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Evaluation of Novel Genetic Algorithm Generated Schemes for Positron Emission Tomography (PET)/Magnetic Resonance Imaging (MRI) Image Fusion

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Abstract

The use and benefits of a multimodality approach in the context of breast cancer imaging are discussed. Fusion techniques that allow multiple images to be viewed simultaneously are discussed. Many of these fusion techniques rely on the use of color tables. A genetic algorithm that generates color tables that have desired properties such as satisfying the order principle, the rows, and columns principle, have perceivable uniformity and have maximum contrast is introduced. The generated 2D color tables can be used for displaying fused datasets. The advantage the proposed method has over other techniques is the ability to consider a much larger set of possible color tables, ensuring that the best one is found. We asked radiologists to perform a set of tasks reading fused PET/MRI breast images obtained using eight different fusion techniques. This preliminary study clearly demonstrates the need and benefit of a joint display by estimating the inaccuracies incurred when using a side-by-side display. The study suggests that the color tables generated by the genetic algorithm are good choices for fusing MR and PET images. It is interesting to note that popular techniques such as the Fire/Gray and techniques based on the HSV color space, which are prevalent in the literature and clinical practice, appear to give poorer performance.

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  1. www.kgbtechnologies.com/fusionviewer/

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Acknowledgements

We wish to thank Drs. M. Lisi, M. McGrath, J. Tam, and M. Khan for their participation in this study. The authors wish to thank the reviewers who helped improve this manuscript with their comments.

This research was partially supported by Carol M. Baldwin Breast Cancer Research Award and by the Department of Radiology at SUNY Upstate Medical University.

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Authors and Affiliations

  1. Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, 54 Lomb Memorial Dr, Rochester, NY, 14623, USA

    K. G. Baum, K. Rafferty & María Helguera

  2. Radiology Department, SUNY Upstate Medical University, Syracuse, NY, 13210, USA

    A. Krol

  3. Honeoye Falls-Lima High School, Honeoye Falls, NY, 14472, USA

    E. Schmidt

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  1. K. G. Baum
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Correspondence to María Helguera.

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Baum, K.G., Schmidt, E., Rafferty, K. et al. Evaluation of Novel Genetic Algorithm Generated Schemes for Positron Emission Tomography (PET)/Magnetic Resonance Imaging (MRI) Image Fusion. J Digit Imaging 24, 1031–1043 (2011). https://doi.org/10.1007/s10278-011-9382-1

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