![Overview of the processing steps: (1) determination of 3D breast volume surfaces: extraction of 3D MR breast surface and reconstruction of 3D mammographic breast surface, (2) ICP-based B-spline registration of the determined surfaces and (3) deformation and simulation of an MR projection image [2].](https://www.researchgate.net/profile/Jan-Ehrhardt/publication/275517441/figure/fig4/AS:668444341043219@1536380958724/Overview-of-the-processing-steps-1-determination-of-3D-breast-volume-surfaces_Q320.jpg)
![Boxplots of the determined distances: left: results (distances in [mm]) for different grid spacings (from 30 × 30 × 30 mm 3 to 60 × 60 × 60 mm 3 ); right: results (for grid spacing 40 × 40 × 40 mm 3 ) before deformation, after deformation and after further manual alignment and deformation of the MR breast.](https://www.researchgate.net/profile/Jan-Ehrhardt/publication/275517441/figure/fig3/AS:383327455465474@1468403795381/Boxplots-of-the-determined-distances-left-results-distances-in-mm-for-different_Q320.jpg)
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Evaluation of a B-Spline-Based Breast Compression Simulation for Correspondence Analysis between MRI and Mammographic Image Data
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Mammography is the most commonly used imaging modality in breast cancer screening and diagnosis. The analysis of 2D mammographic images can be difficult due to the projective nature of the imaging technique and poor contrast between tumorous and healthy fibro-glandular tissue. Contrast-enhanced magnetic resonance imaging (MRI) can overcome these disadvantages by providing a 3D dataset of the breast. The detection of corresponding image structures is challenging due to large breast deformations during the image acquisition. We present a method for analyzing 2D/3D intra-individual correspondences between mammography and MRI datasets. Therefore, an ICP-based B-spline registration is used to approximate the breast deformation differences. The resulting deformed MR image is projected onto the 2D plane to enable a comparison with the 2D mammogram. A first evaluation based on six mammograms revealed an average accuracy of 4.87 mm. In contrast to previous FEM-based approaches, we propose a fast and easy to implement 3D/3D-registration, for simulating the mammographic breast compression.
To investigate a multimodality computer-aided diagnosis (CAD) scheme that combines image information from full-field digital mammography (FFDM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for computerized breast cancer classification.
From a retrospective FFDM database with 432 lesions (255 malignant, 177 benign) and a retrospective DCE-MRI database including 476 lesions (347 malignant, 129 benign), we constructed a multimodality dataset of 213 lesions (168 malignant, 45 benign). Each lesion was present on both FFDM and DCE-MRI images and deemed to be a difficult case given the necessity of having both clinical imaging exams. Using a manually indicated lesion location (ie, a seed point on FFDM images or a region of interest on DCE-MRI images, the computer automatically segmented the mass lesions and extracted lesion features). A subset of features was selected using linear stepwise feature selection and merged by a Bayesian artificial neural network to yield an estimate of the probability of malignancy. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the selected features in distinguishing between malignant and benign lesions.
With leave-one-lesion-out cross-validation on the multimodality dataset, the mammography-only features yielded an area under the ROC curve (AUC) of 0.74 +/- 0.04, and the DCE-MRI-only features yielded an AUC of 0.78 +/- 0.04. The combination of these two modalities, which included a spiculation feature from mammography and two kinetic features from DCE-MRI, yielded an AUC of 0.87 +/- 0.03. The improvement of combining multimodality information was statistically significant as compared to the use of single modality information alone.
A CAD scheme that combines features extracted from FFDM and DCE-MRI images may be advantageous to single-modality CAD in the task of differentiating between malignant and benign lesions.
The paper describes a fast algorithm for scattered data
interpolation and approximation. Multilevel B-splines are introduced to
compute a C2 continuous surface through a set of irregularly
spaced points. The algorithm makes use of a coarse to fine hierarchy of
control lattices to generate a sequence of bicubic B-spline functions
whose sum approaches the desired interpolation function. Large
performance gains are realized by using B-spline refinement to reduce
the sum of these functions into one equivalent B-spline function.
Experimental results demonstrate that high fidelity reconstruction is
possible from a selected set of sparse and irregular samples
We present a new approach for automatic registration of X-ray mammograms and MR images. Multimodal breast cancer diagnosis is supported by automatic localization of small lesions, which are only visible in the mammograms or in the MR image. To cope with the huge deformation of the breast during mammography, a finite element model of the deformable behavior of the breast is applied during the registration. An evaluation of the registration with six clinical data sets resulted in an accurate localization with a mean displacement of 4.3 mm (±1 mm) and 3.9 mm (±1.7 mm) for predicting the lesion position in mammograms and in the MR images, respectively.
Due to their different physical origin, X-ray mammography and Magnetic Resonance Imaging (MRI) provide complementary diagnostic information. However, the correlation of their images is challenging due to differences in dimensionality, patient positioning and compression state of the breast. Our automated registration takes over part of the correlation task. The registration method is based on a biomechanical finite element model, which is used to simulate mammographic compression. The deformed MRI volume can be compared directly with the corresponding mammogram. The registration accuracy is determined by a number of patient-specific parameters. We optimize these parameters - e.g. breast rotation - using image similarity measures. The method was evaluated on 79 datasets from clinical routine. The mean target registration error was 13.2mm in a fully automated setting. On basis of our results, we conclude that a completely automated registration of volume images with 2D mammograms is feasible. The registration accuracy is within the clinically relevant range and thus beneficial for multimodal diagnosis.
X-ray mammography is routinely used in national screening programmes and as a clinical diagnostic tool. Magnetic Resonance Imaging (MRI) is commonly used as a complementary modality, providing functional information about the breast and a 3D image that can overcome ambiguities caused by the superimposition of fibro-glandular structures associated with X-ray imaging. Relating findings between these modalities is a challenging task however, due to the different imaging processes involved and the large deformation that the breast undergoes. In this work we present a registration method to determine spatial correspondence between pairs of MR and X-ray images of the breast, that is targeted for clinical use. We propose a generic registration framework which incorporates a volume-preserving affine transformation model and validate its performance using routinely acquired clinical data. Experiments on simulated mammograms from 8 volunteers produced a mean registration error of 3.8±1.6mm for a mean of 12 manually identified landmarks per volume. When validated using 57 lesions identified on routine clinical CC and MLO mammograms (n=113 registration tasks) from 49 subjects the median registration error was 13.1mm. When applied to the registration of an MR image to CC and MLO mammograms of a patient with a localisation clip, the mean error was 8.9mm. The results indicate that an intensity based registration algorithm, using a relatively simple transformation model, can provide radiologists with a clinically useful tool for breast cancer diagnosis.
Several methods have been proposed to simulate large breast compressions such as those occurring during x-ray mammography. However, the evaluation of these methods against real data is rare. The aim of this study is to learn more about the deformation behavior of breasts and to assess a simulation method.
Magnetic resonance (MR) images of 11 breasts before and after applying a relatively large in vivo compression in the medial direction were acquired. Nonrigid registration was employed to study the deformation behavior. Optimal material properties for finite element modeling were determined and their prediction performance was assessed. The realism of simulated compressions was evaluated by comparing the breast shapes on simulated and real mammograms.
Following image registration, 19 breast compressions from 8 women were studied. An anisotropic deformation behavior, with a reduced elongation in the anterior-posterior direction and an increased stretch in the inferior-superior direction was observed. Using finite element simulations, the performance of isotropic and transverse isotropic material models to predict the displacement of internal landmarks was compared. Isotropic materials reduced the mean displacement error of the landmarks from 23.3 to 4.7 mm, on average, after optimizing material properties with respect to breast surface alignment and image similarity. Statistically significantly smaller errors were achieved with transverse isotropic materials (4.1 mm, P=0.0045). Homogeneous material models performed substantially worse (transverse isotropic: 5.5 mm; isotropic: 6.7 mm). Of the parameters varied, the amount of anisotropy had the greatest influence on the results. Optimal material properties varied less when grouped by patient rather than by compression magnitude (mean: 0.72 vs. 1.44). Employing these optimal materials for simulating mammograms from ten MR breast images of a different cohort resulted in more realistic breast shapes than when using established material models.
Breasts in the prone position exhibited an anisotropic compression behavior. Transverse isotropic materials with an increased stiffness in the anterior-posterior direction improved the prediction of these deformations and produced more realistic mammogram simulations from MR images.