANÇA M, CANDELÁRIA I, et al. Liver MRI: From basic protocol to advanced techniques[J]. Eur J Radiol, 2017, 93: 30-39. DOI: 10.1016/j.ejrad.2017.05.028.
[5]
MAUNG S T, TANPOWPONG N, SATJA M, et al. Non-contrast abbreviated MRI for the detection of hepatocellular carcinoma in patients with liver imaging reporting and data system LR-3 and LR-4 observations in MRI[J]. Br J Radiol, 2024, 97(1162): 1671-1682. DOI: 10.1093/bjr/tqae140.
[6]
SLIPSAGER J M, GLIMBERG S L, SØGAARD J, et al. Quantifying the financial savings of motion correction in brain MRI: A model-based estimate of the costs arising from patient head motion and potential savings from implementation of motion correction[J]. J Magn Reson Imaging, 2020, 52(3): 731-738. DOI: 10.1002/jmri.27112.
[7]
LITTLEJOHNS T J, HOLLIDAY J, GIBSON L M, et al. The UK biobank imaging enhancement of 100, 000 participants:  rationale, data collection, management and future directions[J/OL]. Nat Commun, 2020, 11(1): 2624 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/32457287/. DOI: 10.1038/s41467-020-15948-9.
[8]
SCHREIBER-ZINAMAN J, ROSENKRANTZ A B. Frequency and reasons for extra sequences in clinical abdominal MRI examinations[J]. Abdom Radiol (NY), 2017, 42(1): 306-311. DOI: 10.1007/s00261-016-0877-6.
[9]
YANG Z H, FENG F, ZHENG Z Z, et al. Technical guidelines for magnetic resonance imaging (2nd Edition)[M]. Beijing: Published by Peking Union Medical College, 2023.
[10]
ANDRE J B, BRESNAHAN B W, MOSSA-BASHA M, et al. Toward quantifying the prevalence, severity, and cost associated with patient motion during clinical MR examinations[J]. J Am Coll Radiol, 2015, 12(7): 689-695. DOI: 10.1016/j.jacr.2015.03.007.
[11]
ZAITSEV M, MACLAREN J, HERBST M. Motion artifacts in MRI: A complex problem with many partial solutions[J]. J Magn Reson Imaging, 2015, 42(4): 887-901. DOI: 10.1002/jmri.24850.
[12]
VAN ROOYEN M B, PITCHER R D. The cinderellas of the scanner: magnetic resonance imaging 'pre-scan' and 'post-scan' times: their determinants and impact on patient throughput[J/OL]. SA J RADIOL, 2020, 24(1): 1946 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/33354368/. DOI: 10.4102/sajr.v24i1.1946.
[13]
STREIT U, UHLIG J, LOTZ J, et al. Analysis of core processes of the MRI workflow for improved capacity utilization[J/OL]. Eur J Radiol, 2021, 138: 109648 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/33740625/. DOI: 10.1016/j.ejrad.2021.109648.
[14]
QIAN X L, WU Y L, SPEIER P, et al. Comparison of pilot tone-triggered and electrocardiogram-triggered cardiac magnetic resonance imaging: a prospective clinical feasibility study[J/OL]. J Cardiovasc Magn Reson, 2025, 27(2): 101925 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/40543720/. DOI: 10.1016/j.jocmr.2025.101925.[PubMed]
[15]
WAMPL S, KÖRNER T, MEYERSPEER M, et al. A modular motion compensation pipeline for prospective respiratory motion correction of multi-nuclear MR spectroscopy[J/OL]. Sci Rep, 2024, 14(1): 10781 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/38734781/. DOI: 10.1038/s41598-024-61403-w.[PubMed]
[16]
GOTTWALD L M, BLANKEN C P S, TOURAIS J, et al. Retrospective camera-based respiratory gating in clinical whole-heart 4D flow MRI[J]. J Magn Reson Imaging, 2021, 54(2): 440-451. DOI: 10.1002/jmri.27564.[PubMed]
[17]
ARLOTTO P, GRIMALDI M, NAECK R, et al. An ultrasonic contactless sensor for breathing monitoring[J]. Sensors (Basel), 2014, 14(8): 15371-15386. DOI: 10.3390/s140815371.[PubMed]
[18]
JANG A J, LEE I S, YANG J R. Vital signal detection using multi-radar for reductions in body movement effects[J/OL]. Sensors, 2021, 21(21): 7398 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/34770703/. DOI: 10.3390/s21217398.
[19]
LIANG X Y, BI Z H, YANG C, et al. Free-breathing liver magnetic resonance imaging with respiratory frequency-modulated continuous-wave radar-trigger technique: A preliminary study[J/OL]. Front Oncol, 2022, 12: 918173 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/35719930/. DOI: 10.3389/fonc.2022.918173.[PubMed]
[20]
BI Z H, LIANG X Y, CHEN C Z, et al. Clinical comparison of three respiratory-trigger techniques in free-breathing liver magnetic resonance imaging[J]. Radiol Pract, 2023, 38(2): 216-221. DOI: 10.13609/j.cnki.1000-0313.2023.02.018.
[21]
LEE W, RYU K, LI Z T, et al. MRI retrospective respiratory gating and cardiac sensing by CW Doppler radar: A feasibility study[J]. IEEE Trans Biomed Eng, 2025, 72(1): 112-122. DOI: 10.1109/tbme.2024.3440317.
[22]
KUBICKA F, TAN Q X, MEYER T, et al. Deep-learning-based reconstruction of single-breath-hold 3 mm haste improves abdominal image quality and reduces acquisition time: A quantitative analysis[J/OL]. Curr Oncol, 2025, 32(1): 30 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/39851946/. DOI: 10.3390/curroncol32010030.[PubMed]
[23]
HU C L, LIU Q F, LI H L, et al. Application value of artificial intelligence-assisted compressed sensing technique in liver T2WI scan[J]. Radiol Pract, 2023, 38(4): 508-513. DOI: 10.13609/j.cnki.1000-0313.2023.04.023.
[24]
FANG S, WU M X, CHEN Q, et al. Clinical feasibility of breath-hold fat-suppressed T2-weighted sequence with deep learning reconstruction for liver imaging[J]. Chin J Magn Reson Imaging, 2023, 14(5): 31-35, 40. DOI: 10.12015/issn.1674-8034.2023.05.007.
[25]
LIU K, LI Q, WANG X X, et al. Feasibility of deep learning-reconstructed thin-slice single-breath-hold haste for detecting pancreatic lesions: A comparison with two conventional T2-weighted imaging sequences[J/OL]. Res Diagn Interv Imaging, 2024, 9: 100038 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/39076579/. DOI: 10.1016/j.redii.2023.100038.
[26]
TAN Q X, KUBICKA F, NICKEL D, et al. Optimized deep learning-accelerated single-breath-hold abdominal haste with and without fat saturation improves and accelerates abdominal imaging at 3 Tesla[J/OL]. BMC Med Imaging, 2025, 25(1): 369 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/40968371/. DOI: 10.1186/S12880-025-01838-3.
[27]
SHI Z, JIANG J M, OUYANG H, et al. With 3 types of respiratory acquisition: 3.0 T respiratory triggered acquisition can obtain higher quality DWI images of the upper abdomen[J/OL]. Contrast Media Mol Imaging, 2022, 2022: 9579145 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/35854769/. DOI: 10.1155/2022/9579145.
[28]
JALLOUL M, ANUPINDI S A, VENKATAKRISHNA S S B, et al. Pediatric 3D MRCP imaging: strategies for enhancing exam quality[J]. Abdom Radiol, 2026, 51(1): 127-136. DOI: 10.1007/s00261-025-05063-y.
[29]
SERAI S D, HU H H, AHMAD R, et al. Newly developed methods for reducing motion artifacts in pediatric abdominal mri: tips and pearls[J]. AJR AM J Roentgenol, 2020, 214(5): 1042-1053. DOI: 10.2214/AJR.19.21987.
[30]
ZHAO Y J, PENG C D, WANG S F, et al. The feasibility investigation of AI-assisted compressed sensing in kidney MR imaging: an ultra-fast T2WI imaging technology[J/OL]. BMC Med Imaging, 2022, 22(1): 119 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/35787673/. DOI: 10.1186/s12880-022-00842-1.
[31]
KIM Y, LEE E S, PARK H J, et al. Comparison between conventional breath-hold and respiratory-triggered magnetic resonance cholangiopancreatography with and without compressed sensing: Cross-sectional study[J]. Curr Med Imaging, 2023 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/37018526/. DOI: 10.2174/1573405620666230328093206.
[32]
REITHMEIER B, LAUN F B, FÜHRES T, et al. Relevance of lesion size in navigator-triggered and free-breathing diffusion-weighted liver MRI[J]. EUR Radiol, 2025, 35(4): 2106-2115. DOI: 10.1007/S00330-024-11063-1.
[33]
HU C, WANG P, HE Y, et al. Comparison of respiratory-triggered and breath-holding sequences on 5.0 T magnetic resonance cholangiopancreatography[J]. Chin J Magn Reson Imaging, 2024, 15(11): 130-135, 152. DOI: 10.12015/issn.1674-8034.2024.11.020.
[34]
RUNGE V M, RICHTER J K, HEVERHAGEN J T. Motion in magnetic resonance: new paradigms for improved clinical diagnosis[J]. Invest Radiol, 2019, 54(7): 383-395. DOI: 10.1097/rli.0000000000000566.
[35]
CHEN C, LIU Y M, SIMONETTI O P, et al. Cardiac and respiratory motion extraction for MRI using pilot tone-a patient study[J]. Int J Cardiovasc Imaging, 2024, 40(1): 93-105. DOI: 10.1007/s10554-023-02966-z.
[36]
KIM B S, KIM J H, CHOI G M, et al. Comparison of three free-breathing T2-weighted MRI sequences in the evaluation of focal liver lesions[J]. AJR Am J Roentgenol, 2008, 190(1): W19-W27. DOI: 10.2214/AJR.07.2043.
[37]
LEE S S, BYUN J H, HONG H S, et al. Image quality and focal lesion detection on T2-weighted MR imaging of the liver: comparison of two high-resolution free-breathing imaging techniques with two breath-hold imaging techniques[J]. J Magn Reson Imaging, 2007, 26(2): 323-330. DOI: 10.1002/jmri.21002.
[38]
SHEN L L, YE X D, CHEN C Z, et al. The value of frequency-modulated continuous-wave radar-triggering technology in abdominal MRI examination of elderly patients[J]. Geriatr Health Care, 2023, 29(2): 195-199, 205. DOI: 10.3969/j.issn.1008-8296.2023.02.005.
[39]
YANG Q, DING X Y, GUO Q Y, et al. Advantages of BioMatrix respiratory gating in free-breathing three-dimensional magnetic resonance cholangiopancreatography: a prospective comparative study[J/OL]. Insights Imaging, 2025, 16(1): 137 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/40579676/. DOI: 10.1186/s13244-025-02023-4.

PREV A comparative study on acquisition time and image quality of three different diffusion imaging sequences in the examination of acute cerebral infarction
NEXT Comparative study of T1-mapping and RESOLVE DWI in the quantitative assessment of sacroiliac joint inflammatory activity in axial spondyloarthritis
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn