Abstract: Methods and systems are provided for automatic optimization for ultrasound medical imaging. In one approach, velocity values are unwrapped to avoid aliasing artifacts. Multi-dimensional phase unwrapping is applied to the velocity data. The unwrapped velocity information is used to optimize one or both of the velocity scale (e.g., pulse repetition frequency) and the imaging frequency. For optimizing the scale setting, the distribution of unwrapped velocities from a systolic time period of the heart cycle are used to identify the pulse repetition frequency. For optimizing the imaging frequency, a correlation as a function of depth shows the penetration depth for a given imaging frequency. In a dependent or independent approach, one or more thresholds for velocity or energy in flow imaging are adaptively selected as a function of an amount of clutter. Velocity or other energy information in addition to the clutter information may be used for selecting the thresholds.

Abstract: Phase unwrapping is applied to velocity data to remove aliasing artifacts. Phase unwrapping is applied on multidimensional regions of velocity data. The unwrapped velocity image is displayed. The displayed image may have high sensitivity to slow motion but may also avoid aliasing of fast motion despite being undersampled.

Abstract: Anatomical information is identified from a medical image and/or used for controlling a medical diagnostic imaging system, such as an ultrasound system. To identify anatomical information from a medical image, a processor applies a multi-class classifier. The anatomical information is used to set an imaging parameter of the medical imaging system. The setting or identification may be used in combination or separately.

Abstract: Disclosed are an apparatus and method of adjusting gain of an ultrasound system 100. In particular, subject matter is disclosed for receiving an indication 102 of a rate of change in motion of an object 106, and adjusting a gain based 108, at least in part, on said rate of change in motion, where the gain is adjusted at least partially corresponding to the rate of change in motion of the object 106.

Abstract: Disclosed are a system and method of selecting one or more operational parameters of an ultrasonic imaging system. In particular, methods and means are disclosed for automatically or semi-automatically determining a best operating frequency, or for determining whether a system should operate in a fundamental imaging mode or a harmonic imaging mode.

Abstract: Medical image processing is adaptively optimized in response to selective types of changes or motion. Adaptive optimization is applied in response to limited types of motion or at a controlled time. For example, one type of change, such as change due to heart motion or breathing motion, is distinguished from a different type of change, such as change due to repositioning of an imaging plane within a patient. Imaging parameters are adaptively optimized in response to changes of one type independent of or with minimized contribution from changes of the different type. For example, change due to repositioning of the image plane is detected while accounting for heart motion or breathing motion. Imaging parameters are adaptively optimized once the change due to anatomical motion is removed or accounted for and after detecting a change in an imaging plane position. Any of various adaptive optimizations may be responsive to the identification of one type of change from another type of change.

Abstract: Methods are provided for automatic setting of parameters for contrast agent quantification. Various processes may improve quantification. For example, for consistency in contrast agent quantification, a gain or other setting of an ultrasound system is automatically determined in response to destruction of the contrast agent or at the initiation of the contrast agent quantification procedure. Automatic setting of an adaptive gain provides equalized image intensity for each repetition of a contrast agent quantification procedure based on a same triggering event, the destruction of contrast agent. By synchronizing the adaptive setting algorithms with contrast agent destruction, similar base line information is provided for each iteration of a contrast agent quantification procedure. As another example, the contrast agent gain setting treats acoustic signals representing tissue or other non-contrast agent structure as noise, mapping the tissue values to a substantially constant low value within the dynamic range.

Abstract: Methods and systems are provided for automatic optimization for ultrasound medical imaging. In one approach, velocity values are unwrapped to avoid aliasing artifacts. Multi-dimensional phase unwrapping is applied to the velocity data. The unwrapped velocity information is used to optimize one or both of the velocity scale (e.g., pulse repetition frequency) and the imaging frequency. For optimizing the scale setting, the distribution of unwrapped velocities from a systolic time period of the heart cycle are used to identify the pulse repetition frequency. For optimizing the imaging frequency, a correlation as a function of depth shows the penetration depth for a given imaging frequency. In a dependent or independent approach, one or more thresholds for velocity or energy in flow imaging are adaptively selected as a function of an amount of clutter. Velocity or other energy information in addition to the clutter information may be used for selecting the thresholds.

Abstract: Medical image processing is adaptively optimized in response to selective types of changes or motion. Adaptive optimization is applied in response to limited types of motion or at a controlled time. For example, one type of change, such as change due to heart motion or breathing motion, is distinguished from a different type of change, such as change due to repositioning of an imaging plane within a patient. Imaging parameters are adaptively optimized in response to changes of one type independent of or with minimized contribution from changes of the different type. For example, change due to repositioning of the image plane is detected while accounting for heart motion or breathing motion. Imaging parameters are adaptively optimized once the change due to anatomical motion is removed or accounted for and after detecting a change in an imaging plane position. Any of various adaptive optimizations may be responsive to the identification of one type of change from another type of change.

Abstract: An SNR-adaptive method for processing a plurality of post-detection medical diagnostic ultrasonic images determines a cross-correlation parameter from corresponding multi-dimensional regions of first and second post-detection ultrasonic images, and then uses the cross-correlation parameter to suppress electronic noise in ultrasonic image processing. In some cases the multi-dimensional region of the first and second images are registered prior to cross-correlation to compensate for relative movement between the transducer and the imaged tissue between the first and second images.

Abstract: A medical ultrasonic imaging system uses an adaptive multi-dimensional back-end mapping stage to eliminate loss of information in the back-end, minimize any back-end quantization noise, reduce or eliminate electronic noise, and map the local average of soft tissue to a target display value throughout the image. The system uses spatial variance to identify regions of the image corresponding substantially to soft tissue and a noise frame acquired with the transmitters turned off to determine the mean system noise level. The system then uses the mean noise level and the identified regions of soft tissue to both locally and adaptively set various back-end mapping stages, including the gain and dynamic range.

Abstract: A medical diagnostic ultrasonic imaging system analyzes receive signals generated by the system to adaptively set the interline delay and/or the transmit power to optimize frame rate while reducing or eliminating the wraparound artifact associated with transmit events that are too closely spaced in time for currently prevailing imaging conditions.

Abstract: A medical ultrasonic imaging system uses an adaptive multi-dimensional back-end mapping stage to eliminate loss of information in the back-end, minimize any back-end quantization noise, reduce or eliminate electronic noise, and map the local average of soft tissue to a target display value throughout the image. The system uses spatial variance to identify regions of the image corresponding substantially to soft tissue and a noise frame acquired with the transmitters turned off to determine the mean system noise level. The system then uses the mean noise level and the identified regions of soft tissue to both locally and adaptively set various back-end mapping stages, including the gain and dynamic range.