Undersampling strategies for compressed sensing accelerated MR spectroscopic imaging

Rohini Vidya Shankar, Houchun Harry Hu, Nutandev Bikkamane Jayadev, John C. Chang, Vikram Kodibagkar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Compressed sensing (CS) can accelerate magnetic resonance spectroscopic imaging (MRSI), facilitating its widespread clinical integration. The objective of this study was to assess the effect of different undersampling strategy on CS-MRSI reconstruction quality. Phantom data were acquired on a Philips 3 T Ingenia scanner. Four types of undersampling masks, corresponding to each strategy, namely, low resolution, variable density, iterative design, and a priori were simulated in Matlab and retrospectively applied to the test 1X MRSI data to generate undersampled datasets corresponding to the 2X - 5X, and 7X accelerations for each type of mask. Reconstruction parameters were kept the same in each case(all masks and accelerations) to ensure that any resulting differences can be attributed to the type of mask being employed. The reconstructed datasets from each mask were statistically compared with the reference 1X, and assessed using metrics like the root mean square error and metabolite ratios. Simulation results indicate that both the a priori and variable density undersampling masks maintain high fidelity with the 1X up to five-fold acceleration. The low resolution mask based reconstructions showed statistically significant differences from the 1X with the reconstruction failing at 3X, while the iterative design reconstructions maintained fidelity with the 1X till 4X acceleration. In summary, a pilot study was conducted to identify an optimal sampling mask in CS-MRSI. Simulation results demonstrate that the a priori and variable density masks can provide statistically similar results to the fully sampled reference. Future work would involve implementing these two masks prospectively on a clinical scanner.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2017
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
PublisherSPIE
Volume10137
ISBN (Electronic)9781510607194
DOIs
StatePublished - 2017
EventMedical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging - Orlando, United States
Duration: Feb 12 2017Feb 14 2017

Other

OtherMedical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging
CountryUnited States
CityOrlando
Period2/12/172/14/17

Keywords

  • Compressed sensing
  • Fast imaging
  • Metabolic imaging
  • MRSI
  • Optimal sampling mask
  • Undersampling

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Vidya Shankar, R., Hu, H. H., Bikkamane Jayadev, N., Chang, J. C., & Kodibagkar, V. (2017). Undersampling strategies for compressed sensing accelerated MR spectroscopic imaging. In Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 10137). [101372I] SPIE. https://doi.org/10.1117/12.2254614