Dynamic CT Image Smoothing for Stroke Diagnosis
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Solution Overview
Problem
Computed tomography (CT) perfusion examinations face challenges such as reduced spatial resolution, partial-volume artifacts, low signal-to-noise ratio, and temporal sampling limitations, which affect the quality of dynamic image data sets and hinder accurate diagnosis.
Innovation Solution
A computer-implemented method that involves receiving dynamic CT image data, registering it, and applying smoothing filters like uniform, Gaussian, median, or anisotropic filters to enhance the signal-to-noise ratio and improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If CT perfusion examination is performed to obtain dynamic image data, then diagnostic capability for stroke is improved, but spatial resolution and image quality deteriorate compared to static data sets
Solution Approach 1:
The patent segments the dynamic CT perfusion data into multiple temporal phases (arterial phase, capillary phase, venous phase) and applies different smoothing filter strengths to each phase. This allows optimization for each specific diagnostic task while maintaining overall diagnostic capability across all phases.
Solution Approach 2:
The patent applies adaptive smoothing that varies locally across different regions of the image and different time points. Regions with high contrast agent concentration receive different smoothing treatment compared to low concentration regions, and temporal smoothing adapts to the specific phase of contrast enhancement.
2Object-affected harmful factors
If dose is minimized in CT perfusion examination, then patient safety is improved, but signal-to-noise ratio deteriorates
Solution Approach 1:
The patent converts the harmful noise introduced by low-dose imaging into a beneficial signal by applying temporal smoothing that distinguishes between random noise and true contrast enhancement patterns. The smoothing filter learns from the temporal dynamics to preserve true signal while suppressing noise.
Solution Approach 2:
The patent applies preprocessing steps including motion correction and artifact reduction before the main smoothing operation. This preliminary action prepares the data by reducing dominant noise sources, making the subsequent smoothing more effective at improving SNR without requiring additional dose.
3Adaptability or versatility
If temporal sampling rate is increased to improve dynamic visualization, then flow assessment capability is improved, but data complexity and processing requirements increase
Solution Approach 1:
The patent applies continuous temporal smoothing that operates on the entire time series of dynamic data, creating a continuous representation of contrast agent flow. This allows efficient processing of high-temporal-resolution data by treating it as a continuous signal rather than discrete snapshots, reducing computational complexity.
Solution Approach 2:
The patent implements adaptive smoothing parameters that dynamically adjust based on the local temporal frequency content and contrast agent concentration. This allows the system to handle varying data complexity across different time points and regions, maintaining flow assessment capability while optimizing processing efficiency.
4Reliability
If smoothing filter is applied to dynamic CT data, then signal-to-noise ratio is improved, but temporal detail and edge sharpness deteriorate
Solution Approach 1:
The patent applies different smoothing filter types and strengths to different regions and phases. Edge-preserving filters are used in regions with high gradient magnitude, while stronger smoothing is applied in homogeneous regions. The smoothing parameters are adapted locally based on the detected features and phase of contrast enhancement.
Solution Approach 2:
The patent uses dynamic smoothing parameters that change over time based on the phase of contrast enhancement. During arterial phase with rapid changes, less smoothing is applied to preserve temporal detail. During later phases with more stable contrast distribution, stronger smoothing is applied to improve SNR without losing critical information.
Data Source
AI summary
A data processing technique is provided. In one embodiment, a computer-implemented method includes receiving a set of dynamic computed tomography image data from a computed tomography imaging system, registering the image data, applying a smoothing filter to at least a selection of the registered image data, and outputting the results. The smoothing filter may be, for example, a uniform filter, a triangular filter, a Gaussian filter, a median filter, a percentile filter, a Kuwahara filter, an anisotropic filter, or any combination thereof. Additional methods, systems, and devices are also disclosed.