X-ray imaging optimization using virtual phantoms and computerized observer modelling

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.

VOXSI: A voxelized single- and dual-energy CT scenario generator for quantitative imaging

Background and purpose

Dedicated CT simulation models have the potential to investigate several acquisition, reconstruction, or post-processing parameters without giving any radiation dose to patients. A software program was developed for the simulation and the analysis of single-energy and dual-energy CT images. Simulation and analysis functionalities of the software are described.

Materials and methods

In the software, named VOXSI (VOXelized CT SImulator), the X-ray source, user specified simulation geometry, CT setup and the detector energy response can be varied. CT image reconstructions can be performed with an implementation of the ASTRA toolbox. In the DECT post processing toolkit, GUI tools are provided to calculate effective atomic number, relative electron density, pseudo-monoenergetic images, and material map images. Quantitative CT number validation, based on a RMI 467 tissue characterization phantom model, was performed between experimental and simulated CT scans at three different X-ray tube potentials (80, 120, and 140 kVp) with a third generation CT scanner.

Results

Overall, a good agreement was found for the mean CT numbers of the RMI 467 inserts. For all energies, the maximum difference in CT numbers between experimental and simulated data was below 17 HU for the soft tissues and below 48 HU for the osseous tissues.

Conclusion

The software’s simulation algorithm showed a good agreement between the CT measurements and CT simulations of the RMI 467 phantom at different energies. The capabilities of the software are demonstrated by an elaborated dual-energy CT research example.

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%.

Development of a high resolution voxelised head phantom for medical physics applications

Computational anthropomorphic phantoms have become an important investigation tool for medical imaging and dosimetry for radiotherapy and radiation protection. The development of computational phantoms with realistic anatomical features contribute significantly to the development of novel methods in medical physics. For many applications, it is desirable that such computational phantoms have a real-world physical counterpart in order to verify the obtained results. In this work, we report the development of a voxelised phantom, the HIGH_RES_HEAD, modelling a paediatric head based on the commercial phantom 715-HN (CIRS). HIGH_RES_HEAD is unique for its anatomical details and high spatial resolution (0.18×0.18mm² pixel size). The development of such a phantom was required to investigate the performance of a new proton computed tomography (pCT) system, in terms of detector technology and image reconstruction algorithms. The HIGH_RES_HEAD was used in an ad-hoc Geant4 simulation modelling the pCT system. The simulation application was previously validated with respect to experimental results. When compared to a standard spatial resolution voxelised phantom of the same paediatric head, it was shown that in pCT reconstruction studies, the use of the HIGH_RES_HEAD translates into a reduction from 2% to 0.7% of the average relative stopping power difference between experimental and simulated results thus improving the overall quality of the head phantom simulation. The HIGH_RES_HEAD can also be used for other medical physics applications such as treatment planning studies. A second version of the voxelised phantom was created that contains a prototypic base of skull tumour and surrounding organs at risk.

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.

Exact confidence intervals for channelized Hotelling observer performance in image quality studies

Task-based assessments of image quality constitute a rigorous, principled approach to the evaluation of imaging system performance. To conduct such assessments, it has been recognized that mathematical model observers are very useful, particularly for purposes of imaging system development and optimization. One type of model observer that has been widely applied in the medical imaging community is the channelized Hotelling observer (CHO), which is well-suited to known-location discrimination tasks. In the present work, we address the need for reliable confidence interval estimators of CHO performance. Specifically, we show that the bias associated with point estimates of CHO performance can be overcome by using confidence intervals proposed by Reiser for the Mahalanobis distance. In addition, we find that these intervals are well-defined with theoretically-exact coverage probabilities, which is a new result not proved by Reiser. The confidence intervals are tested with Monte Carlo simulation and demonstrated with two examples comparing X-ray CT reconstruction strategies. Moreover, commonly-used training/testing approaches are discussed and compared to the exact confidence intervals. MATLAB software implementing the estimators described in this work is publicly available at http://code.google.com/p/iqmodelo/.

Implementation of a channelized Hotelling observer model to assess image quality of x-ray angiography systems

Evaluation of flat-panel angiography equipment through conventional image quality metrics is limited by the scope of standard spatial-domain image quality metric(s), such as contrast-to-noise ratio and spatial resolution, or by restricted access to appropriate data to calculate Fourier domain measurements, such as modulation transfer function, noise power spectrum, and detective quantum efficiency. Observer models have been shown capable of overcoming these limitations and are able to comprehensively evaluate medical-imaging systems. We present a spatial domain-based channelized Hotelling observer model to calculate the detectability index (DI) of our different sized disks and compare the performance of different imaging conditions and angiography systems. When appropriate, changes in DIs were compared to expectations based on the classical Rose model of signal detection to assess linearity of the model with quantum signal-to-noise ratio (SNR) theory. For these experiments, the estimated uncertainty of the DIs was less than 3%, allowing for precise comparison of imaging systems or conditions. For most experimental variables, DI changes were linear with expectations based on quantum SNR theory. DIs calculated for the smallest objects demonstrated nonlinearity with quantum SNR theory due to system blur. Two angiography systems with different detector element sizes were shown to perform similarly across the majority of the detection tasks.

An exponential growth of computational phantom research in radiation protection, imaging, and radiotherapy: a review of the fifty-year history

Radiation dose calculation using models of the human anatomy has been a subject of great interest to radiation protection, medical imaging, and radiotherapy. However, early pioneers of this field did not foresee the exponential growth of research activity as observed today. This review article walks the reader through the history of the research and development in this field of study which started some 50 years ago. This review identifies a clear progression of computational phantom complexity which can be denoted by three distinct generations. The first generation of stylized phantoms, representing a grouping of less than dozen models, was initially developed in the 1960s at Oak Ridge National Laboratory to calculate internal doses from nuclear medicine procedures. Despite their anatomical simplicity, these computational phantoms were the best tools available at the time for internal/external dosimetry, image evaluation, and treatment dose evaluations. A second generation of a large number of voxelized phantoms arose rapidly in the late 1980s as a result of the increased availability of tomographic medical imaging and computers. Surprisingly, the last decade saw the emergence of the third generation of phantoms which are based on advanced geometries called boundary representation (BREP) in the form of Non-Uniform Rational B-Splines (NURBS) or polygonal meshes. This new class of phantoms now consists of over 287 models including those used for non-ionizing radiation applications. This review article aims to provide the reader with a general understanding of how the field of computational phantoms came about and the technical challenges it faced at different times. This goal is achieved by defining basic geometry modeling techniques and by analyzing selected phantoms in terms of geometrical features and dosimetric problems to be solved. The rich historical information is summarized in four tables that are aided by highlights in the text on how some of the most well-known phantoms were developed and used in practice. Some of the information covered in this review has not been previously reported, for example, the CAM and CAF phantoms developed in 1970s for space radiation applications. The author also clarifies confusion about 'population-average' prospective dosimetry needed for radiological protection under the current ICRP radiation protection system and 'individualized' retrospective dosimetry often performed for medical physics studies. To illustrate the impact of computational phantoms, a section of this article is devoted to examples from the author's own research group. Finally the author explains an unexpected finding during the course of preparing for this article that the phantoms from the past 50 years followed a pattern of exponential growth. The review ends on a brief discussion of future research needs (a supplementary file '3DPhantoms.pdf' to figure 15 is available for download that will allow a reader to interactively visualize the phantoms in 3D).