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Validation of an algorithm for the nonrigid registration of longitudinal breast MR images using realistic phantoms
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10.1118/1.3414035
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    Affiliations:
    1 Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232
    2 Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232 and Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee 37232
    3 Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232 and MR Clinical Science, Philips Healthcare, Cleveland, Ohio
    4 Department of Radiation Oncology, Vanderbilt University, Nashville, Tennessee 37232
    5 Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37232
    6 Department of Medical Oncology, Vanderbilt University, Nashville, Tennessee 37232
    7 Department of Surgical Oncology, Vanderbilt University, Nashville, Tennessee 37232
    8 Department of Medical Oncology, Vanderbilt University, Nashville, Tennessee 37232
    9 Department of Surgical Oncology, Vanderbilt University, Nashville, Tennessee 37232
    10 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
    11 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
    a) Author to whom correspondence should be addressed. Electronic mail: thomas.yankeelov@vanderbilt.edu
    Med. Phys. 37, 2541 (2010); http://dx.doi.org/10.1118/1.3414035
/content/aapm/journal/medphys/37/6/10.1118/1.3414035
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/37/6/10.1118/1.3414035

Figures

Image of FIG. 1.
FIG. 1.

The tumor is segmented differently (panels a–c) and the registration results (panels d–f) are similar, indicating the registration algorithm is robust to the tumor delineation. Compared to the tumor in the target image (panel g), the tumor in the deformed image is preserved successfully. Moreover, without the constraint, the tumor is substantially compressed (panel h).

Image of FIG. 2.
FIG. 2.

The flow chart of the validation procedure, which includes data acquisition, simulation, transformation, and comparison. The tumor in the pretreatment image is contracted to generate the simulated image in the simulation step. The RPM algorithm is then applied to coregister and the true post-treatment image. This step produces the simulated post-treatment image with known deformation, allowing comparison of the original and modified ABAs on a voxel-by-voxel basis.

Image of FIG. 3.
FIG. 3.

The contracted area is filled using texture from nearby healthy appearing tissue in a postcontrast, fat-suppressed THRIVE image. For each voxel in the contracted area, the closest point on the tumor contour is detected and used to find the voxel in the healthy tissue. The voxel is then filled using the intensity of .

Image of FIG. 4.
FIG. 4.

The original pretreatment, postcontrast, fat-suppressed THRIVE images from four different patients (upper row), the simulated image with tumor contracted by different percentages (middle row), and the close-up views of the tumor regions (lower row). Note that even when the tumor is fully contracted (first column), the simulated image still appears realistic.

Image of FIG. 5.
FIG. 5.

(a) The original postcontrast, fat-suppressed THRIVE breast image, (b) the corresponding simulated image with tumors shrunk by , (c) the simulated post-treatment image after the breast deformation is simulated using the robust point matching algorithm, and (d) the true post-treatment image.

Image of FIG. 6.
FIG. 6.

The histogram of errors of ABA with and without constraint when the tumors are contracted by 95% for six patients. Note that the constrained ABA leads to more compact error distributions, with considerably smaller maximum errors.

Image of FIG. 7.
FIG. 7.

One central slice of tumor contracted by 95% with the color-coded errors (voxel shifts in mm) superimposed on a postcontrast, fat-suppressed THRIVE. In this slice, the original ABA leads to errors up to 7.01 mm, while the ABA with constraint results in errors only up to 2.71 mm.

Image of FIG. 8.
FIG. 8.

The original source image (left panel), the image after the rigid body registration (middle panel), and the image after both the rigid and nonrigid registration (right panel), respectively. The green contour is segmented from the target image (not shown) and copied onto the following images for comparison.

Image of FIG. 9.
FIG. 9.

Three axial, postcontrast, fat-suppressed THRIVE slices at three different time points after rigid body registration (column 1), after nonrigid registration without the constraint (column 2), and with the constraint (column 3). In the fourth row, the zoom-in deformation field without and with the constraint at (the first and second panels) and (the third and fourth panels) are shown, respectively. It is clear that the rigid registration can only provide an approximate registration result, and the original ABA compresses the tumor significantly, although the normal tissues are registered accurately. The modified ABA can perform well on both normal tissues and the tumor.

Tables

Generic image for table
TABLE I.

The mean errors (mm) and standard deviations of ABA with and without constraint over the tumor area when the tumors are contracted by different percentages for six patients. Note that the mean voxel shift errors were calculated through averaging over all voxels of the tumor area from all patients instead of directly averaging the mean shift error of each patient. The nonparametric Wilcoxon signed rank test is applied to each simulated case.

Generic image for table
TABLE II.

The tumor volume changes using ABA with and without constraint when the tumors are contracted by different percentages for six patients. Note that tumor is compressed notably using the unconstrained ABA compared to the constrained ABA.

Generic image for table
TABLE III.

The tumor volume changes, the constraint values, and the bending energy are calculated for the experimental data, after the registration using ABA with and without constraint, respectively. The nonparametric Wilcoxon signed rank test is applied to all data and the values are listed. The results of all the three validation approaches show that the ABA with constraint leads to significantly smaller tumor volume changes, constraint values, and bending energies, indicating the efficiency of the constraint term.

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/content/aapm/journal/medphys/37/6/10.1118/1.3414035
2010-05-13
2014-04-18
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Validation of an algorithm for the nonrigid registration of longitudinal breast MR images using realistic phantoms
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/37/6/10.1118/1.3414035
10.1118/1.3414035
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