What is the minimum amount of simulated breast movement required for visual detection of blurring? An exploratory investigation.

OBJECTIVE Image blurring in mammography can cause significant image degradation and interpretational problems. A potential source is due to paddle movement during image formation. Paddle movement has been shown to be as much as 1.5 mm. No study has yet been performed to determine how much motion would be noticeable visually. The aim of this study is to determine the minimum amount of simulated breast movement at which blurring can be detected visually. METHODS 25 artefact-free mammogram images were selected. Mathematical simulation software was created to mimic the effect of blurring produced by breast movement during exposure. Motion simulation was imposed to 15 levels, from 0.1 to 1.5 mm stepping through 0.1 mm increments. 15 degraded images and 1 without blurring were de-identified, randomized and assessed on a blinded basis by two clinical experts to determine the presence or absence of blurring. Statistical testing was carried out to determine the consistency between the two observers. RESULTS The probability of simulated blurred image detection is the highest for the gaussian method and the lowest for soft-edged mask estimation. CONCLUSION The amount of simulated breast movement at which blurring can be detected visually for gaussian blur, hard-edge mask estimation and soft-edge mask estimation is 0.4, 0.8 and 0.7 mm, respectively. Cohen's kappa for all the levels of simulated blurring is 0.689 (p < 0.05). ADVANCES IN KNOWLEDGE This research establishes the concept of using probability to represent visual detection of blurring rather than defining a hard cut-off level.

[1]  Emily White,et al.  Screening mammography: clinical image quality and the risk of interval breast cancer. , 2002, AJR. American journal of roentgenology.

[2]  S. Susan Young,et al.  Signal Processing and Performance Analysis for Imaging Systems , 2008 .

[3]  Francesc Massanes,et al.  Motion perception in medical imaging , 2011, Medical Imaging.

[4]  D. Okada,et al.  Digital Image Processing for Medical Applications , 2009 .

[5]  Carolyn Hicks Research methods for clinical therapists: applied project design and analysis Carolyn M Hicks Research methods for clinical therapists: applied project design and analysis Churchill LIvingstone 352 Fifth edition £22.99 9780043074301 [Formula: see text]. , 2010, Nurse researcher.

[6]  Consumer Protection,et al.  European guidelines for quality assurance in breast cancer screening and diagnosis. Fourth edition--summary document. , 2008, Annals of oncology : official journal of the European Society for Medical Oncology.

[7]  Anders Tingberg,et al.  Breast compression in mammography: pressure distribution patterns , 2012, Acta radiologica.

[8]  Alastair G. Gale,et al.  Breast Cancer: Measuring radiology performance in breast screening , 2010 .

[9]  Michael A. Webster,et al.  Neural adjustments to image blur , 2002, Nature Neuroscience.

[10]  P Hogg,et al.  Extra patient movement during mammographic imaging: an experimental study. , 2014, The British journal of radiology.

[11]  Peter Hogg,et al.  A method to investigate image blurring due to mammography machine compression paddle movement , 2015 .

[12]  Ehsan Samei,et al.  Does image quality matter? Impact of resolution and noise on mammographic task performance. , 2007, Medical physics.