C-Arm Lung Tomography with AI Enhancement for Low-Radiation Imaging
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Solution Overview
Problem
CT scanning devices are expensive, require licensed operators, and deliver high radiation doses, while C-arm-mounted fluoroscopic imaging devices provide low-quality reconstructed images inadequate for diagnosing small or low-density lesions.
Innovation Solution
A method using a C-arm device to obtain fluoroscopic images, reconstructing a tomographic image, and enhancing it with a trained machine learning model to achieve a CT-like image quality, suitable for diagnosing small lesions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a C-arm-mounted fluoroscopic imaging device is used to obtain images, then the radiation dose to the patient is reduced, but the image quality is insufficient for diagnosing small or low-density lesions
Solution Approach 1:
A trained machine learning model serves as an intermediary between the fluoroscopic images and the diagnostic imaging process. The model enhances the low-quality fluoroscopic images by learning from paired datasets (fluoroscopic images and corresponding CT images), enabling the system to produce enhanced images that maintain diagnostic quality while using the lower-radiation fluoroscopic modality
Solution Approach 2:
The system changes the parameter of image quality by applying machine learning-based enhancement algorithms. These algorithms modify the enhanced image parameters (resolution, contrast, noise reduction) to achieve diagnostic quality comparable to CT scans while maintaining the low radiation dose characteristics of fluoroscopy
2Measurement precision
If a CT scanning device is used to obtain detailed internal images, then the image quality is sufficient for diagnosing small lesions, but the radiation dose to the patient increases significantly
Solution Approach 1:
The system creates a copy of the diagnostic imaging capability by training a machine learning model on paired datasets of fluoroscopic and CT images. The model learns to generate CT-quality images from fluoroscopic inputs, effectively copying the diagnostic information from high-radiation CT scans without requiring actual CT exposure
Solution Approach 2:
The system replaces the expensive, high-radiation CT scanning process with a cheaper, lower-radiation fluoroscopic approach enhanced by machine learning. The enhanced fluoroscopic images serve as a disposable alternative to CT scans for diagnostic purposes, eliminating the need for repeated high-radiation exposure
3Device complexity
If a C-arm device is used instead of a CT scanner, then the equipment cost and operational requirements are reduced, but the reconstructed tomographic image quality is not homogeneous and insufficient for clinical diagnosis
Solution Approach 1:
The system replaces the complex mechanical CT scanning system with a simpler C-arm fluoroscopic device enhanced by machine learning algorithms. The machine learning model compensates for the inferior reconstruction capabilities of the C-arm device by learning optimal image enhancement strategies from training data, substituting computational intelligence for mechanical complexity
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
Generates CT-like images of comparable quality to standard CT scans, enabling early detection of lesions without the need for expensive CT scanners or high radiation, using a C-arm device and machine learning enhancement techniques.
Implementation Method 1
C-arm-mounted fluoroscopic imaging devices, such as X-ray imaging devices
Implementation Method 2
enhancing it with a trained machine learning model to achieve a CT-like image quality
Data Source
AI summary
A method including receiving, from a C-arm device, a plurality of fluoroscopic images of a lung, wherein each fluoroscopic image is obtained with the C-arm device positioned at a particular pose of a plurality of poses traversed by the C-arm device while the C-arm device is moved through a range of motion including a range of rotation, the range of rotation encompassing a sweep angle between 45 degrees and 120 degrees; generating an enhanced tomographic image of the lung, by utilizing: a trained machine learning model and the plurality of fluoroscopic images; and outputting a representation of the enhanced tomographic image, wherein, when tested by a method in which: the lung includes a lesion smaller than 30 millimeters, and the representation is an axial slice showing a boundary of the lesion, the lesion has a contrast-to-noise value of at least 5 as compared to a background of the representation.