Echocardiography Lung Tissue Identification via Center Frequency Estimation
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Solution Overview
Problem
Current ultrasound technologies face challenges in distinguishing between heart and lung tissue, particularly for inexperienced users, due to the high acoustic impedance mismatch and absorption by calcified ribs, leading to compromised image quality and reliance on user expertise for optimal probe placement.
Innovation Solution
An anatomically intelligent echocardiography system that uses ultrasound pulses to estimate center frequencies sub-volume by sub-volume, allowing for automatic identification of the spatial boundary between heart and lung tissue, and provides dynamic user guidance through a display of the identified boundary for optimal probe placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ultrasound is used for cardiac imaging, then soft tissue imaging capability is improved, but image quality deteriorates due to acoustic impedance mismatch with lung tissue and calcified ribs
Solution Approach 1:
The system performs preliminary identification of lung tissue boundaries and rib locations before acquiring the final cardiac image. The probe is guided to positions that anticipate and avoid obstacles, allowing the sonographer to place the probe in optimal locations that prevent lung and rib interference from degrading image quality
Solution Approach 2:
The system provides real-time feedback to the sonographer by displaying identified lung tissue boundaries and rib locations overlaid on the ultrasound image. This feedback loop allows the sonographer to adjust probe position and orientation based on the displayed information, continuously optimizing image quality by avoiding areas with acoustic interference
2Ease of operation
If manual probe placement is used, then flexibility in positioning is improved, but operator expertise requirement increases
Solution Approach 1:
The system performs automatic lung tissue identification and boundary detection without requiring manual intervention. The processing unit automatically analyzes the ultrasound signals, identifies lung tissue regions, and displays boundaries to guide probe placement, allowing the system to serve itself in the complex task of anatomical recognition
Solution Approach 2:
The system replaces the need for human expert judgment and manual probe adjustment with an automated computer-based system. The processing unit uses signal analysis and pattern recognition algorithms to identify anatomical structures and guide probe positioning, substituting mechanical/operator-based positioning with an intelligent automated guidance system
3Loss of energy
If probe placement avoids calcified ribs, then acoustic penetration is improved, but positioning precision deteriorates due to anatomical variability
Solution Approach 1:
The system changes the approach from fixed anatomical landmark-based positioning to dynamic, real-time identification of acoustic pathways. By continuously analyzing ultrasound signal characteristics and identifying regions with good acoustic penetration, the system adapts to individual anatomical variations in rib position and lung boundaries, finding optimal probe placement for each patient's unique anatomy
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
The system enables inexperienced users to achieve high-quality ultrasound images by automatically distinguishing between heart and lung tissue, improving image acquisition and reducing the reliance on user expertise, thus facilitating better cardiac imaging.
Implementation Method 1
an ultrasound system that issues ultrasound pulses to a volume and receives echo data
Implementation Method 2
issues ultrasound pulses to a volume and receives echo data
Implementation Method 3
Based on the received data, center frequency is estimated sub-volume by sub-volume so as to allow heart tissue to be distinguished from lung tissue
Data Source
AI summary
Issuance of ultrasound pulses to a volume and receiving echo data is followed by estimating, based on the received data, center frequency subvolume-by-subvolume. Distinguishing between heart and lung tissue occurs based on a result of the estimating, and may include automatically identifying a spatial boundary (332) between the heart and lung tissue (324, 328), or a user display of center frequencies that allows for visual distinguishing. The issuance can include issuing, ray line by ray line, pair-wise identical, and/or pair-wise mutually inverted, ultrasound pulses. Center frequency calculations may be made for incremental sampling locations of respective imaging depth along each of the A-lines generated from echo data of the rays. The distinguishing might entail averaging center frequencies for locations along an A-line, and applying a central frequency threshold to the average. The leftmost of the qualifying A-lines, i.e., that meet the threshold, may determine the spatial boundary in the current imaging plane.