MODELLING ERRORS IN X-RAY FLUOROSCOPIC IMAGING SYSTEMS USING PHOTOGRAMMETRIC BUNDLE ADJUSTMENT WITH A DATA-DRIVEN SELF-CALIBRATION APPROACH

Abstract. X-ray imaging is a fundamental tool of routine clinical diagnosis. Fluoroscopic imaging can further acquire X-ray images at video frame rates, thus enabling non-invasive in-vivo motion studies of joints, gastrointestinal tract, etc. For both the qualitative and quantitative analysis of static and dynamic X-ray images, the data should be free of systematic biases. Besides precise fabrication of hardware, software-based calibration solutions are commonly used for modelling the distortions. In this primary research study, a robust photogrammetric bundle adjustment was used to model the projective geometry of two fluoroscopic X-ray imaging systems. However, instead of relying on an expert photogrammetrist’s knowledge and judgement to decide on a parametric model for describing the systematic errors, a self-tuning data-driven approach is used to model the complex non-linear distortion profile of the sensors. Quality control from the experiment showed that 0.06mm to 0.09mm 3D reconstruction accuracy was achievable post-calibration using merely 15 X-ray images. As part of the bundle adjustment, the location of the virtual fluoroscopic system relative to the target field can also be spatially resected with an RMSE between 3.10mm and 3.31mm.

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Performance of x-ray imaging systems with optical coupling for demagnification between scintillator and CCD readout

10.1117/12.193177 ◽

1994 ◽

Cited By ~ 1

Author(s):

Hans Roehrig ◽

Tong Yu ◽

William V. Schempp

Keyword(s):

Imaging Systems ◽

Optical Coupling ◽

X Ray ◽

X Ray Imaging

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Fluoroscopy-guided robotic biopsy intervention system

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-0020 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Yusuf Özbek ◽

Michael Vogele ◽

Christian Plattner ◽

Pedro Costa ◽

Mario Griesser ◽

...

Keyword(s):

Percutaneous Biopsy ◽

Practical Experience ◽

Imaging Systems ◽

Robotic Platform ◽

End Effector ◽

X Ray ◽

Needle Path ◽

Custom Made ◽

Fluoroscopic Imaging ◽

Movement Parameters

AbstractFluoroscopy-guided percutaneous biopsy interventions are mostly performed with traditional free-hand technique. The practical experience of the surgeon influences the duration of the intervention and the radiation exposure for patients and him-/herself. Especially when the placement of heavy and long instruments in double oblique angles is required, manual techniques reach their technical limitations very fast. The system presented herein automatizes the needle positioning using only two 2D scans while the robotic platform guides the intervention. These two images were used to plan the needle pathway and to estimate the pose of the robot using a custom-made end-effector with embedded registration fiducials. The estimated pose was subsequently used to transfer the planed needle path to the robot’s coordinate system and finally to compute the movement parameters in order to align the robot with this plan. To evaluate the system, two phantoms with 11 different targets on it were developed. The targets were punctured, and the application accuracy was measured quantitatively. The solution achieved sub-millimetric accuracy for needle placement (min. 0.23, max. 1.04 in mm). Our approach combines the advantages of fluoroscopic imaging and ensures automatic needle alignment with highly reduced X-ray radiation. The proposed system shows promising potential to be a guidance platform that is easy to combine with available fluoroscopic imaging systems and provides valuable help to the physician in more difficult interventions.

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Advanced laser-backlit grazing-incidence x-ray imaging systems for inertial confinement fusion research I Design

Applied Optics ◽

10.1364/ao.40.004570 ◽

2001 ◽

Vol 40 (25) ◽

pp. 4570 ◽

Cited By ~ 7

Author(s):

Guy R. Bennett

Keyword(s):

Inertial Confinement Fusion ◽

Grazing Incidence ◽

Imaging Systems ◽

Inertial Confinement ◽

Fusion Research ◽

X Ray ◽

X Ray Imaging ◽

Confinement Fusion

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Truncated Inception Net: COVID-19 Outbreak Screening using Chest X-rays

10.21203/rs.3.rs-20795/v1 ◽

2020 ◽

Cited By ~ 4

Author(s):

Dipayan Das ◽

KC Santosh ◽

Umapada Pal

Keyword(s):

Neural Network ◽

World Wide ◽

Imaging Techniques ◽

Imaging Systems ◽

Ct Scans ◽

X Rays ◽

X Ray ◽

X Ray Imaging ◽

Proposed Model ◽

Different Types

Abstract Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in less than a couple of months, and the infection, caused by SARS-CoV-2, is spreading at an unprecedented rate. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID- 19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using CXRs.

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Image Segmentation Techniques in Bone Structure Psychiatry

International Journal Of Engineering And Computer Science ◽

10.18535/ijecs/v9i07.4502 ◽

2020 ◽

Vol 9 (07) ◽

pp. 25102-25112

Author(s):

Ajayi Olayinka Adedoyin ◽

Olamide Timothy Tawose ◽

Olu Sunday Adetolaju

Keyword(s):

Image Segmentation ◽

Bone Structure ◽

Performance Comparison ◽

Imaging Systems ◽

Intrinsic Properties ◽

X Ray ◽

Practical Applications ◽

X Ray Imaging ◽

Segmentation Algorithms ◽

Bone Structures

Today, a large number of x-ray images are interpreted in hospitals and computer-aided system that can perform some intelligent task and analysis is needed in order to raise the accuracy and bring down the miss rate in hospitals, particularly when it comes to diagnosis of hairline fractures and fissures in bone joints. This research considered some segmentation techniques that have been used in the processing and analysis of medical images and a system design was proposed to efficiently compare these techniques. The designed system was tested successfully on a hand X-ray image which led to the proposal of simple techniques to eliminate intrinsic properties of x-ray imaging systems such as noise. The performance and accuracy of image segmentation techniques in bone structures were compared and these eliminated time wasting on the choice of image segmentation algorithms. Although there are several practical applications of image segmentation such as content-based image retrieval, machine vision, medical imaging, object detection, recognition tasks, etc., this study focuses on the performance comparison of several image segmentation techniques for medical X-ray images.

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