Image quality based x-ray dose control in cardiac imaging

An automated closed-loop dose control system balances the radiation dose delivered to patients and the quality of images produced in cardiac x-ray imaging systems. Using computer simulations, this study compared two designs of automatic x-ray dose control in terms of the radiation dose and quality of images produced. The first design, commonly in x-ray systems today, maintained a constant dose rate at the image receptor. The second design maintained a constant image quality in the output images. A computer model represented patients as a polymethylmetacrylate phantom (which has similar x-ray attenuation to soft tissue), containing a detail representative of an artery filled with contrast medium. The model predicted the entrance surface dose to the phantom and contrast to noise ratio of the detail as an index of image quality. Results showed that for the constant dose control system, phantom dose increased substantially with phantom size (x5 increase between 20cm and 30 cm thick phantom), yet the image quality decreased by 43% for the same thicknesses. For the constant quality control, phantom dose increased at a greater rate with phantom thickness (>x10 increase between 20 cm and 30 cm phantom). Image quality based dose control could tailor the x-ray output to just achieve the quality required, which would reduce dose to patients where the current dose control produces images of too high quality. However, maintaining higher levels of image quality for large patients would result in a significant dose increase over current practice

Machine vision image quality measurement in cardiac x-ray imaging

The purpose of this work is to report on a machine vision approach for the automated measurement of x-ray image contrast of coronary arteries filled with iodine contrast media during interventional cardiac procedures. A machine vision algorithm was developed that creates a binary mask of the principal vessels of the coronary artery tree by thresholding a standard deviation map of the direction image of the cardiac scene derived using a Frangi filter. Using the mask, average contrast is calculated by tting a Gaussian model to the greyscale profile orthogonal to the vessel centre line at a number of points along the vessel. The algorithm was applied to sections of single image frames from 30 left and 30 right coronary artery image sequences from different patients. Manual measurements of average contrast were also performed on the same images. A Bland-Altman analysis indicates good agreement between the two methods with 95% confidence intervals -0.046 to +0.048 with a mean bias of 0.001. The machine vision algorithm has the potential of providing real-time context sensitive information so that radiographic imaging control parameters could be adjusted on the basis of clinically relevant image content

How much noise can be added in cardiac X-ray imaging without loss in perceived image quality?

Dynamic X-ray imaging systems are used for interventional cardiac procedures to treat coronary heart disease. X-ray settings are controlled automatically by specially-designed X-ray dose control mechanisms whose role is to ensure an adequate level of image quality is maintained with an acceptable radiation dose to the patient. Current commonplace dose control designs quantify image quality by performing a simple technical measurement directly from the image. However, the utility of cardiac X-ray images is in their interpretation by a cardiologist during an interventional procedure, rather than in a technical measurement. With the long term goal of devising a clinically-relevant image quality metric for an intelligent dose control system, we aim to investigate the relationship of image noise with clinical professionals’ perception of dynamic image sequences. Computer-generated noise was added, in incremental amounts, to angiograms of five different patients selected to represent the range of adult cardiac patient sizes. A two alternative forced choice staircase experiment was used to determine the amount of noise which can be added to a patient image sequences without changing image quality as perceived by clinical professionals. Twenty-five viewing sessions (five for each patient) were completed by thirteen observers. Results demonstrated scope to increase the noise of cardiac X-ray images by up to 21% ± 8% before it is noticeable by clinical professionals. This indicates a potential for 21% radiation dose reduction since X-ray image noise and radiation dose are directly related; this would be beneficial to both patients and personnel

X-ray system simulation software tools for radiology and radiography education

Objectives: To develop x-ray simulation software tools to support delivery of radiological science education for a range of learning environments and audiences including individual study, lectures, and tutorials. Methods: Two software tools were developed; one simulated x-ray production for a simple two dimensional radiographic system geometry comprising an x-ray source, beam filter, test object and detector. The other simulated the acquisition and display of two dimensional radiographic images of complex three dimensional objects using a ray casting algorithm through three dimensional mesh objects. Both tools were intended to be simple to use, produce results accurate enough to be useful for educational purposes, and have an acceptable simulation time on modest computer hardware. The radiographic factors and acquisition geometry could be altered in both tools via their graphical user interfaces. A comparison of radiographic contrast measurements of the simulators to a real system was performed. Results: The contrast output of the simulators had excellent agreement with measured results. The software simulators were deployed to 120 computers on campus. Conclusions: The software tools developed are easy-to-use, clearly demonstrate important x-ray physics and imaging principles, are accessible within a standard University setting and could be used to enhance the teaching of x-ray physics to undergraduate students

Does the use of additional X-ray beam filtration during cine acquisition reduce clinical image quality and effective dose in cardiac interventional imaging?

The impact of spectral filtration in digital ('cine') acquisition was investigated using a flat panel cardiac interventional X-ray imaging system. A 0.1-mm copper (Cu) and 1.0-mm aluminium (Al) filter added to the standard acquisition mode created the filtered mode for comparison. Image sequences of 35 patients were acquired, a double-blind subjective image quality assessment was completed and dose-area product (DAP) rates were calculated. Entrance surface dose (ESD) and effective dose (E) rates were determined for 20- and 30-cm phantoms. Phantom ESD fell by 28 and 41 % and E by 1 and 0.7 %, for the 20- and 30-cm phantoms, respectively, when using the filtration. Patient DAP rates fell by 43 % with no statistically significant difference in clinical image quality. Adding 0.1-mm Cu and 1.0-mm Al filtration in acquisition substantially reduces patient ESD and DAP, with no significant change in E or clinical image quality

Artifacts Found During Quality Assurance Testing of Computed Radiography and Digital Radiography Detectors

A series of artifact images, obtained over 5 years of performance testing, of both computed radiography (CR) and integrated digital radiographic X-ray imaging detectors are presented. The images presented are all either flat field or test object images and show artifacts previously either undescribed in the existing literature or meriting further comment. The artifacts described are caused by incorrect flat field corrections, a failing amplifier, damaged detector lines affecting their neighbors, lost information between neighboring detector tiles, image retention, delamination of a detector, poor setup of mechanical movements in CR, suckers damaging a CR plate, inappropriate use of grid suppression software, inappropriate use of a low pass spatial frequency filter, and unsharp masking filters. The causes and significance of the artifacts are explained and categorized as software or hardware related. Actions taken to correct the artifacts are described and explained. This work will help physicists, radiographers, and radiologists identify various image quality problems and shows that quality assurance is useful in identifying artifacts