Abstract: In a method for image guided prostate cancer needle biopsy, a first registration is performed to match a first image of a prostate to a second image of the prostate. Third images of the prostate are acquired and compounded into a three-dimensional (3D) image. The prostate in the compounded 3D image is segmented to show its border. A second registration and then a third registration different from the second registration is performed on distance maps generated from the prostate borders of the first image and the compounded 3D image, wherein the first and second registrations are based on a biomechanical property of the prostate. A region of interest in the first image is mapped to the compounded 3D image or a fourth image of the prostate acquired with the second modality.

Abstract: In a method to correct noise effects in magnetic resonance (MR) images, which is executed in a processor (computer), the processor executes a fitting algorithm in order to calculate initial values for each of selected variables in signal model that models noise effects in a modeled, noise-containing MR image. The processor then iteratively executes the same or a different fitting algorithm, in order to generate final values for each of the selected variables. The processor is provided with an actual, acquired MR image that contains noise, and the processor uses the final values of the selected variables to calculate synthetic signal intensities in the MR image, thereby producing a synthetic MR image with no noise bias effects of errors. This synthetic image is made available in electronic form at an output of the processor, as a data file.

Abstract: In a combined magnetic resonance (MR)-emission tomography apparatus, MR scan data and emission tomography scan data are acquired from a subject in the apparatus. MR image data are generated from the MR scan data, and an attenuation map is generated from the same MR scan data that were used to generate the MR image data. Emission tomography image data are generated by applying a correction algorithm, which uses the attenuation map, to the emission tomography scan data. The MR image data and the emission tomography emission data are then presented.

Abstract: A method and system for fully automatic segmentation the prostate in magnetic resonance (MR) image data is disclosed. Intensity normalization is performed on an MR image of a patient to adjust for global contrast changes between the MR image and other MR scans and to adjust for intensity variation within the MR image due to an endorectal coil used to acquire the MR image. An initial prostate segmentation in the MR image is obtained by aligning a learned statistical shape model of the prostate to the MR image using marginal space learning (MSL). The initial prostate segmentation is refined using one or more trained boundary classifiers.

Abstract: In a method and apparatus for determining the elasticity of a material of an examination object, which is included in a subarea of an acquired image volume, data from the image volume are acquired with a tomographic imaging method in at least two different time phases during a body movement of the examination object. At least two recorded images are registered with one another. A deformation field of the material included in the subarea of the acquired image volume is determined from the images which are registered with one another. The local elasticity of the material of the image volume included in the subarea of the acquired image volume is determined on the basis of the determined deformation field.

Abstract: Disclosed herein is a framework for facilitating visualization. In accordance with one aspect, the framework localizes at least one anatomical structure of interest in image data. The structure of interest is then highlighted by reformatting the image data. The resulting reformatted image data is rendered for display to a user.

Abstract: A method and system for automatic magnetic resonance (MR) volume composition and normalization is disclosed. In one embodiment, a plurality of MR volumes is received. A composite MR volume is generated from the plurality of MR volumes. Volume normalization of the composite MR volume is then performed to correct intensity inhomogeneity in the composite MR volume. The volume normalization of the composite MR volume may be performed using template MR volume or without a template MR volume.

Abstract: Disclosed herein is a framework for identifying signal components in image data. In accordance with one aspect, the framework receives multiple measured signal values corresponding to respective quantified signal components in image data. The framework determines at least one first measure of fit map of a signal model based on the measured signal values. The measured signal values may be swapped to generate swapped signal values. At least one second measure of fit map of the signal model may be determined based on the swapped signal values. The multiple signal components may then be identified by comparing the first and second measure of fit maps.

Abstract: Disclosed herein is a framework for facilitating image processing. In accordance with one aspect, the framework receives first image data acquired by a first modality and one or more articulated models. The one or more articulated models may include at least one section image acquired by the first modality and aligned with a local image acquired by a second modality. The framework may align an anatomical region of the first image data with the section image and non-rigidly register a first region of interest extracted from the section image with a second region of interest extracted from the aligned anatomical region. To generate a segmentation mask of the anatomical region, the registered first region of interest may be inversely mapped to a subject space of the first image data.

Abstract: A method and system for automatic lung segmentation in magnetic resonance imaging (MRI) images and videos is disclosed. A plurality of predetermined key landmarks of a lung are detected in an MRI image. The key landmarks may be detected using discriminative joint contexts representing combinations of multiple key landmarks. A lung boundary is segmented in the MRI image based on the detected key landmarks. The landmark detection and the lung boundary segmentation can be repeated in each frame of an MRI video.

Abstract: A method is disclosed for creating a motion correction for PET data acquired by a PET system from a volume segment of an examination object. The method includes acquisition of MR data within the volume segment by the magnetic resonance system; and determination of a motion model of a motion within the volume segment as a function of the MR data. The motion model, as a function of a respective motion state of the motion, provides a correction specification for PET data which is acquired during this motion state. During acquisition of the MR data, specific MR data is acquired in the center of the k-space or of a straight-line segment which passes through the center of the k-space. The MR data determined is converted by a mathematical function into one value, as a function of which the respective motion state is determined.

Abstract: A database contains variants of protocols for the operation of magnetic resonance tomographs as well as different types of magnetic resonance tomographs. Each variant contains parameter values and is associated with one of the types. In a training phase, relationships are determined between the parameters among one another and/or between the parameters and the associated types and are stored as patterns in a knowledge base. A protocol plan for the operation of a new magnetic resonance tomograph is created later in an application phase using the determined pattern. The method offers the advantage that the efficiency and quality of the automatic conversion of the protocols is improved. The improved quality of the protocol plan reduces operating time and costs for a manual post-processing of the protocols. Furthermore, a higher consistency of the protocols among one another is achieved both between product families and between individual configurations.

Abstract: Automatic prostate localization in T2-weighted MR images facilitate labor-intensive cancer imaging techniques. Methods and systems to accurately segment the prostate gland in MR images are provided and address large variations in prostate anatomy and disease, intensity inhomogeneities, and artifacts induced by endorectal coils. A center of the prostate is automatically detected with a boosted classifier trained on intensity based multi-level Gaussian Mixture Model Expectation Maximization (GMM-EM) segmentations of the raw MR images. A shape model is used in conjunction with Multi-Label Random Walker (MLRW) to constrain the seeding process within MLRW.

Abstract: A method and system for fully automatic segmentation the prostate in magnetic resonance (MR) image data is disclosed. Intensity normalization is performed on an MR image of a patient to adjust for global contrast changes between the MR image and other MR scans and to adjust for intensity variation within the MR image due to an endorectal coil used to acquire the MR image. An initial prostate segmentation in the MR image is obtained by aligning a learned statistical shape model of the prostate to the MR image using marginal space learning (MSL). The initial prostate segmentation is refined using one or more trained boundary classifiers.

Abstract: A method and system for automatic lung segmentation in magnetic resonance imaging (MRI) images and videos is disclosed. A plurality of predetermined key landmarks of a lung are detected in an MRI image. The key landmarks may be detected using discriminative joint contexts representing combinations of multiple key landmarks. A lung boundary is segmented in the MRI image based on the detected key landmarks. The landmark detection and the lung boundary segmentation can be repeated in each frame of an MRI video.

Abstract: A method and system for automatic magnetic resonance (MR) volume composition and normalization is disclosed. In one embodiment, a plurality of MR volumes is received. A composite MR volume is generated from the plurality of MR volumes. Volume normalization of the composite MR volume is then performed to correct intensity inhomogeneity in the composite MR volume. The volume normalization of the composite MR volume may be performed using template MR volume or without a template MR volume.

Abstract: Automatic prostate localization in T2-weighted MR images facilitate labor-intensive cancer imaging techniques. Methods and systems to accurately segment the prostate gland in MR images are provided and address large variations in prostate anatomy and disease, intensity inhomogeneities, and artifacts induced by endorectal coils. A center of the prostate is automatically detected with a boosted classifier trained on intensity based multi-level Gaussian Mixture Model Expectation Maximization (GMM-EM) segmentations of the raw MR images. A shape model is used in conjunction with Multi-Label Random Walker (MLRW) to constrain the seeding process within MLRW.

Abstract: A database contains variants of protocols for the operation of magnetic resonance tomographs as well as different types of magnetic resonance tomographs. Each variant contains parameter values and is associated with one of the types. In a training phase, relationships are determined between the parameters among one another and/or between the parameters and the associated types and are stored as patterns in a knowledge base. A protocol plan for the operation of a new magnetic resonance tomograph is created later in an application phase using the determined pattern. The method offers the advantage that the efficiency and quality of the automatic conversion of the protocols is improved. The improved quality of the protocol plan reduces operating time and costs for a manual post-processing of the protocols. Furthermore, a higher consistency of the protocols among one another is achieved both between product families and between individual configurations.

Abstract: In a method and an apparatus for automatic determination of objects that attenuate high energy/penetrating radiation by magnetic resonance, the magnetic resonance apparatus scans and prepares MR images, and the MR images contain information about the T2 relaxation time constant. Subsequently, penetrating radiation-attenuating objects are determined in the MR images by means of the T2 relaxation time constant.

Abstract: In a method for image guided prostate cancer needle biopsy, a first registration is performed to match a first image of a prostate to a second image of the prostate. Third images of the prostate are acquired and compounded into a three-dimensional (3D) image. The prostate in the compounded 3D image is segmented to show its border. A second registration and then a third registration different from the second registration is performed on distance maps generated from the prostate borders of the first image and the compounded 3D image, wherein the first and second registrations are based on a biomechanical property of the prostate. A region of interest in the first image is mapped to the compounded 3D image or a fourth image of the prostate acquired with the second modality.