Ultrasound Needle Detection via Fourier Transform ROI Analysis
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Solution Overview
Problem
Current ultrasound imaging systems face challenges in accurately identifying a needle in ultrasound images without using additional or specialized equipment, often resulting in false positives due to tissue structures resembling needles.
Innovation Solution
A method involving edge detection, straight line detection, and Fourier transform analysis to identify a region of interest corresponding to a needle signature, allowing for precise identification and highlighting of the needle within the ultrasound image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image analysis techniques are used to identify needle-like structures, then needle detection capability is improved, but false positive rate increases due to tissue structures resembling needles
Solution Approach 1:
The patent applies local quality by analyzing specific regions of interest (ROIs) around detected straight lines rather than the entire image. The system identifies candidate needle regions based on straight line detection, then performs localized Fourier transform analysis only on these ROIs to determine if they exhibit needle-like frequency characteristics, thereby reducing false positives from other tissue structures.
Solution Approach 2:
The patent transitions from spatial domain analysis to frequency domain analysis by applying Fourier transform to the ROI. This dimensional change allows the system to identify needle-like structures based on their characteristic frequency patterns rather than relying solely on spatial appearance, which helps distinguish needles from other linear tissue structures.
2Illumination intensity
If ultrasound beam steering and imaging parameter modification are used to enhance reflective structures, then needle visibility is improved, but imaging artifacts increase as all perpendicular reflective structures are highlighted
Solution Approach 1:
The patent extracts and isolates the needle structure from the rest of the image by detecting straight lines and defining ROIs around them. The Fourier transform analysis is then applied only to these extracted ROIs, allowing the system to enhance needle visibility without enhancing other reflective structures that would create artifacts.
Solution Approach 2:
The patent changes the analysis domain from spatial to frequency domain through Fourier transform. This parameter change allows selective identification of needle-like structures based on their frequency characteristics, enabling enhancement of needle visibility without the need for beam steering that would otherwise highlight all perpendicular reflective structures.
3Measurement precision
If magnetic tracking is used to provide needle trajectory information, then needle identification accuracy is improved, but device complexity increases due to additional bulky hardware and specialized equipment
Solution Approach 1:
The patent replaces mechanical tracking systems (magnetic tracking with bulky hardware) with a computational image analysis system. By using Fourier transform analysis of ultrasound image data, the system can identify needle trajectory and position without requiring any additional mechanical or magnetic tracking equipment.
Solution Approach 2:
The ultrasound imaging system itself performs needle identification and tracking using its own captured image data. The system analyzes the ultrasound images it already acquires, applying edge detection, straight line detection, and Fourier transform analysis to identify the needle, thereby serving its own needle detection needs without external specialized equipment.
Data Source
AI summary
The present embodiments relate generally to ultrasound imaging methods, systems, and apparatus that identify an imaged needle in an ultrasound image. The embodiments involve performing edge detection on the ultrasound image to generate an edge-detected data set corresponding to the ultrasound image; performing a straight line detection operation on the edge-detected data set to detect one or more straight lines; based on the one or more straight lines detected in the edge-detected data set, identifying a region of interest (ROI) on the ultrasound image; transforming the ROI of the ultrasound image from a spatial domain to an analysis domain, to generate a transformed ROI; analyzing the transformed ROI to determine whether the transformed ROI, in the analysis domain, corresponds to a needle signature; and if the transformed ROI corresponds to the needle signature, identifying the detected one or more straight lines as the imaged needle.