Siemens Medical Solutions Usa, Inc.

Acuson Corporation

Siemens Corporation

Kolo Medical, Ltd.

Constantine Simopoulos

John I Jackson

Dorin Comaniciu

Shaohua Kevin Zhou

Kutay F Ustuner

Danhua Zhao

Jin-hyeong Park

Joanne Otsuki

Bogdan Georgescu

Matthew N O'Donnell

Anming He Cai

Robert W Steins

Müge M Bakircioglu

Bhaskar S Ramamurthy

Albert Gee

Rickard C Loftman

Lewis J Thomas

James E Chomas

Jeffrey T Rahn

Kutay F &Uuml;st&uuml;ner

Feng Guo

Gustavo Henrique Monteiro De Barros Carneiro

Jin Hyeong Park

In some examples, a capacitive micromachined ultrasonic transducer (CMUT) array may include a plurality of CMUT elements arranged in a plurality of rows, each row including multiple CMUT elements. A bias voltage supply may be connected for supplying bias voltages to a first row, a second row, and a third row of the plurality of rows. In addition, a processor may be configured by executable instructions to control the bias voltages by applying a first bias voltage to the second row, and a second, different bias voltage to the first and third rows to configure the CMUT elements of the second row to at least one of transmit or receive ultrasonic energy with different efficiency than the CMUT elements of the first row and the third row. In some examples, individual contributions from different rows can be computed from different firing sequences for synthetic aperture beamformation in an elevation dimension.

CMUT signal separation with multi-level bias control

Automated image interpretation is provided with transducer position and/or orientation sensing. The current position of a transducer is used to determine the anatomy being imaged. Other reference information, such as (1) the previous position of the transducer and the known anatomy being imaged at that time or (2) a reference device at a known location, is used in the determination. The anatomy is determined based on position sensing or determined based on position sensing and other anatomy features, such as image analysis.

Automated image interpretation with transducer position or orientation sensing for medical ultrasound

Tissue information is equalized by adaptively controlling gain and mapping input data to output data based on the gain in three-dimensional medical diagnostic imaging. A hypersurface is fit in three spatial dimensions to tissue information in input data. The hypersurface is used to adjust the gain so that input values are mapped to output values with more uniform soft tissue levels.

Gain optimization of volume images for medical diagnostic ultrasonic imaging

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.

Automatic cardiac view classification of echocardiography

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.

Method and system for detection of deformable structures in medical images

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.

Automated view classification with echocardiographic data for gate localization or other purposes

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.

Ultrasonic imaging system

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. In yet another dependent or independent approach, displacement of an imaging plane or other change is detected and used to trigger an automatic update of an imaging parameter for a same or different mode.

Automatic optimization for ultrasound medical imaging

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.

Phase unwrapped velocity display for ultrasound medical imaging

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.

Medical diagnostic imaging optimization based on anatomy recognition

Disclosed are an apparatus and method of adjusting gain of an ultrasound system. In particular, subject matter is disclosed for receiving an indicationof a rate of change in motion of an object, and adjusting a gain based, 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

Ultrasound imaging system having motion adaptive gain

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. As yet another example, by segmenting out blood pools or other areas of contrast agent likely to have contrast agents even after destruction, the resulting gain is more likely sensitive to the detection of perfused contrast agents.

Methods for adaptively varying gain during ultrasound agent quantification

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.

Change detection for optimized medical imaging

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.

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San Francisco, CA, United States of America

Constantine Simopoulos