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Current digital mammography systems primarily employ one of two types of detectors: indirect conversion, typically using a cesium-iodine scintillator integrated with an amorphous silicon photodiode matrix, or direct conversion, using a photoconductive layer of amorphous selenium (a-Se) combined with thin-film transistor array. The goal of this study was to evaluate a methodology for objectively assessing image quality to compare human observer task performance in detecting microcalcification clusters and extended mass-like lesions achieved with different detector types. The proposed assessment methodology uses a novel anthropomorphic breast phantom fabricated with ink-jet printing. In addition to human observer detection performance, standard linear metrics such as modulation transfer function, noise power spectrum, and detective quantum efficiency (DQE) were also measured to assess image quality. An Analogic Anrad AXS-2430 a-Se detector used in a commercial FFDM/DBT system and a Teledyne Dalsa Xineos-2329 with CMOS pixel readout were evaluated and compared. The DQE of each detector was similar over a range of exposures. Similar task performance in detecting microcalcifications and masses was observed between the two detectors over a range of clinically applicable dose levels, with some perplexing differences in the detection of microcalcifications at the lowest dose measurement. The evaluation approach presented seems promising as a new technique for objective assessment of breast imaging technology.
In this study we test two detectors, an amorphous Selenium (a-Se) direct-conversion FPD with a pixel size of 85 μm, integrated in a number of FFDM and DBT systems, and a new CsI columnar scintillator detector with a 49.5 μm pixel and CMOS active pixel readout technology.
Detectors were evaluated, using 1) linear assessment methods, i.e. modulation transfer function (MTF), noise power spectrum (NPS), and detective quantum efficiency (DQE); 2) a human observer reader study in which microcalcification signals were detected in FFDM images of an anthropomorphic breast phantom.
Images were acquired on a Hologic Selenia Dimensions system with a uniform and anthropomorphic phantom. A contrast detail insert of small, low-contrast disks was created using an inkjet printer with iodine-doped ink and inserted in the phantoms. The disks varied in diameter from 210 to 630 μm, and in contrast from 1.1% contrast to 2.2% in regular increments. Human and model observers performed a 4-alternative forced choice experiment. The models were a non-prewhitening matched filter with eye model (NPWE) and a channelized Hotelling observer with either Gabor channels (Gabor-CHO) or Laguerre-Gauss channels (LG-CHO).
With the given phantoms, reader scores were higher in FFDM and DBT than SM. The structure in the phantom background had a bigger impact on outcome for DBT than for FFDM or SM. All three model observers showed good correlation with humans in the uniform background, with ρ between 0.89 and 0.93. However, in the structured background, only the CHOs had high correlation, with ρ=0.92 for Gabor-CHO, 0.90 for LG-CHO, and 0.77 for NPWE.
Because results of any analysis can depend on the phantom structure, conclusions of modality performance may need to be taken in the context of an appropriate model observer and a realistic phantom.
Investigating possible improvements in image quality with energy-weighting photon-counting breast CT
A comparison of lesion detection accuracy using digital mammography and flat-panel CT breast imaging
Development of new breast X-ray imaging technologies or improvements to hardware or software of current systems usually require the accurate assessment of image quality. Image quality assessment methods are also required for quality control (QC) of clinical systems, for example as required by the U.S. Mammography Quality Standards Act (MQSA) program. The gold standard for assessment of image quality is human reader studies assessing diagnostic performance over a cohort of representative clinical images. These clinical trials are often difficult and expensive to perform, and therefore researchers have been studying alternative approaches that can assess diagnostic task performance without imaging patients.
This short course will discuss methods for objectively assessing task performance of breast imaging systems without conducting a clinical trial. One approach that will be discussed is the in silico modeling of a clinical trial. This approach involves complete computer modeling of each step in the imaging chain including: 1) modeling of breast and relevant breast lesions, 2) modeling of the imaging system, and 3) modeling of the observer. Another more experimental approach that will also be discussed involves: 1) development of anthropomorphic physical phantoms with diagnostic features, 2) imaging of these phantoms on breast imaging commercial or prototype systems, and 3) assessment of task performance with either model or human observers.
For maximum efficiency, the proposed in silico and experimental approaches require the development of computer or model observers that can emulate either ideal or human observer task performance. This short course will discuss the use of new machine learning algorithms that can be used to model observer performance in the assessment of breast imaging technology.
This course will describe and make attendees aware of useful open-source software tools that can be downloaded.
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