Quality assurance has been recognized as crucial for the success of population-based breast cancer screening programs using x-ray mammography. Quality guidelines and criteria have been defined in the US as well as the European Union in order to ensure the quality of breast cancer screening. Taplin et al. report that incorrect positioning of the breast is the major image quality issue in screening mammography. Consequently, guidelines and criteria for correct positioning and for the assessment of the positioning quality in mammograms play an important role in the quality standards. In this paper we present a system for the automatic evaluation of positioning quality in mammography according to the existing standardized criteria. This involves the automatic detection of anatomic landmarks in medio- lateral oblique (MLO) and cranio-caudal (CC) mammograms, namely the pectoral muscle, the mammilla and the infra-mammary fold. Furthermore, the detected landmarks are assessed with respect to their proper presentation in the image. Finally, the geometric relations between the detected landmarks are investigated to assess the positioning quality. This includes the evaluation whether the pectoral muscle is imaged down to the mammilla level, and whether the posterior nipple line diameter of the breast is consistent between the different views (MLO and CC) of the same breast. Results of the computerized assessment are compared to ground truth collected from two expert readers.
KEYWORDS: Data modeling, Magnetic resonance imaging, Brain, 3D modeling, Scanners, Neuroimaging, Diagnostics, Process modeling, Head, Image acquisition
In clinical MRI examinations, the geometry of diagnostic scans is defined in an initial planning phase. The operator plans the scan volumes (off-centre, angulation, field-of-view) with respect to patient anatomy in 'scout' images. Often multiple plans are required within a single examination, distracting attention from the patient waiting in the scanner. A novel and robust method is described for automated planning of neurological MRI scans, capable of handling strong shape deviations from healthy anatomy. The expert knowledge required to position scan geometries is learned from previous example plans, allowing site-specific styles to be readily taken into account. The proposed method first fits an anatomical model to the scout data, and then new scan geometries are positioned with respect to extracted landmarks. The accuracy of landmark extraction was measured to be comparable to the inter-observer variability, and automated plans are shown to be highly consistent with those created by expert operators using clinical data. The results of the presented evaluation demonstrate the robustness and applicability of the proposed approach, which has the potential to significantly improve clinical workflow.
The automatic segmentation of the heart's two ventricles from dynamic
("cine") cardiac anatomical images, such as 3D+time short-axis MRI, is of significant clinical importance. Previously published automated
methods have various disadvantages for routine clinical use. This work reports about a novel automatic segmentation method that is very fast, and robust against anatomical variability and image contrast variations. The method is mostly image-driven: it fully exploits the information provided by modern 4D (3D+time) balanced Fast Field Echo (bFFE) cardiac anatomical MRI, and makes only few and plausible assumptions about the images and the imaged heart. Specifically, the method does not need any geometrical shape models nor complex gray-level appearance models. The method simply uses the two ventricles' contraction-expansion cycle, as well as the ventricles' spatial coherence along the time dimension. The performance of the cardiac ventricles segmentation method was demonstrated through a qualitative visual validation on 32 clinical exams: no gross failures for the left-ventricle (right-ventricle) on 32 (30) of the exams were found. Also, a clinical validation of resulting quantitative cardiac functional parameters was performed against a manual quantification of 18 exams; the automatically computed Ejection Fraction (EF) correlated well to the manually computed one: linear regression with RMS=3.7% (RMS expressed in EF units).
Registration of medical images, i.e. the integration of two or more images into a common geometrical system of reference so that corresponding image structures correctly align, is an active field of current research. Registration algorithms in general are composed of three main building blocks: a geometrical transformation is applied in order to transform the images into the geometrical system of reference, a similarity measure puts the comparison of the images into quantifiable terms, and an optimization algorithm searches for that transformation that leads to optimal similarity between the images. Whereas in the literature fixed configurations of registration algorithms are investigated, here we present a modular toolbox containing several similarity measures, transformation classes and optimization strategies. Derivative-free optimization is applicable for any similarity measure, but is not fast enough in clinical practice. Hence we consider much faster derivative-based Gauss-Newton and Levenberg-Marquardt optimization algorithms that can be used in conjunction with frequently needed similarity measures for which derivatives can be easily obtained. The implemented similarity measures, geometrical transformations and optimization methods can be freely combined in order to configure a registration algorithm matching the requirements of a particular clinical application. Test examples show that particular algorithm configurations out of this toolbox allow e.g. for an improved lesion identification and localization in PET-CT or MR registration applications.
B-splines are a well-known approach for non-rigid image registration. Though successfully applied to various medical applications they exhibit a high computational complexity mainly because of the lack of dedicated optimization methods. In this work we focus on a Levenberg-Marquardt type optimization routine. As a similarity measure we use least-squares functionals such as the sum of squared differences, the cross-correlation and the local correlation measure, respectively. The latter is used for multi-modality registration tasks. The proposed registration algorithm consists of three main parts. In each iteration step one has to (a) build a linear system of equations, (b) solve this system and compute an update, (c) determine the step length for the following iteration step. Appropriate stopping criteria ensure the termination of the registration task. A standard approach for (c) and several modifications are investigated. Using a quadratic model we are able to avoid additional execution of (b) during the step length adaption. Several solvers (Cholesky, CG, pre-conditioning) for (b) have been evaluated. Also, modifications on the most time consuming task (a) are investigated, leading to a speed-up by a factor up to 30. Finally, the algorithm is embedded in a multi-scale framework (both on image and on parameter level) providing additional regularization, an increased capture range and speed-up. Convergence tests have been successfully applied for a priori known transformations. Feasibility of the proposed approach is also shown for clinical applications including PET-CT registrations (19 data sets) and MR mammography.
Intensity based registration algorithms have proved to be accurate and robust for 3D-3D registration tasks. However, these methods utilise the information content within an image, and therefore their performance is hindered for image data that is sparse. This is the case for the registration of a single image slice to a 3D image volume. There are some important applications that could benefit from improved slice-to-volume registration, for example, the planning of magnetic resonance (MR) scans or cardiac MR imaging, where images are acquired as stacks of single slices. We have developed and validated an information based slice-to-volume registration algorithm that uses vector valued probabilistic images of tissue classification that have been derived from the original intensity images. We believe that using such methods inherently incorporates into the registration framework more information about the images, especially in images containing severe partial volume artifacts. Initial experimental results indicate that the suggested method can achieve a more robust registration compared to standard intensity based methods for the rigid registration of a single thick brain MR slice, containing severe partial volume artifacts in the through-plane direction, to a complete 3D MR brain volume.
KEYWORDS: Image segmentation, Motion models, Data modeling, Heart, Magnetic resonance imaging, Expectation maximization algorithms, Cardiovascular magnetic resonance imaging, Monte Carlo methods, 3D modeling, Tissues
The quantitative analysis of cardiac cine MRI sequences requires automated, robust, and fast image processing algorithms for the 4D (3D + time) segmentation of the heart chambers. The use of shape models has proven efficient in extracting the cardiac volumes for single phases, but less attention has been focused on incorporating prior knowledge about the cardiac motion. To explicitly address the temporal aspect of the segmentation problem, this paper proposes a full Bayesian model, where the prior information is represented by a cardiac shape and motion model. In this framework, the solution of the segmentation is defined by means of a probability distribution over the parameters of the space-time problem. The computed solution, obtained by means of sequential Monte Carlo techniques, has the advantage of being both spatially and temporally coherent. Furthermore, the method does not require any particular representation of the shape or of the motion model; it is therefore generic and highly flexible.
KEYWORDS: Image segmentation, Data modeling, 3D modeling, Magnetic resonance imaging, Cardiovascular magnetic resonance imaging, Natural surfaces, Statistical modeling, Medical imaging, Eye models, Binary data
Cardiac MRI has improved the diagnosis of cardiovascular diseases by enabling the quantitative assessment of functional parameters. This requires an accurate identification of the myocardium of the left ventricle. This paper describes a novel segmentation technique for automated delineation of the myocardium. We propose to use prior knowledge by integrating a statistical shape model and a spatially varying feature model into a deformable mesh adaptation framework. Our shape model consists of a coupled, layered triangular mesh of the epi- and endocardium. It is adapted to the image by iteratively carrying out i) a surface detection and ii) a mesh reconfiguration by energy minimization. For surface detection a feature search is performed to find the point with the best feature combination. To accommodate the different tissue types the triangles of the mesh are labeled, resulting in a spatially varying feature model. The energy function consists of two terms: an external energy term, which attracts the triangles towards the features, and an internal energy term, which preserves the shape of the mesh. We applied our method to 40 cardiac MRI data sets (FFE-EPI) and compared the results to manual segmentations. A mean distance of about 3 mm with a standard deviation of 2 mm to the manual segmentations was achieved.
The purpose of the proposed template propagation method is to support the comparative analysis of image pairs even when large deformations (e.g. from movement) are present. Starting from a position where valid starting estimates are known, small sub-volumes (templates) are registered rigidly. Propagating registration results to neighboring templates, the algorithm proceeds layer by layer until corresponding points for the whole volume are available. Template classification is important for defining the templates to be registered, for propagating registration results and for selecting successfully registered templates which finally represent the motion vector field. This contribution discusses a template selection and classification strategy based on the analysis of the similarity measure in the vicinity of the optimum. For testing the template propagation and classification methods, deformation fields of four volume pairs exhibiting considerable deformations have been estimated and the results have been compared to corresponding points picked by an expert. In all four cases, the proposed classification scheme was successful. Based on homologous points resulting from template propagation, an elastic transformation was performed.
The analysis of functional MR images of the brain such as FMRI and neuro perfusion is significantly limited by movement of the head during image acquisition. Already small motions introduce artifacts in voxel-based statistical analysis and restrict the assessment of functional information. The retrospective compensation of head motion is usually addressed by image registration techniques which spatially align the images of the time-series. In this paper we investigate the relevance of intermediate interpolation during the registration process, similarity measure and optimization scheme by means of statistical consistency of the registration results. Experiments show that cubic and quartic interpolation remarkably improve the consistency when compared to linear methods. The use of larger interpolation kernels, however, does not result in further improvements. Measures based on the mean squared error are successfully applied to FMRI time- series which provide constant tissue-to-image transfer. However, they are not suitable for neuro perfusion imaging since the change of image intensity during the inflow of the contrast agent affords measures typically applied in multi- modality registration. Our results indicate that a recently proposed measure based on local correlation is preferable to mutual information in the case of neuro perfusion.
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