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 MRimages 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-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 ( deviation) for the ABA with constraint was , , , and for the 25%, 50%, 75%, and 95% contraction simulations, respectively. In comparison, the average voxel shift error for the unconstrained ABA was , , , and , respectively. These voxel shift errors translate into compression of the tumor volume: The ABA with constraint returned volumetric errors of , , , and for the 25%, 50%, 75%, and 95% contraction simulations, respectively, whereas the unconstrained ABA returned volumetric errors of , , , and . The ABA with constraint yields a smaller mean shift error, as well as a smaller volume error ( 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 MRimages 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.
The authors thank the National Institutes of Health for funding through Grant Nos. NCI 1R01CA129961, NIBIB 1K25 EB005936, and NCI 1P50 098131, and the Vanderbilt-Ingram Cancer Center Institutional Grant (NIH Grant No. P30 CA68485). The authors thank Dr. J. Christopher Gatenby, Ph.D., Donna Butler, Wanda Smith, Debbie Boner, Robin Greene-Avison, and Darla Freehardt for expert technical assistance, and Dr. John Huff, M.D., for many informative discussions. The authors also thank the authors of the RPM algorithm, Dr. Haili Chui, Ph.D., and Dr. Anand Rangarajan, Ph.D., for providing the MATLAB® code for their RPM algorithm.
II. MATERIALS AND METHODS
II.A. Data acquisition
II.B. Registration algorithms
II.C. Validation approach for simulated data
II.D. Validation approach for experimental data
III.A. Validation results for simulated data
III.B. Validation results for experimental data
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