Proc. SPIE. 8669, Medical Imaging 2013: Image Processing
KEYWORDS: Principal component analysis, Data modeling, Tumors, Image segmentation, 3D modeling, Image registration, Lung, Data acquisition, Motion models, Prototyping
Treatment of tumor sites affected by respiratory motion requires knowledge of the position and the shape of
the tumor and the surrounding organs during breathing. As not all structures of interest can be observed in
real-time, their position needs to be predicted from partial information (so-called surrogates) like motion of
diaphragm, internal markers or patients surface. Here, we present an approach to model respiratory lung motion
and predict the position and shape of the lungs from surrogates. 4D-MRI lung data of 10 healthy subjects was
acquired and used to create a model based on Principal Component Analysis (PCA). The mean RMS motion
ranged from 1.88 mm to 9.66 mm. Prediction was done using a Bayesian approach and an average RMSE of
1.44 mm was achieved.
This work applies fractional differentiation (differentiation to non-integer order) to the gradients determined
from image intensities for enhanced image registration. The technique is used to correct known simulated
deformations of volumetric breast MR data using two algorithms: direct registration of gradient magnitude
images and an extension of a previously published method that incorporates both image intensity and image
gradient information to enhance registration performance. Better recovery of known deformations are seen
when using non-integer order derivatives: half-derivative breast images are better registered when these
methods are incorporated into a standard diffusion-based registration algorithm.
Proc. SPIE. 7963, Medical Imaging 2011: Computer-Aided Diagnosis
KEYWORDS: Mathematical modeling, Breast, Optical spheres, Computer aided diagnosis and therapy, Data modeling, Tissues, Mammography, Nipple, Digital breast tomosynthesis
To improve cancer detection in mammography, breast exams usually consist of two views per breast. To combine
information from both views, radiologists and multiview computer-aided detection (CAD) systems need to match
corresponding regions in the two views. In digital breast tomosynthesis (DBT), finding corresponding regions
in ipsilateral volumes may be a difficult and time-consuming task for radiologists, because many slices have to
be inspected individually. In this study we developed a method to quickly estimate corresponding locations in
ipsilateral tomosynthesis views by applying a mathematical transformation. First a compressed breast model is
matched to the tomosynthesis view containing a point of interest. Then we decompress, rotate and compress
again to estimate the location of the corresponding point in the ipsilateral view. In this study we use a simple
elastically deformable sphere model to obtain an analytical solution for the transformation in a given DBT
case. The model is matched to the volume by using automatic segmentation of the pectoral muscle, breast
tissue and nipple. For validation we annotated 181 landmarks in both views and applied our method to each
location. Results show a median 3D distance between the actual location and estimated location of 1.5 cm; a
good starting point for a feature based local search method to link lesions for a multiview CAD system. Half of
the estimated locations were at most 1 slice away from the actual location, making our method useful as a tool
in mammographic workstations to interactively find corresponding locations in ipsilateral tomosynthesis views.
This paper presents a novel approach to X-ray mammography - MRI registration. The proposed method uses
an intensity-based technique and an affine transformation matrix to approximate the 3D deformation of the
breast resulting from the compression applied during mammogram acquisition. The registration is driven by a
similarity measure that is calculated at each iteration of the algorithm between the target X-ray mammogram and
a simulated X-ray image, created from the MR volume. Although the similarity measure is calculated in 2D, we
compute a 3D transformation that is updated at each iteration. We have performed two types of experiments.
In the first set, we used simulated X-ray target data, for which the ground truth deformation of the volume
was known and thus the results could be validated. For this case, we examined the performance of 4 different
similarity measures and we show that Normalized Cross Correlation and Gradient Difference perform best. The
calculated mean reprojection error was for both similarity measures 4mm, for an initial misregistration of 14mm.
In the second set of experiments, we present the initial results of registering real X-ray mammograms with MR
volumes. The results indicate that the breast boundaries were registered well and the volume was deformed in
3D in a similar way to the deformation of the breast during X-ray mammogram acquisition. The experiments
were carried out on five patients.
Stable features under simulated mammographic compressions, which will become candidate landmarks for a temporal
mammographic feature-based registration algorithm, are discussed in this paper. Using these simulated mammograms,
we explore the extraction of features based on standard intensity projection images and local phase projection images.
One approach to establishing corresponding features is by template matching using a similarity measure. Simulated
mammographic projections from deformed MR volumes are employed, as the mean projected 3D displacements are
computed and therefore validation of the technique is possible. Tracking is done by template matching using normalized
cross correlation as the similarity measure. The performance of standard projection images and local phase projection
images is compared. The preliminary results reveal that although the majority of the points within the breast are difficult
to track, a small number may be successfully tracked, which is indicative of their stability and thus their suitability as
candidate landmarks. Whilst matching using the standard projection images achieves an overall error of 14.46mm, this
error increases to 22.7mm when computing local phase of the projection images. These results suggest that using local
phase alone does not improve template matching. For the identification of stable landmarks for feature-based
mammogram registration, we conclude that intensity based template matching using normalized correlation is a feasible
approach for identifying stable features.
Proc. SPIE. 7623, Medical Imaging 2010: Image Processing
KEYWORDS: Breast, Visual process modeling, Magnetic resonance imaging, Image segmentation, Image restoration, 3D modeling, Image registration, Medical imaging, Computed tomography, Affine motion model
This paper describes the development of a cylindrical affine transformation model for image registration. The
usefulness of the model for initial alignment was demonstrated for the application of registering prone and
supine 3D MR images of the breast. Final registration results visually improved when using the cylindrical affine
transformation model instead of none or a Cartesian affine transformation model before non-rigid registration.
This paper describes a novel method for registering multimodal breast images. The method is based on guiding
initial alignment by a 3D statistical deformation model (SDM) followed by a standard non-rigid registration
method for fine alignment. The method was applied to the problem of compensating for large breast compressions,
namely registering magnetic resonance (MR) images to tomosynthesis images and X-ray mammograms. The
SDM was based on simulating plausible breast compressions for a population of 20 subjects via finite element
models created from segmented 3D MR breast images. Leave-one-out tests on simulated data showed that using
SDM guided registration rather than affine registration for the initial alignment led on average to lower mean
registration errors, namely 3.2 mm versus 4.2 mm for MR to tomosynthesis images (17.1 mm initially) and
5.0 mm versus 6.2 mm for MR to X-ray mammograms (15.0 mm initially).
We have previously proposed a system for image-guided breast surgery that compensates for the deformation of the
breast during patient set-up. Since breast surgery is performed with the patient positioned supine, but MR imaging is
performed with the patient positioned prone, a large soft tissue deformation must be accounted for. A biomechanical
model can help to constrain the associated registrations. However the necessary material properties for breast tissue
under such strains are not available in the literature. This paper describes a method to determine these properties. We
first show that the stress-free or 'reference' state of an object can be approximated by submerging it in liquid of a similar
density. MR images of the breast submerged in water and in a pendulous prone position are acquired. An intensity-based
non-rigid image registration algorithm is used to establish point-by-point correspondence between these images. A finite
element model of the breast is then constructed from the submerged images and the deformation to free-pendulous is
simulated. The material properties for which the model deformation best fits the observed deformation are determined.
Assuming neo-Hookean material properties, the initial shear moduli of fibroglandular and adipose tissue are found to be
0.4 kPa and 0.3 kPa respectively.
Image registration is a very important procedure in medical imaging analysis. However, the intensive computations involved in image registration have to some extent made it impractical for interactive use as well as limiting its general availability. This paper presents our current Grid project to facilitate image registration tasks. We have set up an image registration Grid by combining the attractive features of both Globus and Condor distributed computing environments. In order to make it more convenient to use, we have also developed a web interface for potential clients to specify and submit their image registration jobs to the Grid. The initial experiments in 3D breast MR images have shown encouraging results and demonstrated the suitability of Grid technology to this type of computationally intensive applications. The image registration Grid makes it much more straightforward for different institutes to use the identical registration program and protocols to register images consistently, quickly and efficiently. This can greatly improve data sharing and comparative studies in multi-centre trials. The Grid presented here could be an important step for clinical applications of image registration. Future work will focus on refining the Grid with the aim of upgrading it to a Grid Service and testing the system more extensively with medical imaging dataset.
This work presents a validation study for non-rigid registration of 3D contrast enhanced magnetic resonance mammography images. We are using our previously developed methodology for simulating physically plausible, biomechanical tissue deformations using finite element methods to compare two non-rigid registration algorithms based on single-level and multi-level free-form deformations using B-splines and normalized mutual information. We have constructed four patient-specific finite element models and applied the solutions to the original post-contrast scans of the patients, simulating tissue deformation between image acquisitions. The original image pairs were registered to the FEM-deformed post-contrast images using different free-form deformation mesh resolutions. The target registration error was computed for each experiment with respect to the simulated gold standard on a voxel basis. Registration error and single-level free-form deformation resolution were found to be intrinsically related: the smaller the spacing, the higher localized errors, indicating local registration failure. For multi-level free-form deformations, the registration errors improved for increasing mesh resolution. This study forms an important milestone in making our non-rigid registration framework applicable for clinical routine use.
We present initial results from evaluating the accuracy with which biomechanical breast models based on finite element methods can predict the displacements of tissue within the breast. We investigate the influence of different tissue elasticity values, Poisson's ratios, boundary conditions, finite element solvers and mesh resolutions on one data set. MR images were acquired before and after compressing a volunteer's breast gently. These images were aligned using a 3D non-rigid registration algorithm. The boundary conditions were derived from the result of the non-rigid registration or by assuming no patient motion at the deep or medial side. Three linear and two non-linear elastic material models were tested. The accuracy of the BBMs was assessed by the Euclidean distance of twelve corresponding anatomical landmarks. Overall, none of the tested material models was obviously superior to another regarding the set of investigated values. A major average error increase was noted for partially inaccurate boundary conditions at high Poisson's ratios due to introduced volume change. Maximal errors remained, however, high for low Poisson's ratio due to the landmarks closeness to the inaccurate boundary conditions. The choice of finite element solver or mesh resolution had almost no effect on the performance outcome.
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