KEYWORDS: Interference (communication), X-ray imaging, X-rays, Quantization, Image quality, Image processing, Signal to noise ratio, Chest, Signal processing, Medical imaging
This work aims at defining an information-theoretic quality assessment technique for cardiovascular X-ray
images, using a full-reference scheme (relying on averaging a sequence to obtain a noiseless reference). With the
growth of advanced signal processing in medical imaging, such an approach will enable objective comparisons
of the quality of processed images. A concept for describing the quality of an image is to express it in terms
of its information capacity. Shannon has derived this capacity for noisy channel coding. However, for X-ray
images, the noise is signal-dependent and non-additive, so that Shannon's theorem is not directly applicable.
To overcome this complication, we exploit the fact that any invertible mapping on a signal does not change
its information content. We show that it is possible to transform the images in such a way that the Shannon
theorem can be applied. A general method for calculating such a transformation is used, given a known relation
between signal mean and noise standard deviation. After making the noise signal-independent, it is possible to
assess the information content of an image and to calculate an overall quality metric (e.g. information capacity)
which includes the effects of sharpness, contrast and noise. We have applied this method on phantom images
under different acquisition conditions and computed the information capacity for those images. We aim to show
that the results of this assessment are consistent with variations in noise, contrast and sharpness, introduced by
system settings and image processing.
KEYWORDS: 3D modeling, Modulation transfer functions, X-rays, X-ray imaging, 3D image processing, 3D image reconstruction, Image quality, 3D metrology, Imaging systems, Sensors
Nowadays, 2D X-ray systems are used more and more for 3-dimensional rotational X-ray imaging (3D-RX) or volume imaging, such as 3D rotational angiography. However, it is not evident that the application of settings for optimal 2D images also guarantee optimal conditions for 3D-RX reconstruction results. In particular the search for a good compromise between patient dose and IQ may lead to different results in case of 3D imaging. For this purpose we developed an additional 3D-RX module for our full-scale image quality & patient dose (IQ&PD) simulation model, with specific calculations of patient dose under rotational conditions, and contrast, sharpness and noise of 3D images.
The complete X-ray system from X-ray tube up to and including the display device is modelled in separate blocks for each distinguishable component or process. The model acts as a tool for X-ray system design, image quality optimisation and patient dose reduction. The model supports the decomposition of system level requirements, and takes inherently care of the prerequisite mutual coherence between component requirements. The short calculation times enable comprehensive multi-parameter optimisation studies.
The 3D-RX IQ&PD performance is validated by comparing calculation results with actual measurements performed on volume images acquired with a state-of-the-art 3D-RX system. The measurements include RXDI dose index, signal and contrast based on Hounsfield units (H and ΔH), modulation transfer function (MTF), noise variance (σ2) and contrast-to-noise ratio (CNR).
Further we developed a new 3D contrast-delta (3D-CΔ) phantom with details of varying size and contrast medium material and concentration. Simulation and measurement results show a significant correlation.
We have developed a full-scale image quality (IQ) simulation model as a tool for X-ray system design, image quality optimization and patient dose reduction. The IQ model supports the (de-)composition of system level requirements and simulates several types of automatic X-ray control technique. The model is implemented in LabVIEW. The X-ray system is modeled in distinguishable components and processes, which allows isolation of sub-systems and exclusion of devices. All relevant patient dose and IQ items such as contrast, sharpness, lag and noise are calculated and additionally combined in IQ "figures of merit" (FOM). Some characteristic application examples will be presented: In a general image magnification study we compare magnification techniques, such as geometric enlargement, image intensifier zooming and digital processing. In an optimization study we apply a new IQ FOM that contains not only imaging properties of the system, but also detail information in terms of material, size and thickness. Combining the IQ simulation model with a Pareto trade-off algorithm appears to be a promising optimization approach. In addition to the mentioned employment, the IQ simulation model is also suitable for comparison studies on the performance of flat detectors versus image intensifier television detectors, application related studies and fine tuning of specific settings and adjustments, design of test objects and development of measuring methods.
One of the issues in (alpha) -Si:H X-ray detectors is signal to noise ratio for low dose fluoroscopic applications. An optimized sensitivity of the X-ray detection system together with low and isotropic system noise characteristics are primary pre-conditions needed for maximum image quality. However, in spite of high DQE numbers of this Flat Detector technology in radiological and fluoroscopic application areas, a SNR for low dose fluoroscopy is found, which is inferior to that found with Image Intensifier-TV based systems. The problem area is a small dose range, producing gray levels just above absolute dark. Except for the dark level, these levels can (dependent on the application area) contain clinically relevant information. Since scatter affects the darker parts of the relevant image areas there will be noise in those areas, caused by X-ray quantum statistics and readout noise. The objective of the simulations is to investigate the influence of the various system noise components on the image quality. A level of system noise can be found where the subjective image quality is mainly determined by the X-ray quantum statistics and where the readout noise does not necessarily have to be invisible in totally dark parts. The simulation concerns a threshold contrast detail detectability (TCDD) observation test, where observers score discs of various diameter and absorption in an image sequence (being a fixed scene of the test object with (temporal) X-ray noise and system noise). The dynamic sequence is based upon total simulation, i.e. the test object as well as the X-ray noise and the system noise components were simulated. To verify the simulations also an image sequence was acquired on a Flat Detector system. The observations are done at various dose levels, with and without post processing to obtain noise reduction like it is used in clinical practice for this kind of system. The sequences are observed on a medical CRT display.
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