Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Blood movement investigated by magnetic resonance (MR) velocity mapping is generally presented in the form of velocity components in one or more chosen velocity encoding directions. By viewing these components separately, it is difficult for MR practitioners to conceptualize and comprehend the underlying flow structures, especially when the image data have strong background noise. A flow visualization technique that adapts the idea of particle tracing used in classical fluid dynamics for visualizing flow is presented. The flow image processing relies on the strong correlation beten the principal flow direction estimated from the distribution of the modulus of the velocity field and the direction derived from the raw image data. By correlation calculation, severe background noise can be eliminated. Flow pattern rendering and animation provide an efficient way for representing internal flow structures. >
['Guang-Zhong Yang', 'Peter Burger', 'Philip J. Kilner', 'Raad H. Mohiaddin']
I am writing a research paper on a idea In vivo blood flow visualization with magnetic resonance imaging
[{"from": "human", "value": "I am writing a research paper on a idea In vivo blood flow visualization with magnetic resonance imaging"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Blood movement investigated by magnetic resonance (MR) velocity mapping is generally presented in the form of velocity components in one or more chosen velocity encoding directions. By viewing these components separately, it is difficult for MR practitioners to conceptualize and comprehend the underlying flow structures, especially when the image data have strong background noise. A flow visualization technique that adapts the idea of particle tracing used in classical fluid dynamics for visualizing flow is presented. The flow image processing relies on the strong correlation beten the principal flow direction estimated from the distribution of the modulus of the velocity field and the direction derived from the raw image data. By correlation calculation, severe background noise can be eliminated. Flow pattern rendering and animation provide an efficient way for representing internal flow structures. >"}]