With its high sensitivity, dynamic contrast-enhanced MR imaging (DCE-MRI) of the breast is today one of the first-line
tools for early detection and diagnosis of breast cancer, particularly in the dense breast of young women. However, many
relevant findings are very small or occult on targeted ultrasound images or mammography, so that MRI guided biopsy is
the only option for a precise histological work-up [1]. State-of-the-art software tools for computer-aided diagnosis of
breast cancer in DCE-MRI data offer also means for image-based planning of biopsy interventions. One step in the MRI
guided biopsy workflow is the alignment of the patient position with the preoperative MR images. In these images, the
location and orientation of the coil localization unit can be inferred from a number of fiducial markers, which for this
purpose have to be manually or semi-automatically detected by the user.
In this study, we propose a method for precise, full-automatic localization of fiducial markers, on which basis a virtual
localization unit can be subsequently placed in the image volume for the purpose of determining the parameters for
needle navigation. The method is based on adaptive thresholding for separating breast tissue from background followed
by rigid registration of marker templates. In an evaluation of 25 clinical cases comprising 4 different commercial coil
array models and 3 different MR imaging protocols, the method yielded a sensitivity of 0.96 at a false positive rate of
0.44 markers per case. The mean distance deviation between detected fiducial centers and ground truth information that
was appointed from a radiologist was 0.94mm.
J. Bieberstein, C. Schumann, A. Weihusen, T. Boehler, S. Wirtz, P. Bruners, D. Schmidt, C. Trumm, M. Niethammer, G. Haras, R.-T. Hoffmann, A. Mahnken, P. Pereira, H.-O. Peitgen
Radiofrequency (RF) ablation is an image-guided minimally invasive therapy which destroys a tumor by locally
inducing electrical energy into the malignant tissue through a radiofrequency applicator. Treatment success is essentially
dependent on the accurate placement of the RF applicator. In the case of CT-guided RF ablation of liver tumors, a central
problem during monitoring is the reduced quality and information content in the peri-interventional images compared to
the images used for planning. Therefore, the question of how to effectively transfer information from the planning scan
into the peri-interventional scan in order to support the interventionalist is of high interest. Key to such an enhancement
of peri-interventional scans is an adequate registration of the pre- and peri-interventional image, which also needs to be
fast since intervention duration is still a challenge. We present an approach for the fast and automatic registration of a
high quality CT volume scan of the liver to a spiral CT scan of lower quality. Our method combines an approximate pre-registration
to compensate large displacements and a rigid registration of a liver subvolume for further refinement. The
method focuses on the position of the tumor to be ablated and the corresponding access path. Thereby, it achieves both
fast and precise results in the region of interest. A preliminary evaluation, on 37 data sets from 20 different patients,
shows that the registration is performed within a maximum of 18 seconds, while obtaining high accuracy in the relevant
region of the liver comprising tumor and the planned access path.
Correction of patient motion is a fundamental preprocessing step for dynamic contrast-enhanced (DCE) breast MRI, removing artifacts induced by involuntary movement and facilitating quantitative analysis of contrast agent kinetics. Image registration algorithms commonly employed for this task align subsequent temporal images of the dynamic MRI by maximizing intensity-, correlation- or entropy-based similarity measures between image pairs. To compensate for global patient motion, frequently an initial affine linear or rigid transformation is estimated. Subsequently, local image variablity is reduced by maximizing local similarity measures and using viscous fluid or elastic regularization terms. We present a novel iterative scheme combining local and global registration into one single algorithm, limiting computational overhead, reducing interpolation artifacts and generally improving the quality of registration results. The relation between local and global motion is adjusted by the introduction of corresponding flexible weighting functions, allowing for a sound combination of both registration types and a potentially wider range of computable transformations. The proposed method is evaluated on both synthetic images and clinical breast MRI data. The results demonstrate that our method works stable and reliably compensates for common motion artifacts typical to DCE MR mammography.
The automatic segmentation of relevant structures such as skin edge, chest wall, or nipple in dynamic contrast
enhanced MR imaging (DCE MRI) of the breast provides additional information for computer aided diagnosis (CAD) systems. Automatic reporting using BI-RADS criteria benefits of information about location of those
structures. Lesion positions can be automatically described relatively to such reference structures for reporting
purposes. Furthermore, this information can assist data reduction for computation expensive preprocessing such
as registration, or for visualization of only the segments of current interest. In this paper, a novel automatic method for determining the air-breast boundary resp. skin edge, for approximation of the chest wall, and locating of the nipples is presented. The method consists of several steps which are built on top of each other. Automatic threshold computation leads to the air-breast boundary which is then analyzed to determine the location of the nipple. Finally, results of both steps are starting point for approximation of the chest wall. The proposed process was evaluated on a large data set of DCE MRI recorded by T1 sequences and yielded reasonable results in all cases.
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