J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2025 , Vol. 30 >Issue 3: 555 - 565

DOI: https://doi.org/10.1007/s12204-023-2677-9

Medicine-Engineering Interdisciplinary

Improved Sensitivity Encoding Parallel Magnetic Resonance Imaging Reconstruction Algorithm Based on Efficient Sum of Outer Products Dictionary Learning

Expand
  • Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

Received date: 2022-09-22

  Accepted date: 2022-09-22

  Online published: 2025-06-06

Fold

Abstract

Sensitivity encoding (SENSE) is a parallel magnetic resonance imaging (MRI) reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction. The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms, resulting in a limited reconstruction accuracy. Therefore, we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning (SOUPDIL) with the SENSE model, called SOUPDIL-SENSE. The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques. The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity, eliminates the noise and artifacts of the reconstructed images, and improves the reconstruction accuracy.

Cite this article

Duan Jizhong, Su Yan . Improved Sensitivity Encoding Parallel Magnetic Resonance Imaging Reconstruction Algorithm Based on Efficient Sum of Outer Products Dictionary Learning[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(3) : 555 -565 . DOI: 10.1007/s12204-023-2677-9

References

[1] FANG L L, WANG X, LIAN Z Y, et al. Supervoxelbased  brain tumor segmentation with multimodal MRI  images [J]. Signal, Image and Video Processing, 2022, 16(5): 1215-1223. 

[2] LIU Y L, YI Z Y, ZHAO Y J, et al. Calibrationless  parallel imaging reconstruction for multislice MR data  using low-rank tensor completion [J]. Magnetic Resonance  in Medicine, 2021, 85(2): 897-911. 

[3] SHENG J H, YIN J, WANG L Y, et al. Parallel MR  image reconstruction based on triple cycle optimization [J]. Scientific Reports, 2022, 12: 7783. 

[4] CAO C H, DUANW, HU K, et al. Compressive sensing MR imaging based on adaptive tight frame and reference  image [J]. IET Image Processing, 2020, 14(14): 3508-3515. 

[5] HU Z Q, ZHAO C L, ZHAO X, et al. Joint reconstruction  framework of compressed sensing and nonlinear  parallel imaging for dynamic cardiac magnetic resonance  imaging [J]. BMC Medical Imaging, 2021, 21(1): 182.

[6] SHENG J H, SHI Y C, ZHANG Q. Improved parallel  magnetic resonance imaging reconstruction with multiple  variable density sampling [J]. Scientific Reports, 2021, 11: 9005. 

[7] PAN T, DUAN J Z, WANG J F, et al. Iterative selfconsistent  parallel magnetic resonance imaging reconstruction  based on nonlocal low-rank regularization [J]. Magnetic Resonance Imaging, 2022, 88: 62-75. 

[8] HALDAR J P, ZHUO J W. P-LORAKS: Low-rank  modeling of local k-space neighborhoods with parallel  imaging data [J]. Magnetic Resonance in Medicine, 2016, 75(4): 1499-1514. 

[9] SHIN P J, LARSON P E Z, OHLIGER M A, et al. Calibrationless parallel imaging reconstruction based  on structured low-rank matrix completion [J]. Magnetic Resonance in Medicine, 2014, 72(4): 959-970. 

[10] UECKER M, LAI P,MURPHYM J, et al. ESPIRiT— An eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA [J]. Magnetic Resonance in Medicine, 2014, 71(3): 990-1001. 

[11] LIU B, ZOU Y M, YING L. Sparsesense: Application  of compressed sensing in parallel MRI [C]//2008 International Conference on Information Technology and Applications in Biomedicine. Shenzhen: IEEE, 2008: 127-130. 

[12] RAMANI S, FESSLER J A. Parallel MR image reconstruction  using augmented Lagrangian methods [J]. IEEE Transactions on Medical Imaging, 2011, 30(3): 694-706. 

[13] KIM T H, SETSOMPOP K, HALDAR J P. LORAKS  makes better SENSE: Phase-constrained partial Fourier SENSE reconstruction without phase calibration [J]. Magnetic Resonance in Medicine, 2017, 77(3): 1021-1035. 

[14] YASHTINI M. Euler’s elastica-based algorithm for Parallel MRI reconstruction using SENSitivity Encoding [J]. Optimization Letters, 2020, 14(6): 1435-1458. 

[15] DUAN J Z, BAO Z W, LIU Y. Eigenvector-based SPIRiT parallel MR imaging reconstruction based on  p pseudo-norm joint total variation [J]. Magnetic Resonance Imaging, 2019, 58: 108-115. 

[16] ZHANG P, MANG A, HE J W, et al. Diffeomorphic  shape matching by operator splitting in 3D cardiology  imaging [J]. Journal of Optimization Theory and Applications, 2021, 188(1): 143-168. 

[17] RAVISHANKAR S, BRESLER Y. MR image reconstruction  from highly undersampled k-space data by  dictionary learning [J]. IEEE Transactions on Medical Imaging, 2011, 30(5): 1028-1041. 

[18] RAVISHANKAR S, NADAKUDITI R R, FESSLER J A. Efficient sum of outer products dictionary learning (SOUP-DIL) and its application to inverse problems [J]. IEEE Transactions on Computational Imaging, 2017, 3(4): 694-709. 

[19] ZHANG Y L, LI X W, ZHAO G Y, et al. Signal reconstruction  of compressed sensing based on alternating  direction method of multipliers [J]. Circuits, Systems,  and Signal Processing, 2020, 39(1): 307-323. 

[20] RUBINSTEIN R, ZIBULEVSKY M, ELAD M. Efficient  implementation of the K-SVD algorithm using  batch orthogonal matching pursuit [EB/OL]. [2022-09- 22]. https://csaws.cs.technion.ac.il/∼ronrubin/Publications/ KSVD-OMP-v2.pdf 

[21] SOUZA R, LUCENA O, GARRAFA J, et al. An open,  multi-vendor, multi-field-strength brain MR dataset  and analysis of publicly available skull stripping methods  agreement [J]. NeuroImage, 2018, 170: 482-494. 

[22] YE X J, CHEN YM, HUANG F. Computational acceleration  for MR image reconstruction in partially parallel  imaging [J]. IEEE Transactions on Medical Imaging, 2011, 30(5): 1055-1063. 

[23] KNOLL F, ZBONTAR J, SRIRAM A, et al. fastMRI: A publicly available raw k-space and DICOM dataset  of knee images for accelerated MR image reconstruction  using machine learning [J]. Radiology Artificial Intelligence, 2020, 2(1): e190007.

Options
Outlines

/

〈 〉