There are a number of parameters that, taken together, characterize the performance of x-ray imaging systems. For computed radiography (CR) and direct or digital radiography (DR) systems, these parameters can include spatial resolution, noise, detector efficiency, exposure response, dark image signal level, and image artifacts.
With CR and DR systems, the modulation transfer function (MTF) of the imaging system is often used to characterize the system's contrast and spatial resolution. MTF is a 2D (two-dimensional) function of spatial frequency and is usually measured for both x and y directions of the acquired image. Techniques for MTF measurement may employ a target or phantom, an angular slit, or an angular edge. A resolution target may employ a commercially available bar-target or a star-pattern target, for example. Alternately, a custom-made bar target of varying resolution, such as described in U.S. Pat. No. 5,420,441 entitled “Automatic Technique for Calibrating A Storage Phosphor Reader” to Newman et al., can be used. MTF can be estimated either using a human observer to identify the blur frequency point of the target or calculating the visibility modulation. The error of the estimation depends on the orientation/resolution of the target and on subjective criteria of the observer. This assessment requires that the target be perfectly aligned in the x or y direction, and the severity of the error increases when the target resolution is close to the Nyquist frequency of the imaging system.
Still other methods use a narrow slit to measure the line spread function, followed by Fourier transformation to obtain the MTF of the imaging system in the slit transverse direction. The slit, much narrower than the sampling pitch (pixel size) of the imaging system and long enough to cover at least one pixel in the slit transverse direction, is oriented at a slight angle to the y or x direction in order to achieve super sampling for aliasing reduction. Although it can provide a MTF measurement, this method relies on a delicately made, expensive slit target. Yet another method, used with some digital radiography imaging systems, uses a sharp and straight edge target to measure the edge spread function of the imaging system. The MTF in the edge transverse direction can be obtained from the edge spread function by taking the Fourier transform of its derivative.
The noise of the imaging system determines the system low-contrast resolution as well as the x-ray detective efficiency etc. The noise characteristics can be described by the noise power spectrum (NPS) of the imaging system, which is also a 2D function of spatial frequency. To obtain the NPS, a flat image region is generally used for Fourier analysis. Because the system noise level is also x-ray exposure dependent, the NPS is often measured at a certain exposure level to facilitate comparison between imaging systems.
Detective efficiency at each point (u, v) is a secondary parameter of the imaging system that can be readily calculated from the system MTF and NPS:
      DQE    ⁡          (              u        ,        v            )        ∼                    MTF        2            ⁡              (                  u          ,          v                )                    NPS      ⁡              (                  u          ,          v                )            
Response accuracy, linearity, and uniformity are among the parameters for characterizing exposure response, the relationship between the output of the imaging system (image pixel values) and the incident x-ray exposure. Ideally, the exposure response or logarithmic exposure response should be linear and equal for all the pixels across the whole image. Exposure accuracy and linearity describe how accurately and linearly the output of the imaging system can track the incident x-ray exposure. Response uniformity describes the inter-pixel response variation. Each of these parameters is usually measured using the same x-ray spectrum, but at different exposure levels.
The dark image signal level determines the baseline noise of the imaging system and is independent of x-ray exposure. For a CR image, this corresponds to the signal level that would result from reading an erased phosphor screen, and for a DR image, this corresponds to the accumulated noise level before the x-ray exposure and during the readout process.
Artifacts in images are often unpredictable and may take the form of spots, lines and low-frequency modulations, either periodic or non-periodic. White and dark spot artifacts are usually caused by foreign dust/dirt residing on the image receptor or may be caused by bad pixels (within a DR detector). There are two major types of line artifacts, periodic (banding), and non-periodic (streaks). Either artifact, given enough magnitude, can result in objectionable image quality.
Other parameters for image quality that are unique to CR imaging include scan linearity and scan accuracy. For a CR imaging system, a laser beam provides raster scanning for reading the signal from the storage phosphor screen. Because there are moving optical devices, the image pixel size and the pixel aspect ratio can exhibit a degree of spatial variation. Scan accuracy gives a measure of the geometric integrity of the image.
Although there is no variable geometry related quality assurance issues for DR, since the imaging pixels are solid state elements manufactured on an evenly distributed grid, the locations of failed pixels and the individual pixel response correction are unique to DR and need to be characterized as part of the calibration process.
Because there are a number of diverse parameters to be measured for maintaining image quality in digital x-ray imaging systems, the image quality and Quality Assurance (QA) process presents a challenge for designing a quick, accurate, easy to use, and fully automatic method/procedure to conduct the measurements. In general, most of the proposed methods for QA testing rely either on visually reading image pixel values from a computer screen or on printing a test image on film and then using visual examination combined with film densitometer measurements. However, as noted in commonly assigned U.S. Pat. No. 6,409,383 entitled “Automated And Quantitative Method For Quality Assurance Of Digital Radiography Imaging Systems” to Wang et al., existing methods are limited by the quality of the film printer and densitometer. Other methods can be relatively difficult and time-consuming.
Automated methods, such as that disclosed in commonly assigned U.S. Pat. No. 5,420,441 entitled “Automated Technique For Calibrating A Storage Phosphor Reader” to Newman et al., often employ a patterned target or phantom. Specially made for this type of testing, the phantom is imaged in a test sequence. Then, analysis of the phantom image is performed to derive system performance parameters. These derived parameters can then be compared with corresponding pre-stored threshold values of a normal imaging system.
Radiography phantoms of various types have been disclosed. For example, commonly assigned U.S. Pat. No. 6,409,383 entitled “Automated and Quantitative Method for Quality Assurance of Digital Radiography Imaging Systems” to Wang et al., discloses a phantom formed from a rigid copper sheet having a pattern of milled or punched apertures. This phantom, designed for use with cassettes of various dimensions, is designed for general radiography systems having x-ray tube voltage in the 60-130 kVp range.
By comparison with general radiography systems, mammography systems operate at a lower x-ray tube voltage range. Typical x-ray tube voltage for mammography imaging is in the range of 25-35 kVp. As is well known, lower kVp x-rays are more easily attenuated than are rays in a higher range. Therefore, a phantom for a general radiography system, such as that disclosed in the '383 Wang et al. patent, is not suitable for use with the mammography system because the attenuation of the phantom is too high. Instead, a phantom designed for mammography systems must have substantial portions that are permeable to substantially lower kVp levels.
Phantoms especially designed for the lower exposure levels of mammography have been developed. For example, one known mammography phantom, the Artinis Contrast-Detail Phantom, from Artinis Medical Systems, Zetten, The Netherlands, employs an array of gold discs having increasing thicknesses and diameters. The deposited gold discs are distributed on an aluminum base and encased within a PMMA (Polymethyl methacrylate) cover. However, because this type of phantom uses precious metals deposited in dots of exacting thicknesses and diameters, it is difficult to fabricate and is very expensive.
One principle for phantom design relates to the overall response region of the imaging system detector. It is desired that the phantom be designed to show system sensitivity over the linear response region of the system under test. At the same time, such a phantom should be reasonably robust, so that it can be handled and used repeatedly for testing and calibrating the mammography system. In CR mammography imaging, the X-ray cassette that is used has a directional bias; the imaged tissue is intended to lie on the side of the cassette that is nearest to the chest wall of the patient. Thus, a phantom designed for such a system should be particularly arranged with the same directional bias to provide the optimal measurement conditions for breast tissue imaging. Lower cost solutions would be particularly advantageous.
Thus, it can be appreciated that there is a need for a phantom that is configured for the demands of the mammography system and is relatively robust and capable of supporting automated calibration of the mammography imaging apparatus.
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