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A Novel Linear Model for Quantitative Magnetic Resonance Imaging Based On Spin Echo Sequence: A Feasibility Study

S Li*, Y Zhang, H Wu#, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, BeijingCN, #corresponding author: Hao Wu; email: hao.wu@bjcancer.org

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose:
To date, the existing quantitative magnetic resonance imaging (MRI) methods usually either provide information on a single parameter at a time or have complex mathematical models. This study proposed a novel linear model for calculating quantitative T2-map, T1-map and ?-map images based on spin echo (SE) sequence. Investigation was also performed to validate whether the linear model can achieve quantitative imaging.

Methods:
Scans of whole brain were performed on two healthy volunteers using a clinical 3T MR scanner (GE MR750) with a spin echo sequence. Typical acquisition parameters included: TR, 2000 ms; TE, 9 ms, 15 ms, 30 ms, 45 ms, 60 ms and 75 ms; FOV, 25 cm; matrix, 256×256; slice thickness, 5 mm; slice number, 20. The new method integrated two models of generating T2-map and ?-map into one mathematical model, and then separately generated T1-map by another improved model. The time for generating quantitative images were recorded and compared with the results of the existing research method. Structural similarity index (SSIM) was used to evaluate the image quality of quantitative images.

Results:
Compared with the existing linear fitting method, the results showed that the total time for generating quantitative images of the three parameters using the proposed method was 1/11 of the reference method. The mean structural similarity index of T2-map, ?-map and T1-map were approximately 1.0, 1.0 and 0.9, respectively.

Conclusion:
The new linear model can realize quantitative imaging based on SE sequence. Compared with the existing linear fitting method, the proposed method improved computing efficiency while maintained the image quality.

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Funding Support, Disclosures, and Conflict of Interest: This work was supported by Capital's Funds for Health Improvement and Research[2018-4-1027]; Fundamental Research Funds for the Central Universities/Peking University Clinical Medicine Plus X - Young Scholars Project(PKU2020LCXQ019); National Key R&D Program of China(2019YFF01014405); Ministry of Education Science and Technology Development Center[2018A01019]; National Natural Science Foundation of China[11505012,11905150].

Keywords

MRI, Quantitative Imaging, Image Analysis

Taxonomy

IM- MRI : Quantitative Imaging

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