Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  are characterizing atherosclerotic disease in patients and animal models using multiple MR images having different contrasts.  use intravascular and surface array coils giving high signal-to-noise but significant sensitivity inhomogeneity. In human carotid images, bias was corrected using a modified adaptive fuzzy c-mean method with a mechanical membrane model of the bias field. Noise reduction filtering, background segmentation, outlier class identification, and signal normalization re all designed to address specific, significant technical issues. In a synthetic image having a bias field measured from our MR system, variations across an area comparable to a carotid artery re reduced from 60% to <5% with processing while the misclassification rate was kept below 4% even with poor SNR (<7). Human carotid images re qualitatively improved and large regions of skeletal muscle re flattened and normalized for inter- and intra-subject variation. The method should facilitate interpretation of artery gray scales for manual plaque characterization and enable computerized plaque classification.
['Olivier Salvado', 'Claudia M. Hillenbrand', 'Shaoxiong Zhang', 'Jasjit S. Suri', 'David L. Wilson']
I am writing a research paper on a idea MR signal inhomogeneity correction for visual and computerized atherosclerosis lesion assessment
[{"from": "human", "value": "I am writing a research paper on a idea MR signal inhomogeneity correction for visual and computerized atherosclerosis lesion assessment"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  are characterizing atherosclerotic disease in patients and animal models using multiple MR images having different contrasts.  use intravascular and surface array coils giving high signal-to-noise but significant sensitivity inhomogeneity. In human carotid images, bias was corrected using a modified adaptive fuzzy c-mean method with a mechanical membrane model of the bias field. Noise reduction filtering, background segmentation, outlier class identification, and signal normalization re all designed to address specific, significant technical issues. In a synthetic image having a bias field measured from our MR system, variations across an area comparable to a carotid artery re reduced from 60% to <5% with processing while the misclassification rate was kept below 4% even with poor SNR (<7). Human carotid images re qualitatively improved and large regions of skeletal muscle re flattened and normalized for inter- and intra-subject variation. The method should facilitate interpretation of artery gray scales for manual plaque characterization and enable computerized plaque classification."}]