Method for scanning for second object on or within first object starts by receiving information on first object and second object. Task lists are generated that include at least one task action based on information on first object and second object. Based on task lists, beamer is then signaled to generate and send first signal to first probe unit to perform first beam firing. Receiver processes first data signal from first probe unit that is then analyzed to determine if first object is identified using processed first data signal. Upon determination that first object is identified, based on task list, beamer is signaled to generate and send second signal to second probe unit to perform second beam firing. Receiver processes second data signal from second probe unit that is then analyzed to determine if second object is identified using processed second data signal. Other embodiments are described.

Ultrasound imaging systems including transducer probes having wireless tags, and associated systems and methods, are described herein. For example, the wireless tags can store supplemental data about the transducer probes, and the ultrasound system can include a base unit configured to wirelessly communicate with nearby ones of the wireless tags to receive the supplemental data. The base unit can be further configured to display the transducer probes that are nearby. In some embodiments, the operator can filter or sort the displayed nearby transducer probes based on the supplemental data to identify a particular one of the nearby transducer devices that has one or more desired attributes.

Method for scanning for second object on or within first object starts by receiving information on first object and second object. Task lists are generated that include at least one task action based on information on first object and second object. Based on task lists, beamer is then signaled to generate and send first signal to first probe unit to perform first beam firing. Receiver processes first data signal from first probe unit that is then analyzed to determine if first object is identified using processed first data signal. Upon determination that first object is identified, based on task list, beamer is signaled to generate and send second signal to second probe unit to perform second beam firing. Receiver processes second data signal from second probe unit that is then analyzed to determine if second object is identified using processed second data signal. Other embodiments are described.

Method for scanning for second object on or within first object starts by receiving information on first object and second object. Task lists are generated that include at least one task action based on information on first object and second object. Based on task lists, beamer is then signaled to generate and send first signal to first probe unit to perform first beam firing. Receiver processes first data signal from first probe unit that is then analyzed to determine if first object is identified using processed first data signal. Upon determination that first object is identified, based on task list, beamer is signaled to generate and send second signal to second probe unit to perform second beam firing. Receiver processes second data signal from second probe unit that is then analyzed to determine if second object is identified using processed second data signal. Other embodiments are described.

The present disclosure describes ultrasound imaging systems and methods for ultrasonically inspecting biological tissue. An ultrasound imaging system according to the present disclosure may be configured to automatically apply tissue-specific imaging parameter settings (312, 314) based upon the automatic identification of the type of tissue being scanned. Tissue type identification (315) may be performed automatically for the images in the live image stream (304) and thus adjustments to the imaging settings may be applied automatically by using a neural network (320) and thus dynamically during the exam obviating the need for the sonographer to manually switch presets or adjust the imaging settings when moving to different portion of the anatomy.

An information providing method which is implementable by using an ultrasound apparatus includes obtaining ultrasound image data which relates to an object; displaying, on a first area of a screen, a gain setup window for setting a gain of the obtained ultrasound image data; receiving a gain which is set by a user on the gain setup window; and displaying, on a second area of the screen, an ultrasound image of the object to which the set gain is applied.

An apparatus and method for generating high quality, high frame rate images in a handheld or hand-carried ultrasonic imaging machine. The apparatus includes an image enhancer to reduce speckle noise and improve the compressibility of the resulting image. This approach reduces the required hardware and power consumption to satisfy the physical space, power, and limited processing power of a handheld probe and enables high quality images to be transmitted efficiently over low-bandwidth connections.

Methods and systems for storing and processing a plurality of fractions of imaging data thereby minimizing the amount of cache memory necessary while controlling data loss and the resulting image quality.

Rather than trying to automate what an experienced user does, rules designed for processor implementation are used for color flow imaging optimization by an image processor of an ultrasound scanner. By determining a characteristic of a scanned target, a priori information is provided. This a priori information, such as a size of a primary target, is used to select the optimization to be used. Different types of optimization may be used for different characteristics of the primary target. The values for settings may be different for different characteristics.

A computing system includes a memory configured to store instructions, and one or more processors configured to execute the instructions to receive data relating to an image or a user, determine a feature from the data, identify a user preference from a user profile, obtain a model, and segment the image based on the feature, the user preference, and the model. The model is generated by determining a historical feature from historical data as an input, determining a desired output, obtaining a preliminary model based on the input and the desired output, determining an actual output of the preliminary model, determining error criteria between the actual output and the desired output, and generating the model by updating the preliminary model based on the error criteria.