| Patent application number | Description | Published |
|---|
| 20080211812 | Method and system for detection and registration of 3D objects using incremental parameter learning - A method and system for detecting 3D objects in images is disclosed. In particular, a method and system for Ileo-Cecal Valve detection in 3D computed tomography (CT) images using incremental parameter learning and ICV specific prior learning is disclosed. First, second, and third classifiers are sequentially trained to detect candidates for position, scale, and orientation parameters of a box that bounds an object in 3D image. In the training of each sequential classifier, new training samples are generated by scanning the object's configuration parameters in the current learning projected subspace (position, scale, orientation), based on detected candidates resulting from the previous training step. This allows simultaneous detection and registration of a 3D object with full 9 degrees of freedom. ICV specific prior learning can be used to detect candidate voxels for an orifice of the ICV and to detect initial ICV box candidates using a constrained orientation alignment at each candidate voxel. | 09-04-2008 |
| 20080240337 | Model-Based Heart Reconstruction and Navigation - A method to obtain a patient based organ model from patient data, having steps of obtaining a computerized organ model based upon at least one data set of patients, the computerized organ model having a set of classifiers that are used to determine physical parameters of the patients heart, placing the patient in a diagnostic scanner device, taking representative data images of a patients organ while changing position of the image scan, the data images taken with ECG synchronization; and preparing the patient based organ model by evaluating the representative data images of the patients organ with the set of classifiers in the computerized organ model. | 10-02-2008 |
| 20080240532 | System and Method for Detection of Fetal Anatomies From Ultrasound Images Using a Constrained Probabilistic Boosting Tree - A method for detecting fetal anatomic features in ultrasound images includes providing an ultrasound image of a fetus, specifying an anatomic feature to be detected in a region S determined by parameter vector θ, providing a sequence of probabilistic boosting tree classifiers, each with a pre-specified height and number of nodes. Each classifier computes a posterior probability P(y|S) where yε{−1,+1}, with P(y=+1|S) representing a probability that region S contains the feature, and P(y=−1|S) representing a probability that region S contains background information. The feature is detected by uniformly sampling a parameter space of parameter vector θ using a first classifier with a sampling interval vector used for training said first classifier, and having each subsequent classifier classify positive samples identified by a preceding classifier using a smaller sampling interval vector used for training said preceding classifier. Each classifier forms a union of its positive samples with those of the preceding classifier. | 10-02-2008 |
| 20080262814 | Method and system for generating a four-chamber heart model - A method and system for building a statistical four-chamber heart model from 3D volumes is disclosed. In order to generate the four-chamber heart model, each chamber is modeled using an open mesh, with holes at the valves. Based on the image data in one or more 3D volumes, meshes are generated and edited for the left ventricle (LV), left atrium (LA), right ventricle (RV), and right atrium (RA). Resampling to enforce point correspondence is performed during mesh editing. Important anatomic landmarks in the heart are explicitly represented in the four-chamber heart model of the present invention. | 10-23-2008 |
| 20080275335 | Methods and apparatus for virtual coronary mapping - A virtual map of vessels of interest in medical procedures, such as coronary angioplasty is created so that doses of contrasting agent given to a patient may be reduced. A position of a coronary guidewire is determined and locations of vessel boundaries are found. When the contrast agent has dissipated, virtual maps of the vessels are created as new images. The locations of the determined vessel boundaries are imported to a mapping system and an image obtained without using a contrast agent is modified based on the imported locations of vessel boundaries. This creates a virtual map of the vessels. | 11-06-2008 |
| 20080281203 | System and Method for Quasi-Real-Time Ventricular Measurements From M-Mode EchoCardiogram - A method for measuring ventricular dimensions from M-mode echocardiograms, includes providing a digitized M-mode echocardiogram image, running a plurality of local classifiers, where each local classifier trained to detect a landmark on either an end-diastole (ED) line or an end-systole (ES) line in the image, recording all possible landmarks detected by the classifiers, where a search range in an N-dimensional parameter space defined by the landmarks for each dimension is reduced to a union of subsets, where each dimension of the parameter space corresponds a landmark, for each combination of possible landmarks, checking if an order of the landmarks is consistent with a known ordering of the landmarks, and if the order is consistent, running a global detector on each consistent combination of landmarks to find a landmark combination with a highest detection probability as a confirmed landmark detection, where the landmarks are used for measuring ventricular dimensions. | 11-13-2008 |
| 20090010509 | Method and system for detection of deformable structures in medical images - A method and system for detection of deformable structures in medical images is disclosed. Deformable structures can represent blood flow patterns in images such as Doppler echocardiograms. A probabilistic, hierarchical, and discriminant framework is used to detect such deformable structures. This framework integrates evidence from different primitive levels via a progressive detector hierarchy, including a series of discriminant classifiers. A target deformable structure is parameterized by a multi-dimensional parameter, and primitives or partial parameterizations of the parameter are determined. An input image is received, and a series of primitives are sequentially detected using the progressive detector hierarchy, in which each detector or classifier detects a corresponding primitive. The final detector detects configuration candidates for the deformable structure. | 01-08-2009 |
| 20090010512 | System and method for coronary digital subtraction angiography - A method and system for extracting coronary vessels fluoroscopic image sequences using coronary digital subtraction angiography (DSA) are disclosed. A set of mask images of a coronary region is received, and a sequence of contrast images for the coronary region is received. For each contrast image, vessel regions are detected in the contrast image using learning-based vessel segment detection and a background region of the contrast image is determined based on the detected vessel regions. Background motion is estimated between one of the mask images and the background region of the contrast image, and the mask image is warped based on the estimated background motion to generate an estimated background layer. The estimated background layer is subtracted from the contrast image to extract a coronary vessel layer for the contrast image. | 01-08-2009 |
| 20090034808 | Automatic Cardiac View Classification of Echocardiography - A method for view classification includes providing a frame of an object of interest, detecting a region of interest within the object of interest for each of a plurality of detectors (e.g., binary classifiers), wherein each binary classifier corresponds to a different view, performing a global view classification using a multiview classifier for each view, outputting a classification for each view, fusing outputs of the multiview classifiers, and determining and outputting a classification of the frame based on a fused output of the multiview classifiers. | 02-05-2009 |
| 20090074272 | Method and system for polyp segmentation for 3D computed tomography colonography - A method and system for polyp segmentation in computed tomography colonogrphy (CTC) volumes is disclosed. The polyp segmentation method utilizes a three-staged probabilistic binary classification approach for automatically segmenting polyp voxels from surrounding tissue in CTC volumes. Based on an input initial polyp position, a polyp tip is detected in a CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary. | 03-19-2009 |
| 20090074280 | Automated Detection of Planes From Three-Dimensional Echocardiographic Data - A plane position for a standard view is detected from three-dimensional echocardiographic data. The position of the plane within the volume is defined by translation, orientation (rotation), and/or scale. Possible positions are detected and other possible positions are ruled out. The classification of the possible positions occurs sequentially by translation, then orientation, and then scale. The sequential process may limit calculations required to identify the plane position for a desired view. | 03-19-2009 |
| 20090080728 | Method and system for vessel segmentation in fluoroscopic images - A method and system for vessel segmentation in fluoroscopic images is disclosed. Hierarchical learning-based detection is used to perform the vessel segmentation. A boundary classifier is trained and used to detect boundary pixels of a vessel in a fluoroscopic image. A cross-segment classifier is trained and used to detect cross-segments connecting the boundary pixels. A quadrilateral classifier is trained and used to detect quadrilaterals connecting the cross segments. Dynamic programming is then used to combine the quadrilaterals to generate a tubular structure representing the vessel. | 03-26-2009 |
| 20090080729 | Method and system for evaluating image segmentation based on visibility - A method and system for evaluating image segmentation is disclosed. In order to quantitatively evaluate an image segmentation technique, synthetic image data is generated and the synthetic image data is segmented to extract an object using the segmentation technique. This segmentation results in a foreground containing the extracted object and a background. The visibility of the extracted object is quantitatively measured based on the intensity distributions of the segmented foreground and background. The visibility is quantitatively measured by calculating the Jeffries-Matusita distance between the foreground and background intensity distributions. This method can be used to evaluate segmentation of vessels in fluoroscopic image sequences by coronary digital subtraction angiography (DSA). | 03-26-2009 |
| 20090080732 | System and Method for Performing Probabilistic Classification and Decision Support Using Multidimensional Medical Image Databases - A system and method for providing decision support to a physician during a medical examination is disclosed. Data is received from a sensor representing a particular medical measurement. The received data includes image data. The received data and context data is analyzed with respect to one or more sets of training models. Probability values for the particular medical measurement and other measurements to be taken are derived based on the analysis and based on identified classes. The received image data is compared with training images. Distance values are determined between the received image data and the training images, and the training images are associated with the identified classes. Absolute value feature sensitivity scores are derived for the particular medical measurement and other measurements to be taken based on the analysis. The probability values, distance values and absolute value feature sensitivity scores are outputted to the user. | 03-26-2009 |
| 20090080745 | Method and system for measuring left ventricle volume - A method and system for measuring the volume of the left ventricle (LV) in a 3D medical image, such as a CT, volume is disclosed. Heart chambers are segmented in the CT volume, including at least the LV endocardium and the LV epicardium. An optimal threshold value is automatically determined based on voxel intensities within the LV endocardium and voxel intensities between the LV endocardium and the LV epicardium. Voxels within the LV endocardium are labeled as blood pool voxels or papillary muscle voxels based on the optimal threshold value. The LV volume can be measured excluding the papillary muscles based on the number of blood pool voxels, and the LV volume can be measured including the papillary muscles based on the total number of voxels within the LV endocardium. | 03-26-2009 |
| 20090080747 | User interface for polyp annotation, segmentation, and measurement in 3D computed tomography colonography - A method and system for providing a user interface for polyp annotation, segmentation, and measurement in computer tomography colonography (CTC) volumes is disclosed. The interface receives an initial polyp position in a CTC volume, and automatically segments the polyp based on the initial polyp position. In order to segment the polyp, a polyp tip is detected in the CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary. The segmented polyp is displayed in the user interface, and a user can modify the segmented polyp boundary using the interface. The interface can measure the size of the segmented polyp in three dimensions. The user can also use the interface for polyp annotation in CTC volumes. | 03-26-2009 |
| 20090088640 | Automated View Classification With Echocardiographic Data For Gate Localization Or Other Purposes - A view represented by echocardiographic data is classified. A probabilistic boosting network is used to classify the view. The probabilistic boosting network may include multiple levels where each level has a multi-class local structure classifier and a plurality of local-structure detectors corresponding to the respective multiple classes. In each level, the local structure is classified as a particular view and then the local structure is detected to determine whether the currently selected local structure corresponds to the class. The view classification may be used to determine gate locations, such as a gate for spectral Doppler analysis. | 04-02-2009 |
| 20090090873 | Method and system for detection of contrast injection in fluoroscopic image sequences - A method and system for detecting a spatial and temporal location of a contrast injection in a fluoroscopic image sequence is disclosed. Training volumes generated by stacking a sequence of 2D fluoroscopic images in time order are annotated with ground truth contrast injection points. A heart rate is globally estimated for each training volume, and local frequency and phase is estimated in a neighborhood of the ground truth contrast injection point for each training volume. Frequency and phase invariant features are extracted from each training volume based on the heart rate, local frequency and phase, and a detector is trained based on the training volumes and the features extracted for each training volume. The detector can be used to detect the spatial and temporal location of a contrast injection in a fluoroscopic image sequence. | 04-09-2009 |
| 20090093717 | Automated Fetal Measurement From Three-Dimensional Ultrasound Data - A fetal parameter or anatomy is measured or detected from three-dimensional ultrasound data. An algorithm is machine-trained to detect fetal anatomy. Any machine training approach may be used. The machine-trained classifier is a joint classifier, such that one anatomy is detected using the ultrasound data and the detected location of another anatomy. The machine-trained classifier uses marginal space such that the location of anatomy is detected sequentially through translation, orientation and scale rather than detecting for all location parameters at once. The machine-trained classifier includes detectors for detecting from the ultrasound data at different resolutions, such as in a pyramid volume. | 04-09-2009 |
| 20090123050 | Method and system for automatic quantification of aortic valve function from 4D computed tomography data using a physiological model - A method and system for modeling the aortic valve in 4D image data, such as 4D CT and echocardiography, is disclosed. An initial estimate of a physiological aortic valve model is determined for at least one reference frame of a 4D image sequence based on anatomic features in the reference frame. The initial estimate is refined to generate a final estimate in the reference frame. A dynamic model of the aortic valve is then generated by estimating the physiological aortic valve model for each remaining frame of the 4D image sequence based on the final estimate in the reference frame. The aortic valve can be quantitatively evaluated using the dynamic model. | 05-14-2009 |
| 20090154785 | Method and system for dynamic pulmonary trunk modeling in computed tomography and magnetic resonance imaging - A method and system for modeling the pulmonary trunk in 4D image data, such as 4D CT and MRI data, is disclosed. Bounding boxes are detected in frames of the 4D image data. Anatomic landmarks are detected in the frames of the 4D image data based on the bounding boxes. Ribs or centerlines of the pulmonary artery are detected in the frames of the 4D image data based on the anatomic landmarks, and a physiological pulmonary trunk model is fit the frames of the 4D image data based on the detected ribs and anatomic landmarks. The boundary of the pulmonary trunk is detected in order to refine the boundary of the pulmonary trunk model in the frames of the 4D image data, resulting in a dynamic model of the pulmonary trunk. The pulmonary trunk can be quantitatively evaluated using the dynamic model. | 06-18-2009 |
| 20090190811 | Method and system for left ventricle endocardium surface segmentation using constrained optimal mesh smoothing - A method and system for left ventricle (LV) endocardium surface segmentation using constrained optimal mesh smoothing is disclosed. The LV endocardium surface in the 3D cardiac volume is initially segmented in a 3D cardiac volume, such as a CT volume, resulting in an LV endocardium surface mesh. A smoothed LV endocardium surface mesh is generated by smoothing the LV endocardium surface mesh using constrained optimal mesh smoothing. The constrained optimal mesh smoothing determines an optimal adjustment for each point on the LV endocardium surface mesh by minimizing an objective function based at least on a smoothness measure, subject to a constraint bounding the adjustment for each point. The adjustment for each point can be constrained to prevent adjustments inward toward the blood pool in order to ensure that the smoothed LV endocardium surface mesh encloses the entire blood pool. | 07-30-2009 |
| 20090304251 | Method and System for Detecting 3D Anatomical Structures Using Constrained Marginal Space Learning - A method and apparatus for detecting 3D anatomical objects in medical images using constrained marginal space learning (MSL) is disclosed. A constrained search range is determined for an input medical image volume based on training data. A first trained classifier is used to detect position candidates in the constrained search range. Position-orientation hypotheses are generated from the position candidates using orientation examples in the training data. A second trained classifier is used to detect position-orientation candidates from the position-orientation hypotheses. Similarity transformation hypotheses are generated from the position-orientation candidates based on scale examples in the training data. A third trained classifier is used to detect similarity transformation candidates from the similarity transformation hypotheses, and the similarity transformation candidates define the position, translation, and scale of the 3D anatomic object in the medical image volume. | 12-10-2009 |
| 20090310837 | Method and System for Automatic Detection and Measurement of Mitral Valve Inflow Patterns in Doppler Echocardiography - A method and system for segmentation of mitral valve inflow (MI) patterns in Doppler echocardiogram images is disclosed. Trained root detectors are used to detect left root candidates, right root candidates, and peak candidates in an input Doppler echocardiogram image. Two global structure detectors, a single triangle detector for non-overlapping E-waves and A-waves and a double triangle detector for overlapping E-waves and A-waves, are used to detect single triangle candidates and double triangle candidates based on the left root, right root, and peak candidates. A shape profile is used to determine a shape probability for each of the single triangle candidates and each of the double triangle candidates. The best single triangle candidate and the best double triangle candidate are selected based on shape probability and detection probability. One of the best single triangle candidate and the best double triangle candidate is selected as the final segmentation result based on a shape probability comparison. | 12-17-2009 |
| 20100027865 | Method and System for Brain Tumor Segmentation in 3D Magnetic Resonance Images - A method and system for brain tumor segmentation in multi-spectral 3D MRI images is disclosed. A trained probabilistic boosting tree (PBT) classifier is used to determine, for each voxel in a multi-spectral 3D MR image sequence, a probability that the voxel is part of a brain tumor. The brain tumor is then segmented in the multi-spectral 3D MRI image sequence using graph cuts segmentation based on the probabilities determined using the trained PBT classifier and intensities of the voxels in the multi-spectral 3D MR image sequence. | 02-04-2010 |
| 20100034446 | System and Method for Coronary Digital Subtraction Angiography - A method and system for extracting coronary vessels fluoroscopic image sequences using coronary digital subtraction angiography (DSA) are disclosed. A set of mask images of a coronary region is received, and a sequence of contrast images for the coronary region is received. For each contrast image, vessel regions are detected in the contrast image using learning-based vessel segment detection and a background region of the contrast image is determined based on the detected vessel regions. Background motion is estimated between one of the mask images and the background region of the contrast image by estimating a motion field between the mask image and the background image and performing covariance-based filtering over the estimated motion field. The mask image is then warped based on the estimated background motion to generate an estimated background layer. The estimated background layer is subtracted from the contrast image to extract a coronary vessel layer for the contrast image. | 02-11-2010 |
| 20100040272 | Method and System for Left Ventricle Detection in 2D Magnetic Resonance Images - A method and system for left ventricle (LV) detection in 2D magnetic resonance imaging (MRI) images is disclosed. In order to detect the LV in a 2D MRI image, a plurality of LV candidates are detected, for example using marginal space learning (MSL) based detection. Candidates for distinctive anatomic landmarks associated with the LV are then detected in the 2D MRI image. In particular, apex candidates and base candidates are detected in the 2D MRI image. One of the LV candidates is selected as a final LV detection result using component-based voting based on the detected LV candidates, apex candidates, and base candidates. | 02-18-2010 |
| 20100067760 | Method and System for Automatic Coronary Artery Detection - A method and system for coronary artery detection in 3D cardiac volumes is disclosed. The heart chambers are segmented in the cardiac volume, and an initial estimation of a coronary artery is generated based on the segmented heart chambers. The initial estimation of the coronary artery is then refined based on local information in the cardiac volume in order to detect the coronary artery in the cardiac volume. The detected coronary artery can be extended using 3D dynamic programming. | 03-18-2010 |
| 20100067764 | Method and System for Automatic Landmark Detection Using Discriminative Joint Context - A method and system for detecting anatomic landmarks in medical images is disclosed. In order to detect multiple related anatomic landmarks, a plurality of landmark candidates are first detected individually using trained landmark detectors. A joint context is then generated for each combination of the landmark candidates. The best combination of landmarks in then determined based on the joint context using a trained joint context detector. | 03-18-2010 |
| 20100067768 | Method and System for Physiological Image Registration and Fusion - A method and system for physiological image registration and fusion is disclosed. A physiological model of a target anatomical structure in estimated each of a first image and a second image. The physiological model is estimated using database-guided discriminative machine learning-based estimation. A fused image is then generated by registering the first and second images based on correspondences between the physiological model estimated in each of the first and second images. | 03-18-2010 |
| 20100070249 | Method and System for Generating a Personalized Anatomical Heart Model - A method and system for generating a patient specific anatomical heart model is disclosed. Volumetric image data, such as computed tomography (CT) or echocardiography image data, of a patient's cardiac region is received. Individual models for multiple heart components, such as the left ventricle (LV) endocardium, LV epicardium, right ventricle (RV), left atrium (LA), right atrium (RA), mitral valve, aortic valve, aorta, and pulmonary trunk, are estimated in said volumetric cardiac image data. A patient specific anatomical heart model is generated by integrating the individual models for each of the heart components. | 03-18-2010 |
| 20100074499 | Method and System for Segmentation of Brain Structures in 3D Magnetic Resonance Images - A method and system for segmenting multiple brain structures in 3D magnetic resonance (MR) images is disclosed. After intensity standardization of a 3D MR image, a meta-structure including center positions of multiple brain structures is detected in the 3D MR image. The brain structures are then individually segmented using marginal space learning (MSL) constrained by the detected meta-structure. | 03-25-2010 |
| 20100076296 | Method and System for Automatic Detection of Coronary Stenosis in Cardiac Computed Tomography Data - A method and system for automatic coronary stenosis detection in computed tomography (CT) data is disclosed. Coronary artery centerlines are obtained in an input cardiac CT volume. A trained classifier, such as a probabilistic boosting tree (PBT) classifier, is used to detect stenosis regions along the centerlines in the input cardiac CT volume. The classifier classifies each of the control points that define the coronary artery centerlines as a stenosis point or a non-stenosis point. | 03-25-2010 |
| 20100080434 | Method and System for Hierarchical Parsing and Semantic Navigation of Full Body Computed Tomography Data - A method and apparatus for hierarchical parsing and semantic navigation of a full or partial body computed tomography CT scan is disclosed. In particular, organs are segmented and anatomic landmarks are detected in a full or partial body CT volume. One or more predetermined slices of the CT volume are detected. A plurality of anatomic landmarks and organ centers are then detected in the CT volume using a discriminative anatomical network, each detected in a portion of the CT volume constrained by at least one of the detected slices. A plurality of organs, such as heart, liver, kidneys, spleen, bladder, and prostate, are detected in a sense of a bounding box and segmented in the CT volume, detection of each organ bounding box constrained by the detected organ centers and anatomic landmarks. Organ segmentation is via a database-guided segmentation method. | 04-01-2010 |
| 20100119137 | Method and System for Anatomic Landmark Detection Using Constrained Marginal Space Learning and Geometric Inference - A method and apparatus for detecting multiple anatomical landmarks in a 3D volume. A first anatomical landmark is detected in a 3D volume using marginal space learning (MSL). Locations of remaining anatomical landmarks are estimated in the 3D volume based on the detected first anatomical landmark using a learned geometric model relating the anatomical landmarks. Each of the remaining anatomical landmarks is then detected using MSL in a portion of the 3D volume constrained based on the estimated location of each remaining landmark. This method can be used to detect the anatomical landmarks of the crista galli (CG), tip of the occipital bone (OB), anterior of the corpus callosum (ACC), and posterior of the corpus callosum (PCC) in a brain magnetic resonance imaging (MRI) volume. | 05-13-2010 |
| 20100121181 | Method and System for Guidewire Tracking in Fluoroscopic Image Sequences - A method and system for tracking a guidewire in a fluoroscopic image sequence is disclosed. In order to track a guidewire in a fluoroscopic image sequence, guidewire segments are detected in each frame of the fluoroscopic image sequence. The guidewire in each frame of the fluoroscopic image sequence is then detected by rigidly tracking the guidewire from a previous frame of the fluoroscopic image sequence based on the detected guidewire segments in the current frame. The guidewire is then non-rigidly deformed in each frame based on the guidewire position in the previous frame. | 05-13-2010 |
| 20100142787 | Method and System for Left Ventricle Detection in 2D Magnetic Resonance Images Using Ranking Based Multi-Detector Aggregation - A method and system for left ventricle (LV) detection in 2D magnetic resonance imaging (MRI) images is disclosed. In order to detect the LV in a 2D MRI image, a plurality of LV candidates are detected, for example using marginal space learning (MSL) based detection. Candidates for distinctive anatomic landmarks associated with the LV are then detected in the 2D MRI image. In particular, apex candidates and base candidates are detected in the 2D MRI image. One of the LV candidates is selected as a final LV detection result by ranking the LV candidates based on the LV candidates, the apex candidates, and the base candidates using a trained ranking model. | 06-10-2010 |
| 20100239147 | Method and System for Dynamic Pulmonary Trunk Modeling and Intervention Planning - A method and system for modeling the pulmonary trunk in 4D image data, such as 4D CT data, and model-based percutaneous pulmonary valve implantation (PPVI) intervention is disclosed. A patient-specific dynamic pulmonary trunk data is generated from 4D image data of a patient. The patient is automatically classified as suitable for PPVI intervention or not suitable for PPVI intervention based on the generated patient-specific dynamic pulmonary trunk model. | 09-23-2010 |
| 20100239148 | Method and System for Automatic Aorta Segmentation - A method and system for aorta segmentation in a 3D volume, such as a C-arm CT volume is disclosed. The aortic root is detected in the 3D volume using marginal space learning (MSL) based segmentation. The aortic arch is detected in the 3D volume using MSL based segmentation. The ascending aorta is tracked from the aortic root to the aortic arch in the 3D volume, and the descending aorta is tracked from the aortic arch in the 3D volume. | 09-23-2010 |
| 20100240996 | VALVE ASSESSMENT FROM MEDICAL DIAGNOSTIC IMAGING DATA - Heart valve operation is assessed with patient-specific medical diagnostic imaging data. To deal with the complex motion of the passive valve tissue, a hierarchal model is used. Rigid global motion of the overall valve, non-rigid local motion of landmarks of the valve, and surface motion of the valve are modeled sequentially. For the non-rigid local motion, a spectral trajectory approach is used in the model to determine location and motion of the landmarks more efficiently than detection and tracking. Given efficiencies in processing, more than one valve may be modeled at a same time. A graphic overlay representing the valve in four dimensions and/or quantities may be provided during an imaging session. One or more of these features may be used in combination or independently. | 09-23-2010 |
| 20100254582 | System and Method for Detecting Landmarks in a Three-Dimensional Image Volume - A method and apparatus for detecting vascular landmarks in a 3D image volume, such as a CT volume, is disclosed. One or more guide slices are detected in a 3D image volume. A set of landmark candidates for multiple target vascular landmarks are then detected based on the guide slices. A node potential value for each landmark candidate is generated based on an error value determined using spatial histogram-based error regression, and edge potential values for pairs of landmark candidates are generated based on a bifurcation analysis of the image volume using vessel tracing. The optimal landmark candidate for each target landmark is then determined using a Markov random field model based on the node potential values and the edge potential values. | 10-07-2010 |
| 20100280352 | Method and System for Multi-Component Heart and Aorta Modeling for Decision Support in Cardiac Disease - A method and system for generating a patient specific anatomical heart model is disclosed. Volumetric image data, such as computed tomography (CT), echocardiography, or magnetic resonance (MR) image data of a patient's cardiac region is received. Individual models for multiple heart components, such as the left ventricle (LV) endocardium, LV epicardium, right ventricle (RV), left atrium (LA), right atrium (RA), mitral valve, aortic valve, aorta, and pulmonary trunk, are estimated in said volumetric cardiac image data. A multi-component patient specific anatomical heart model is generated by integrating the individual models for each of the heart components. Fluid Structure Interaction (FSI) simulations are performed on the patient specific anatomical model, and patient specific clinical parameters are extracted based on the patient specific heart model and the FSI simulations. Disease progression modeling and risk stratification are performed based on the patient specific clinical parameters. | 11-04-2010 |
| 20110033102 | System and Method for Coronary Digital Subtraction Angiography - A method and system for extracting coronary vessels fluoroscopic image sequences using coronary digital subtraction angiography (DSA) are disclosed. A set of mask images of a coronary region is received, and a sequence of contrast images for the coronary region is received. For each contrast image, a motion estimate is calculated between each of the mask images and a background region of the contrast image and a covariance is calculated for each motion estimate. Multiple background layer predictions are generated by generating a background layer prediction for each mask image based on the calculated motion estimate and covariance. The multiple background layer estimates are combined using statistical fusion to generate a final estimated background layer. The final estimated background layer is subtracted from the contrast image to extract a coronary vessel layer for the contrast image. | 02-10-2011 |
| 20110060576 | Method and System for Computational Modeling of the Aorta and Heart - A method and system for generating a patient specific anatomical heart model is disclosed. A sequence of volumetric image data, such as computed tomography (CT), echocardiography, or magnetic resonance (MR) image data of a patient's cardiac region is received. A multi-component patient specific 4D geometric model of the heart and aorta estimated from the sequence of volumetric cardiac imaging data. A patient specific 4D computational model based on one or more of personalized geometry, material properties, fluid boundary conditions, and flow velocity measurements in the 4D geometric model is generated. Patient specific material properties of the aortic wall are estimated using the 4D geometrical model and the 4D computational model. Fluid Structure Interaction (FSI) simulations are performed using the 4D computational model and estimated material properties of the aortic wall, and patient specific clinical parameters are extracted based on the FSI simulations. Disease progression modeling and risk stratification are performed based on the patient specific clinical parameters. | 03-10-2011 |
| 20110064189 | Method and System for Needle Tracking in Fluoroscopic Image Sequences - A method and system for tracking a needle in a fluoroscopic image sequence is disclosed. In order to track a needle in a fluoroscopic image sequence, the needle is initialized in a first frame of the fluoroscopic image sequence. Needle segments are detected in each subsequent frame of the fluoroscopic image sequence, and the needle is detected in each frame of the fluoroscopic image by tracking the needle from a previous frame of the fluoroscopic image sequence based on the detected needle segments in the current frame. | 03-17-2011 |
| 20110087443 | Three-Dimensional Visualization and Analysis Method and System for Non-Destructive Examination of a Rotor Bore using Ultrasound - A method and apparatus for three-dimensional (3D) visualization and analysis for automatic non-destructive examination of a rotor bore using ultrasound. Data is acquired by scanning the rotor bore with an ultrasound pulser/transducer producing a plurality of one-dimensional ultrasound scans, each scan having a plurality of sample points. Each sample point is associated with a voxel of a regular 3D grid having a plurality of voxels. A Gaussian kernel is associated with each sample point and a value for a particular voxel is determined based on a weighted sum of sample points whose kernels cover the particular voxel. The values for the other voxels of the regular 3D grid are determined similarly. A 3D visualization of the rotor bore can be displayed to a user. | 04-14-2011 |
| 20110096964 | Method and System for Automatic Extraction of Personalized Left Atrium Models - A method and system for automatic extraction of personalized left atrium models is disclosed. A left atrium chamber body is segmented from a 3D image volume. At least one pulmonary venous ostium is detected on the segmented left atrium chamber body. At least one pulmonary vein trunk connected to the left atrium chamber body is segmented based on the detected pulmonary venous ostia. | 04-28-2011 |
| 20110096969 | Method and System for Shape-Constrained Aortic Valve Landmark Detection - A system and method for performing shape-constrained aortic valve landmark detection using 3D medical images is provided. A rigid global shape defining initial positions of a plurality of aortic valve landmarks is detected within a 3D image. Each of the plurality of aortic valve landmarks is detected based on the initial positions. | 04-28-2011 |
| 20110116698 | Method and System for Segmentation of the Prostate in 3D Magnetic Resonance Images - A method and system for fully automatic segmentation the prostate in multi-spectral 3D magnetic resonance (MR) image data having one or more scalar intensity values per voxel is disclosed. After intensity standardization of multi-spectral 3D MR image data, a prostate boundary is detected in the multi-spectral 3D MR image data using marginal space learning (MSL). The detected prostate boundary is refined using one or more trained boundary detectors. The detected prostate boundary can be split into patches corresponding to anatomical regions of the prostate and the detected prostate boundary can be refined using trained boundary detectors corresponding to the patches. | 05-19-2011 |
| 20110142318 | STENT VIEWING USING A LEARNING BASED CLASSIFIER IN MEDICAL IMAGING - Stent viewing is provided in medical imaging. Stent images are provided with minimal or no user input of spatial locations. Images showing contrast agent are distinguished from other images in a sequence. After aligning non-contrast images, the images are compounded to enhance the stent. The contrast agent images are used to identify the vessel. A contrast agent image is aligned with the enhanced stent or other image to determine the relative vessel location. An indication of the vessel wall may be displayed in an image also showing the stent. A preview images may be output. A guide wire may be used to detect the center line for vessel identification. Various detections are performed using a machine-trained classifier or classifiers. | 06-16-2011 |
| 20110144480 | STENT MARKER DETECTION USING A LEARNING BASED CLASSIFIER IN MEDICAL IMAGING - Stent marker detection is automatically performed. Stent markers in fluoroscopic images or other markers in other types of imaging are detected using a machine-learnt classifier. Hierarchal classification may be used, such as detecting individual markers with one classifier and then detecting groups of markers (e.g., a pair) with a joint classifier. The detection may be performed in a single image and without user indication of a location. | 06-16-2011 |
| 20110153286 | Method and System for Virtual Percutaneous Valve Implantation - A method and system for virtual percutaneous valve implantation is disclosed. A patient-specific anatomical model of a heart valve is estimated based on 3D cardiac medical image data and an implant model representing a valve implant is virtually deployed into the patient-specific anatomical model of the heart valve. A library of implant models, each modeling geometrical properties of a corresponding valve implant, is maintained. The implant models maintained in the library are virtually deployed into the patient specific anatomical model of the heart valve to select an implant type and size and deployment location and orientation for percutaneous valve implantation. | 06-23-2011 |
