Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In Magnetic Resonance Imaging (MRI) studies, for clinical applications and for research as ll, reduction of scanning time is an essential issue. This time reduction could be obtained by using fast acquisition sequences, such as EPI and spiral k-space trajectories, and by acquiring less data, this being possible based on the new sampling theories that gave rise to the so called Compressed Sampling (CS for short). Hover the main assumption for the application of CS to Fourier data is that magnitude and phase are both sparse in some given domain. This assumption is not always true for fast acquisition sequences because of the non-homogeneities of the main magnetic field. In this article  propose a new model for MRI with different regularization penalties for magnitude and phase. Magnitude regularization exploits the sparsity assumption on the signal and the suggested penalty for phase takes into account its smoothness.  show results of numerical experiments with simulated data.
['Marcelo Victor Wüst Zibetti', 'Alvaro R. De Pierro']
I am writing a research paper on a idea Separate magnitude and phase regularization in MRI with incomplete data: Preliminary results
[{"from": "human", "value": "I am writing a research paper on a idea Separate magnitude and phase regularization in MRI with incomplete data: Preliminary results"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In Magnetic Resonance Imaging (MRI) studies, for clinical applications and for research as ll, reduction of scanning time is an essential issue. This time reduction could be obtained by using fast acquisition sequences, such as EPI and spiral k-space trajectories, and by acquiring less data, this being possible based on the new sampling theories that gave rise to the so called Compressed Sampling (CS for short). Hover the main assumption for the application of CS to Fourier data is that magnitude and phase are both sparse in some given domain. This assumption is not always true for fast acquisition sequences because of the non-homogeneities of the main magnetic field. In this article  propose a new model for MRI with different regularization penalties for magnitude and phase. Magnitude regularization exploits the sparsity assumption on the signal and the suggested penalty for phase takes into account its smoothness.  show results of numerical experiments with simulated data."}]