Authors:
P. Barca
1
;
2
;
R. M. Lamastra
1
;
D. Caramella
3
;
A. C. Traino
4
;
R. M. Tucciariello
1
and
M. E. Fantacci
1
;
2
Affiliations:
1
Department of Physics, University of Pisa, Pisa, Italy
;
2
INFN, Pisa Section, Pisa, Italy
;
3
Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
;
4
Unit of Medical Physics, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy
Keyword(s):
Synthesized Mammography, Digital Mammography, Image Quality, Modulation Transfer Function, Noise Power Spectrum, Contrast to Noise Ratio, ACR Phantom.
Abstract:
The recent introduction of digital breast tomosynthesis (DBT) have lead to improvements in sensitivity and specificity of breast cancer detection, especially in cases of tumors developed in dense breasts. Since DBT provides tomographic slices of an entire tissue volume, it reduces the inherent tissue overlapping limitation of digital mammography (DM). In addition, DBT combined with DM has been proven to decrease recall and increase invasive cancer detection rates in breast cancer screening. However, the employment of DBT+DM implies a not negligible increment of patients absorbed dose. Therefore, Synthesized mammograms (SMs) generated from the DBT data have been recently introduced to eliminate the need of an additional DM. However, several studies showed differences between DM and SM images and some studies found contrasting results in terms of image quality when DM and SM images were compared. In our phantom study, we objectively compare image quality of SM and DM images in terms of
noise, spatial resolution and contrast properties. Additionally, a qualitative analysis of the ACR mammographic phantom was performed in both modalities to assess the detectability of different features. SM images were characterized by different texture with respect to DM images, showing lower overall performances in terms of contrast-to-noise ratio and modulation transfer function. However, the goal of SM images is to provide a useful two-dimensional guide complementary to the DBT dataset and the performances in terms of high-contrast features detectability were satisfactory in comparison to those obtained in DM.
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