Abstract: A system and method for detecting motion is presented. The system and method includes identifying a region of interest in the plurality of images corresponding to a subject of interest. Furthermore, the system and method includes determining signal characteristics corresponding to the region of interest. In addition, the system and method includes generating a composite signal, where the composite signal comprises an aggregate of the signal characteristics corresponding to the region of interest. The system and method also includes analyzing the composite signal to detect motion in the region of interest.

Abstract: A system and method for estimating image intensity bias and segmentation tissues is presented. The system and method includes obtaining a first image data set and at least a second image data set, wherein the first and second image data sets are representative of an anatomical region in a subject of interest. Furthermore, the system and method includes generating a baseline bias map by processing the first image data set. The system and method also includes determining a baseline body mask by processing the second image data set. In addition, the system and method includes estimating a bias map corresponding to a sub-region in the anatomical region based on the baseline body mask. Moreover, the system and method includes segmenting one or more tissues in the anatomical region based on the bias map.

Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring an ultrasound image, generating a Fisher-tippett (FT) distribution-based edge feature map from the acquired ultrasound image, generating gradient concentration (GC) scores for pixels of the acquired ultrasound image using the FT distribution-based edge feature map, and identifying a candidate lesion region within the acquired ultrasound image based on the GC scores.

Abstract: In one embodiment, a method includes performing a magnetic resonance (MR) imaging sequence to acquire MR image slices or volumes of a first station representative of a portion of a patient; applying a first phase field algorithm to the first station to determine a body contour of the patient in the first station; identifying a contour of a first anatomy of interest within the body contour of the first station using the first phase field algorithm or a second phase field algorithm; segmenting the first anatomy of interest based on the identified contour of the first anatomy of interest; correlating first attenuation information to the segmented first anatomy of interest; and modifying a positron emission tomography (PET) image based at least on the first correlated attenuation information.

Abstract: A method for detecting a lesion in an anatomical region of interest is presented. The method includes identifying one or more candidate mass regions in each of a plurality of 3D ultrasound images acquired at different view angles from the anatomical region of interest. Single-view features corresponding to each candidate mass region are identified. For a candidate mass region, a similarity metric between the single-view features corresponding to the candidate mass region and the single-view features corresponding to the other candidate mass regions is determined. The candidate mass region is classified based at least on the similarity metric. A system for imaging and a non-transitory computer readable media for detection of the lesion are also presented.

Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring an ultrasound image, generating a Fisher-tippett (FT) distribution-based edge feature map from the acquired ultrasound image, generating gradient concentration (GC) scores for pixels of the acquired ultrasound image using the FT distribution-based edge feature map, and identifying a candidate lesion region within the acquired ultrasound image based on the GC scores.

Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring ultrasound information, determining discriminative descriptors that describe the texture of a candidate lesion region, and classifying each of the discriminative descriptors as one of a top boundary pixel, a lesion interior pixel, a lower boundary pixel, or a normal tissue pixel. The method also includes determining a pattern of transitions between the classified discriminative descriptors, and classifying the candidate lesion region as a lesion or normal tissue based on the pattern of transitions between the classified discriminative descriptors.

Abstract: A method for generating an image is provided. The method comprises: acquiring a first set of image data using a first imaging modality; sorting the first set of image data into a plurality of gates to generate a plurality of gated data sets; reconstructing each gated data set to generate a respective gated image for each gated data set; registering the respective gated images to generate a plurality of registered images; and generating a median image from the plurality of registered images, wherein each voxel of the median image is a respective median value of the corresponding voxels of the plurality of registered images.

Abstract: In one embodiment, a method includes performing a magnetic resonance (MR) imaging sequence to acquire MR image slices or volumes of a first station representative of a portion of a patient; applying a first phase field algorithm to the first station to determine a body contour of the patient in the first station; identifying a contour of a first anatomy of interest within the body contour of the first station using the first phase field algorithm or a second phase field algorithm; segmenting the first anatomy of interest based on the identified contour of the first anatomy of interest; correlating first attenuation information to the segmented first anatomy of interest; and modifying a positron emission tomography (PET) image based at least on the first correlated attenuation information.

Abstract: A method for automatic mismatch correction is presented. The method includes identifying a feature of interest in a reference image volume and a target image volume. Furthermore, the method includes computing a cost matrix based on one or more pairs of image slices in the reference image volume and the target image volume. The method also includes identifying one or more longest common matching regions in the reference image volume and the target image volume based on the computed cost matrix. In addition, the method includes aligning the reference image volume and the target image volume based on the identified one or more longest common matching regions. A non-transitory computer readable medium including one or more tangible media, where the one or more tangible media include code adapted to perform the method for automatic mismatch correction is also presented.

Abstract: An imaging workflow system includes a workstation for acquiring patient information and requesting a patient scan. The request for a patient scan includes scan information for performing the scan. A registration module receives the scan information and the patient information. The registration module automatically schedules the patient scan based on the scan information and the patient information. The registration module determines an imaging protocol based on the patient information and the scan information. An imaging module within an imaging system receives the imaging protocol. The imaging module automatically sets scan parameters based on the imaging protocol. The imaging system scans the patient based on the scan parameters to acquire image data. A user interface controls the patient scan. The user interface includes a display to display images generated from the acquired image data.

Abstract: A method and apparatus are provided for reducing motion related imaging artifacts. The method includes determining an internal motion for of two regions of the object, each region having a different level of motion, scanning the first region using a first scan protocol based on the motion, scanning a second region using a second different scan protocol based on the motion, and generating an image of the object based on the first and second regions.

Abstract: A method for automatic mismatch correction is presented. The method includes identifying a feature of interest in a reference image volume and a target image volume. Furthermore, the method includes computing a cost matrix based on one or more pairs of image slices in the reference image volume and the target image volume. The method also includes identifying one or more longest common matching regions in the reference image volume and the target image volume based on the computed cost matrix. In addition, the method includes aligning the reference image volume and the target image volume based on the identified one or more longest common matching regions. A non-transitory computer readable medium including one or more tangible media, where the one or more tangible media include code adapted to perform the method for automatic mismatch correction is also presented.

Abstract: A method for generating an image is provided. The method comprises: acquiring a first set of image data using a first imaging modality; sorting the first set of image data into a plurality of gates to generate a plurality of gated data sets; reconstructing each gated data set to generate a respective gated image for each gated data set; registering the respective gated images to generate a plurality of registered images; and generating a median image from the plurality of registered images, wherein each voxel of the median image is a respective median value of the corresponding voxels of the plurality of registered images.

Abstract: A method is provided for generating a metavolume for reconstructing an image. The method provides for acquiring a plurality of low-resolution images of an imaged volume, the plurality of low-resolution images obtained from one or more imaging planes and merging the plurality of low-resolution images using a statistical operation to generate a metavolume. Systems and computer programs that afford functionality of the type defined by this method may be provided by the present technique.

Abstract: An apparatus, system and method to determine a coordinate system of a heart includes an imager and a computer. The computer is programmed to acquire a first set of initialization imaging data from an anatomical region of a free-breathing subject. A portion of the first set of initialization imaging data includes organ data, which includes cardiac data. The computer is further programmed to determine a location of a central region of a left ventricle of a heart, where the location is based on the organ data and a priori information. The computer is also programmed to determine a short axis of the left ventricle based on the determined location, acquire a first set of post-initialization imaging data from the free-breathing subject from an imaging plane orientation based on the determination of the short axis, and reconstruct at least one image from the first set of post-initialization imaging data.

Abstract: A technique for automatically labeling a CT image of the brain with anatomical information. The anatomical information is obtained from an atlas of the brain prepared from an MR image of the brain. The atlas contains image data that is referenced to the Talairach coordinate system. The atlas is aligned to the CT image and the coordinate system of the CT image data is transformed to the Talairach coordinate system. The alignment of the CT image and the atlas is performed using anatomical landmarks that are visible on both the CT image and the atlas. The CT image is then labeled automatically with the anatomical information in the atlas.

Abstract: A system and method for automatic computation of MR imaging scan parameters include a computer programmed to acquire a first set of MR data from an imaging subject, the first set of MR data comprising a plurality of slices acquired at a first field-of-view. The computer is also programmed to reconstruct the plurality of slices into a plurality of localizer images and identify a 3D object based on the plurality of localizer images. The computer is further programmed to prescribe a scan, execute the prescribed scan to acquire a second set of MR data, and reconstruct the second set of MR data into an image. The prescribed scan includes one of a reduced field-of-view based on a boundary of the 3D object and a shim region based on the boundary of the 3D object.

Abstract: A technique is provided for utilizing a model of an anatomical feature to facilitate the segmentation of the anatomical feature from its background in a medical image. A global alignment of the model with a region in the patient's image data that generally corresponds to the anatomical feature is performed in one embodiment. Internal structural features within the model are then aligned with their corresponding structural features in the patient's image data. The portion of the patient's image data that is aligned with the model of the anatomical feature is then segmented from the remaining portions of the patient's image data that are not aligned with the model.

Abstract: A system and method for MR image scan and analysis include an MRI apparatus that includes a magnetic resonance imaging (MRI) system and a computer programmed to automatically prescribe a first scanning protocol based on the selected examination, acquire a first set of MR data of an imaging object via application of the first scanning protocol, and reconstruct a first image from the first set of MR data. The computer is also programmed to automatically prescribe a second scanning protocol based on the first image, acquire a second set of MR data of the imaging object via application of the second scanning protocol, reconstruct a second image from the second set of MR data, and quantify a first parameter of the imaging object based on the second image.