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The Effect of Registration on Voxel-Wise Tofts Model Parameters and Uncertainties from DCE-MRI of Early-Stage Breast Cancer Patients Using 3DSlicer

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Abstract

We quantitatively investigate the influence of image registration, using open-source software (3DSlicer), on kinetic analysis (Tofts model) of dynamic contrast enhanced MRI of early-stage breast cancer patients. We also show that registration computation time can be reduced by reducing the percent sampling (PS) of voxels used for estimation of the cost function. DCE-MRI breast images were acquired on a 3T-PET/MRI system in 13 patients with early-stage breast cancer who were scanned in a prone radiotherapy position. Images were registered using a BSpline transformation with a 2 cm isotropic grid at 100, 20, 5, 1, and 0.5PS (BRAINSFit in 3DSlicer). Signal enhancement curves were analyzed voxel-by-voxel using the Tofts kinetic model. Comparing unregistered with registered groups, we found a significant change in the 90th percentile of the voxel-wise distribution of Ktrans. We also found a significant reduction in the following: (1) in the standard error (uncertainty) of the parameter value estimation, (2) the number of voxel fits providing unphysical values for the extracellular-extravascular volume fraction (ve > 1), and (3) goodness of fit. We found no significant differences in the median of parameter value distributions (Ktrans, ve) between unregistered and registered images. Differences between parameters and uncertainties obtained using 100PS versus 20PS were small and statistically insignificant. As such, computation time can be reduced by a factor of 2, on average, by using 20PS while not affecting the kinetic fit. The methods outlined here are important for studies including a large number of post-contrast images or number of patient images.

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Acknowledgments

The authors would like to thank Siemens Canada for supplying the breast coil used in this study.

Funding

The authors would also like to acknowledge grant funding from the Ontario Research Fund–Research Excellence (OCAIRO Project RE-04-026), the Canadian institutes of Health Research (149080), and the Breast Cancer Society of Canada for funding support.

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Authors and Affiliations

  1. Medical Biophysics, Western University, London, ON, Canada

    Matthew Mouawad, Frank S. Prato, R. Terry Thompson, Stewart Gaede & Neil Gelman

  2. Lawson Imaging, Lawson Health Research Institute, London, ON, Canada

    Heather Biernaski, Frank S. Prato, R. Terry Thompson, Stewart Gaede & Neil Gelman

  3. Surgical Oncology, London Regional Cancer Program, London, ON, Canada

    Muriel Brackstone

  4. Oncology, Western University, London, ON, Canada

    Muriel Brackstone & Michael Lock

  5. Radiation Oncology, London Regional Cancer Program, London, ON, Canada

    Michael Lock

  6. Medical Imaging, Western University, London, ON, Canada

    Anat Kornecki, Olga Shmuilovich, Ilanit Ben-Nachum, Frank S. Prato, R. Terry Thompson & Neil Gelman

  7. Diagnostic Imaging, St. Joseph’s Health Center, London, ON, Canada

    Anat Kornecki, Olga Shmuilovich & Ilanit Ben-Nachum

  8. Physics and Engineering, London Regional Cancer Program, London, ON, Canada

    Stewart Gaede

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  1. Matthew Mouawad
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Correspondence to Matthew Mouawad.

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Mouawad, M., Biernaski, H., Brackstone, M. et al. The Effect of Registration on Voxel-Wise Tofts Model Parameters and Uncertainties from DCE-MRI of Early-Stage Breast Cancer Patients Using 3DSlicer. J Digit Imaging 33, 1065–1072 (2020). https://doi.org/10.1007/s10278-020-00374-6

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  • DOI: https://doi.org/10.1007/s10278-020-00374-6

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