KEYWORDS: Metals, Digital filtering, Computed tomography, Tissues, Image segmentation, Modulation transfer functions, Signal to noise ratio, Image filtering, Data modeling, Bone
In CT imaging, high absorbing objects such as metal bodies may cause significant artifacts, which may, for example, result in dose inaccuracies in the radiation therapy planning process. In this work, we aim at reducing the local and global image artifact, in order to improve the overall dose accuracy. The key part f this approach is the correction of the original projection data in those regions, which feature defects caused by rays traversing the high attenuating objects in the patient. The affected regions are substituted by model data derived from the original tomogram deploying a segmentation method. Phantom and climnical studies demonstrate that the proposed method significantly reduces the overall artifacts while preserving the information content of the image as much as possible. The image quality improvements were quantified by determining the signal-to-noise ratio, the artifact level and the modulation transfer function. The proposed method is computationally efficient and can easily be integrated into commercial CT scanners and radiation therapy planning software.
The determination of the treatment parameters in radiation therapy requires the segmentation of the patient anatomy. This procedure is usually performed by manual contouring of 2D slices, which may require several hours. The burden is considerably increased in the context of IMRT and 4D adaptive radiotherapy. Intensity Modulated Radiation Therapy (IMRT) offers the increased ability to accurately conform the radiation dose to the target. IMRT is typically applied in fractions over 30 days. 4D or adaptive radiotherapy aims at compensating the significant anatomical changes during the course of treatment based on additional imagery. The development of fast and robust automated segmentation tools is crucial for these novel treatment methods to succeed. The purpose of this paper is to automate organ contouring of 3D CT data and time series of 3D CT in radiation therapy planning (RTP). Automated organ delineation in CT is challenging due to poor soft tissue contrast and high inter- and intra-patient organ variability. This paper presents an automated model-based concept for organ delineation, based on adaptation of 3D deformable surface models to the boundaries of the anatomical structures of interest in 3D CT and time-series thereof. A feasibility study with 40 3D clinical datasets and a 3D time series with 19 datasets was done for the risk organs (bladder, rectum, and femoral heads) of the pelvic area. The results of the validation study show that the presented model-based approach is accurate (1-1.7 mm mean error) for the tested anatomical structures, and allows a significant reduction of time compared to manual organ contouring (minutes vs. hours).
KEYWORDS: Image segmentation, Bone, Head, 3D modeling, Feature extraction, Medical imaging, Modeling, Databases, Systems modeling, Magnetic resonance imaging
The model of image features is critical to the robustness and accuracy of deformable models. Usually, an edge detector is used for this purpose, because the object boundary is expected to correspond with a strong directed gradient in the image. Two methods are presented to make a feature model more specific and suitable for a given object class for which this assumption is too weak. One aims at a better conformance of the model with the image features by a spatially varying parameterisation of clustered features that is learnt from a training set. The other discriminates the object surface from adjacent false attractors that have similar gradient properties by additional grey value properties. The clustered feature model was successfully applied in left ventricle segmentation to delineate the epicardium in cardiac MR images for which the image gradient reverses sign along the surface. The discriminating feature approach successfully prevented false attractions in CT bone segmentation to strong edges within other nearby bones (shown for femur head). In this case, the grey value beyond the attempted gradient position discriminated well the desired bone surface edges from these false edges.
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 efficient representation of shape and shape variability is a key issue in computerized 3D image processing. One of the common goals is the ability to express as much shape variability as necessary with as few parameters as possible. In this paper we focus on the capture of shape variability on the basis of free surface vibration modes. We do not model the interior of an elastic object, but rather its triangulated surface. As in the case of 3D statistical point-distribution models (PDM) we assume that the shape of an anatomical object can efficiently be approximated by a weighted sum of a mean shape and a number of variation modes. The variation modes are in our case Eigenvectors of a stiffness-matrix. Based on a given surface triangulation we define a physical model by placing mass points at the vertices and coil- and leaf-spring elements at the edge positions of the triangulation. Ordered by wavelength, the resulting free vibration modes can be used to efficiently approximate shape variability in a coarse to fine manner, similar to a Fourier decomposition. As real-object examples from the medical image-processing domain, we applied the method to triangulated surfaces of segmented lumbar vertebra and femor-head from CT data sets. A comparison to corresponding statistical shape models shows, that natural variability of anatomical shape can efficiently be approximated by free surface vibration modes.
Segmentation methods based on adaptation of deformable models have found numerous applications in medical image analysis. Many efforts have been made in the recent years to improve their robustness and reliability. In particular, increasingly more methods use a priori information about the shape of the anatomical structure to be segmented. This reduces the risk of the model being attracted to false features in the image and, as a consequence, makes the need of close initialization, which remains the principal limitation of elastically deformable models, less crucial for the segmentation quality. In this paper, we present a novel segmentation approach which uses a 3D anatomical statistical shape model to initialize the adaptation process of a deformable model represented by a triangular mesh. As the first step, the anatomical shape model is parametrically fitted to the structure of interest in the image. The result of this global adaptation is used to initialize the local mesh refinement based on an energy minimization. We applied our approach to segment spine vertebrae in CT datasets. The segmentation quality was quantitatively assessed for 6 vertebrae, from 2 datasets, by computing the mean and maximum distance between the adapted mesh and a manually segmented reference shape. The results of the study show that the presented method is a promising approach for segmentation of complex anatomical structures in medical images.
Locally recurrent breast carcinoma and skin metastasisses on the chest wall can be difficult to treat. Conventional treatments like radiation-, chemo- and hormonal therapy have shown poor results in these patients. In comparison to this, PDT has some advantages and less side effects. We can observe a tumor accumulation of a systemic applied photosensitizer (PS). The PS can be stimulated by light of a wavelength of 630 nm and a phototoxic effect in the tumor occurs. We treated 7 patients with locally recurrent breast carcinoma 15 times with PDT. The intravenous application of the PS (Photofrin II, 1.5 mg/kg BW) was done 24 - 96 hours before local laser light radiation. The light source was an Ar-Dye laser with a wavelength of 630 nm. Due to a local tumor necrosis we observed a tumor reduction in each case. In 5 patients we saw a complete local remission with a good cosmetic result. Side effects were rare. All patients suffered from pain in the treated area. No major phototoxicity effects were seen. PDT can induce complete local tumor remissions in patients with cutaneous metastasisses after locally recurrent breast carcinoma. In absence of other metastasisses PDT is possibly a curative treatment. One of the major advantages of this treatment are the rare side effects, rare complications and the possible repetition of the PDT.
HPV associated bowenoid papulosis of the anogenital region are classified as carcinoma in situ. The treatment can be difficult and recurrence rates are high. Extended surgical resections may have complications such as anal sphincter insufficiency. PDT does have some advantages and less side effects in the treatment of these tumors. We treated one female patient with an extended perianal bowenoid papulosis. Previous surgical resection led to local recurrence and partial sphincter insufficiency. Twenty-four hours before local laser light radiation (Ar-Dye laser, 630 nm wavelength), a systemic photosensitizer was applied (Photofrin II, 1.5 mg/kg BW). Four courses of PDT were performed within one year. We observed a total tumor necrosis in every radiation area. The previous sphincter insufficiency improved during the sessions. Side effects were rare. Pain in the radiation was stopped within 2 - 3 days under pain medication. PDT can induce a total local tumor necrosis in perianal bowenoid papulosis. Concerning local expansion, PDT can be a curable treatment.
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