Amber J. Gislason-Lee

Dose optimization in cardiac x-ray imaging.

PURPOSE The aim of this research was to optimize x-ray image quality to dose ratios in the cardiac catheterization laboratory. This study examined independently the effects of peak x-ray tube voltage (kVp), copper (Cu), and gadolinium (Gd) x-ray beam filtration on the image quality to radiation dose balance for adult patient sizes. METHODS Image sequences of polymethyl methacrylate (PMMA) phantoms representing two adult patient sizes were captured using a modern flat panel detector based x-ray imaging system. Tin and copper test details were used to simulate iodine-based contrast medium and stents∕guide wires respectively, which are used in clinical procedures. Noise measurement for a flat field image and test detail contrast were used to calculate the contrast to noise ratio (CNR). Entrance surface dose (ESD) and effective dose measurements were obtained to calculate the figure of merit (FOM), CNR(2)∕dose. This FOM determined the dose efficiency of x-ray spectra investigated. Images were captured with 0.0, 0.1, 0.25, 0.4, and 0.9 mm Cu filtration and with a range of gadolinium oxysulphide (Gd2O2S) filtration. RESULTS Optimum x-ray spectra were the same for the tin and copper test details. Lower peak tube voltages were generally favored. For the 20 cm phantom, using 2 Lanex Fast Back Gd2O2S screens as x-ray filtration at 65 kVp provided the highest FOM considering ESD and effective dose. Considering ESD, this FOM was only marginally larger than that from using 0.4 mm Cu at 65 kVp. For the 30 cm phantom, using 0.25 mm copper filtration at 80 kVp was most optimal; considering effective dose the FOM was highest with no filtration at 65 kVp. CONCLUSIONS These settings, adjusted for x-ray tube loading limits and clinically acceptable image quality, should provide a useful option for optimizing patient dose to image quality in cardiac x-ray imaging. The same optimal x-ray beam spectra were found for both the tin and copper details, suggesting that iodine contrast based imaging and visualization of interventional devices could potentially be optimized for dose using similar x-ray beam spectra.

Selecting stimuli parameters for video quality assessment studies based on perceptual similarity distances

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.

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

Abstract. 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.

Methods for the analysis of ordinal response data in medical image quality assessment

The assessment of image quality in medical imaging often requires observers to rate images for some metric or detectability task. These subjective results are used in optimization, radiation dose reduction or system comparison studies and may be compared to objective measures from a computer vision algorithm performing the same task. One popular scoring approach is to use a Likert scale, then assign consecutive numbers to the categories. The mean of these response values is then taken and used for comparison with the objective or second subjective response. Agreement is often assessed using correlation coefficients. We highlight a number of weaknesses in this common approach, including inappropriate analyses of ordinal data and the inability to properly account for correlations caused by repeated images or observers. We suggest alternative data collection and analysis techniques such as amendments to the scale and multilevel proportional odds models. We detail the suitability of each approach depending upon the data structure and demonstrate each method using a medical imaging example. Whilst others have raised some of these issues, we evaluated the entire study from data collection to analysis, suggested sources for software and further reading, and provided a checklist plus flowchart for use with any ordinal data. We hope that raised awareness of the limitations of the current approaches will encourage greater method consideration and the utilization of a more appropriate analysis. More accurate comparisons between measures in medical imaging will lead to a more robust contribution to the imaging literature and ultimately improved patient care.

Context sensitive cardiac x-ray imaging: a machine vision approach to x-ray dose control

Abstract. 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.

Comprehensive assessment of patient image quality and radiation dose in latest generation cardiac x-ray equipment for percutaneous coronary interventions.

Abstract. 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.

Can Image Enhancement Allow Radiation Dose to Be Reduced Whilst Maintaining the Perceived Diagnostic Image Quality Required for Coronary Angiography

OBJECTIVE The aim of this research was to quantify the reduction in radiation dose facilitated by image processing alone for percutaneous coronary intervention (PCI) patient angiograms, without reducing the perceived image quality required to confidently make a diagnosis. METHODS Incremental amounts of image noise were added to five PCI angiograms, simulating the angiogram as having been acquired at corresponding lower dose levels (10-89% dose reduction). 16 observers with relevant experience scored the image quality of these angiograms in 3 states-with no image processing and with 2 different modern image processing algorithms applied. These algorithms are used on state-of-the-art and previous generation cardiac interventional X-ray systems. Ordinal regression allowing for random effects and the delta method were used to quantify the dose reduction possible by the processing algorithms, for equivalent image quality scores. RESULTS Observers rated the quality of the images processed with the state-of-the-art and previous generation image processing with a 24.9% and 15.6% dose reduction, respectively, as equivalent in quality to the unenhanced images. The dose reduction facilitated by the state-of-the-art image processing relative to previous generation processing was 10.3%. CONCLUSION Results demonstrate that statistically significant dose reduction can be facilitated with no loss in perceived image quality using modern image enhancement; the most recent processing algorithm was more effective in preserving image quality at lower doses. Advances in knowledge: Image enhancement was shown to maintain perceived image quality in coronary angiography at a reduced level of radiation dose using computer software to produce synthetic images from real angiograms simulating a reduction in dose.

Noise estimation in cardiac x-ray imaging: a machine vision approach

We propose a method to automatically parameterize noise in cardiac x-ray image sequences. The aim was to provide context-sensitive imaging information for use in regulating dose control feedback systems that relates to the experience of human observers. The algorithm locates and measures noise contained in areas of approximately equal signal level. A single noise metric is derived from the dominant noise components based on their magnitude and spatial location in relation to clinically relevant structures. The output of the algorithm was compared to noise and clinical acceptability ratings from 28 observers viewing 40 different cardiac x-ray imaging sequences. Results show good agreement and that the algorithm has the potential to augment existing control strategies to deliver x-ray dose to the patient on an individual basis.

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 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.

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 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.