Abstract: Physically-constrained modeling of a heart is provided. Patient-specific data may be used to estimate heart anatomy locations. A model is applied to the data for estimation. For increased accuracy of estimation, the biomechanics of the heart, such as the valve, may be used to constrain the estimation. By applying a dynamic system between estimated anatomy locations of different times, the locations may be deformed or refined. The modeled heart and/or valve may be used to estimate hemodynamics. The resulting velocities or other motion information may be used to emulate ultrasound Doppler imaging for comparing with acquired ultrasound Doppler data. The comparison may validate the modeling.

Abstract: Flow information (e.g., velocity and acceleration) and/or pressure gradient information are determined with ultrasound. Both velocity and acceleration are simultaneously estimated using a model and least squares analysis. Anti-aliasing of velocity information may be provided using the model and least squares analysis. Pressure gradient is calculated from velocity information automatically, more likely providing consistent measurements. Consistency may be increased further by automatically positioning a region of interest in either a multidimensional spatial image or a spatial-temporal image. A parametric image of pressure gradient for each spatial location within the image is generated as well as a delta pressure curve. Any single one or combinations of two or more of the features described above may be used.

Abstract: Computerized characterization of cardiac wall motion is provided. Quantities for cardiac wall motion are determined from a four-dimensional (i.e., 3D+time) sequence of ultrasound data. A processor automatically processes the volume data to locate the cardiac wall through the sequence and calculate the quantity from the cardiac wall position or motion. 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. Where the sequence extends over multiple cycles, the cycles are automatically divided for independent tracking of the cardiac wall. The cardiac wall from one cycle may be used to propagate to another cycle for initializing the tracking. Independent tracking in each cycle may reduce or avoid inaccuracies due to drift.

Abstract: 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.

Abstract: Rotations at multiple longitudinal levels (e.g., rotations from different short axis views) are determined. Three-dimensional representations are generated as a function of the rotations, such as distorting the representation to show rotation and/or modulating the display values as a function of the rotations. Twist and/or torsion of the heart may be calculated from the rotations.

Abstract: 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.

Abstract: Contrast agent enhanced medical diagnostic imaging is provided. Frames of data from common phase periods are grouped. Motion correction is performed within each common phase group. An image representing contrast agents is formed from a combination of the frames within each common phase, motion corrected group.

Abstract: 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.

Abstract: Rotations at multiple longitudinal levels (e.g., rotations from different short axis views) are determined. Three-dimensional representations are generated as a function of the rotations, such as distorting the representation to show rotation and/or modulating the display values as a function of the rotations. Twist and/or torsion of the heart may be calculated from the rotations.