Ultrasound RF Signal Processing by Target Region for Image Quality
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Solution Overview
Problem
Existing ultrasound diagnostic apparatuses struggle to enhance image quality considering both the depth of the subject and the specific characteristics of various objects within the imaging region, such as organs and blood vessels, as current image quality improvement processing is based solely on signal amount changes, which may not be suitable for each object.
Innovation Solution
An ultrasound diagnostic apparatus that specifies a target region corresponding to an object in the subject, applying normal signal processing outside the region and specific signal processing inside the region, tailored to the object's characteristics, using a learning device or user input to define the target region and execute appropriate signal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If image quality improvement processing is performed based only on signal amount change, then processing simplicity is maintained, but image quality for specific objects cannot be optimized
Solution Approach 1:
The imaging region is divided into multiple target regions corresponding to different objects (e.g., gallbladder, pancreas, liver). Each target region is processed independently with object-specific signal processing parameters, allowing optimization for each object type while maintaining overall system simplicity through automated region segmentation and classification.
Solution Approach 2:
Different signal processing parameters are applied to different target regions based on object characteristics. For example, visibility enhancement parameters are used for gallbladder lumen detection, while deep sensitivity parameters are used for pancreas imaging. This local customization optimizes image quality for each object without requiring complex manual adjustment across the entire image.
2Manufacturing precision
If different signal processing is applied to each target region, then image quality for specific objects is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary automated segmentation and classification of target regions before signal processing. By pre-identifying objects and their types in the imaging region, the system prepares object-specific processing parameters in advance, eliminating the need for complex real-time decision-making during signal processing and reducing overall device complexity.
Solution Approach 2:
The ultrasound diagnostic apparatus automatically performs target region specification, object classification, and signal processing parameter selection without requiring manual intervention. The system self-adjusts processing parameters based on detected objects, reducing operational complexity while maintaining high image quality for specific objects.
3Ease of operation
If normal signal processing is applied uniformly across all regions, then device operation is simple, but visibility and sensitivity for specific objects are insufficient
Solution Approach 1:
The signal processing parameters are dynamically adjusted based on the detected object type and target region. The system automatically transitions between different processing modes (e.g., visibility enhancement for gallbladder, deep sensitivity for pancreas) without requiring manual reconfiguration, maintaining operational simplicity while achieving object-specific optimization.
Solution Approach 2:
The system changes signal processing parameters according to the specific object being imaged. Different gain settings, filtering parameters, and enhancement algorithms are applied based on object characteristics, automatically adjusting parameters to optimize visibility and sensitivity for each target object while keeping the user interface simple.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances ultrasound image quality by considering both the depth and the specific characteristics of objects within the imaging region, resulting in improved image quality for each object, such as enhanced visibility or sensitivity as required.
Implementation Method 1
transmits and receives ultrasound waves to and from a subject
Data Source
AI summary
A target region specifying unit specifies a target region corresponding to an object in a subject in a data space of an RF signal, by analyzing ultrasound data, which is at least one of the RF signal transmitted from a transmission/reception unit, a coordinate transformation signal transmitted from an image formation unit, or an ultrasound image (B-mode image) transmitted from the image formation unit. A signal processing unit executes normal signal processing, which is signal processing according to a depth of the subject, with respect to the RF signal outside the target region (that is, RF signal in a non-target region) and executes specific signal processing different from the normal signal processing with respect to the RF signal inside the target region. The image formation unit forms the ultrasound image based on the RF signal subjected to the signal processing by the signal processing unit.