Parallel implementation of L + S signal recovery in dynamic MRI


Dynamic MRI is useful to diagnose different diseases, e.g. cardiac ailments, by monitoring the structure and function of the heart and blood flow through the valves. Faster data acquisition is highly desirable in dynamic MRI, but this may lead to aliasing artifacts due to under-sampling. Advanced image reconstruction algorithms are required to obtain aliasing-free MR images from the acquired under-sampled data. One major limitation of using the advanced reconstruction algorithms is their computationally expensive and time-consuming nature, which make them infeasible for clinical use, especially for applications like cardiac MRI. L + S decomposition model is an approach provided in literature which separates the sparse and low-rank information in dynamic MRI. However, L + S decomposition model is a computationally complex process demanding significant computation time. In this paper, a parallel framework is proposed to accelerate the image reconstruction process of L + S decomposition model using GPU. Experiments are performed on cardiac perfusion dataset (\(256\, \times \,256\, \times \,40\, \times \,12\)) and cardiac cine dataset (\(256\, \times \,256\, \times \,11\, \times \,30\)) using NVIDIA’s GeForce GTX780 GPU and Core-i7 CPU. The results show that the proposed method provides up to 18 × speed-up including the memory transfer time (i.e. data transfer between the CPU and GPU) and ~ 46 × speed-up without memory transfer for the cardiac perfusion dataset in our experiments. This level of improvement in the reconstruction time will increase the usefulness of L + S reconstruction by making it feasible for clinical applications.

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Correspondence to Sohaib A. Qazi.

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Sohaib Ayyaz Qazi declares that he has no conflict of interest. Fareena Tariq declares that he has no conflict of interest. Irfan Ullah declares that he has no conflict of interest. Hammad Omer declares that he has no conflict of interest.

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Qazi, S.A., Tariq, F., Ullah, I. et al. Parallel implementation of L + S signal recovery in dynamic MRI. Magn Reson Mater Phy (2020).

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  • Cardiac MRI
  • GPU computing
  • MRI
  • Reconstruction
  • CUDA
  • Compressed sensing