Purpose: The relevance of presampling modulation transfer function (MTF) measurements in digital mammography (DM) quality control (QC) is examined. Two studies are presented: a case study on the impact of a reduction in MTF on the technical image quality score and analysis of the robustness of routine QC MTF measurements.
Approach: In the first study, two needle computed radiography (CR) plates with identical sensitivities were used with differences in the 50% point of the MTF (fMTF0.5) larger than the limiting value in the European guidelines (>10 % change between successive measurements). Technical image quality was assessed via threshold gold thickness of the CDMAM phantom and threshold microcalcification diameter of the L1 structured phantom. For the second study, presampling MTF results from 595 half-yearly QC tests of 55 DM systems (16 types, six manufacturers) were analyzed for changes from the baseline value and changes in fMTF0.5 between successive tests.
Results: A reduction of 20% in fMTF0.5 of the two CR plates was observed. There was a tendency to a lower score for task-based metrics, but none were significant. Averaging over 55 systems, the absolute relative change in fMTF0.5 between consecutive tests (with 95% confidence interval) was 3% (2.5% to 3.4%). Analysis of the maximum relative change from baseline revealed changes of up to −10 % for one a-Se based system and −15 % for a group of CsI-based systems.
Conclusions: A limit of 10% is a relevant action level for investigation. If exceeded, then the impact on performance has to be verified with extra metrics.
This work examined the impact of the presampling Modulation Transfer Function (MTF) on detectability of lesion-like targets in digital mammography. Two needle CR plates (CR1 and CR2) with different MTF curves but identical detector response (sensitivity) were selected. The plates were characterized by MTF, normalized noise power spectrum (NNPS) and detective quantum efficiency (DQE). Three image quality phantoms were applied to study the impact of the difference in MTF: first, the CDMAM contrast-detail phantom to give gold thickness threshold (T); second, a 3D structured phantom with lesion models (calcifications and masses), evaluated via a 4-alternative forced-choice study to give threshold diameter (dtr) and third, a detectability index (d') from a 50 mm PMMA flat field image and an 0.2 mm Al contrast square. MTF coefficient of variation was ~1%, averaged up to 5 mm-1. At 5 mm-1, a significant 24% reduction in MTF was observed. The lower MTF caused a 12% reduction in NNPS for CR2 compared to CR1 (at detector air kerma 117 μGy). At 5 mm-1, there was a drop in DQE of 34% for CR2 compared to CR1. For the test objects, there was a trend to lower detectability for CR2 (lower MTF) for all but one parameter, however none of the changes were significant. The MTF is a sensitive and easily applied means of tracking changes in sharpness before these changes are uncovered using lesion simulating objects in test objects.
KEYWORDS: Modulation transfer functions, Chromium, Digital mammography, Computing systems, Gold, Photon counting, Signal to noise ratio, Polymethylmethacrylate, Chest, Radiology
The purpose was to find the correlation between a Figure of Merit (FoM) calculated from a new (simple) test object for Quality Control in digital mammography and CDMAM threshold thicknesses. The FoM included the signal difference to noise ratio, modulation transfer function of the complete system (including scatter and grid) and normalized noise power spectrum. The pre-programmed exposure settings for clinical work were used, as was done for the CDMAM
acquisitions. The FoM is calculated from 2 images only (an image from the QC test object and an image of a corresponding homogeneous plate imaged with the same exposure settings). This FoM was evaluated in frequencies that match with the diameters of the gold disks in the CDMAM phantom. Computerized CDMAM analysis uses 16 images
per system. The software program "cdcom" (www.euref.org) was used for the 4-AFC experiment. All matrices were averaged, smoothed with a Gaussian filter and psychometric curves were fitted through the correctly detected fractions to obtain the threshold thickness with a detectability of 62.5% for all diameters.
Images have been acquired on 10 different systems (2 computed radiography (CR) systems, 6 direct radiology (DR) systems and 2 photon counting systems).
The reproducibility of the QC metrics from images of the new phantom was assessed. The standard error on the mean of the FoM was for the highest frequency 8.1% for a CR system and 5.6% for a DR system. The main component in this error is due to the NNPS and the limited number of independent pixels used in this analysis.
Parameters calculated from both phantoms are sensitive to variation in mean glandular dose levels. Present results show a weak correlation (R2=0.60) between the FoM at 5lp/mm and CDMAM threshold values for the 0.1mm objects when all system data are pooled. If evaluated for separate systems, the correlation holds promise for automated, periodic performance evaluations of digital mammography systems with the simplified phantom.
The contributors to image noise of two computed radiography (CR) detector systems-a state-of-the-art and a wellchosen laboratory CR image plate-were studied by two different methods. Method 1 analyzes the image noise content of a series of images obtained at a wide range of different X-ray exposure levels. It uses a model to fit the observed exposure dependence of the normalized noise power spectrum (NNPS): It distinguishes between an NNPS component that is independent of the exposure level and mainly due to correlated noise, and an NNPS component which is inversely proportional to the exposure level and consists mainly of quantum noise. Method 2 analyzes several images taken at the same exposure level and distinguishes between correlated noise, which remains unchanged in repeated exposures, and uncorrelated noise which is different in each image. The results of the two methods allowed the relevant noise contributions in CR images to be quantitatively determined. The novel laboratory image plate showed a significant reduction of correlated noise with an accompanying increase in the DQE. The results also served to estimate a possible improvement of DQE if an appropriate flat field correction is made for these CR systems.
The purpose of this study is to describe a method that allows the calculation of a contrast-detail curve for a particular
system configuration using simulated micro calcifications into clinical mammograms.
We made use of simulated templates of micro calcifications and adjusted their x-ray transmission coefficients and
resolution to the properties of the mammographic system under consideration (4). We expressed the thickness of the
simulated micro calcifications in terms of Al equivalence.
In a first step we validated that the thickness of very small Al particles with well known size and thickness can be
calculated from their x-ray transmission characteristics at a particular X-ray beam energy.
Then, micro calcifications with equivalent diameters in the plane of the detector ranging from 300 to 800 μm and
thicknesses, expressed in Al equivalent, covering 77 to 800 μm were simulated into the raw data of real clinical images.
The procedure was tested on 2 system configurations: the GE Senographe 2000 D and the Se based Agfa Embrace
DM1000 system. We adapted the X-ray transmissions and spatial characteristics of the simulated micro calcifications
such that the same physical micro calcification could be simulated into images with the specific exposure parameters
(Senographe 2000D: 28 kVp-Rh/Rh, Embrace DM1000: 28 kVp-Mo/Rh), compressed breast thickness (42+/-5mm) and
detector under consideration. After processing and printing, 3 observers scored the visibility of the micro calcifications.
We derived contrast-detail curves. This psychophysical method allows to summarize the performance of a digital
mammography detector including processing and visualization.
KEYWORDS: Modulation transfer functions, Sensors, X-rays, Digital mammography, Signal attenuation, Quantum efficiency, X-ray detectors, Data modeling, Fourier transforms, Polymethylmethacrylate
X-ray detector systems can be characterized by their measured or estimated detective quantum efficiency (DQE). Assessment of DQE includes a measurement of the modulation transfer function (MTF) and the normalized noise power spectrum (NNPS). The incoming X-ray quantum flux has to be estimated. In this paper, the influence of the different possibilities regarding the measurement methods and phantoms, the X-ray quantum flux estimation models and the exposure geometry on the DQE of a full field digital mammography detector is assessed. Physical models were used to fit MTF measurements from bar-pattern and edge phantoms. The NNPS was calculated by 2D-FFT on a large number of flat-field subimages. The flux was calculated using anode spectra models (Boone, 1997) and attenuation data (NIST). We compared the influence of scattered radiation MTF calculations of both phantoms were similar. The edge method is preferred for practical reasons. NNPS data were similar to 1D synthetic-slit measurements. DQE data compared well with literature. Different exposure geometry conditions (with scattered radiation) showed similar results but a siginificantly lower DQE than in absence of scattered radiation. DQE assessment is feasible using normal exposure conditions, an edge phantom and calculated estimations of the flux.
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