90 research outputs found

    Practical application of contrast-enhanced magnetic resonance mammography [CE-MRM] by an algorithm combining morphological and enhancement patterns

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    The purpose of this article is to report our practical utilization of dynamic contrast-enhanced magnetic resonance mammography [DCE-MRM] in the diagnosis of breast lesions. In many European centers, was preferred a high-temporal acquisition of both breasts simultaneously in a large FOV. We preferred to scan single breasts, with the aim to combine the analysis of the contrast intake and washout with the morphological evaluation of breast lesions. We followed an interpretation model, based upon a diagnostic algorithm, which combined contrast enhancement with morphological evaluation, in order to increase our confidence in diagnosis. DCE-MRM with our diagnostic algorithm has identified 179 malignant and 41 benign lesions; final outcome has identified 178 malignant and 42 benign lesions, 3 false positives and 2 false negatives. Sensitivity of CE-MRM was 98.3%; specificity, 95.1%; positive predictive value 98.9%; negative predictive,. value, 92.8% and accuracy, 97.7%. (C) 2008 Elsevier Ltd. All rights reserve

    A Comprehensive Framework for Image Guided Breast Surgery

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    Fast registration of contrast-enhanced magnetic resonance images of the breast

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    Master'sMASTER OF ENGINEERIN

    Hierarchical alignment of breast DCE-MR images by groupwise registration and robust feature matching: Breast DCE-MR image registration

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    Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) shows high sensitivity in detecting breast cancer. However, its performance could be affected by patient motion during the imaging. To overcome this problem, it is necessary to correct patient motion by deformable registration, before using the DCE-MRI to detect breast cancer. However, deformable registration of DCE-MR images is challenging due to the dramatic contrast change over time (especially between the precontrast and postcontrast images). Most existing methods typically register each postcontrast image onto the precontrast image independently, without considering the dynamic contrast change after agent uptake. This could lead to the inconsistency among the aligned postcontrast images in the precontrast image space, which will eventually result in worse performance in cancer detection. In this paper, the authors present a novel hierarchical registration framework to address this problem

    Automatic correspondence between 2D and 3D images of the breast

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    Radiologists often need to localise corresponding findings in different images of the breast, such as Magnetic Resonance Images and X-ray mammograms. However, this is a difficult task, as one is a volume and the other a projection image. In addition, the appearance of breast tissue structure can vary significantly between them. Some breast regions are often obscured in an X-ray, due to its projective nature and the superimposition of normal glandular tissue. Automatically determining correspondences between the two modalities could assist radiologists in the detection, diagnosis and surgical planning of breast cancer. This thesis addresses the problems associated with the automatic alignment of 3D and 2D breast images and presents a generic framework for registration that uses the structures within the breast for alignment, rather than surrogates based on the breast outline or nipple position. The proposed algorithm can adapt to incorporate different types of transformation models, in order to capture the breast deformation between modalities. The framework was validated on clinical MRI and X-ray mammography cases using both simple geometrical models, such as the affine, and also more complex ones that are based on biomechanical simulations. The results showed that the proposed framework with the affine transformation model can provide clinically useful accuracy (13.1mm when tested on 113 registration tasks). The biomechanical transformation models provided further improvement when applied on a smaller dataset. Our technique was also tested on determining corresponding findings in multiple X-ray images (i.e. temporal or CC to MLO) for a given subject using the 3D information provided by the MRI. Quantitative results showed that this approach outperforms 2D transformation models that are typically used for this task. The results indicate that this pipeline has the potential to provide a clinically useful tool for radiologists

    Standardized Platform for Coregistration of Noncurrent Diffuse Optical and Magnetic Resonance Breast Images Obtained in Different Geometries

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    We present a novel methodology for combining breast image data obtained at different times, in different geometries, and by different techniques. We combine data based on diffuse optical tomography (DOT) and magnetic resonance imaging (MRI). The software platform integrates advanced multimodal registration and segmentation algorithms, requires minimal user experience, and employs computationally efficient techniques. The resulting superposed 3-D tomographs facilitate tissue analyses based on structural and functional data derived from both modalities, and readily permit enhancement of DOT data reconstruction using MRI-derived a-priori structural information. We demonstrate the multimodal registration method using a simulated phantom, and we present initial patient studies that confirm that tumorous regions in a patient breast found by both imaging modalities exhibit significantly higher total hemoglobin concentration (THC) than surrounding normal tissues. The average THC in the tumorous regions is one to three standard deviations larger than the overall breast average THC for all patients

    Multimodal breast imaging: Registration, visualization, and image synthesis

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    The benefit of registration and fusion of functional images with anatomical images is well appreciated in the advent of combined positron emission tomography and x-ray computed tomography scanners (PET/CT). This is especially true in breast cancer imaging, where modalities such as high-resolution and dynamic contrast-enhanced magnetic resonance imaging (MRI) and F-18-FDG positron emission tomography (PET) have steadily gained acceptance in addition to x-ray mammography, the primary detection tool. The increased interest in combined PET/MRI images has facilitated the demand for appropriate registration and fusion algorithms. A new approach to MRI-to-PET non-rigid breast image registration was developed and evaluated based on the location of a small number of fiducial skin markers (FSMs) visible in both modalities. The observed FSM displacement vectors between MRI and PET, distributed piecewise linearly over the breast volume, produce a deformed Finite-Element mesh that reasonably approximates non-rigid deformation of the breast tissue between the MRI and PET scans. The method does not require a biomechanical breast tissue model, and is robust and fast. The method was evaluated both qualitatively and quantitatively on patients and a deformable breast phantom. The procedure yields quality images with average target registration error (TRE) below 4 mm. The importance of appropriately jointly displaying (i.e. fusing) the registered images has often been neglected and underestimated. A combined MRI/PET image has the benefits of directly showing the spatial relationships between the two modalities, increasing the sensitivity, specificity, and accuracy of diagnosis. Additional information on morphology and on dynamic behavior of the suspicious lesion can be provided, allowing more accurate lesion localization including mapping of hyper- and hypo-metabolic regions as well as better lesion-boundary definition, improving accuracy when grading the breast cancer and assessing the need for biopsy. Eight promising fusion-for-visualization techniques were evaluated by radiologists from University Hospital, in Syracuse, NY. Preliminary results indicate that the radiologists were better able to perform a series of tasks when reading the fused PET/MRI data sets using color tables generated by a newly developed genetic algorithm, as compared to other commonly used schemes. The lack of a known ground truth hinders the development and evaluation of new algorithms for tasks such as registration and classification. A preliminary mesh-based breast phantom containing 12 distinct tissue classes along with tissue properties necessary for the simulation of dynamic positron emission tomography scans was created. The phantom contains multiple components which can be separately manipulated, utilizing geometric transformations, to represent populations or a single individual being imaged in multiple positions. This phantom will support future multimodal breast imaging work

    Numerical Approaches for Solving the Combined Reconstruction and Registration of Digital Breast Tomosynthesis

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    Heavy demands on the development of medical imaging modalities for breast cancer detection have been witnessed in the last three decades in an attempt to reduce the mortality associated with the disease. Recently, Digital Breast Tomosynthesis (DBT) shows its promising in the early diagnosis when lesions are small. In particular, it offers potential benefits over X-ray mammography - the current modality of choice for breast screening - of increased sensitivity and specificity for comparable X-ray dose, speed, and cost. An important feature of DBT is that it provides a pseudo-3D image of the breast. This is of particular relevance for heterogeneous dense breasts of young women, which can inhibit detection of cancer using conventional mammography. In the same way that it is difficult to see a bird from the edge of the forest, detecting cancer in a conventional 2D mammogram is a challenging task. Three-dimensional DBT, however, enables us to step through the forest, i.e., the breast, reducing the confounding effect of superimposed tissue and so (potentially) increasing the sensitivity and specificity of cancer detection. The workflow in which DBT would be used clinically, involves two key tasks: reconstruction, to generate a 3D image of the breast, and registration, to enable images from different visits to be compared as is routinely performed by radiologists working with conventional mammograms. Conventional approaches proposed in the literature separate these steps, solving each task independently. This can be effective if reconstructing using a complete set of data. However, for ill-posed limited-angle problems such as DBT, estimating the deformation is difficult because of the significant artefacts associated with DBT reconstructions, leading to severe inaccuracies in the registration. The aim of my work is to find and evaluate methods capable of allying these two tasks, which will enhance the performance of each process as a result. Consequently, I prove that the processes of reconstruction and registration of DBT are not independent but reciprocal. This thesis proposes innovative numerical approaches combining reconstruction of a pair of temporal DBT acquisitions with their registration iteratively and simultaneously. To evaluate the performance of my methods I use synthetic images, breast MRI, and DBT simulations with in-vivo breast compressions. I show that, compared to the conventional sequential method, jointly estimating image intensities and transformation parameters gives superior results with respect to both reconstruction fidelity and registration accuracy
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