Abstract: Image enhancement is provided for medical diagnostic ultrasound. Knowledge-based detection of anatomy or artifact identifies locations to be enhanced. The knowledge-based detection of the locations may avoid identification of other anatomy or artifacts. The image enhancement is applied to the identified locations and not others.

Abstract: A system and method for multi-modality fusion for 3D printing of a patient-specific organ model is disclosed. A plurality of medical images of a target organ of a patient from different medical imaging modalities are fused. A holistic mesh model of the target organ is generated by segmenting the target organ in the fused medical images from the different medical imaging modalities. One or more spatially varying physiological parameter is estimated from the fused medical images and the estimated one or more spatially varying physiological parameter is mapped to the holistic mesh model of the target organ. The holistic mesh model of the target organ is 3D printed including a representation of the estimated one or more spatially varying physiological parameter mapped to the holistic mesh model. The estimated one or more spatially varying physiological parameter can be represented in the 3D printed model using a spatially material property (e.g.

Abstract: Image enhancement is provided for medical diagnostic ultrasound. Knowledge-based detection of anatomy or artifact identifies locations to be enhanced. The knowledge-based detection of the locations may avoid identification of other anatomy or artifacts. The image enhancement is applied to the identified locations and not others.

Abstract: Image enhancement is provided for medical diagnostic ultrasound. Knowledge-based detection of anatomy or artifact identifies locations to be enhanced. The knowledge-based detection of the locations may avoid identification of other anatomy or artifacts. The image enhancement is applied to the identified locations and not others.

Abstract: A system and method for multi-modality fusion for 3D printing of a patient-specific organ model is disclosed. A plurality of medical images of a target organ of a patient from different medical imaging modalities are fused. A holistic mesh model of the target organ is generated by segmenting the target organ in the fused medical images from the different medical imaging modalities. One or more spatially varying physiological parameter is estimated from the fused medical images and the estimated one or more spatially varying physiological parameter is mapped to the holistic mesh model of the target organ. The holistic mesh model of the target organ is 3D printed including a representation of the estimated one or more spatially varying physiological parameter mapped to the holistic mesh model. The estimated one or more spatially varying physiological parameter can be represented in the 3D printed model using a spatially material property (e.g.

Abstract: In personalized object creation, for implants, medical imaging is used to derive a model personalized to a patient. The model may be of a dynamic structure, such as part of the cardiovascular system, and is used to print the implant itself. The model may be used to print a mold to create the implant, a scaffold on which to grow tissue, and/or tissue itself. In another or additional approach, the medical imaging information is used to determine tissue properties. Differences in a material property of the anatomy is mapped to different materials used by a multi-material 3D printer, resulting in a printed object reflecting the size, shape, and/or other material property of the anatomy of the patient.

Abstract: Image enhancement is provided for medical diagnostic ultrasound. Knowledge-based detection of anatomy or artifact identifies locations to be enhanced. The knowledge-based detection of the locations may avoid identification of other anatomy or artifacts. The image enhancement is applied to the identified locations and not others.

Abstract: Flow estimation is provided. The flow is predicted. A mathematical, logic, machine learning or other model is used to predict flow. For example, the boundary conditions associated with a previous flow, the previous flow, and current boundary conditions are used to predict the current flow. The current flow is corrected using the predicted flow. Velocities may be unaliased based on the predicted flow. The predicted flow may replace the current flow. Prediction may additionally or alternatively be used in determination of lateral or elevational flow.

Abstract: Flow estimation is provided. The flow is predicted. A mathematical, logic, machine learning or other model is used to predict flow. For example, the boundary conditions associated with a previous flow, the previous flow, and current boundary conditions are used to predict the current flow. The current flow is corrected using the predicted flow. Velocities may be unaliased based on the predicted flow. The predicted flow may replace the current flow. Prediction may additionally or alternatively be used in determination of lateral or elevational flow.

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: Three-dimensional cardiac border is delineated in medical imaging. A view is labeled, such as identifying a two-dimensional view as an apical four-chamber view. A three-dimensional border is detected as a function of the view label. For example, the view is associated from a plane through a volume and a known orientation relative to the heart. Labeling the view indicates the orientation of the heart in the scanned volume. By determining the orientation of the heart, border detection processes may be simplified or assisted.

Abstract: Parametric imaging of a surface is provided on a medical diagnostic ultrasound imaging system. A bull's eye or Beutel surface representing the scanned tissue, such as a portion of the heart, is formed from planar views, such as apical 4 chamber, apical 2 chamber and long axis views of the heart. Dynamic clips or videos of the parametric imaging provide temporally useful information to a user. The parametric imaging may include information determined from data at different locations or different times, such as strain, velocity, tissue displacement, velocity, or wall thickness. The ultrasound data may be responsive to contrast agents. The ultrasound data may be acquired with a three-dimensional scan.