ter Bm Bart Haar Romeny

Magnetic resonance imaging techniques for visualization of the subthalamic nucleus

The authors reviewed 70 publications on MR imaging-based targeting techniques for identifying the subthalamic nucleus (STN) for deep brain stimulation in patients with Parkinson disease. Of these 70 publications, 33 presented quantitatively validated results. There is still no consensus on which targeting technique to use for surgery planning; methods vary greatly between centers. Some groups apply indirect methods involving anatomical landmarks, or atlases incorporating anatomical or functional data. Others perform direct visualization on MR imaging, using T2-weighted spin echo or inversion recovery protocols. The combined studies do not offer a straightforward conclusion on the best targeting protocol. Indirect methods are not patient specific, leading to varying results between cases. On the other hand, direct targeting on MR imaging suffers from lack of contrast within the subthalamic region, resulting in a poor delineation of the STN. These deficiencies result in a need for intraoperative adaptation of the original target based on test stimulation with or without microelectrode recording. It is expected that future advances in MR imaging technology will lead to improvements in direct targeting. The use of new MR imaging modalities such as diffusion MR imaging might even lead to the specific identification of the different functional parts of the STN, such as the dorsolateral sensorimotor part, the target for deep brain stimulation.

Quantification of collagen orientation in 3D engineered tissue

Tissue engineered heart valves are a promising alternative for current heart valve replacements. However, the mechanical properties of these valves are insufficient for implantation at the aortic position [1]. Collagen orientation is important to improve the mechanical properties of tissue engineered valves. Two-photon laser-scanning microscopy allows us to study the influence of strain on collagen orientation in 3D. A method based on the 2nd order derivative of the 3D image structure was used to determine the general orientation of the collagen fibers with automatic scale selection of the operator. We studied the effect of strain on collagen orientation. Alignment in the direction of the applied strain is seen. Histograms show that the distribution of orientations becomes smaller for increased strain. This indicates that the collagen fibers align more.

The use of a reference tissue arterial input function with low-temporal-resolution DCE-MRI data

Pharmacokinetic modeling is a promising quantitative analysis technique for cancer diagnosis. However, diagnostic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast is commonly performed with low temporal resolution. This limits its clinical utility. We investigated for a range of temporal resolutions whether pharmacokinetic parameter estimation is impacted by the use of data-derived arterial input functions (AIFs), obtained via analysis of dynamic data from a reference tissue, as opposed to the use of a standard AIF, often obtained from the literature. We hypothesized that the first method allows the use of data at lower temporal resolutions than the second method. Test data were obtained by downsampling high-temporal-resolution rodent data via a k-space-based strategy. To fit the basic Tofts model, either the data-derived or the standard AIF was used. The resulting estimates of K(trans) and v(e) were compared with the standard estimates obtained by using the original data. The deviations in K(trans) and v(e), introduced when lowering temporal resolution, were more modest using data-derived AIFs compared with using a standard AIF. Specifically, lowering the resolution from 5 to 60 s, the respective changes in K(trans) were 2% (non-significant) and 18% (significant). Extracting the AIF from a reference tissue enables accurate pharmacokinetic parameter estimation for low-temporal-resolution data.

Cardiac left atrium CT image segmentation for ablation guidance

Catheter ablation is an increasingly important curative procedure for atrial fibrillation. Knowledge of the local wall thickness is essential to determine the proper ablation energy. This paper presents the first semi-automatic atrial wall thickness measurement method for ablation guidance. It includes both endocardial and epicardial atrial wall segmentation on CT image data. Segmentation is based on active contours, Otsus multiple threshold method and hysteresis thresholding. Segmentation results were compared to contours manually drawn by two experts, using repeated measures analysis of variance. The root mean square differences between the semi-automatic and the manually drawn contours were comparable to intra-observer variation (endocardium: p = 0.23, epicardium: p = 0.18). Mean wall thickness difference is significant between one of the experts on one side, and the presented method and the other expert on the other side (p ≪ 0.001). Wall thicknesses found were in the range of 0.5–5.5mm, corresponding to values presented in literature.

Optic flow from multi-scale dynamic anchor point attributes

Optic flow describes the apparent motion that is present in an image sequence. We show the feasibility of obtaining optic flow from dynamic properties of a sparse set of multi-scale anchor points. Singular points of a Gaussian scale space image are identified as feasible anchor point candidates and analytical expressions describing their dynamic properties are presented. Advantages of approaching the optic flow estimation problem using these anchor points are that (i) in these points the notorious aperture problem does not manifest itself, (ii) it combines the strengths of variational and multi-scale methods, (iii) optic flow definition becomes independent of image resolution, (iv) computations of the components of the optic flow field are decoupled and that (v) the feature set inducing the optic flow field is very sparse (typically

Object Matching in the Presence of Non-Rigid Deformations Close to Similarities

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Scale Space Texture Classification Using Combined Classifiers with Application to Ultrasound Tissue Characterization

of the number of pixels in a frame). A dense optic flow vector field is obtained through projection into a Sobolev space defined by and consistent with the dynamic constraints in the anchor points. As opposed to classical optic flow estimation schemes the proposed method accounts for an explicit scale component of the vector field, which encodes some dynamic differential flow property.

Pharmacokinetic models in clinical practice: What model to use for DCE-MRI of the breast?

In this paper we address the problem of object retrieval based on scale-space interest points, namely top-points. The original retrieval algorithm can only cope with scale-Euclidean transformations. We extend the algorithm to the case of non-rigid transformations like affine and perspective transformations and investigate its robustness. The proposed algorithm is proven to be highly robust under various degrading factors, such as noise, occlusion, rendering artifacts, etc. and can deal with multiple occurrences of the object.

Curvature Estimation for Enhancement of Crossing Curves

Texture is often considered as a repetitive pattern and the constructing structure is known as texel. The granularity of a texture, i.e. the size of a texel, is different from one texture to another and hence inspiring us applying scale space techniques to texture classification. In this paper Gaussian kernels with different variances (σ2) are convolved with the textures from Brodatz album to generate the textures in different scales. After some preprocessing and feature extraction using principal component analysis (PCA), the features are fed to a combined classifier for classification. The learning curves are used to evaluate the performance of the texture classifier system designed. The results of classification show that the scale space texture classification approach used can significantly improve the performance of the classification especially for small training set size. This is very important in applications where the training set data is limited. The application of this method to ultrasound liver tissue characterization for discrimination of normal liver from cirrhosis yields promising results.

Optic flow based on multi-scale anchor point movement and discontinuity-preserving regularization

Pharmacokinetic modeling is increasingly used in DCE-MRI high risk breast cancer screening. Several models are available. The most common models are the standard and extended Tofts, the shutter-speed, and the Brix model. Each model and the meaning of its parameters is explained. It was investigated which models can be used in a clinical setting by simulating a range of sampling rates and noise levels representing different MRI acquisition schemes. In addition, an investigation was performed on the errors introduced in the estimates of the pharmacokinetic parameters when using a physiologically less complex model, i.e. the standard Tofts model, to fit curves generated with more complex models. It was found that the standard Tofts model is the only model that performs within an error margin of 20% on parameter estimates over a range of sampling rates and noise levels. This still holds when small complex physiological effects are present.