Performance of a new star-bar phantom designed for MTF calculations in x-ray imaging systems

PURPOSE

A new phantom, designed and manufactured for modulation transfer function (MTF) calculations is presented in this work. The phantom has a star-bar pattern and is manufactured in stainless steel. Modulation transfer function determinations are carried out with the new phantom and with an edge phantom to compare their performance and to compare them with previous theoretical predictions.

METHODS

The phantoms are imaged in an x-ray imaging system using different beam qualities and different entrance air KERMA. Methods, previously developed for synthetic images and simulations, are adapted to real measurements, solving practical implementation issues.

RESULTS

In the case of the star-bar, in order to obtain optimal MTF determinations it is necessary to accurately determine the center of the pattern. Also, to avoid underestimates in MTF calculations, the length in pixels of each of the scanning circumferences must be an integer multiple of the number of cycles in the pattern. Both methods, star-bar and edge, give similar mean values of the MTF in all cases analyzed. Also, the dependence with frequency of the experimental MTF standard deviation (SD) agrees with the theoretical expressions presented in previous works. In this regard, the precision is better for the star-bar method than for the edge and differences in precision between both methods are higher for the lowest beam quality.

CONCLUSIONS

The star-bar phantom can be used for MTF determinations with the advantage of having an improved precision. However, precision is reduced when the radiation quality increases. This fact suggests that, for the highest beam qualities, materials with an attenuation coefficient greater than that of steel should be used to manufacture the phantom.

Use of a computer simulator to investigate optimized tube voltage for chest imaging of average patients with a digital radiography (DR) imaging system

Objective:

The aim of this study was to investigate via computer simulation a proposed improvement to clinical practice by deriving an optimized tube voltage (kVp) range for digital radiography (DR) chest imaging.

Methods:

A digitally reconstructed radiograph algorithm was used which was capable of simulating DR chest radiographs containing clinically relevant anatomy. Five experienced image evaluators graded clinical image criteria, i.e. overall quality, rib, lung, hilar, spine, diaphragm and lung nodule in images of 20 patients at tube voltages across the diagnostic energy range. These criteria were scored against corresponding images of the same patient reconstructed at a specific reference kVp. Evaluators were blinded to kVp. Evaluator score for each criterion was modelled with a linear mixed effects algorithm and compared with the score for the reference image.

Results:

Score was dependent on tube voltage and image criteria in a statistically significant manner for both. Overall quality, hilar, diaphragm and spine criteria performed poorly at low and high tube voltages, peaking at 80–100 kVp. Lung and lung nodule demonstrated little variation. Rib demonstrated superiority at low kVp.

Conclusion:

A virtual clinical trial has been performed with simulated chest DR images. Results indicate mid-range tube voltages of 80–100 kVp are optimum for average adults.

Advances in knowledge:

There are currently no specific recommendations for optimized tube voltage parameters for DR chest imaging. This study, validated with images containing realistic anatomical noise, has investigated and recommended an optimal tube voltage range.

A method to incorporate the effect of beam quality on image noise in a digitally reconstructed radiograph (DRR) based computer simulation for optimisation of digital radiography

The use of computer simulated digital x-radiographs for optimisation purposes has become widespread in recent years. To make these optimisation investigations effective, it is vital simulated radiographs contain accurate anatomical and system noise. Computer algorithms that simulate radiographs based solely on the incident detector x-ray intensity ('dose') have been reported extensively in the literature. However, while it has been established for digital mammography that x-ray beam quality is an important factor when modelling noise in simulated images there are no such studies for diagnostic imaging of the chest, abdomen and pelvis. This study investigates the influence of beam quality on image noise in a digital radiography (DR) imaging system, and incorporates these effects into a digitally reconstructed radiograph (DRR) computer simulator. Image noise was measured on a real DR imaging system as a function of dose (absorbed energy) over a range of clinically relevant beam qualities. Simulated 'absorbed energy' and 'beam quality' DRRs were then created for each patient and tube voltage under investigation. Simulated noise images, corrected for dose and beam quality, were subsequently produced from the absorbed energy and beam quality DRRs, using the measured noise, absorbed energy and beam quality relationships. The noise images were superimposed onto the noiseless absorbed energy DRRs to create the final images. Signal-to-noise measurements in simulated chest, abdomen and spine images were within 10% of the corresponding measurements in real images. This compares favourably to our previous algorithm where images corrected for dose only were all within 20%.

Detection and quantification of coronary calcium from dual energy chest x-rays: Phantom feasibility study

PURPOSE

We have demonstrated the ability to identify coronary calcium, a reliable biomarker of coronary artery disease, using nongated, 2-shot, dual energy (DE) chest x-ray imaging. Here we will use digital simulations, backed up by measurements, to characterize DE calcium signals and the role of potential confounds such as beam hardening, x-ray scatter, cardiac motion, and pulmonary artery pulsation. For the DE calcium signal, we will consider quantification, as compared to CT calcium score, and visualization.

METHODS

We created stylized and anatomical digital 3D phantoms including heart, lung, coronary calcium, spine, ribs, pulmonary artery, and adipose. We simulated high and low kVp x-ray acquisitions with x-ray spectra, energy dependent attenuation, scatter, ideal detector, and automatic exposure control (AEC). Phantoms allowed us to vary adipose thickness, cardiac motion, etc. We used specialized dual energy coronary calcium (DECC) processing that includes corrections for scatter and beam hardening.

RESULTS

Beam hardening over a wide range of adipose thickness (0-30 cm) reduced the change in intensity of a coronary artery calcification (ΔI_CAC ) by < 3% in DECC images. Scatter correction errors of ±50% affected the calcium signal (ΔI_CAC ) in DECC images ±9%. If a simulated pulmonary artery fills with blood between exposures, it can give rise to a residual signal in DECC images, explaining pulmonary artery visibility in some clinical images. Residual misregistration can be mostly compensated by integrating signals in an enlarged region encompassing registration artifacts. DECC calcium score compared favorably to CT mass and volume scores over a number of phantom perturbations.

CONCLUSION

Simulations indicate that proper DECC processing can faithfully recover coronary calcium signals. Beam hardening, errors in scatter estimation, cardiac motion, calcium residual misregistration etc., are all manageable. Simulations are valuable as we continue to optimize DE coronary calcium image processing and quantitative analysis.

A GPU Simulation Tool for Training and Optimisation in 2D Digital X-Ray Imaging

Conventional radiology is performed by means of digital detectors, with various types of technology and different performance in terms of efficiency and image quality. Following the arrival of a new digital detector in a radiology department, all the staff involved should adapt the procedure parameters to the properties of the detector, in order to achieve an optimal result in terms of correct diagnostic information and minimum radiation risks for the patient. The aim of this study was to develop and validate a software capable of simulating a digital X-ray imaging system, using graphics processing unit computing. All radiological image components were implemented in this application: an X-ray tube with primary beam, a virtual patient, noise, scatter radiation, a grid and a digital detector. Three different digital detectors (two digital radiography and a computed radiography systems) were implemented. In order to validate the software, we carried out a quantitative comparison of geometrical and anthropomorphic phantom simulated images with those acquired. In terms of average pixel values, the maximum differences were below 15%, while the noise values were in agreement with a maximum difference of 20%. The relative trends of contrast to noise ratio versus beam energy and intensity were well simulated. Total calculation times were below 3 seconds for clinical images with pixel size of actual dimensions less than 0.2 mm. The application proved to be efficient and realistic. Short calculation times and the accuracy of the results obtained make this software a useful tool for training operators and dose optimisation studies.