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Visual\u2010search (VS) model observers that employ morphological features in sequential search and analysis stages have less need for such information and fewer task constraints. The authors compared four VS observers against human observers and an existing scanning model observer in a pilot study that quantified how mass detection and localization in simulated digital breast tomosynthesis (DBT) can be affected by the number <jats:italic>P<\/jats:italic> of acquired projections.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods:<\/jats:title><jats:p>Digital breast phantoms with embedded spherical masses provided single\u2010target cases for a localization receiver operating characteristic (LROC) study. DBT projection sets based on an acquisition arc of 60\u00b0 were generated for values of <jats:italic>P<\/jats:italic> between 3 and 51. DBT volumes were reconstructed using filtered backprojection with a constant 3D Butterworth postfilter; extracted 2D slices were used as test images. Three imaging physicists participated as observers. A scanning channelized nonprewhitening (CNPW) observer had knowledge of the mean lesion\u2010absent images. The VS observers computed an initial single\u2010feature search statistic that identified candidate locations as local maxima of either a template matched\u2010filter (MF) image or a gradient\u2010template MF (GMF) image. Search inefficiencies that modified the statistic were also considered. Subsequent VS candidate analyses were carried out with (i) the CNPW statistical discriminant and (ii) the discriminant computed from GMF training images. These location\u2010invariant discriminants did not utilize covariance information. All observers read 36 training images and 108 study images per <jats:italic>P<\/jats:italic> value. Performance was scored in terms of area under the LROC curve.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results:<\/jats:title><jats:p>Average human\u2010observer performance was stable for <jats:italic>P<\/jats:italic> between 7 and 35. In the absence of search inefficiencies, the VS models based on the GMF analysis provided the best correlation (Pearson <jats:italic>\u03c1<\/jats:italic> \u2265 0.62) with the human results. The CNPW\u2010based VS observers deviated from the humans primarily at lower values of <jats:italic>P<\/jats:italic>. In this limited study, search inefficiencies allowed for good quantitative agreement with the humans for most of the VS observers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions:<\/jats:title><jats:p>The computationally efficient training requirements for the VS observer are suitable for high\u2010resolution imaging, indicating that the observer framework has the potential to overcome important task limitations of current model observers for x\u2010ray applications.<\/jats:p><\/jats:sec>","DOI":"10.1118\/1.4942485","type":"journal-article","created":{"date-parts":[[2016,3,1]],"date-time":"2016-03-01T18:00:53Z","timestamp":1456855253000},"page":"1563-1575","update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":33,"title":["Visual\u2010search observers for assessing tomographic x\u2010ray image quality"],"prefix":"10.1002","volume":"43","author":[{"given":"Howard C.","family":"Gifford","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihua","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mini","family":"Das","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2016,3]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1364\/JOSAA.27.002648"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3676183"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3692176"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3357288"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.878826"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2756603"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1076\u20106332(03)00062\u2010X"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.771018"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4824055"},{"key":"e_1_2_8_11_1","volume-title":"Handbook of Medical Imaging: Physics and Psychophysics","author":"Beutel J.","year":"2000"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.431177"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/23.790820"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2422051997"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1167\/11.5.14"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4818824"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3231814"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2043598"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2015.02.015"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2008503"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2007.913241"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2005.859210"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1364\/JOSAA.20.000430"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2006.889163"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.3233\/FI\u20102000\u2010411207"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1113\/jphysiol.1973.sp010133"},{"key":"e_1_2_8_27_1","volume-title":"Fundamentals of Digital Image Processing","author":"Jain A. 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