Barry David Daly MD, Abstract Co-Author: Research funded, Koninklijke Philips Electronics NV

Amar Dhanantwari, Abstract Co-Author: Employee, Koninklijke Philips Electronics NV

Seth Jay Kligerman MD, Abstract Co-Author: Author, Amirsys, Inc Research Grant, Riverain Medical

Katrina M. Read MS, Abstract Co-Author: Employee, Koninklijke Philips Electronics NV

Barton Frederick Lane MD, Presenter: Nothing to Disclose

Deepa Natarajamani, Abstract Co-Author: Nothing to Disclose

IMR has great potential for simultaneous radical reduction in radiation dose and almost noise-free thin slice image generation in CT without loss of image quality.

Currently available Iterative Reconstruction techniques partially remove image noise and typically allow moderate dose reduction improvements in clinical CT. Iterative Model Reconstruction (IMR) (Philips Healthcare, Highland Heights, OH) is an iterative algorithm that uses a model-based approach that achieves very low noise and improved image quality through iterative minimization of the penalty based cost function. The algorithm uses a knowledge based approach to accurately determine the data statistic and image statistic models, which are coupled with the system model in the reconstruction.

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IMR has great potential for simultaneous radical reduction in radiation dose and almost noise-free thin slice image generation in CT without loss of image quality.

Daly, B, Dhanantwari, A, Kligerman, S, Read, K, Lane, B, Natarajamani, D, Iterative Model Reconstruction Technique For Ultra Low Noise, Low Dose CT.  Radiological Society of North America 2012 Scientific Assembly and Annual Meeting, November 25 - November 30, 2012 ,Chicago IL. http://archive.rsna.org/2012/12028102.html