Optimization of multi-parameter MRI with flexible design sequences in the saddle area based on artificial intelligence-assisted compressed sensing technology

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• Technical Article •

LUO Hedan LIU Yangyingqiu ZHANG Haonan LIU Na ZHANG Yukun YUAN Chang SUN Jiayi SONG Qingwei MIAO Yanwei

Cite this article as: LUO H D, LIU Y Y Q, ZHANG H N, et al. Optimization of multi-parameter MRI with flexible design sequences in the saddle area based on artificial intelligence-assisted compressed sensing[J]. Chin J Magn Reson Imaging, 2024, 15(12): 150-156. DOI:10.12015/issn.1674-8034.2024.12.022.

Abstract

[Abstract] Objective To investigate the impact of artificial intelligence-assisted compressed sensing (ACS) technology on the imaging of the MULTI-parametric MR imaging with flexible design sequences (MTP) in the saddle area, and to optimize and select the most suitable acceleration factor (AF).Materials and Methods Forty-one patients were prospectively included. There were 27 patients with sellar lesions and 14 healthy volunteers. All subjects underwent MTP sequential scanning with different AF using 3.0 T MRI. These included AF=3 for sensitivity encoding (SENSE) and AF=3, 4, 5 and 6 for ACS technology (abbreviated SENSE3, ACS3, ACS4, ACS5 and ACS6, respectively). The images of T1 map, R2^* map, T2^* map, T1WI and proton density weighted imaging (PDWI) were obtained from the MTP sequence. The signal intensity (SI) and quantitative parameters of the lesions and gray matter were measured by two observers on the parameter maps and weighted maps of different AF sequences, respectively. SI, standard deviation (SD) and quantitative parameters of white matter were measured. The quantitative parameter values, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), between the two groups of different AF were calculated respectively. According to image artifacts, lesion significance and gray matter demarcation clarity, five-point scoring method was used to evaluate the image quality. Kappa was used to test the consistency of subjective scores between the two observers. Intra-class correlation coefficients (ICC) were used to test the consistency of objective measurements between two observers. The differences in quantitative values, SNR, CNR and subjective scores among different AF were analyzed using Friedman rank sum test or single factor ANOVA test.Results There was a good agreement between the two observers (ICC: 0.836-0.998, Kappa: 0.839-0.909). The subjective scores and measurement data of senior observers were selected for follow-up analysis. There was no significant difference in quantitative values among different AF sequences (P>0.05). The measured SNR and CNR were different under different AF, and there was statistical significance (P<0.05). After optimization, the scanning time of ACS sequences with different AF (3-6) was reduced by 21.21%, 40.77%, 52.62% and 61.16%, respectively, compared with that of SENSE3 sequences. Compared with SENSE3 sequence, SNR and CNR of T1WI image and PDWI image were increased when ACS3 and 4 were used, and the difference was statistically significant (P<0.05). When ACS5, 6, the SNR of PDWI images increased, and the difference was statistically significant (P<0.05). There was no significant difference in other data (P>0.05). Compared with SENSE3 sequence images, the subjective score of gray-white matter demarcation clarity in ACS5 and 6 sequences was lower among different AFS, and the difference was statistically significant. The subjective score of lesion significance of ACS6 sequence decreased, and the difference was statistically significant. There was no significant difference in other data (P>0.05).Conclusions The results of this study show that using ACS can further optimize MTP sequences. Comprehensively considering time and image quality, ACS4 is recommended, with scan time reduced by 40.77%, and able to obtain reliable quantitative parameters and qualitative images.

[Keywords] multi-parameter synthetic sequence；compressed sensing；artificial intelligence；saddle region；magnetic resonance imaging

Contributor Information

LUO Hedan^{1,
2} LIU Yangyingqiu³ ZHANG Haonan¹ LIU Na¹ ZHANG Yukun¹ YUAN Chang¹ SUN Jiayi¹ SONG Qingwei¹ MIAO Yanwei^1*

¹ Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian116011, China

² Department of Radiology, Dalian Public Health Clinical Center, Dalian116031, China

³ Department of Radiology, Zibo Central Hospital, Zibo255000, China

Corresponding author: MIAO Y W, E-mail: ywmiao716@163.com

Conflicts of interest None.

Publication Information

Received 2024-06-25

Accepted 2024-12-10

DOI: 10.12015/issn.1674-8034.2024.12.022

Text

References

[1]

GONÇALVES F G, SERAI S D, ZUCCOLI G. Synthetic brain MRI: review of current concepts and future directions[J]. Top Magn Reson Imaging, 2018, 27(6): 387-393. DOI: 10.1097/RMR.0000000000000189.

[2]

JI S, YANG D J, LEE J, et al. Synthetic MRI: technologies and applications in neuroradiology[J]. J Magn Reson Imaging, 2022, 55(4): 1013-1025. DOI: 10.1002/jmri.27440.

[3]

YE Y Q, LYU J Y, HU Y C, et al. MULTI-parametric MR imaging with fLEXible design (MULTIPLEX)[J]. Magn Reson Med, 2022, 87(2): 658-673. DOI: 10.1002/mrm.28999.

[4]

LIU Y W, NIU H J, YIN H X, et al. A comparative study on phantom verification of T1 and T2 relaxation values determined by synthetic MRI and conventional mapping methods[J]. Chin J Magn Reson Imag, 2022, 13(4): 89-93. DOI: 10.12015/issn.1674-8034.2022.04.016.

[5]

LIU H M, YIN G P, BIE F, et al. Comparison of image quality of brain between conventional MR sequence and compilation sequence[J]. Chin J Med Imag Technol, 2019, 35(2): 268-271. DOI: 10.13929/j.1003-3289.201805163.

[6]

HAGIWARA A, HORI M, COHEN-ADAD J, et al. Linearity, bias, intrascanner repeatability, and interscanner reproducibility of quantitative multidynamic multiecho sequence for rapid simultaneous relaxometry at 3 T: a validation study with a standardized phantom and healthy controls[J]. Invest Radiol, 2019, 54(1): 39-47. DOI: 10.1097/RLI.0000000000000510.

[7]

ANDICA C, HAGIWARA A, HORI M, et al. Automated brain tissue and myelin volumetry based on quantitative MR imaging with various in-plane resolutions[J]. J De Neuroradiol, 2018, 45(3): 164-168. DOI: 10.1016/j.neurad.2017.10.002.

[8]

LEE S M, CHO H H, YOU S K, et al. Time-saving synthetic magnetic resonance imaging protocols for pediatric neuroimaging: impact of echo train length and bandwidth on image quality[J]. Pediatr Radiol, 2022, 52(12): 2401-2412. DOI: 10.1007/s00247-022-05389-5.

[9]

WANG J N, ZHU J Y, ZHANG B, et al. The study of T1 and T2 values in differentiating of benign and malignant gliomas[J]. Chin J Magn Reson Imag, 2020, 11(6): 416-421. DOI: 10.12015/issn.1674-8034.2020.06.004.

[10]

FUJITA S, HAGIWARA A, HORI M, et al. 3D quantitative synthetic MRI-derived cortical thickness and subcortical brain volumes: scan-rescan repeatability and comparison with conventional T1-weighted images[J]. J Magn Reson Imaging, 2019, 50(6): 1834-1842. DOI: 10.1002/jmri.26744.

[11]

FUJITA S, HAGIWARA A, OTSUKA Y, et al. Deep learning approach for generating MRA images from 3D quantitative synthetic MRI without additional scans[J]. Invest Radiol, 2020, 55(4): 249-256. DOI: 10.1097/RLI.0000000000000628.

[12]

LEE H K, SONG J S, JANG W, et al. Improved single breath-hold SSFSE sequence for liver MRI based on compressed sensing: evaluation of image quality compared with conventional T2-weighted sequences[J/OL]. Diagnostics, 2022, 12(9): 2164 [2024-06-24]. https://pubmed.ncbi.nlm.nih.gov/36140565/. DOI: 10.3390/diagnostics12092164.

[13]

BISCHOFF L M, KATEMANN C, ISAAK A, et al. T2 turbo spin echo with compressed sensing and propeller acquisition (sampling k-space by utilizing rotating blades) for fast and motion robust prostate MRI: comparison with conventional acquisition[J]. Invest Radiol, 2023, 58(3): 209-215. DOI: 10.1097/RLI.0000000000000923.

[14]

QIU J X, LIU J, BI Z X, et al. An investigation of 2D spine magnetic resonance imaging (MRI) with compressed sensing (CS)[J]. Skeletal Radiol, 2022, 51(6): 1273-1283. DOI: 10.1007/s00256-021-03954-x.

[15]

YOON S, PARK S H, HAN D. Uncover this tech term: compressed sensing magnetic resonance imaging[J]. Korean J Radiol, 2023, 24(12): 1293-1302. DOI: 10.3348/kjr.2023.0743.

[16]

BUSTIN A, FUIN N, BOTNAR R M, et al. From compressed-sensing to artificial intelligence-based cardiac MRI reconstruction[J/OL]. Front Cardiovasc Med, 2020, 7: 17 [2024-06-24]. https://pubmed.ncbi.nlm.nih.gov/32158767/. DOI: 10.3389/fcvm.2020.00017.

[17]

SUI H, GONG Y, LIU L, et al. Comparison of artificial intelligence-assisted compressed sensing (ACS) and routine two-dimensional sequences on lumbar spine imaging[J/OL]. J Pain Res, 2023, 16: 257-267 [2024-06-24]. https://pubmed.ncbi.nlm.nih.gov/36744117/. DOI: 10.2147/JPR.S388219.

[18]

KOZAK B M, JAIMES C, KIRSCH J, et al. MRI techniques to decrease imaging times in children[J]. Radiographics, 2020, 40(2): 485-502. DOI: 10.1148/rg.2020190112.

[19]

LIU H B, DENG D L, ZENG W L, et al. AI-assisted compressed sensing and parallel imaging sequences for MRI of patients with nasopharyngeal carcinoma: comparison of their capabilities in terms of examination time and image quality[J]. Eur Radiol, 2023, 33(11): 7686-7696. DOI: 10.1007/s00330-023-09742-6.

[20]

LIU K, XI B, SUN H T, et al. The clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) for evaluation of uncooperative patients with brain diseases: comparison with the conventional T2-FLAIR with parallel imaging[J]. Acta Radiol, 2023, 64(5): 1943-1949. DOI: 10.1177/02841851221139125.

[21]

LI H L, HU C L, YANG Y, et al. Single-breath-hold T2WI MRI with artificial intelligence-assisted technique in liver imaging: As compared with conventional respiratory-triggered T2WI[J/OL]. Magn Reson Imaging, 2022, 93: 175-180 [2024-06-24]. https://pubmed.ncbi.nlm.nih.gov/35987419/. DOI: 10.1016/j.mri.2022.08.012.

[22]

ZHAO Q, XU J J, YANG Y X, et al. AI-assisted accelerated MRI of the ankle: clinical practice assessment[J/OL]. Eur Radiol Exp, 2023, 7(1): 62 [2024-06-24]. https://pubmed.ncbi.nlm.nih.gov/37857868/. DOI: 10.1186/s41747-023-00374-5.

[23]

ZHENG Z F, ZHANG D P, MAO L, et al. Comparative study of image quality between synthetic MRI and conventional MRI[J]. Chin J Magn Reson Imag, 2019, 10(5): 361-365. DOI: 10.12015/issn.1674-8034.2019.05.009.

[24]

LÜ Q Q, LIAO J J, REN N N, et al. The study of image quality at synthetic brian MRI in children[J]. J Clin Radiol, 2023, 42(11): 1795-1799. DOI: 10.13437/j.cnki.jcr2023.11.013.

[25]

VANDERHASSELT T, NAEYAERT M, WATTÉ N, et al. Synthetic MRI of preterm infants at term-equivalent age: evaluation of diagnostic image quality and automated brain volume segmentation[J]. AJNR Am J Neuroradiol, 2020, 41(5): 882-888. DOI: 10.3174/ajnr.A6533.

[26]

ZOU M S, ZHOU Q, LI R C, et al. Image quality using synthetic brain MRI: an age-stratified study[J]. Acta Radiol, 2023, 64(5): 2010-2023. DOI: 10.1177/02841851231152098.

[27]

KONAR A S, SHAH A D, PAUDYAL R, et al. Quantitative synthetic magnetic resonance imaging for brain metastases: a feasibility study[J/OL]. Cancers, 2022, 14(11): 2651 [2024-06-24]. https://pubmed.ncbi.nlm.nih.gov/35681631/. DOI: 10.3390/cancers14112651.

[28]

SACCO S, BALLATI F, GAETANI C, et al. Multi-parametric qualitative and quantitative MRI assessment as predictor of histological grading in previously treated meningiomas[J]. Neuroradiology, 2020, 62(11): 1441-1449. DOI: 10.1007/s00234-020-02476-y.

[29]

MAURER G D, TICHY J, HARTER P N, et al. Matching quantitative MRI parameters with histological features of treatment-Naïve IDH wild-type glioma[J/OL]. Cancers, 2021, 13(16): 4060 [2024-06-24]. https://pubmed.ncbi.nlm.nih.gov/34439213/. DOI: 10.3390/cancers13164060.

[30]

BAI Y, SHEN Y, CHEN R S, et al. Magnetic resonance fingerprinting for preoperative differentiation between gonadotroph and non-gonadotroph pituitary macroadenomas[J]. Eur Radiol, 2021, 31(11): 8420-8428. DOI: 10.1007/s00330-021-07950-6.

[31]

NAYAK A M, CHOUDHARI A, PATKAR D P, et al. Pituitary volume and iron overload evaluation by 3T MRI in thalassemia[J]. Indian J Pediatr, 2021, 88(7): 656-662. DOI: 10.1007/s12098-020-03629-w.

[32]

WANG Q Z, ZHAO W L, XING X Y, et al. Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study[J]. Eur Radiol, 2023, 33(12): 8585-8596. DOI: 10.1007/s00330-023-09823-6.

[33]

NI M, HE M, YANG Y X, et al. Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective[J]. Eur Radiol, 2024, 34(5): 3046-3058. DOI: 10.1007/s00330-023-10368-x.

[34]

LUO H D, ZHANG H N, GAO B B, et al. United compressed sensing technology for optimizing one-stop threedimensional modulated flip angles technique in refocusing imaging with extended echo train (3D-MATRIX) imaging of cranial nerves[J]. Chin J Med Imag Technol, 2023, 39(6): 905-909. DOI: 10.13929/j.issn.1003-3289.2023.06.024.

[35]

ZHANG Y K, CHANG P P, GAO B B, et al. 3.0T three-dimensional high resolution vessel wall MRI for displaying lenticulostriate artery changes of cerebral small vessel disease[J]. Chin J Med Imag Technol, 2023, 39(5): 665-669. DOI: 10.13929/j.issn.1003-3289.2023.05.005.

[36]

YUAN C, ZHANG Y K, CAO J J, et al. Optimization of artificial intelligence-assisted compressed sensing sequences for cerebral and carotid 3D-TOF-MRA[J]. Chin J Magn Reson Imag, 2024, 15(4): 139-144, 152. DOI: 10.12015/issn.1674-8034.2024.04.022.

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