KEYWORDS: Modulation transfer functions, Digital mammography, X-rays, Sensors, Scintillators, Computing systems, Quantum efficiency, Photodiodes, Signal detection, Signal processing
The physical performance characteristics of a clinical full-field digital mammography (FFDM) system were analyzed for different target/filter conditions using theoretical modeling and experimental measurements. The signal and noise propagation through the various stages of the FFDM system was simulated as a cascaded process and used to compute the frequency dependent detective quantum efficiency (DQE) of the system. The presampling modulation transfer function (MTF) of the system and the noise power spectra (NPS) of the system were measured under the different spectral conditions as used in the theoretical model at an exposure close to 10-mR from which corresponding DQEs were computed. The experimental zero frequency DQE after filtering the x-ray beam through 45-mm acrylic was estimated at 0.51, 0.48, and 0.46 for Mo/Mo, Mo/Rh, and Rh/Rh respectively. A good agreement between the theoretical and experimental results was observed. The clinical digital mammography system appears to exhibit favorable physical characteristics and similar models could be used to design and optimize other imaging systems.
KEYWORDS: Image compression, Mammography, Digital mammography, Image filtering, Eye, Digital imaging, Medical imaging, Eye models, Performance modeling, Data storage
The objective of this study was to evaluate an image compression technique for digital mammography using a nonprewhitening matched filter with an eye filter (NPWE) and channelized Hotelling numerical observer models. A total of 1024 images were cropped from clinical digital mammograms and used as backgrounds. The images were acquired using a clinical full-field digital mammography (FFDM) system and masses of sizes 30, 40, and 60 pixels (100 μm pixel size) were simulated. In addition, microcalcifications were synthetically extracted from clinical digital mammograms and used in the study. Image compression was achieved using a compression software (JPEG 2000, Aware Inc., Bedford, MA) at compression ratios 1:1, 15:1 and 30:1. The channelized Hotelling observer model was investigated only for the mass type lesions by transforming the images to channel space and computing the Hotelling trace for each compression condition. The NPWE model was investigated for both lesions and micocalcifications at all compression conditions and the detection indices were computed by assuming Gaussian statistics and by the 'percent correct’ detection method. The results of the study indicated a reduction in detection with increased compression for microcalcifications at 30:1 compression while almost no variation in detection index was observed for the simulated masses.
KEYWORDS: Digital mammography, Imaging systems, Data modeling, Signal to noise ratio, Signal detection, Error analysis, Sensors, Modulation transfer functions, X-ray imaging
In this investigation we studied the imaging characteristics of a mammographic screen-film (MinR-2000, Eastman Kodak Co.) and an amorphous-silicon flat-panel digital mammography system (Senographe 2000D, GE Medical Systems) based on information perception by human observers. The focus of the study was to utilize an effective means to estimate the contrast-detail characteristics of x-ray imaging systems at various threshold levels to evaluate system performance with reduced observer subjectivity. We obtained three images of a contrast-detail phantom (CDMAM, Nuclear Associates) with screen-film and three images with digital mammography under identical exposure conditions. The digital images were printed using dry film printer (DryView 8600, Eastman Kodak Co.) after being windowed/leveled appropriately by two experienced radiologists. Seven observers reviewed the images and 'proportion correct' detection data were computed for each observer. A psychophysical signal detection model that hypothesizes a continuous decision variable internal to the observer with Gaussian probability density functions was used to fit the experimental observer data. Projection data from the detection curves at 50%, 62.5%, and 75% threshold levels were used to generate contrast-detail diagrams. Digital mammography, on average, exhibited lower (better) threshold contrast-detail characteristics compared to screen-film mammography.
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