Physical and psychophysical characterization of a GE senographe DS clinical system

Lanconelli, Nico; Rivetti, Stefano; Golinelli, Paola; Sansone, Raffaele; Bertolini, Marco; Borasi, Giovanni

doi:10.1117/12.710025

Cited by 5 publications

(18 citation statements)

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“…Consequently, FineView was found to improve the DQE at lower DAK but to decrease the DQE at high DAK for higher spatial frequencies. Hence, the data of Lanconelli et al (2007) suggest that FineView changes DQE at different DAK settings. This result is not consistent with the study of Souchay et al (2004).…”

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confidence: 88%

“…The manufacturer does not expect any change in the detective quantum efficiency (DQE) of the system when FineView is activated (Souchay et al 2004). Lanconelli et al (2007) published a physical characterization of a GE Senographe DS system looking at the effect of FineView. The signal transfer property (STP) was characterized and showed no change with FineView.…”

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confidence: 99%

“…This result is not consistent with the study of Souchay et al (2004). The effect of FineView was also investigated by Lanconelli et al (2007) using the CDMAM phantom by scoring images acquired at 70 and 180 μGy with three experienced human observers; they found no change in threshold contrast detectability with FineView activated. This is unusual given that DQE and threshold contrast are normally closely linked (Aufrichtig 1999, Marshall 2006.…”

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confidence: 99%

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Characterization of the effects of the FineView algorithm for full field digital mammography

et al. 2012

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The aim of this study was to characterize the effect of an image processing algorithm (FineView) on both quantitative image quality parameters and the threshold contrast detail response of the GE Senographe DS full-field digital mammography system. The system was characterized using signal transfer property, pre-sampling modulation transfer function (MTF), normalized noise power spectrum (NNPS) and detective quantum efficiency (DQE) of the system. An algorithmic modulation transfer function (MTF(a)) was calculated from images acquired at a reduced detector air kerma (DAK) and with the FineView algorithm enabled. Two sets of beam conditions were used: Mo/Mo/28 kV and Rh/Rh/29 kV, both with 2 mm added Al filtration at the x-ray tube. Images were acquired with and without FineView at four DAK levels from 14 to 378 µGy. The threshold contrast detail response was assessed using the CDMAM contrast-detail test object which was imaged under standard clinical conditions with and without FineView at three DAK levels from 24 to 243 µGy. The images were scored by both human observers and by automated scoring software. Results indicated an improvement of up to 125% at 5 mm⁻¹ in MTF(a) when FineView was activated, particularly at high DAK levels. A corresponding increase of up to 425% at 5 mm⁻¹ was also seen in the NNPS, again with the same DAK dependence. FineView did not influence DQE, an indication that the signal to noise ratio transfer of the system remained unchanged. FineView did not affect the threshold contrast detectability of the system, a result that is consistent with the DQE results.

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confidence: 88%

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confidence: 99%

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confidence: 99%

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Characterization of the effects of the FineView algorithm for full field digital mammography

et al. 2012

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“…The reported values from human readers and for CDCOM analysis are better than those from other FFDM systems in similar conditions. 2,18,19…”

Section: Iiib Contrast-detail Analysismentioning

confidence: 99%

Physical and psychophysical characterization of a novel clinical system for digital mammography

et al. 2009

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The novel clinical unit based on direct-conversion detector and optical reading presents great results in terms of both physical and psychophysical characterizations. The good spatial resolution, combined with excellent noise properties, allows the achievement of very good DQE, better than those published for clinical FFDM systems. The psychophysical analysis confirms the excellent behavior of the AMULET unit.

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“…Postprocessing techniques may be available in dedicated or general use available software packages [ 2 – 7 ]. General Electric has developed dedicated software named “Fine View” and “Premium View” (GE Healthcare Milwaukee, WI) to restore the loss in spatial resolution [ 8 ]. However, postprocessing techniques alter the quality characteristics of the original image in a contradicting manner [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Figure of Image Quality and Information Capacity in Digital Mammography

Michail

Kalyvas

Valais

et al. 2014

BioMed Research International

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Objectives. In this work, a simple technique to assess the image quality characteristics of the postprocessed image is developed and an easy to use figure of image quality (FIQ) is introduced. This FIQ characterizes images in terms of resolution and noise. In addition information capacity, defined within the context of Shannon's information theory, was used as an overall image quality index. Materials and Methods. A digital mammographic image was postprocessed with three digital filters. Resolution and noise were calculated via the Modulation Transfer Function (MTF), the coefficient of variation, and the figure of image quality. In addition, frequency dependent parameters such as the noise power spectrum (NPS) and noise equivalent quanta (NEQ) were estimated and used to assess information capacity. Results. FIQs for the “raw image” data and the image processed with the “sharpen edges” filter were found 907.3 and 1906.1, correspondingly. The information capacity values were 60.86 × 103 and 78.96 × 103 bits/mm2. Conclusion. It was found that, after the application of the postprocessing techniques (even commercial nondedicated software) on the raw digital mammograms, MTF, NPS, and NEQ are improved for medium to high spatial frequencies leading to resolving smaller structures in the final image.

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Physical and psychophysical characterization of a GE senographe DS clinical system

Cited by 5 publications

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Characterization of the effects of the FineView algorithm for full field digital mammography

Characterization of the effects of the FineView algorithm for full field digital mammography

Physical and psychophysical characterization of a novel clinical system for digital mammography

Figure of Image Quality and Information Capacity in Digital Mammography

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