Validation of an algorithm for the nonrigid registration of longitudinal breast MR images using realistic phantoms

Authors

  • Li Xia,

    1. Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232
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  • Dawant Benoit M.,

    1. Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232 and Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee 37232
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  • Welch E. Brian,

    1. Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232 and MR Clinical Science, Philips Healthcare, Cleveland, Ohio
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  • Chakravarthy A. Bapsi,

    1. Department of Radiation Oncology, Vanderbilt University, Nashville, Tennessee 37232
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  • Xu Lei,

    1. Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37232
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  • Mayer Ingrid,

    1. Department of Medical Oncology, Vanderbilt University, Nashville, Tennessee 37232
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  • Kelley Mark,

    1. Department of Surgical Oncology, Vanderbilt University, Nashville, Tennessee 37232
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  • Meszoely Ingrid,

    1. Department of Medical Oncology, Vanderbilt University, Nashville, Tennessee 37232
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  • Means-Powell Julie,

    1. Department of Surgical Oncology, Vanderbilt University, Nashville, Tennessee 37232
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  • Gore John C.,

    1. Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee 37232; Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee 37232; Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37232; and Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee 37232
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  • Yankeelov Thomas E.

    1. Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee 37232; Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee 37232; Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37232; and Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee 37232
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  • 0094-2405/2010/37(6)/2541/12/$30.00

Abstract

Purpose

The authors present a method to validate coregistration of breast magnetic resonance images obtained at multiple time points during the course of treatment. In performing sequential registration of breast images, the effects of patient repositioning, as well as possible changes in tumor shape and volume, must be considered. The authors accomplish this by extending the adaptive bases algorithm (ABA) to include a tumor-volume preserving constraint in the cost function. In this study, the authors evaluate this approach using a novel validation method that simulates not only the bulk deformation associated with breast MR images obtained at different time points, but also the reduction in tumor volume typically observed as a response to neoadjuvant chemotherapy.

Methods

For each of the six patients, high-resolution 3D contrast enhanced T1-weighted images were obtained before treatment, after one cycle of chemotherapy and at the conclusion of chemotherapy. To evaluate the effects of decreasing tumor size during the course of therapy, simulations were run in which the tumor in the original images was contracted by 25%, 50%, 75%, and 95%, respectively. The contracted area was then filled using texture from local healthy appearing tissue. Next, to simulate the post-treatment data, the simulated (i.e., contracted tumor) images were coregistered to the experimentally measured post-treatment images using a surface registration. By comparing the deformations generated by the constrained and unconstrained version of ABA, the authors assessed the accuracy of the registration algorithms. The authors also applied the two algorithms on experimental data to study the tumor volume changes, the value of the constraint, and the smoothness of transformations.

Results

For the six patient data sets, the average voxel shift error (mean±standard deviation) for the ABA with constraint was 0.45±0.37, 0.97±0.83, 1.43±0.96, and 1.80±1.17mm for the 25%, 50%, 75%, and 95% contraction simulations, respectively. In comparison, the average voxel shift error for the unconstrained ABA was 0.46±0.29, 1.13±1.17, 2.40±2.04, and 3.53±2.89mm, respectively. These voxel shift errors translate into compression of the tumor volume: The ABA with constraint returned volumetric errors of 2.70±4.08%, 7.31±4.52%, 9.28±5.55%, and 13.19±6.73% for the 25%, 50%, 75%, and 95% contraction simulations, respectively, whereas the unconstrained ABA returned volumetric errors of 4.00±4.46%, 9.93±4.83%, 19.78±5.657%, and 29.75±15.18%. The ABA with constraint yields a smaller mean shift error, as well as a smaller volume error (p=0.03125 for the 75% and 95% contractions), than the unconstrained ABA for the simulated sets. Visual and quantitative assessments on experimental data also indicate a good performance of the proposed algorithm.

Conclusions

The ABA with constraint can successfully register breast MR images acquired at different time points with reasonable error. To the best of the authors’ knowledge, this is the first report of an attempt to quantitatively assess in both phantoms and a set of patients the accuracy of a registration algorithm for this purpose.

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