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GPU based parallel framework for receiver coil sensitivity estimation in SENSE reconstruction
Muhammad Adil Khalil1, Afaq Ashfaq1, Hassan Shahzad2
1Medical Image Processing Research Group (MIPRG), Department of Electrical & Computer Engineering, COMSATS University Islamabad, Pakistan.
Abstract:
Magnetic Resonance Imaging (MRI) uses non-ionizing radiations and is safer as compared to CT and X-ray imaging. MRI is broadly used around the globe for medical diagnostics. One main limitation of MRI is its long data acquisition time. Parallel MRI (pMRI) was introduced in late 1990's to reduce the MRI data acquisition time. In pMRI, data is acquired by under-sampling the Phase Encoding (PE) steps which introduces aliasing artefacts in the MR images. SENSitivity Encoding (SENSE) is a pMRI based method that reconstructs fully sampled MR image from the acquired under-sampled data using the sensitivity information of receiver coils. In SENSE, precise estimation of the receiver coil sensitivity maps is vital to obtain good quality images. Eigen-value method (a recently proposed method in literature for the estimation of receiver coil sensitivity information) does not require a pre-scan image unlike other conventional methods of sensitivity estimation. However, Eigen-value method is computationally intensive and takes a significant amount of time to estimate the receiver coil sensitivity maps. This work proposes a parallel framework for Eigen-value method of receiver coil sensitivity estimation that exploits its inherent parallelism using Graphics Processing Units (GPUs). We evaluated the performance of the proposed algorithm on in-vivo and simulated MRI datasets (i.e. human head and simulated phantom datasets) with Peak Signal-to-Noise Ratio (PSNR) and Artefact Power (AP) as evaluation metrics. The results show that the proposed GPU implementation reduces the execution time of Eigen-value method of receiver coil sensitivity estimation (providing up to 30 times speed up in our experiments) without degrading the quality of the reconstructed image.
Insights
This study introduces a faster method for Magnetic Resonance Imaging (MRI) using Graphics Processing Units (GPUs) to speed up receiver coil sensitivity estimation in parallel MRI (pMRI). The GPU-accelerated Eigen-value method significantly reduces computation time without compromising image quality.
Area of Science:
- Medical Imaging
- Computational Imaging
- Biomedical Engineering
Background:
- Magnetic Resonance Imaging (MRI) is a crucial diagnostic tool, but its long data acquisition times limit efficiency.
- Parallel MRI (pMRI) accelerates MRI by undersampling data, which can introduce aliasing artifacts.
- Accurate receiver coil sensitivity map estimation is vital for reconstructing high-quality images in pMRI methods like SENSitivity Encoding (SENSE).
Purpose of the Study:
- To develop and evaluate a parallel computing framework for the Eigen-value method of receiver coil sensitivity estimation.
- To accelerate the computationally intensive Eigen-value method using Graphics Processing Units (GPUs).
- To assess the performance and image quality preservation of the GPU-accelerated method.
Main Methods:
- A parallel framework was designed to leverage the inherent parallelism of the Eigen-value method.
- Graphics Processing Units (GPUs) were utilized for accelerating the sensitivity map estimation process.
- The proposed algorithm was evaluated on in-vivo (human head) and simulated phantom MRI datasets.
Main Results:
- The GPU implementation of the Eigen-value method significantly reduced execution time, achieving up to a 30-fold speedup in experimental tests.
- The parallel framework effectively reduced the computational burden of sensitivity estimation.
- Image quality, assessed using Peak Signal-to-Noise Ratio (PSNR) and Artefact Power (AP), was maintained without degradation.
Conclusions:
- The proposed GPU-accelerated parallel framework offers a substantial speed improvement for Eigen-value based receiver coil sensitivity estimation in pMRI.
- This acceleration makes the Eigen-value method more practical for clinical MRI workflows.
- The method successfully reduces MRI acquisition time without compromising diagnostic image quality.
