Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Compressed sensing (CS) is a new signal acquisition paradigm that enables the reconstruction of signals and images from a low number of samples. A particularly exciting application of CS is Magnetic Resonance Imaging (MRI), where CS significantly speeds up scan time by requiring far fer measurements than standard MRI techniques. Such a reduction in sampling time leads to less por consumption, less need for patient sedation, and more accurate images. This accuracy increase is especially pronounced in pediatric MRI where patients have trouble being still for long scan periods. Although such gains are already significant, even further improvements can be made by utilizing past MRI scans of the same patient. Many patients require repeated scans over a period of time in order to track illnesses and the prior scans can be used as references for the current image. This allows samples to be taken adaptively, based on both the prior scan and the current measurements. #R##N#Work by izman has shown that so-called reference based adaptive-ighted temporal Compressed Sensing MRI (LACS-MRI) requires far fer samples than standard Compressed Sensing (CS) to achieve the same reconstruction signal-to-noise ratio (RSNR). The method uses a mixture of reference-based and adaptive-sampling. In this work,  test this methodology by using various adaptive sensing schemes, reconstruction methods, and image types.  create a thorough catalog of reconstruction behavior and success rates that is interesting from a mathematical point of view and is useful for practitioners.  also solve a grayscale compensation toy problem that supports the insensitivity of LACS-MRI to changes in MRI acquisition parameters and thus showcases the reliability of LACS-MRI in possible clinical situations.
['Samuel Birns', 'Bohyun Kim', 'Stephanie Ku', 'Kevin Stangl', 'Deanna Needell']
I am writing a research paper on a idea A Practical Study of Longitudinal Reference Based Compressed Sensing for MRI
[{"from": "human", "value": "I am writing a research paper on a idea A Practical Study of Longitudinal Reference Based Compressed Sensing for MRI"}, {"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) is a new signal acquisition paradigm that enables the reconstruction of signals and images from a low number of samples. A particularly exciting application of CS is Magnetic Resonance Imaging (MRI), where CS significantly speeds up scan time by requiring far fer measurements than standard MRI techniques. Such a reduction in sampling time leads to less por consumption, less need for patient sedation, and more accurate images. This accuracy increase is especially pronounced in pediatric MRI where patients have trouble being still for long scan periods. Although such gains are already significant, even further improvements can be made by utilizing past MRI scans of the same patient. Many patients require repeated scans over a period of time in order to track illnesses and the prior scans can be used as references for the current image. This allows samples to be taken adaptively, based on both the prior scan and the current measurements. #R##N#Work by izman has shown that so-called reference based adaptive-ighted temporal Compressed Sensing MRI (LACS-MRI) requires far fer samples than standard Compressed Sensing (CS) to achieve the same reconstruction signal-to-noise ratio (RSNR). The method uses a mixture of reference-based and adaptive-sampling. In this work,  test this methodology by using various adaptive sensing schemes, reconstruction methods, and image types.  create a thorough catalog of reconstruction behavior and success rates that is interesting from a mathematical point of view and is useful for practitioners.  also solve a grayscale compensation toy problem that supports the insensitivity of LACS-MRI to changes in MRI acquisition parameters and thus showcases the reliability of LACS-MRI in possible clinical situations."}]