Chinese Journal of Magnetic Resonance >

2022 , Vol. 39 >Issue 3: 243 - 257

DOI: https://doi.org/10.11938/cjmr20222976

Articles

Accelerating T1ρ Dispersion Imaging with Multiple Relaxation Signal Compensation

  • Yuan-yuan LIU ,
  • Yu-xin YANG ,
  • Qing-yong ZHU ,
  • Zhuo-xu CUI ,
  • Jing CHENG ,
  • Cong-cong LIU ,
  • Dong LIANG ,
  • Yan-jie ZHU
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  • 1. Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    2. Department of Biomedical Engineering, Chongqing University of Technology, Chongqing 400054, China
    3. Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Received date: 2022-02-16

  Online published: 2022-04-28

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Abstract

Magnetic resonance imaging (MRI) can quantify characteristic values of tissues, serving as an important tool for scientific and clinical research. Magnetic resonance T1ρ relaxation time reflects the low-frequency motional processes between water and macromolecules. At high fields of 3 T and above, T1ρ is greatly affected by the chemical exchange between water and exchangeable protons, and T1ρ dispersion measured with varying spin-lock fields can be utilized to analyze and quantify the proton exchange process. However, it is time-consuming to obtain T1ρ-weighted images with different spin-lock fields, which limits its application. To solve this problem, a fast T1ρ dispersion imaging method based on multiple relaxation signal compensation strategy is proposed in this work, which compensates the T1ρ-weighted images at different locking frequencies to the same signal strength level, and combines the low-rank plus sparse model in the reconstruction. Experimental results show that the proposed method achieves good reconstruction results even when the acceleration factor is up to 7.

Key words： magnetic resonance quantitative imaging; T1ρ dispersion; signal compensation; low-rank plus sparse

Cite this article

Yuan-yuan LIU , Yu-xin YANG , Qing-yong ZHU , Zhuo-xu CUI , Jing CHENG , Cong-cong LIU , Dong LIANG , Yan-jie ZHU . Accelerating T1ρ Dispersion Imaging with Multiple Relaxation Signal Compensation[J]. Chinese Journal of Magnetic Resonance, 2022 , 39(3) : 243 -257 . DOI: 10.11938/cjmr20222976

References

1 DEONI S C, PETERS T M, RUTT B KHigh-resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2[J].Magn Reson Med,2005,53(1):237-241.
2 GANZETTI M, WENDEROTH N, MANTINI DWhole brain myelin mapping using T1-and T2-weighted MR imaging data[J].Front Hum Neurosci,2014,8,671.
3 KNIGHT M J, MCCANN B, TSIVOS D, et alQuantitative T2 mapping of white matter: applications for ageing and cognitive decline[J].Phys Med Biol,2016,61(15):5587-5605.
4 SENER R NDiffusion MRI: apparent diffusion coefficient (ADC) values in the normal brain and a classification of brain disorders based on ADC values[J].Comput Med Imaging Graph,2001,25(4):299-326.
5 TAYLOR A J, SALERNO M, DHARMAKUMAR R, et alT1 Mapping: basic techniques and clinical applications[J].JACC Cardiovasc Imaging,2016,9(1):67-81.
6 ALI S O, FESSAS P, KAGGIE J D, et alEvaluation of the sensitivity of R1ρ MRI to pH and macromolecular density[J].Magn Reson Imaging,2019,58,156-161.
7 WANG Y X, ZHANG Q, LI X, et alT1ρ magnetic resonance: basic physics principles and applications in knee and intervertebral disc imaging[J].Quant Imaging Med Surg,2015,5(6):858-885.
8 LI X, WYATT C, RIVOIRE J, et alSimultaneous acquisition of T1ρ and T2 quantification in knee cartilage: repeatability and diurnal variation[J].J Magn Reson Imaging,2014,39(5):1287-1293.
9 WANG Y X, YUAN JEvaluation of liver fibrosis with T1ρ MR imaging[J].Quant Imaging Med Surg,2014,4(3):152-155.
10 HARIS M, SINGH A, CAI K, et alT1ρ MRI in Alzheimer's disease: detection of pathological changes in medial temporal lobe[J].J Neuroimaging,2011,21(2):e86-90.
11 COBB J G, XIE J, GORE J CContributions of chemical exchange to T1ρ dispersion in a tissue model[J].Magn Reson Med,2011,66(6):1563-1571.
12 COBB J G, XIE J, GORE J CContributions of chemical and diffusive exchange to T1ρ dispersion[J].Magn Reson Med,2013,69(5):1357-1366.
13 LUSTIG M, DONOHO D, PAULY J MSparse MRI: the application of compressed sensing for rapid MR imaging[J].Magn Reson Med,2007,58(6):1182-1195.
14 BHAVE S, LINGALA S G, JOHNSON C P, et alAccelerated whole-brain multi-parameter mapping using blind compressed sensing[J].Magn Reson Med,2016,75(3):1175-1186.
15 PANDIT P, RIVOIRE J, KING K, et alAccelerated T1ρ acquisition for knee cartilage quantification using compressed sensing and data-driven parallel imaging: a feasibility study[J].Magn Reson Med,2016,75(3):1256-1261.
16 KAMESH IYER S, MOON B, HWUANG E, et alAccelerated free-breathing 3D T1ρ cardiovascular magnetic resonance using multicoil compressed sensing[J].J Cardiovasc Magn R,2019,21,5.
17 李嫣嫣, 李律, 李雪松, 等基于同伦l0范数最小化重建的三维动态磁共振成像[J].波谱学杂志,2022,39(1):20-32.
17 LI Y Y, LI L, LI X S, et al3D dynamic MRI with homotopic l0 minimization rconstruction[J].Chinese J Magn Reson,2022,39(1):20-32.
18 詹嘉莹, 涂章仁, 杜晓凤, 等基于低秩矩阵的非均匀采样NMR波谱重建进展[J].波谱学杂志,2020,37(3):255-272.
18 ZHAN J Y, TU Z R, DU X F, et alProgresses on low-rank reconstruction for non-uniformly sampled NMR spectra[J].Chinese J Magn Reson,2020,37(3):255-272.
19 HALDAR J P, LIANG Z P. Low-rank approximations for dynamic imaging[C]// 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011: 1052-1055.
20 JACOB M, MANI M P, YE J CStructured low-rank algorithms: theory, magnetic resonance applications, and links to machine learning[J].IEEE Signal Process Mag,2020,37(1):54-68.
21 ZIBETTI M V W, SHARAFI A, OTAZO R, et alAccelerating 3D- T1ρ mapping of cartilage using compressed sensing with different sparse and low rank models[J].Magn Reson Med,2018,80(4):1475-1491.
22 PENG X, YING L, LIU Y Y, et alAccelerated exponential parameterization of T2 relaxation with model-driven low rank and sparsity priors (MORASA)[J].Magn Reson Med,2016,76(6):1865-1878.
23 ZHU Y J, LIU Y Y, YING L, et alSCOPE: signal compensation for low-rank plus sparse matrix decomposition for fast parameter mapping[J].Phys Med Biol,2018,63(18):185009.
24 BOUWMANS T, SOBRAL A, JAVED S, et alDecomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset[J].Comput Sci Rev,2017,23,1-71.
25 ONG F, LUSTIG MBeyond low rank plus sparse: multiscale low rank matrix decomposition[J].IEEE J Sel Topics Signal Process,2016,10(4):672-687.
26 HUANG W Q, KE Z W, CUI Z X, et alDeep low-rank plus sparse network for dynamic MR imaging[J].Med Image Anal,2021,73.
27 OTAZO R, CANDES E, SODICKSON D KLow-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components[J].Magn Reson Med,2015,73(3):1125-1136.
28 PETROV A Y, HERBST M, STENGER V AImproving temporal resolution in fMRI using a 3D spiral acquisition and low rank plus sparse (L plus S) reconstruction[J].Neuroimage,2017,157,660-674.
29 PRUESSMANN K P, WEIGER M, BORNERT P, et alAdvances in sensitivity encoding with arbitrary k-space trajectories[J].Magn Reson Med,2001,46(4):638-651.
30 MORE J JThe Levenberg-Marquardt algorithm: implementation and theory[J].Numerical analysis,1978,105-116.
31 MITREA B G, KRAFFT A J, SONG R, et alPaired self-compensated spin-lock preparation for improved T1ρ quantification[J].J Magn Reson,2016,268,49-57.
32 RICH A, GREGG M, JIN N, et alCartesian sampling with variable density and Adjustable temporal resolution (CAVA)[J].Magn Reson Med,2020,83(6):2015-2025.
33 WANG Z, BOVIK A C, SHEIKH H R, et alImage quality assessment: from error visibility to structural similarity[J].IEEE Trans Image Process,2004,13(4):600-612.
34 MASON A, RIOUX J, CLARKE SComparison of objective image quality metrics to expert radiologists' scoring of diagnostic quality of MR Images[J].IEEE Trans Med Imaging,2020,39(4):1064-1072.
35 ZHU Y, LIU Y, YING L, et alA 4-minute solution for submillimeter whole-brain T1ρ quantification[J].Magn Reson Med,2021,85(6):3299-3307.
36 KORZHNEV D M, OREKHOV V Y, KAY L EOff-resonance R1ρ NMR studies of exchange dynamics in proteins with low spin-lock fields: An application to a fyn SH3 domain[J].J Am Chem Soc,2005,127(2):713-721.
37 王婉婷, 苏适, 贾森, 等基于虚拟线圈和卷积神经网络的多层同时激发图像重建[J].波谱学杂志,2020,37(4):407-421.
37 WANG W T, SU S, JIA S, et alReconstruction of simultaneous multi-slice MRI data by combining virtual conjugate coil technology and convolutional neural network[J].Chinese J Magn Reson,2020,37(4):407-421.
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