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ASIC modelling of SENSE for parallel MRI
COMSATS Univ Islamabad, Pakistan.
COMSATS Univ Islamabad, Pakistan.
Linköping University, Department of Electrical Engineering, Integrated Circuits and Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-2144-6795
COMSATS Univ Islamabad, Pakistan.
2019 (English)In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 109, p. 53-61Article in journal (Refereed) Published
Abstract [en]

Magnetic Resonance Imaging (MRI) is widely used in medical diagnostics and image reconstruction is a vital part of MRI systems. In Parallel MRI (pMRI), imaging process is accelerated by acquiring less data (undersampled) using multiple receiver coils and offline reconstruction algorithms are applied to reconstruct the fully sampled image. In this research, an Application Specific Integrated Circuits (ASIC) model of SENSE (a pMRI algorithm) is presented which reconstructs the image from the undersampled data right on the data acquisition module of the scanner. The proposed ASIC HDL architecture is compared with SENSE reconstruction model implemented on FPGAs, Multi-core CPU and Graphics Processing Units. The proposed architecture is validated using simulated brain data with 8-channel receiver coils and a human cardiac dataset with 20-channel receiver coils. The quality of the reconstructed images is analyzed using Artifact Power (0.0098), Peak Signal-to-Noise Ratio (53.4) and Structured Similarity Index (0.871) which validate the quality of the reconstructed images using the proposed design. The results show that the proposed ASIC HDL SENSE reconstruction model is similar to 8000 times faster as compared to the multi-core CPU reconstruction, similar to 700 times faster than the GPU implementation and similar to 16 times faster as compared to the FPGA reconstruction model. The proposed architecture is suitable for image reconstruction right on the data acquisition system of the scanner and will open new ways for faster image reconstruction on portable MRI scanners.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2019. Vol. 109, p. 53-61
Keywords [en]
MRI reconstruction; Parallel computing; ASIC modelling; FPGA; Parallel MRI
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-158990DOI: 10.1016/j.compbiomed.2019.04.028ISI: 000472590500006PubMedID: 31035071OAI: oai:DiVA.org:liu-158990DiVA, id: diva2:1338118
Available from: 2019-07-19 Created: 2019-07-19 Last updated: 2019-07-19

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • de-DE
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
  • rtf