Showing papers by "Rafael Wiemker published in 2010"

Combined correction of recovery effect and motion blur for SUV quantification of solitary pulmonary nodules in FDG PET/CT

Abstract: Objective We evaluate a fully data-driven method for the combined recovery and motion blur correction of small solitary pulmonary nodules (SPNs) in F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT).

Visualization apparatus for visualizing an image data set

Abstract: The invention relates to a visualization apparatus (1) for visualizing an image data set. The visualization apparatus (1) comprises an image data set providing unit (2) for providing the image data set, a differential property determination unit (5) for determining local differential properties for different regions of the image data set, an assigning unit (6) for assigning visualization properties to the different regions of the image data set depending on the determined local differential properties, wherein a visualization property defines the visualization of a region, to which the visualization property is assigned, and a display unit (7) for displaying the visualization properties assigned to the different regions of the image data set. By displaying the visualization properties assigned to the different regions of the image data set different objects can visually be separated from each other without requiring large computational costs.

Scan plan field of view adjustor, determiner, and/or quality assessor

Abstract: A method includes using a pre-scan image to define a scan field of view for a region of interest of a patient to be scanned for at least one image acquisition of a series of image acquisitions of a scan plan, performing an image acquisition of the series based on a corresponding scan field of view for the image acquisition, and determining, via a processor (120), a next field of view for a next image acquisition of the series based on available image related data.

Quantitative perfusion analysis

Abstract: A system is disclosed for quantitative analysis of perfusion images comprising image elements having intensity values associated therewith. The system comprises a frequency distribution computing subsystem (1) for computing a plurality of frequency distributions of the intensity values of at least part of the images. The system comprises a perfusion information extractor (2) for extracting information relating to perfusion from the plurality of frequency distributions. The perfusion information extractor (2) comprises a shift detector (3) for detecting a shift of the intensity values of the frequency distribution. The perfusion information extractor (2) is arranged for extracting the information relating to perfusion, based on the detected shift. A user interface element (8) enables a user to indicate a boundary between the core region and the rim region by a single degree of freedom. A vesselness subsystem (9) associates a vesselness value with an image element.

Large-volume multi-tined expandable RF ablation in pig livers: comparison of 2D and volumetric measurements of the ablation zone

Abstract: To compare two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) measurements of ablation zones (AZs) related to the shaft of two different large-volume monopolar multi-tined expandable electrodes. Percutaneous radiofrequency (RF) ablation was performed in 12 pigs (81.6 ± 7.8 kg) using two electrodes (LeVeen 5 cm, Rita XL 5 cm; n = 6 in each group). Contrast-enhanced CT with the electrode shaft in place evaluated the AZ. The largest sphere centred on the electrode shaft within the AZ was calculated (1) based on the 2D axial CT image in the plane of the shaft assuming rotational symmetry of the AZ and (2) using prototype software and the 3D volume data of the AZ measured with CT. The mean largest diameter of a sphere centred on the electrode shaft was always smaller using the 3D data of the AZ than using 2D CT measurements assuming rotational symmetry of the AZ (3D vs 2D): LeVeen 18.2 ± 4.8 mm; 24.5 ± 3.1 mm; p = 0.001; Rita XL 20.0 ± 3.7 mm; 28.8 ± 4.9 mm; p = 0.0002. All AZ showed indentations around the tines. Two-dimensional CT measurements assuming rotational symmetry of the AZ overestimate the largest ablated sphere centred on the electrode shaft compared with 3D CT measurements.

Rule based decision support and patient-specific visualization system for optimal cancer staging

Abstract: A system including a display and a processor and a corresponding method for identifying a tumor in a patient image, classifying the tumor based on a predetermined classification system and determining a recommendation regarding a lymph node biopsy based on the tumor identified in the patient image, the classification of the tumor and a predetermined rule.

Filter learning and evaluation of the Computer Aided Visualization and Analysis (CAVA) paradigm for pulmonary nodules using the LIDC-IDRI database

Abstract: We present a simple rendering scheme for thoracic CT datasets which yields a color coding based on local differential geometry features rather than Hounsfield densities. The local curvatures are computed on several resolution scales and mapped onto different colors, thereby enhancing nodular and tubular structures. The rendering can be used as a navigation device to quickly access points of possible chest anomalies, in particular lung nodules and lymph nodes. The underlying principle is to use the nodule enhancing overview as a possible alternative to classical CAD approaches by avoiding explicit graphical markers. For performance evaluation we have used the LIDC-IDRI lung nodule data base. Our results indicate that the nodule-enhancing overview correlates well with the projection images produced from the IDRI expert annotations, and that we can use this measure to optimize the combination of differential geometry filters.

Heterogeneity of kinetic curve parameters as indicator for the malignancy of breast lesions in DCE MRI

Abstract: Dynamic contrast enhanced Breast MRI (DCE BMRI) has emerged as powerful tool in the diagnostic work-up of breast cancer. While DCE BMRI is very sensitive, specificity remains to be an issue. Consequently, there is a need for features that support the classification of enhancing lesions into benign and malignant lesions. Traditional features include the morphology and the texture of a lesion, as well as the kinetic parameters of the time-intensity curves, i.e., the temporal change of image intensity at a given location. The kinetic parameters include initial contrast uptake of a lesion and the type of the kinetic curve. The curve type is usually assigned to one of three classes: persistent enhancement (Type I), plateau (Type II), and washout (Type III). While these curve types show a correlation with the tumor type (benign or malignant), only a small sub-volume of the lesion is taken into consideration and the curve type will depend on the location of the ROI that was used to generate the kinetic curve. Furthermore, it has been shown that the curve type significantly depends on which MR scanner was used as well as on the scan parameters. Recently, it was shown that the heterogeneity of a given lesion with respect to spatial variation of the kinetic curve type is a clinically significant indicator for malignancy of a tumor. In this work we compare four quantitative measures for the degree of heterogeneity of the signal enhancement ratio in a tumor and evaluate their ability of predicting the dignity of a tumor. All features are shown to have an area under the ROC curve of between 0.63 and 0.78 (for a single feature).

A completely automated processing pipeline for lung and lung lobe segmentation and its application to the LIDC-IDRI data base

Abstract: Automated segmentation of lung lobes in thoracic CT images has relevance for various diagnostic purposes like localization of tumors within the lung or quantification of emphysema. Since emphysema is a known risk factor for lung cancer, both purposes are even related to each other. The main steps of the segmentation pipeline described in this paper are the lung detector and the lung segmentation based on a watershed algorithm, and the lung lobe segmentation based on mesh model adaptation. The segmentation procedure was applied to data sets of the data base of the Image Database Resource Initiative (IDRI) that currently contains over 500 thoracic CT scans with delineated lung nodule annotations. We visually assessed the reliability of the single segmentation steps, with a success rate of 98% for the lung detection and 90% for lung delineation. For about 20% of the cases we found the lobe segmentation not to be anatomically plausible. A modeling confidence measure is introduced that gives a quantitative indication of the segmentation quality. For a demonstration of the segmentation method we studied the correlation between emphysema score and malignancy on a per-lobe basis.

Towards automatic determination of total tumor burden from PET images

Abstract: Quantification of potentially cancerous lesions from imaging modalities, most prominently from CT or PET images, plays a crucial role both in diagnosing and staging of cancer as well as in the assessment of the response of a cancer to a therapy, e.g. for lymphoma or lung cancer. For PET imaging, several quantifications which might bear great discriminating potential (e.g. total tumor burden or total tumor glycolysis) involve the segmentation of the entirety of all of the cancerous lesions. However, this particular task of segmenting the entirety of all cancerous lesions might be very tedious if it has to be done manually, in particular if the disease is scattered or metastasized and thus consists of numerous foci; this is one of the reasons why only few clinical studies on those quantifications are available. In this work, we investigate a way to aid the easy determination of the entirety of cancerous lesions in a PET image of a human. The approach is designed to detect all hot spots within a PET image and rank their probability of being a cancerous lesion. The basis of this component is a modified watershed algorithm; the ranking is performed on a combination of several, primarily morphological measures derived from the individual basins. This component is embedded in a software suite to assess response to a therapy based on PET images. As a preprocessing step, potential lesions are segmented and indicated to the user, who can select the foci which constitute the tumor and discard the false positives. This procedure substantially simplifies the segmentation of the entire tumor burden of a patient. This approach of semi-automatic hot spot detection is evaluated on 17 clinical datasets.

System and method for determining a property of blur in a blurred image

Abstract: A system and a method of determining a property of blur in an image are provided. According to other aspects a medical image acquisition apparatus, a medical workstation and a computer program product are provided. The system (100) comprises a receiver (102) for receiving the image of an object-of- interest of a body. The image comprises blur. Further, the system comprises a determining subsystem (122) for determining a value of a characteristic of the blur in the image on individual lines of a plurality of lines intersecting with the object-of-interest at different angles. Thus, the lines extend in different directions. The determination of the value comprises analyzing the image along the respective lines. The system further comprises an obtaining subsystem (126) for obtaining a direction in which the value of the characteristic of the blur is maximal, based on the determined values on the individual lines of the plurality of lines, which lines extend in different directions.

Anatomical modeling of the bronchial tree

Abstract: The bronchial tree is of direct clinical importance in the context of respective diseases, such as chronic obstructive pulmonary disease (COPD). It furthermore constitutes a reference structure for object localization in the lungs and it finally provides access to lung tissue in, e.g., bronchoscope based procedures for diagnosis and therapy. This paper presents a comprehensive anatomical model for the bronchial tree, including statistics of position, relative and absolute orientation, length, and radius of 34 bronchial segments, going beyond previously published results. The model has been built from 16 manually annotated CT scans, covering several branching variants. The model is represented as a centerline/tree structure but can also be converted in a surface representation. Possible model applications are either to anatomically label extracted bronchial trees or to improve the tree extraction itself by identifying missing segments or sub-trees, e.g., if located beyond a bronchial stenosis. Bronchial tree labeling is achieved using a naive Bayesian classifier based on the segment properties contained in the model in combination with tree matching. The tree matching step makes use of branching variations covered by the model. An evaluation of the model has been performed in a leaveone- out manner. In total, 87% of the branches resulting from preceding airway tree segmentation could be correctly labeled. The individualized model enables the detection of missing branches, allowing a targeted search, e.g., a local rerun of the tree-segmentation segmentation.