Dose reduction remains an important goal in interventional x-ray. We propose an image quality (IQ) measure called the visibility overshoot index. Given a patient image and a specified clinical task, the index quantifies the maximum acceptable dose reduction. The dose control system can then use this information to deliver the minimum dose necessary for detection of clinical signals, reducing unnecessary radiation exposure. We developed an experimental visual model to estimate signal detectability as a function of image features such as noise and signal contrast. The model is used to find a feature’s threshold–the maximum change in noise or signal contrast where signal detectability remains possible. An automated algorithm measures the magnitudes of these features on a frame. Visibility overshoot is expressed in terms of the image features: the noise overshoot and contrast overshoot indices are the ratio of the threshold to measured noise/contrast. The indices demonstrate good agreement with detector dose, channelized hotelling observer results, and clinicians’ judgments. In our study of a cylindrical object phantom acquired at seven dose levels, we found that the noise overshoot index is linearly related to the square root of detector dose and the CHO detectability index, with Pearson correlation 0.995–1.0 for signals 1-4 mm diameter. For interventional cardiology and neurology sequences acquired at standard and 25–30% dose, the index and clinicians rank IQ similarly. Results on the phantom suggest at least 15% dose reduction could be achieved in fluoroscopy mode. Our patient-specific IQ approach could bring additional dose savings to clinical practice.
This study aimed to determine whether a reduction in radiation dose was found for percutaneous coronary interventional (PCI) patients using a cardiac interventional x-ray system with state-of-the-art image enhancement and x-ray optimization, compared to the current generation x-ray system, and to determine the corresponding impact on clinical image quality. Patient procedure dose area product (DAP) and fluoroscopy duration of 131 PCI patient cases from each x-ray system were compared using a Wilcoxon test on median values. Significant reductions in patient dose (p≪0.001) were found for the new system with no significant change in fluoroscopy duration (p=0.2); procedure DAP reduced by 64%, fluoroscopy DAP by 51%, and “cine” acquisition DAP by 76%. The image quality of 15 patient angiograms from each x-ray system (30 total) was scored by 75 clinical professionals on a continuous scale for the ability to determine the presence and severity of stenotic lesions; image quality scores were analyzed using a two-sample t-test. Image quality was reduced by 9% (p≪0.01) for the new x-ray system. This demonstrates a substantial reduction in patient dose, from acquisition more than fluoroscopy imaging, with slightly reduced image quality, for the new x-ray system compared to the current generation system.
Cardiologists use x-ray image sequences of the moving heart acquired in real-time to diagnose and treat cardiac patients. The amount of radiation used is proportional to image quality; however, exposure to radiation is damaging to patients and personnel. The amount by which radiation dose can be reduced without compromising patient care was determined. For five patient image sequences, increments of computer-generated quantum noise (white + colored) were added to the images, frame by frame using pixel-to-pixel addition, to simulate corresponding increments of dose reduction. The noise adding software was calibrated for settings used in cardiac procedures, and validated using standard objective and subjective image quality measurements. The degraded images were viewed next to corresponding original (not degraded) images in a two-alternative-forced-choice staircase psychophysics experiment. Seven cardiologists and five radiographers selected their preferred image based on visualization of the coronary arteries. The point of subjective equality, i.e., level of degradation where the observer could not perceive a difference between the original and degraded images, was calculated; for all patients the median was 33%±15% dose reduction. This demonstrates that a 33%±15% increase in image noise is feasible without being perceived, indicating potential for 33%±15% dose reduction without compromising patient care.
Modern cardiac x-ray imaging systems regulate their radiation output based on the thickness of the patient to maintain an acceptable signal at the input of the x-ray detector. This approach does not account for the context of the examination or the content of the image displayed. We have developed a machine vision algorithm that detects iodine-filled blood vessels and fits an idealized vessel model with the key parameters of contrast, diameter, and linear attenuation coefficient. The spatio-temporal distribution of the linear attenuation coefficient samples, when appropriately arranged, can be described by a simple linear relationship, despite the complexity of scene information. The algorithm was tested on static anthropomorphic chest phantom images under different radiographic factors and 60 dynamic clinical image sequences. It was found to be robust and sensitive to changes in vessel contrast resulting from variations in system parameters. The machine vision algorithm has the potential of extracting real-time context sensitive information that may be used for augmenting existing dose control strategies.
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 20 cm 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.
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.
This work presents a methodology to optimize the selection of multiple parameter levels of an image acquisition, degradation, or post-processing process applied to stimuli intended to be used in a subjective image or video quality assessment (QA) study. It is known that processing parameters (e.g. compression bit-rate) or technical quality measures (e.g. peak signal-to-noise ratio, PSNR) are often non-linearly related to human quality judgment, and the model of either relationship may not be known in advance. Using these approaches to select parameter levels may lead to an inaccurate estimate of the relationship between the parameter and subjective quality judgments – the system’s quality model. To overcome this, we propose a method for modeling the relationship between parameter levels and perceived quality distances using a paired comparison parameter selection procedure in which subjects judge the perceived similarity in quality. Our goal is to enable the selection of evenly sampled parameter levels within the considered quality range for use in a subjective QA study. This approach is tested on two applications: (1) selection of compression levels for laparoscopic surgery video QA study, and (2) selection of dose levels for an interventional X-ray QA study. Subjective scores, obtained from the follow-up single stimulus QA experiments conducted with expert subjects who evaluated the selected bit-rates and dose levels, were roughly equidistant in the perceptual quality space - as intended. These results suggest that a similarity judgment task can help select parameter values corresponding to desired subjective quality levels.
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 fitting 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.
We investigated the potential for digital tomosynthesis (DT) to reduce pediatric x-ray dose while maintaining
image quality. We utilized the DT feature (VolumeRadTM) on the GE DefiniumTM 8000 flat panel system installed in the
Winnipeg Children's Hospital. Facial bones, cervical spine, thoracic spine, and knee of children aged 5, 10, and 15 years
were represented by acrylic phantoms for DT dose measurements. Effective dose was estimated for DT and for
corresponding digital radiography (DR) and computed tomography (CT) patient image sets. Anthropomorphic phantoms
of selected body parts were imaged by DR, DT, and CT. Pediatric radiologists rated visualization of selected anatomic
features in these images. Dose and image quality comparisons between DR, DT, and CT determined the usefulness of
tomosynthesis for pediatric imaging.
CT effective dose was highest; total DR effective dose was not always lowest - depending how many
projections were in the DR image set. For the cervical spine, DT dose was close to and occasionally lower than DR
dose. Expert radiologists rated visibility of the central facial complex in a skull phantom as better than DR and
comparable to CT. Digital tomosynthesis has a significantly lower dose than CT. This study has demonstrated DT
shows promise to replace CT for some facial bones and spinal diagnoses. Other clinical applications will be evaluated in
the future.
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