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Effect of image quality on calcification detection in digital mammography
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1.
1. E. D. Pisano, C. Gatsonis, E. Hendrick, M. Yaffe, J. Baum, S. Acharyya, E. F. Conant, L. L. Fajardo, L. Bassett, C. D’Orsi, R. Jong, and M. Rebner, “Diagnostic performance of digital versus film mammography for breast-cancer screening,” N. Engl. J. Med. 353, 17731784 (2005).
http://dx.doi.org/10.1056/NEJMoa052911
2.
2. P. Skaane and A. Skjennald, “Screen-film mammography versus full-field digital mammography with soft-copy reading: Randomized trial in a population-based screening program—The Oslo II study,” Radiology 232, 197204 (2004).
http://dx.doi.org/10.1148/radiol.2321031624
3.
3. R. E. Hendrick, E. B. Cole, E. D. Pisano, S. Acharyya, H. Marques, M. A. Cohen, R. A. Jong, G. E. Mawdsley, K. M. Kanal, C. J. D’Orsu, M. Rebner, and C. Gatsonis, “Accuracy of soft-copy digital mammography versus that of screen-film mammography according to digital manufacturer: ACRIN DMIT reterospective multireader study,” Radiology 247, 3848 (2008).
http://dx.doi.org/10.1148/radiol.2471070418
4.
4. X. J. Rong, C. C Shaw, S. K. Thompson, K. T. Krugh, C. Lai, D. A. Johnston, M. R. Lemocks, X. Liu, G. J. Whitman, M. J. Dryden, and T. W. Stephens, “Micro-calcification detectability for four mammographic detectors: Flat-panel, CCD, CR and screen/film,” Med. Phys. 29, 20522062 (2002).
http://dx.doi.org/10.1118/1.1500768
5.
5. G. Schueller, C. Riedl, R. Mallek, K. Eibenberger, H. Langenberger, E. Kaindl, C. Kulinna-Cosentini, M. Rudas, and T. Helbich, “Image quality, lesion detection and diagnostic efficacy in digital mammography: Full-field digital mammography versus computed radiography-based mammography using digital storage phosphor plates,” Eur. J. Radiol. 67, 487496 (2008).
http://dx.doi.org/10.1016/j.ejrad.2007.08.016
6.
6. M. Ruschin, P. Timberg, M. Bäth, B. Hemdal, T. Svahn, R. Saunders, E. Samei, I. Andersson, S. Mattsson, D. P. Chakraborty, and A. Tingberg, “Dose dependence of mass and microcalcification detection in digital mammography: Free response human observer studies,” Med. Phys. 34, 400407 (2007).
http://dx.doi.org/10.1118/1.2405324
7.
7. M. Yakabe, S. Sakai, H. Yabuuchi, Y. Matsuo, K. Takeshi, T. Setoguchi, M. Cho, M. Masuda, and M. Sasaki, “Effect of dose reduction on the ability of digital mammography to detect simulated micro-calcifications,” J. Digit Imaging 23, 520526 (2010).
http://dx.doi.org/10.1007/s10278-009-9203-y
8.
8. E. Samei, R. Saunders, J. Baker, and D. Delong, “Digital Mammography: Effects of reduced radiation dose on diagnostic performance,” Radiology 243, 396404 (2007).
http://dx.doi.org/10.1148/radiol.2432061065
9.
9. D. P. Chakraborty, “New developments in observer performance methodology in medical imaging,” Semin. Nucl. Med. 41, 401418 (2011).
http://dx.doi.org/10.1053/j.semnuclmed.2011.07.001
10.
10. E. B. Cole, E. D. Pisano, D. Zeng, K. E. Muller, S. R. Aylward, S. Park, C. Kuzmiak, M. Koomen, D. Pavic, R. Walsh, J. Baker, E. I. Gimenez, R. Freimanis, and K. Muller, “The effects of gray scale image processing on digital mammography interpretation performance,” Acad. Radiol. 12, 585595 (2005).
http://dx.doi.org/10.1016/j.acra.2005.01.017
11.
11. F. Zanca, J. Jacobs, C. V. Ongeval, F. Claus, V. Celis, C. Geniets, V. Provost, H. Pauwels, G. Marchal, and H. Bosmans, “Evaluation of clinical image processing algorithms used in digital mammography,” Med. Phys. 36, 765775 (2009).
http://dx.doi.org/10.1118/1.3077121
12.
12. T. Uematsu, “Detection of masses and calcifications by soft-copy reading: Comparison of two post-processing algorithms for full-field digital mammography,” Jpn. J. Radiol. 27, 168175 (2009).
http://dx.doi.org/10.1007/s11604-009-0315-6
13.
13. E. B. Cole, E. D. Pisano, E. D. Kistner, K. E. Muller, M. E. Brown, S. A. Feig, R. A. Jong, A. D. A. Maidment, M. J. Staiger, C. M. Kuzmiak, R. I. Freimanis, N. Lesko, E. L. Rosen, R. Walsh, M. Williford, and P. Braeuming, “Diagnostic accuracy of digital mammography in patients with dense breasts who underwent problem-solving mammography: Effects of image processing and lesion type,” Radiology 226, 153160 (2002).
http://dx.doi.org/10.1148/radiol.2261012024
14.
14. A. Mackenzie, A. Workman, D. R. Dance, M. Yip, K. Wells, and K. C. Young, “Conversion of mammographic images to appear with noise and sharpness characteristics of a different detector and x-ray system,” Med. Phys. 39, 27212734 (2012).
http://dx.doi.org/10.1118/1.4704525
15.
15. European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis, The European Protocol for the Quality Control of the Physical and Technical Aspects of Mammography Screening, Part B: Digital Mammography, 4th ed. (European Commission, Luxembourg, 2006).
16.
16. A. K. Carton, H. Bosmans, C. Vanongeval, G. Souverijns, G. Marchal, J. Jacobs, D. Vandenbroucke, H. Pauwels, and K. Nijs, “Contrast threshold of 4 full field digital mammography systems using different measurement methods,” in IWDM, LNCS (Springer-Verlag, Berlin, Heidelberg, 2006), Vol. 4046, pp. 593600.
17.
17. F. Zanca, D. P. Chakraborty, C. V. Ongeval, J. Jacobs, F. Claus, G. Marchal, and H. Bosmans, “An improved method for simulating microcalcifications in digital mammograms,” Med. Phys. 35, 40124018 (2008).
http://dx.doi.org/10.1118/1.2968334
18.
18. A. K. Carton, H. Bosmans, C. Van Ongeval, G. Souverijns, F. Rogge, A. Van Steen, and G. Marchal, “Development and validation of a simulation procedure to study the visibility of micro-calcifications in digital mammograms,” Med. Phys. 30, 22342241 (2003).
http://dx.doi.org/10.1118/1.1591193
19.
19. S. L. Hillis, N. A. Obuchowski, and K. S. Berbaum, “Power estimation for multireader ROC methods: An updated and unified approach,” Acad. Radiol. 18, 129142 (2011).
http://dx.doi.org/10.1016/j.acra.2010.09.007
20.
20. A. K. Carton, H. Bosmans, D. Vandenbroucke, G. Souverjins, C. V. Ongeval, O. Dragusin, and G. Marchal, “Quantification of Al-equivalent thickness of just visible micro-calcifications in full field digital mammograms,” Med. Phys. 31, 21652176 (2004).
http://dx.doi.org/10.1118/1.1758352
21.
21. F. Zanca, C. V. Ongeval, N. Marshall, T. Meylaers, K. Michielsen, G. Marchal, and H. Bosmans, “The relationship between the attenuation properties of breast micro-calcifications and aluminum,” Phys. Med. Biol. 55, 10571068 (2010).
http://dx.doi.org/10.1088/0031-9155/55/4/010
22.
22. D. R. Dance, C. L. Skinner, K. C. Young, and R. E. van Engen, “Additional factors for the estimation of mean glandular breast dose using the UK mammography dosimetry protocol,” Phys. Med. Biol. 45, 32253240 (2000).
http://dx.doi.org/10.1088/0031-9155/45/11/308
23.
23. D. R. Dance and G. J. Day, “The computation of scatter in mammography by Monte Carlo methods,” Phys. Med. Biol. 29, 237247 (1984).
http://dx.doi.org/10.1088/0031-9155/29/3/003
24.
24. J. M. Boone, K. K. Lindfors, V. N. Cooper III, and J. A. Seibert, “Scatter/primary in mammography: Comprehensive results,” Med. Phys. 27, 24082416 (2000).
http://dx.doi.org/10.1118/1.1312812
25.
25. I. Sechopoulos, S. Suryanarayanan, S. Vedantham, C. J. D’Orsi, and A. Karellas, “Scatter radiation in digital tomosynthesis of the breast,” Med. Phys. 34, 564576 (2007).
http://dx.doi.org/10.1118/1.2428404
26.
26. F. Zanca, G. Zhang, N. Marshall, E. Shaheen, E. Salvagnini, G. Marchal, and H. Bosmans, “Software framework for simulating clusters of micro-calcifications in digital mammography,” in IWDM, LNCS (Springer-Verlag, Berlin, Heidelberg, 2010), Vol. 6136, 689696.
27.
27. M. Yip, A. Mackenzie, E. Lewis, D. R. Dance, K. C. Young, W. Christmas, and K. Wells, “Image resampling in mammographic image simulation,” Phys. Med. Biol. 56, N275N286 (2011).
http://dx.doi.org/10.1088/0031-9155/56/22/N02
28.
28. J. Jacobs, F. Zanca, and H. Bosmans, “A novel platform to simplify human observer performance experiments in clinical reading environments,” Proc. SPIE 7966, 79660B (2011).
http://dx.doi.org/10.1117/12.878322
29.
29. NHS Breast Screening Programmes, Quality Assurance Guidelines for Breast Cancer Screening Radiology, 2nd ed., edited by J. Liston and R. Wilson (Sheffield, England, 2011), Publication No. 59.
30.
30. E. Samei et al., “Assessment of display performance for medical imaging systems: Executive summary of AAPM TG18 report,” Med. Phys. 32, 12051226 (2005).
http://dx.doi.org/10.1118/1.1861159
31.
31. J. Wei, H. P. Chan, C. Zhou, Y. T. Wu, B. Sahiner, L. M. Hadjiiski, M. A. Roubidoux, and M. A. Helvie, “Computer-aided detection of breast masses: Four-view strategy for screening mammography,” Med. Phys. 38, 18671876 (2011).
http://dx.doi.org/10.1118/1.3560462
32.
32. F. Zanca, C. V. Ongeval, J. Jacobs, G. Marchal, and H. Bosmans, “A quantitative method for evaluating the detectability of lesions in digital mammography,” Radiat. Prot. Dosim. 129, 214218 (2008).
http://dx.doi.org/10.1093/rpd/ncn049
33.
33. D. P. Chakraborty and T. Svahn, “Estimating the parameters of a model of visual search from ROC data: An alternative method for fitting proper ROC curves,” Proc. SPIE 7966, 79660L1 (2011).
http://dx.doi.org/10.1117/12.878231
34.
34. D. P. Chakraborty and K. S. Berbaum, “Observer studies involving detection and localization: Modelling, analysis, and validation,” Med. Phys. 31, 23132330 (2004).
http://dx.doi.org/10.1118/1.1769352
35.
35. D. D. Dorfman, K. S. Berbaum, and C. E. Metz, “Receiver operating characteristic rating analysis: Generalization to the population of readers and patients with jackknife method,” Invest. Radiol. 27, 723731 (1992).
http://dx.doi.org/10.1097/00004424-199209000-00015
36.
36. B. Zheng, J. K. Leader, G. Abrams, B. Shindel, V. Catullo, W. F. Good, and D. Gur, “Computer-aided detection schemes: The effect of limiting the number of cued regions in each case,” AJR 182, 579583 (2004).
37.
37. H. P. Chan, J. Wei, Y. Zheng, M. Helvie, R. H. Moore, B. Sahiner, L. Hadjiiski, and D. B. Kopans, “Computer-aided detection of masses in digital tomosynthesis mammography: Comparison of three approaches,” Med. Phys. 35, 40874095 (2008).
http://dx.doi.org/10.1118/1.2968098
38.
38. K. C. Young, J. J. H. Cook, and J. M. Oduko, “Automated and human determination of threshold contrast for digital mammography systems,” in IWDM, LNCS (Springer-Verlag, Berlin, Heidelberg, 2006), Vol. 4046, pp. 266272.
39.
39. K. C. Young, A. Alsager, J. M Oduko, H. Bosmans, B. Verbrugge, T. Geertse, and R. van Engen, “Evaluation of software for reading images of the CDMAM test object to assess digital mammography systems,” Proc. SPIE 6913, 69131C (2008).
http://dx.doi.org/10.1117/12.770571
40.
40. A. Workman, I. Castellano, E Kulama, C. P. Lawinski, N. Marshall, and K. C. Young, “Commissioning and routine testing of full field digital mammography systems,” NHSBSP Equipment Report No. 0604, Version 2 (2006).
41.
41. N. W. Marshall, K. Lemmens, and H. Bosmans, “Physical evaluation of a needle photostimulable phosphor based CR mammography system,” Med. Phys. 39, 811824 (2012).
http://dx.doi.org/10.1118/1.3675403
42.
42. J. M. Oduko, K. C. Young, and A. Burch, “A survey of patient doses from digital mammography systems in the UK in 2007 to 2009,” in Digital Mammography, Lecture Notes in Computer Science (Springer-Verlag, Berlin, Heidelberg, 2010), Vol. 6136, pp. 365370.
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/39/6/10.1118/1.4718571
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Figures

Image of FIG. 1.

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FIG. 1.

Examples of clusters used in the observer study inserted in four different categories of background: (a) fatty, (b) mixed without high density structure, (c) mixed with high density structure, and (d) glandular. Each image segment is 200 × 200 pixels (pixel size of 70 μm.

Image of FIG. 2.

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FIG. 2.

Characterization measurements on Hologic Selenia DR system and simulated CR used in study. (a) NEQ for DR and simulated CR (incident air kerma to detector = 89 μGy) and (b) MTF for DR and simulated CR.

Image of FIG. 3.

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FIG. 3.

Processed regions of interest (200 × 200 pixels) from an image in the study at all image qualities; (a) normal dose DR images with Hologic image processing, (b) normal dose DR images with Agfa image processing, (c) half dose DR with Agfa image processing, (d) quarter dose DR images Agfa image processing, (e) normal dose CR with Agfa image processing, and (f) half dose CR with Agfa image processing.

Image of FIG. 4.

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FIG. 4.

CDMAM test object.

Image of FIG. 5.

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FIG. 5.

Characteristics of extracted calcification clusters: (a) Cluster diameters and (b) number of calcifications in a cluster.

Image of FIG. 6.

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FIG. 6.

AFROC curves for two observers [(a) and (b)] inspecting the six image qualities. The crosses show the raw data points calculated using the confidence scores for three of the image qualities. The associated error bars show the 95% confidence intervals estimated using bootstrapping. Data points and error bars for the remaining three image qualities have been omitted to reduce clutter in the figure.

Image of FIG. 7.

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FIG. 7.

Reader-averaged AFROC curves showing performance at all six image qualities.

Image of FIG. 8.

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FIG. 8.

Difference in FoM between several image quality pairs (error bars indicate 95% confidence intervals).

Image of FIG. 9.

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FIG. 9.

Threshold gold thickness at five different image qualities: DR at normal, half, and quarter dose levels shown with disc points, and CR at normal and half dose levels shown with square points: (a) 0.1 mm gold disc diameter and (b) 0.25 mm gold disc diameter. Acceptable and achievable standards as set in the European protocol (Ref. 15) are also shown along with dose limit for a breast thickness equivalent to 50 mm PMMA.

Image of FIG. 10.

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FIG. 10.

Reader-averaged JAFROC FoM from the observer study plotted against the threshold gold thickness from CDMAM phantom images at the same IQ for (a) 0.10 mm and (b) 0.25 mm gold disc diameter. DR at normal, half, and quarter dose levels shown with cross points, and CR at normal and half dose levels shown with diamond points The results from the observer study include only images processed using Agfa image processing. The CDMAM analysis was performed on the unprocessed image. Error bars represent the 95% confidence interval. The acceptable and achievable limits as set in the European protocol (Ref. 15) are displayed as dashed lines.

Tables

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TABLE I.

Reader-averaged JAFROC and ROC FoM and reader-averaged LLF at a NLF equal to 0.1 for all six image qualities. Image qualities 1 and 2 are clinical images processed with both Hologic and Agfa Musica-2 image processing. Image qualities 3–6 are simulated image qualities from the original clinical images.

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/content/aapm/journal/medphys/39/6/10.1118/1.4718571
2012-05-17
2014-04-16

Abstract

Purpose:

This study aims to investigate if microcalcification detection varies significantly when mammographicimages are acquired using different image qualities, including: different detectors,dose levels, and different image processing algorithms. An additional aim was to determine how the standard European method of measuring image quality using threshold gold thickness measured with a CDMAM phantom and the associated limits in current EU guidelines relate to calcification detection.

Methods:

One hundred and sixty two normal breast images were acquired on an amorphous selenium direct digital (DR) system. Microcalcification clusters extracted from magnified images of slices of mastectomies were electronically inserted into half of the images. The calcification clusters had a subtle appearance. All images were adjusted using a validated mathematical method to simulate the appearance of images from a computed radiography(CR)imagingsystem at the same dose, from both systems at half this dose, and from the DR system at quarter this dose. The original 162 images were processed with both Hologic and Agfa (Musica-2) image processing. All other image qualities were processed with Agfa (Musica-2) image processing only. Seven experienced observers marked and rated any identified suspicious regions. Free response operating characteristic (FROC) and ROC analyses were performed on the data. The lesion sensitivity at a nonlesion localization fraction (NLF) of 0.1 was also calculated. Images of the CDMAM mammographic test phantom were acquired using the automatic setting on the DR system. These images were modified to the additional image qualities used in the observer study. The images were analyzed using automated software. In order to assess the relationship between threshold gold thickness and calcification detection a power law was fitted to the data.

Results:

There was a significant reduction in calcification detection using CR compared with DR: the alternative FROC (AFROC) area decreased from 0.84 to 0.63 and the ROC area decreased from 0.91 to 0.79 (p < 0.0001). This corresponded to a 30% drop in lesion sensitivity at a NLF equal to 0.1. Detection was also sensitive to the dose used. There was no significant difference in detection between the two image processing algorithms used (p > 0.05). It was additionally found that lower threshold gold thickness from CDMAM analysis implied better cluster detection. The measured threshold gold thickness passed the acceptable limit set in the EU standards for all image qualities except half doseCR. However, calcification detection varied significantly between image qualities. This suggests that the current EU guidelines may need revising.

Conclusions:

Microcalcification detection was found to be sensitive to detector and dose used. Standard measurements of image quality were a good predictor of microcalcification cluster detection.

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Scitation: Effect of image quality on calcification detection in digital mammography
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/39/6/10.1118/1.4718571
10.1118/1.4718571
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