Dynamic Contrast Enhanced Breast MR Imaging (DCE BMRI) has emerged as a modality for breast cancer diagnosis. In
this modality a temporal sequence of volume images of the breasts is acquired, where a contrast agent is injected after
acquisition of the first 3D image. Since the introduction of the modality, research has been directed at the development
of computer-aided support for the diagnostic workup. This includes automatic segmentation of mass-like lesions, lesion
characterization, and lesion classification. Robustness, user-independence, and reproducibility of the results of
computerized methods are essential for such methods to be acceptable for clinical application.
A previously proposed and evaluated computerized lesion segmentation method has been further analyzed in this study.
The segmentation method uses as input a subtraction image (post-contrast - pre-contrast) and a user defined region of
interest (ROI). Previous evaluation studies investigated the robustness of the segmentation against variations in the user
selected ROI. Robustness of the method against variations in the image data itself has so far not been investigated. To fill
this gap is the purpose of this study.
In this study, the segmentation algorithm was applied to a series of subtraction images built from the pre-contrast volume
and all available post-contrast image volumes, successively. This provides set of typically 4-5 delineations per lesion,
each based on a different phase of the dynamic sequence.
Analysis of the apparent lesion volumes derived from these delineations and comparison to manual delineations showed
that computerized segmentation is more robust and reproducible than manual segmentation, even if computer
segmentations are computed on subtraction images derived from different dynamic phases of the DCE MRI study, while
all manual segmentations of a lesion are derived from one and the same dynamic phase of the study.
Furthermore, it could be shown that the rate of apparent change of lesion volume over the course of a DCE MRI study is
significantly dependent on the lesion type (benign vs. malignant).
Dynamic contrast enhanced breast MRI (DCE BMRI) is an emerging tool for breast cancer diagnosis. There is
a clear clinical demand for computer-aided diagnosis (CADx) tools to support radiologists in the diagnostic
reading process of DCE BMRI studies. A crucial step in a CADx system is the segmentation of tumors,
which allows for accurate assessment of the 3D lesion size and morphology. In this paper we propose a semiautomatic
segmentation procedure for suspicious breast lesions. The proposed methodology consists of four steps:
(1) Robust seed point selection. This interaction mode ensures robustness of the segmentation result against
variations in seed-point placement. (2) Automatic intensity threshold estimation in the subtraction image.
(3)Connected component analysis based on the estimated threshold. (4) A post-processing step that includes
non-enhancing portions of the lesion into the segmented area and removes attached vessels. The proposed
methodology was applied to DCE BMRI data acquired at different institutions using different protocols.
Finite element method based patient-specific wall stress in
abdominal aortic aneurysm (AAA) may provide a more accurate rupture
risk predictor than the currently used maximum transverse diameter.
In this study, we have investigated the sensitivity of the wall
stress in AAA with respect to geometrical variations. We have
acquired MR and CT images for four patients with AAA. Three
individual users have delineated the AAA vessel wall contours on the
image slices. These contours were used to generate synthetic feature images for a deformable model based segmentation method. We investigated the reproducibility and the influence of the user variability on the wall stress. For sufficiently smooth models of the AAA wall, the peak wall stress is reproducible for three out of the four AAA geometries. The 0.99 percentiles of the wall stress show
excellent reproducibility for all four AAAs. The variations induced by user variability are larger than the errors caused by the segmentation variability. The influence of the user variability appears to be similar for MR and CT. We conclude that the peak wall stress in AAA is sensitive to small geometrical variations. To increase reproducibility it appears to be best not to allow too much geometrical detail in the simulations. This could be achieved either by using a sufficiently smooth geometry representation or by using a more robust statistical parameter derived from the wall stress distribution.
In recent years, simulations of the blood flow and the wall mechanics in the vascular system with patient-specific boundary conditions by using computational fluid dynamics (CFD) and computational solid mechanics (CSM) have gained significant interest. A common goal of such simulations is to help predict the development of vascular diseases over time. However, the validity of such simulations and therefore the validity of the predictions are often questioned by physicians. The aim of the research reported in this paper is to validate CFD simulations performed on patient-specific models of abdominal aorta aneurysms (AAAs) using patient-specific blood velocity inflow profiles. Patient-specific AAA geometries were derived from images originating from Computed Tomography (CT) or Magnetic Resonance (MR) imaging. Patient-specific flow profiles were measured with Phase-Contrast MR imaging (Quantitative flow, Qflow). Such profiles, determined at the inflow site of the AAA, were used as inflow boundary condition for CFD simulations. Qflow images that were taken on a number of planes along the AAA were used for the validation of the simulation results. To compare the measured with the simulated flow we have generated synthetic Qflow images from the simulated velocities on cut-planes positioned and oriented according to the planes of the validation images. The comparison of the real with the simulated flow profiles was performed visually and by quantitatively comparing flow values on cross sections of the AAA in the measured and the synthetic Qflow images. In a preliminary study on two patients we found a reasonable agreement between the measured and the simulated flow profiles.
In recent years, the assessment of patient-specific hemodynamic information of the cardiovascular system has become an important issue. It is believed that this information will improve the diagnosis and treatment of cardiovascular diseases. Realistic patient geometries and flow velocities acquired from image data can nowadays be used as input for computational fluid dynamics (CFD) simulations of the blood flow through the cardiovascular system. Results obtained from these simulations have to be comprehensively visualized so that the physician can understand them and draw diagnostic and/or therapeutic conclusions. The aim of the research reported in this paper is to provide methods for the combined comprehensive visualization of the anatomical information segmented from image data with the hemodynamic information acquired by CFD simulations based on these image data. Several methods are known for the visualization of the blood flow velocity, e.g. flow streamlines, particle traces or simple cut planes through the vessel with a color-coded overlay of the flow velocity. To make these flow visualizations more understandable for the physician, we have developed methods to generate combined visualizations of the simulated blood flow velocity and the patient’s anatomy segmented from the image data. First results of these methods show that the perception of CFD simulation results of blood flow is much better when it is combined with anatomical information of surrounding structures. Physicians reacted very enthusiastically during presentations of results of our new visualization methods. Results will be demonstrated at the conference.