Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Compressed sensing (CS) magnetic resonance imaging (MRI) reconstruction reduces the scan time by undersampling the data but increases the image reconstruction time because a non-linear optimization problem must be iteratively solved to reconstruct the images. The growing demand for reducing the examination time in cardiac MRI led us to investigate opportunities to accelerate this non-linear optimization problem to facilitate the migration of CS into the clinical environment. Using 3D steady-state free precession MRI images from 5 patients,  compared the speed and output quality of CS reconstruction using central processing unit (CPU), CPU with OpenMP parallelization, and graphics processing unit (GPU) platforms. Mean reconstruction time was 13.1 ± 3.8 minutes for the CPU, 11.6 ± 3.6 minutes for the CPU with OpenMP parallelization, and 2.5 ± 0.3 minutes for the CPU with OpenMP plus GPU. GPU and CPU reconstructed image quality as assessed by image subtraction re comparable. Additional developments needed for implementation of rapid CS image reconstruction in the clinical environment are discussed.
['Majid Sabbagh', 'Martin Uecker', 'Andrew J. Powell', 'Miriam Leeser', 'Mehdi H. Moghari']
I am writing a research paper on a idea Cardiac MRI compressed sensing image reconstruction with a graphics processing unit
[{"from": "human", "value": "I am writing a research paper on a idea Cardiac MRI compressed sensing image reconstruction with a graphics processing unit"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Compressed sensing (CS) magnetic resonance imaging (MRI) reconstruction reduces the scan time by undersampling the data but increases the image reconstruction time because a non-linear optimization problem must be iteratively solved to reconstruct the images. The growing demand for reducing the examination time in cardiac MRI led us to investigate opportunities to accelerate this non-linear optimization problem to facilitate the migration of CS into the clinical environment. Using 3D steady-state free precession MRI images from 5 patients,  compared the speed and output quality of CS reconstruction using central processing unit (CPU), CPU with OpenMP parallelization, and graphics processing unit (GPU) platforms. Mean reconstruction time was 13.1 \u00b1 3.8 minutes for the CPU, 11.6 \u00b1 3.6 minutes for the CPU with OpenMP parallelization, and 2.5 \u00b1 0.3 minutes for the CPU with OpenMP plus GPU. GPU and CPU reconstructed image quality as assessed by image subtraction re comparable. Additional developments needed for implementation of rapid CS image reconstruction in the clinical environment are discussed."}]