Literature DB >> 19415382

Effect of dose reduction on the ability of digital mammography to detect simulated microcalcifications.

Mari Yakabe1, Shuji Sakai, Hidetake Yabuuchi, Yoshio Matsuo, Takeshi Kamitani, Taro Setoguchi, Mayumi Cho, Masafumi Masuda, Masayuki Sasaki.   

Abstract

The purpose of this article was to report the relationship between radiation dose and the ability of sentence digital mammography to detect microcalcifications. All images were acquired by computed radiography and an anthropomorphic breast phantom. The tube voltage and anode/filter combination used were 28 kVp and Mo/Mo. Simulated microcalcifications with an approximate diameter of 250-350 μm were positioned on the phantom. Groups of six microcalcifications were arranged in one of two patterns, a line cluster 1 cm long or a hexagonal cluster 4 mm wide. One of the six microcalcifications was removed to create a negative control. Each cluster was placed on 25 different points. Four levels of milliampere-second (mAs) values were applied: 100%, 50%, 25%, and 12.5%. Five staff radiologists participated in an observer performance test. All observers used a workstation with a 3-megapixel monochrome LCD monitor. The areas under the receiver-operating characteristics curves (AUC) were used to compare diagnostic performance among the four doses. The overall AUC scores were 0.97 with 100% mAs, 0.93 (n.s.) with 50%, 0.90 (p < 0.05) with 25%, and 0.81 (p < 0.01) with 12.5% mAs. Among the negative series, the percentage of images on which observers were able to identify the removed microcalcification point decreased from 88.8% with 100% mAs to 83.6% (n.s.) with 50%, 74.8% (p < 0.001) with 25%, and 67.2% (p < 0.001) with 12.5% mAs. A certain level of dose reduction in digital mammography may be an option.

Mesh:

Year:  2009        PMID: 19415382      PMCID: PMC3046683          DOI: 10.1007/s10278-009-9203-y

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  25 in total

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6.  Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study.

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