Is binary trading reliably 4d ultrasound

For example, different probabilistic models are trained, such as one for detecting the myocardium in a given frame of ultrasound data and another for tracking the myocardium between frames. The memory 14 may store data representing instructions executable by a programmed processor, such as the processor 12, for characterizing cardiac motion from ultrasound information. One or more frames may correspond to a given phase of the heart cycle. Automatic semantic processing provides myocardial mechanics. The systems, methods and instructions herein may instead or additionally be used for other cyclical or repetitive motion characterization, such as analysis of diaphragm motion or a gait while jogging. The knowledge from the learning provides the typical myocardium position for different phases. Since time separates frame acquisition, two frames may overlap or represent the closest times to the desired phase. Computerized characterization of cardiac wall motion is provided.

The data is stored for or during processing by the processor 12. Act 30 is performed for each cycle without continuing tracking from the other cycle. The left ventricle chamber expands and contracts throughout the heart cycle. For another cycle, the initialization may not rely on the motion prior. Data matching or speckle tracking may be used for initialization in the other sequence. The planes may be detected for one or more of the frames. By way of introduction, the preferred embodiments described below include methods, computer readable media and systems for computerized characterization of cardiac wall motion.

The tracking model may use the output of the detection model. The motion prior mesh represents the shape of the myocardium. These patterns may be used to analyze motion abnormalities. In alternative embodiments, the dimensionality is not reduced. Alternatively, the motion prior is used. For example, the boundary is initialized in both the first and last frames of the sequence. Each classifier uses the data sets and annotations specific to the anatomy being classified. The volume over time may be low pass filtered.

The motion prior is used in a Bayesian objective function with speckle tracking, boundary detection, and mass conservation. In one embodiment, the volume is calculated by detecting the myocardium. Tracking of additional cycles may be provided. It is therefore intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is the following claims, including all equivalents, that are intended to define the spirit and scope of this invention. While discussed herein for use on the myocardium or left ventricle with ultrasound data, the approach may be considered a general framework. Siemens Corporation, Siemens Medical Solutions USA, Inc. Act 26 may not be performed where the sequence represents a single cycle or less or where the tracking is to be performed through multiple cycles without countering drift or with continuous, uniform processing.

The tracking may be to adjacent frames, such that half the sequence uses the forward tracked boundary and the other half uses the reverse tracked boundary. The features are determined from the gradient information. Figure 7 also shows several LV motion representations in a low dimensional space on a graph. Any types of features from the ultrasound data may be used. The present invention is defined by the following claims, and nothing in this section should be taken as a limitation on those claims. Acts 28 and 30 represent the inner cycle tracking for one cycle.

Act 32 represents acts 28 and 30 being performed for different heart cycles, so is performed in parallel, before, or after acts 28 and 30. Cartesian coordinate system and a local heart coordinate system. Figure 2 shows a method for computerized characterization of cardiac motion from ultrasound data. An imaging system or work station uploads the instructions. Any scan pattern may be used. Additional, different or fewer components may be provided. The estimating is also a function of volumetric tracking of the myocardium from the first frame to a second frame of the sequence of the frames. In another example, act 34 is not performed and an image is generated instead.

The two boundaries are averaged, interpolated, otherwise combined, or used to select one. The quantity is projected to a Cartesian coordinate system and a local heart coordinate system. Using ED frames as the beginning of a heart cycle, the peaks in the volume indicate the beginning of each cycle. The division is based on the volume within the myocardium. Any number of features may be used, such as tens, hundreds, or thousands. For example, the processor 12 or another processor tracks one or more points and calculates spatial parameter values for each point in a model. Patent Application Serial Nos. The ultrasound data is input to the processor 12 or the memory 14. For example, the identity of the myocardium is performed using relative relationships of points rather than absolute coordinates.

In one example, steerable features are used. To automate the clinical workflow and facilitate the subsequent processing tasks, such as ventricular wall motion tracking, standard cardiac MPR planes may be automatically detected from a 3D volume. Additional, different or fewer acts than shown may be used. For example, speckle or tissue is tracked using correlation or minimum sum of differences calculations. The boundary in a given frame of the sequence is based on the mesh, but may be different than the motion prior mesh depending on the other features. The detectors are trained on a large number of annotated 3D ultrasound volumes. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing and the like. Cardiac motion may be classified as a function of the values. In alternative embodiments, the classifier is manually programmed.

This task is particularly challenging for the ultrasound data because of the noise and missing data. The tracking is performed in forward and reverse temporal directions. The frames may be obtained in real time with the scanning. Each frame of data represents the volume at a given time. This framework may have advantages as compared to mere image registration or optical flow tracking. The classifier is trained from a training data set using a computer. Bayesian objective function to achieve accurate and robust tracking of the whole myocardium volume. Given an input volume, the mean LV shape is aligned to the detected pose or data. One may be selected, an interpolation frame may be formed, or the frames may be averaged.

Other types of features may alternatively or additionally be used. Any frame rate is used, such as 15 or more frames a second or heart beat. The displacement and velocity are determined relative to the heart. The tissue boundary may have one or more gaps. However, most existing methods for measuring myocardial strain are limited to measurements in one or two dimensions. The processor identifies the boundary from the ultrasound data of the frame with or without data from other frames. The ultrasound data is grouped into frames. Figure 3 shows another embodiment of the method with respect to representative ultrasound medical images.

The probabilistic models may be joint or dependent. For each frame in the sequence, the volume is calculated. The knowledge of the probabilities, as fit to the data of each frame, indicates the location or local offset based on the motion prior. The estimation and analysis of cardiac motion may provide important information for the quantification of the elasticity and contractility of the myocardium. The components and the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. The normalized or extracted image data is used to calculate values for one or more parameters. For example, the estimation and analysis of cardiac motion provides important information for the elasticity and contractility of the myocardium. Figure 7 shows two temporally aligned LV motion sequences with 16 frames per cardiac cycle.

The features are calculated from the ultrasound data, such as centering a window or kernel over each location and determining the steerable features for each window position. The medical imaging cardiac example is used herein. The sequence is over a portion of a heart cycle, over multiple cycles, or any length. Published Patent Application No. These cues are used as input feature vectors. In one embodiment, a binary boosting classifier with a tree and cascade structure is used. The initialization of act 28 is performed using the detector for one cycle.

Detecting motion over multiple cycles may be more useful than over a single cycle, especially where drift in tracking is avoided. The memory 14 stores the ultrasound frame or image data. Alternatively, the frames are acquired from a memory or transfer where the scan occurred previously in an examination no longer occurring. Any number of expert annotated sets of ultrasound data is used. In one embodiment, the type of features used is gradient features. In one embodiment, the dimensionality is reduced. The results are then fused into a single estimate by averaging the computed deformations and the procedure is repeated until the full 4D model is estimated for the complete sequence.

In one embodiment, ECG information is used to determine the frames associated with phases. In act 28, a processor identifies a myocardium in one or more frames. In other embodiments, the acts are performed in a different order. The motion may be determined by differences between absolute positions of the heart wall in different frames. The 3D deformation of the myocardial wall is captured by fusing the information from multiple cues, including speckle tracking, boundary detection and motion prior. The sequences from different cycles may be normalized as a function of time.

In yet other embodiments, the instructions are stored within the imaging system on a hard drive, random access memory, cache memory, buffer, removable media or other device. The fitting locally deforms the model. Where the sequence extends over multiple cycles, the cycles are automatically divided for independent tracking of the cardiac wall. The user configures the system to perform heart scanning prior to activation. In another embodiment, the motion prior is used for initializing the tracking in act 28. The image represents the volume within the patient, such as being a rendering of the volume from one or more viewing directions or an MPR image. In an example embodiment, the 4D ultrasound sequences are acquired with a medical diagnostic ultrasound scanner with the average volume size of size 200 x 200 x 140 voxels and resolution of 1 mm in the x, y and z directions. The motion of the heart wall, such as the myocardium, is determined. To facilitate the analysis, the tracking result X is temporally aligned to a reference cycle length. The acts are performed in the order shown.

Features may be identified using image processing. The calculation occurs without user input. Bayesian objective function with integration of a plurality of cues, including myocardium boundary detection, speckle tracking and mass conservation. Each frame includes the ultrasound data sufficient to detect values for the voxels representing the volume of the patient. American Society of Echocardiography. Various machine learning is used for locating and tracking the cardiac wall, such as using a motion prior learned from training data for locating and the motion prior, speckle tracking, boundary detection, and mass conservation cues for tracking with another machine learned classifier.

The gaps are closed by curve fitting or interpolation. The processor 12 is one or more general processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed device for processing medical ultrasound data. The views are detected from features. The estimation is through a cycle or portion of a cycle. Figure 4 shows an example division. The transducer 18 is a piezoelectric or capacitive device operable to convert between acoustic and electrical energy.

Cartesian coordinate with polar coordinate spacing between planes, or other format. The sequences are in a clip stored in a CINE loop, DICOM images or other format. Image quality measurements based on image intensities and speckleness scores are integrated in a weighted likelihood estimation to handle noise and signal dropouts in ultrasound data. The system 10 uses the transducer 18 to scan a volume. The scale is normalized and the model aligned to the data. Because of the cyclic motion pattern of each cycle, the ED frame of the second cycle may also be initialized by propagating the motion from the ED frame of the first cycle. The same or different sized windows are used for different anatomies. For example, a local or remote workstation without the transducer 18 receives ultrasound data and characterizes cardiac motion. The quantities are determined with minimal user input.

In one embodiment, the motion prior information is used throughout a cycle to determine the volume in act 26. ISOMAP or other algorithm. The region enclosed by the boundary is the cavity volume. The frames are acquired by scanning the patient with ultrasound. The tracking is performed by image analysis. In a first aspect, a method is provided for computerized characterization of cardiac motion from ultrasound data. The tracking of the frames of one cycle is independent of the tracking of the frames of another cycle and vice versa. The mesh may be input as absolute coordinates of the control points or may use the manifold learning and be input as relative locations. The processor identifies the boundary using a classifier or detector. Alternatively, both forward and reverse tracking are performed over the entire sequence.

Ultrasound data representing a volume is provided in response to the scanning. The known cases may be spatially aligned or registered, such as by aligning the coordinate system to the heart. Unsupervised manifold learning is capable of discovering the nonlinear degrees of freedom that underlie complex natural observations. In yet another embodiment, the motion prior is also or alternatively used as an input feature for tracking in act 30. Any boundary detection may be used. Other classifiers may be used. The initialization for the other cycle may be dependent on the previous cycle while the tracking is not. In act 20, a sequence of frames of ultrasound data are obtained. As a result, two boundaries are provided for each frame. In one embodiment, the instructions are stored on a removable media drive for reading by a medical diagnostic imaging system or a workstation networked with imaging systems.

The processor 12 implements a software program, such as code generated manually or programmed or a trained classification system. In a third action, automatic tracking initialization is provided. Alternatively, the same classifier detects the boundary at any phase. Efficient optimization, such as manifold learning, is used to achieve high speed. Different frames represent substantially the same volume at different times. For example, nq may be 150 while n is 771. As represented in Figure 8, a local heart coordinate system describes the LV deformation. For tracking in one temporal direction through the sequence, the boundary is identified in one frame.

Each detector not only provides a binary decision for a given sample, but also a confidence value associated with the decision. Knowledge is embedded in large annotated data repositories where expert clinicians manually indicate the anatomies. For example, the boundary in the last from of one cycle is propagated to the first frame of the next cycles. The features represent directional gradients. The window is translated, rotated, and scaled as part of searching for an anatomy. In one embodiment, the process proceeds through a number of actions. The LV for the whole myocardium is located.

As the scanning occurs for a subsequent frame of data, the most recently acquired frame of data is processed. Combinations of these uses may be provided. Speckle tracking alone may be provided. The initialization is based on other cues. Further aspects and advantages of the invention are discussed below in conjunction with the preferred embodiments. For example, features are calculated for each frame of data from the data itself for detecting the myocardium. As another example, low, high, or bandpass filtering is applied. The first deforms a representative model to the data of each frame based on learned posterior probabilities. In a second aspect, an automatic semantic processing framework is provided for estimation of volume myocardial mechanics.

Any features may be used. The detector represents a model of the anatomy where the model is fit by application of the matrix to the ultrasound data. Both features may be efficiently computed and be effective as a feature space for boosting classifiers. The boundary may then be tracked in forward and reverse directions through the portion of the sequence of the given cycle. Automatic temporal adjustment is provided for when ECG is not available or not correct. The machine learning process may operate to determine a desired subset or set of features to be used for a given classification task. The annotation is provided for each frame of data. The system scans, detects the boundary, tracks the boundary, and calculates the values without further input.

As represented in act 32, the tracking for an additional cycle is performed independently. In one embodiment, the system 10 is a medical diagnostic imaging system, such as an ultrasound imaging system. Alternatively, the gaps are identified and the tissue boundary is closed by connecting a flat or curved surface between the tissue boundary points closest to the gap. For example, Haar, steerable or other features are also input. The instructions are for characterizing cardiac motion from ultrasound information. Other beginning phases may be used. In act 22, one or more frames in the heart cycle are identified.

For example, the boundaries in the first and last frames of the sequence are identified. The locations or mesh of the myocardium in the last or adjacent frame of one cycle is propagated to the first or adjacent frame from the other cycle. March 7, 2004, is used, the disclosure of which is incorporated herein by reference. Since two machine learned classifiers are used for boundary detection given the input volumetric sequence, both an optical flow tracker and a boundary detection tracker are used. The display 16 may be configured by the output of the processor 12 to display information to the user. The functions, acts or tasks illustrated in the figures or described herein are performed by the programmed processor 12 executing the instructions stored in the memory 14 or a different memory.

The estimation tracks the boundary from one frame to another frame. To prevent or limit drifting, the ventricular wall motion is tracked in both forward and backward directions based on learned motion priors. The result is a mesh of detected positions of the boundary in the frame. Since myocardial tissue is virtually incompressible, the tissue deforms in all three dimensions simultaneously. The window function defining the data is a cube, but may have other volume shapes. The temporal deformations are first aligned by 4D generalized procrustes analysis. While the invention has been described above by reference to various embodiments, it should be understood that many changes and modifications can be made without departing from the scope of the invention. For example, about 200 hundred ultrasound sequences representing the whole or at least a majority of the myocardium are annotated. The knowledge is used as a motion prior.

Boundary detection and motion prediction are combined with image intensity information to prevent drifting. Each frame is detected data representing the volume. Since the different frames represent different times, the different frames are associated with different phases of the heart cycle. LV wall motion may be preferred to visual assessments. In other embodiments, the system 10 is a computer, workstation or server. The heart or other anatomy identified in act 28 is for initializing the tracking. The classifier is configured or trained for distinguishing between the desired groups of states or to identify options and associated probabilities. For example, the gradients throughout the frame are calculated.

In one embodiment, the myocardial motion pattern is quantized using statistical atlases of deformation. The relative location is maintained while reducing the dimensionality. Additional, different, or fewer cues may be provided. For example, the user configures the system to scan a volume of a patient and to provide values or displays for one or more specific parameters. One or more values or quantities are calculated for the parameters. DR, and circumferential DC. Alternatively, the same initialization may be used for other cycles.

The separation allows for independent tracking through each cycle to prevent or limit the drifting over a long sequence. Moreover, in the figures, like reference numerals designate corresponding parts throughout the different views. The myocardium changes during a cardiac cycle. Any learning based approach for boundary detection may be used. For each phase, a classifier is trained and used. The parameters are variables, such as any myocardial mechanical characteristic. The classification may be performed using the motion information described above. Standard views are used to visualize the cardiac structures and are the starting point of many echocardiographic examinations.

For tracking from different temporal directions, the boundary is initialized in two or more frames. Independent tracking in each cycle may reduce or avoid inaccuracies due to drift. The classifier learns various feature vectors for distinguishing between a desired anatomy and information not being detected. Alternatively, the transducer 18 is a wobbler for mechanical scanning in one dimension and electrical scanning in another dimension. In other embodiments, the initialization of the tracking for each cycle is independent of information form other cycles. The ultrasound data may be processed prior to calculating features. Efficient optimization may achieve high speed performance. Given three spatial dimensions, time, and a large number of voxels in each volume, the dimensionality of the data for detecting the boundary is high.

Boundary detection and motion prediction are combined with image intensity to prevent drifting in multiple cardiac cycles. Upon activation, the scanning occurs and the boundary is identified without further user input. In one embodiment, the transmit beam is wide enough for reception along a plurality of scan lines. Doppler mode, contrast agent, harmonic, or other ultrasound modes of imaging. In a fourth action, ventricular wall motion tracking is provided. The improved tracking accuracy and robustness may better track ventricular wall motion reliably in multiple cardiac cycles. The ultrasound data is beamformed or detected. The frames representing the heart at the desired phases are determined from the volume within the myocardium.

The frames of ultrasound data may be used to determine the frames for particular phases. Other features may be used. By including both approaches, temporal consistency and smooth motion may more likely be assured, and drifting and outliers may be avoided. The volumetric ultrasound data is acquired from a cardiomyopathy patient. In another embodiment, a plane, collimated or diverging transmit waveform is provided for reception along a plurality, large number, or all scan lines. For example, volume, displacement, velocity, twist, torsion, strain, strain rate, principal strain, radius, curvature, contraction front map, relaxation front map, coronary map, or combinations thereof are calculated. The storage media includes instructions for obtaining a sequence of frames of ultrasound data, the frames representing a volume of a patient at a different times, identifying first and second cycles of the heart from the sequence of the frames of the ultrasound data, and propagating a first location of a myocardium from a first frame of the frames of the first cycle to a second location of the myocardium in a second frame of the frames of the second cycle, the second location being an initial location of the myocardium used in volumetric tracking of the myocardium through the frames of the second cycle.

The 4D generalized procrustes analysis is used to align all resampled motion vectors to remove the translation, rotation and scaling in the global coordinate systems, while keeping the shape variations and motion patterns inside the motion vectors. In act 26, the sequence of frames is divided into cycles. In alternative embodiments, the estimation is through more than a cycle. The system 10 includes a processor 12, a memory 14, a transducer 18, and a display 16. Other now known or later developed tracking methods may be used. For example, the detection discussed below for act 28 as part of tracking is performed for each frame of data to calculate volume. The same or different features are used for classification in each stage. Any block size may be used, such as 11 x 11 x 11. For example, act 24 is not performed. The motion prior alone may be used to reduce drift and account for individual patient variation from the motion prior, the estimation uses information in addition to or instead of the motion prior. The automatic or semiautomatic operations discussed herein are implemented, at least in part, by the instructions.

Different types of features may be used for the same classifier, or all of the features are of a same type for a given classifier. The variation of the volume, such as shown in the example of Figure 4, indicates the different cycles. In act 34, one or more cardiac parameters are calculated. The feature vectors may be processed, such as filtered, prior to input. In alternative embodiments, other tracking may be used. Doppler detector, harmonic response detector, contrast agent detector, scan converter, filter, combinations thereof, or other now known or later developed medical diagnostic ultrasound system components. Using a model of the heart indicating the spatial position of the planes relative to the heart orientation represented by the locations of the detected features, the positions of the planes relative to the volume represented by the frame of data are determined. However, the ECG information might be unavailable or incorrect in some data.

The template changes for each pair of frames between which the boundaries are tracked. Control points representing the mesh are positioned using the detector. Bayesian or neural network classifiers. The frames represent a volume of a patient at different times. The boundary is identified automatically, such as without user input. The boundary is tracked between images based on minimum stress or distortion of the previous boundary. Global and regional myocardial mechanics are estimated based on the tracking result. In a second action, automatic data navigation is provided.

In a first action, automatic temporal segmentation is provided. The myocardium is tracked through each cycle separate from other cycles. Although visual wall motion scoring is the clinically established method for assessment of regional myocardial function, this methodology may be variable between observers. Figure 1 shows a system 10 for characterizing cardiac motion. For Doppler, the ultrasound data may include samples from a plurality of reception events performed along each scan line or estimated velocity or energy samples for each scan line. The cardiac wall from one cycle may be used to propagate to another cycle for initializing the independent tracking.

In another embodiment, the instructions are stored in a remote location for transfer through a computer network or over telephone communications to the imaging system or workstation. The variation in volume indicates relative cycle timing. Both the endocardial and epicardial boundaries are detected, but just one boundary may be identified in other embodiments. Siemens Corporate Research, Inc. For application, the processor 12 calculates features for classification. Propagation is performed by interpolation. The tracked cardiac wall from a plurality of cycles may be fused temporally to enhance tracking accuracy and robustness. The parameters are calculated based on the tracking. The best match of data for or surrounding each location is identified in subsequent images.

Given the cardiac cycle and phase information estimated in act 22, the input ultrasound sequence is split into individual cardiac cycles. Because of the acquisition nature of the ultrasound data, noise and signal dropouts may introduce artifacts. The data is combined or processed to have fewer variables. Any search region may be used. In alternative embodiments, user input during the process is provided. We can provide you with the best experience on Yellowpages.

The processor 12 is one or more general processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed device for processing medical ultrasound data The processor 12 implements a software program, such as code generated manually or programmed or a trained classification system. As represented in FIG. As illustrated in FIG. The method of claim 1 wherein estimating comprises using the motion prior in a Bayesian objective function with integration of a plurality of cues, including myocardium boundary detection, speckle tracking and mass conservation. Circle Cardiovascular Imaging Inc. For example, n q may be 150 while n is 771. LV motion representations in a low dimensional space on a graph.

The cardiac wall from one cycle may be used to propagate to another cycle for initializing the tracking. University Of Florida Research Foundation, Inc. IEEE Int Symp Biomend Imaging, May 14, vol. Siemens Medical Solutions Usa, Inc. The variation of the volume, such as shown in the example of FIG. Various machine learning is used for locating and tracking the cardiac wall, such as using a motion prior learned from training data for initially locating the cardiac wall and the motion prior, speckle tracking, boundary detection, and mass conservation cues for tracking with another machine learned classifier.

The method of claim 1 wherein estimating comprises volumetric tracking between pairs of the frames in a forward direction and volumetric tracking between pairs of frames in a reverse direction, the forward and reverse directions being temporally through the sequence, and comprises combining the trackings from the forward and reverse directions. LV motion sequences with 16 frames per cardiac cycle. May 14, Proc IEEE Int Symp Biomed Imaging, vol. The method of claim 1 further comprising dividing the sequence into first and second heart cycles based on a volume within the myocardium, wherein identifying for the first heart cycle comprises identifying with the motion prior, wherein identifying for the second heart cycle comprises identifying with propagation of myocardium locations from a first frame of the first heart cycle to a second frame of the second heart cycle, and wherein estimating comprises volumetric tracking the myocardium through the first and second heart cycles independently.