An ultrasonic diagnostic imaging system has a user control by which a user positions the user's selection of a heart chamber border in relation to two myocardial boundaries identified by a deformable heart model. The user's border is positioned by a single degree of freedom control which positions the border as a function of a single user-determined value. This overcomes the vagaries of machine-drawn borders and their mixed acceptance by clinicians, who can now create repeatably-drawn borders and exchange the control value for use by others to obtain the same results.

A method for delineating a ventricle from MRI data relating to the heart of a patient, the method comprising: a) providing a contrast-enhanced cine MRI dataset; b) providing one or more additional MRI datasets; c) segmenting one or more features on the additional MRI dataset or datasets; d) mapping the segmented features to the contrast-enhanced cine MRI dataset; and e) using the segmented features as mapped in step d) to assist segmentation of the ventricle on the contrast-enhanced cine MRI dataset.
A corresponding device and computer program are also disclosed.

The present invention relates to the field of medical monitoring, and in particular non-contact monitoring of one or more physiological parameters in a region of a patient during surgery. Systems, methods, and computer readable media are described for generating a pulsation field and/or a pulsation strength field of a region of interest (ROI) in a patient across a field of view of an image capture device, such as a video camera. The pulsation field and/or the pulsation strength field can be generated from changes in light intensities and/or colors of pixels in a video sequence captured by the image capture device. The pulsation field and/or the pulsation strength field can be combined with indocyanine green (ICG) information regarding ICG dye injected into the patient to identify sites where blood flow has decreased and/or ceased and that are at risk of hypoxia.

A time sequenced series of optical images of a patient is obtained at a rate faster than cardiac frequency, wherein the time sequenced series of images capture one or more physical properties of intrinsic contrast. A cross-correland signal from the patient is obtained. A cross-correlated wavelet transform analysis is applied to the time sequenced series of optical images to yield a spatiotemporal representation of cardiac frequency phenomena. The cross-correlated wavelet transform analysis comprises performing a wavelet transform on the time-sequenced series of optical images to obtain a wavelet transformed signal, cross-correlating the wavelet transformed signal with the cross-correland signal to obtain a cross-correlated signal, filtering the cross-correlated signal at cardiac frequency to obtain a filtered signal, and performing an inverse wavelet transform on the filtered signal to obtain a spatiotemporal representation of the time sequenced series of optical images. Images of the cardiac frequency phenomena are generated.

A multi-modality phantom is provided. The multi-modality phantom includes a container and an insert. The container defines an exterior that is separated from an interior space and designed to receive a tissue-mimicking medium for an ultrasound imaging process. The container further includes at least one access port formed in the container to perform the ultrasound imaging process of the interior space. The insert can be dimensioned to be selectively arranged within the interior space of the container. The insert includes imaging features arranged to simulate an environment and constructed to yield simultaneous imaging results when performing the ultrasound imaging process and at least one non-ultrasound imaging process.

A method for selecting one or more targets for non-invasively treating a cardiac arrhythmia in a patient includes receiving a mapping associated with the patient's heart and generating a segmented model of the mapping associated with the patient's heart. The segmented model divides the mapping into a plurality of segments. The method includes identifying one or more abnormality in the segmented model of the mapping associated with the patient's heart, determining which segment or segments of the plurality of segments include the identified one or more abnormality, and selecting a target for non-invasive treatment of the cardiac arrhythmia based on the determined segment or segments of the plurality of segments that include the identified one or more abnormality.

In a method of segmenting a medical image, a segmentation of the medical image is displayed (102) to a user, the segmentation comprising a contour representing a feature in the medical image. A user input is then received (104), the user input indicting a correction to the contour in the segmentation of the medical image. A shape constraint is determined (106) from the contour and the indicated correction to the contour and the shape constraint is provided (108) as an input parameter to a segmentation model to perform a new segmentation of the medical image.

A method includes calculating a first medial-axis tree graph of a volume of an organ of a patient in a first computerized anatomical map of the volume, acquired at a first time. A second medial-axis tree graph is calculated, of a volume of the organ of the patient in a second computerized anatomical map of the volume, acquired at a second time that is different from the first time. A deviation is detected and estimated, between the first and second tree-graphs. Using the estimated deviation, the first and second medial-axis tree graphs are registered with one another. Using the registered first and second tree graphs, the first and second computerized anatomical maps are combined.

A system for patient cardiac imaging and tissue modeling. The system includes a patient imaging device that can acquire patient cardiac imaging data. A processor is configured to receive the cardiac imaging data. A user interface and display allow a user to interact with the cardiac imaging data. The processor includes fat identification software conducting operations to interact with a trained learning network to identify fat tissue in the cardiac imaging data and to map fat tissue onto a three-dimensional model of the heart. A preferred system uses an ultrasound imaging device as the patient imaging device. Another preferred system uses an MRI or CT image device as the patient imaging device.

A medical information processing apparatus according to an embodiment includes processing circuitry. The processing circuitry obtains image data rendering a blood vessel of a patient. The processing circuitry performs a fluid analysis on the obtained image data and calculates an index value related to a blood flow in the blood vessel with respect to each of a plurality of positions in the blood vessel. With respect to the index values to be calculated, the processing circuitry selects a position in which a first value is to be obtained from among the plurality of positions or selects a value serving as the first value from among the index values exhibited in positions. The processing circuitry causes a display to display the first value in a predetermined display region thereof used for displaying the first value.