Image Quality of Digital Breast Tomosynthesis: Optimization in Image Acquisition and Reconstruction by Gang Wu A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Medical Biophysics University of Toronto c Copyright 2014 by Gang Wu Abstract Image Quality of Digital Breast Tomosynthesis: Optimization in Image Acquisition and Reconstruction Gang Wu Doctor of Philosophy Graduate Department of Medical Biophysics University of Toronto 2014 Breast cancer continues to be the most frequently diagnosed cancer in Canadian women. Currently, mammography is the clinically accepted best modality for breast cancer detection and the regular use of screening has been shown to contribute to reduced mortality. However, mammography suffers from several drawbacks which limit its sensitivity and specificity. As a potential solution, digital breast to- mosynthesis (DBT) uses a limited number (typically 10{20) of low-dose x-ray projections to produce a three-dimensional tomographic representation of the breast. The reconstruction of DBT images is challenged by such incomplete sampling. The purpose of this thesis is to evaluate the effect of image acquisition parameters on image quality of DBT for various reconstruction techniques and to optimize these, with three specific goals: A) Develop a better power spectrum estimator for detectability calcu- lation as a task-based image quality index; B) Develop a paired-view algorithm for artifact removal in DBT reconstruction; and C) Increase dose efficiency in DBT by reducing random noise. A better power spectrum estimator was developed using a multitaper technique, which yields reduced bias and variance in estimation compared to the conventional moving average method. This gives us an improved detectability measurement with finer frequency steps. The paired-view scheme in DBT recon- struction provides better image quality than the commonly used sequential method. A simple ordering like the \side-to-side" method can achieve less artifact and higher image quality in reconstructed slices. The new denoising algorithm developed was applied to the projection views acquired in DBT before reconstruction. The random noise was markedly removed while the anatomic details were maintained. With the help of this artifact-removal technique used in reconstruction and the denoising method em- ployed on the projection views, the image quality of DBT is enhanced and lesions should be more readily detectable. ii Dedication I dedicate this thesis to my parents for their constant support and unconditional love. I love you dearly. iii Acknowledgements I would like to thank the following people who helped, encouraged and supported me throughout my Ph.D. degree: • My supervisor Dr. Martin Yaffe and members of my supervisory committee Dr. Anne Martel, Dr. Simon Graham and Dr. James Mainprize. Thanks for your guidance, patience and great help all these years; • Colleagues in Dr. Yaffe’s group, especially those who work on tomosynthesis, Gord Mawdsley, Aili Bloomquist, Sam Shen, Cindy Xinying Wang and Albert Tyson who give me valuable suggestions and spark new ideas in many discussions; • My fellow graduate students Melissa Hill and Olivier Alonzo-Proulx for their help, support and useful discussions; • All members of the Yaffe lab, past and present, who have been great colleagues and friends; • My appreciation also goes to many scholars and friends working in Sunnybrook, Dr. Sam Shen, Dr. Wen Shi, Dr. Peizhu Sun, and Dr. Jianhua Yin. Thank you for imparting wisdoms on me, about living, loving and learning; • Finally, I am grateful for my friends and family and I want to thank them for their continued love and support. iv Contents List of Tables viii List of Figures x Glossary xi Academic Curriculum Vitae 1 Peer Reviewed Publications . 1 Conference Proceedings . 1 1 Introduction 3 1.1 Breast Cancer and Mammography . 4 1.1.1 Breast Cancer . 4 1.1.2 Breast Tissue . 4 1.1.3 Strength and Limitation of Mammography . 5 1.2 Digital Breast Tomosynthesis (DBT) . 6 1.2.1 Tomosynthesis Geometry for Image Acquisition . 7 1.2.2 Reconstruction Techniques . 7 1.3 Image Quality . 8 1.3.1 Contrast . 9 1.3.2 Spatial Resolution . 9 1.3.3 Noise . 10 1.3.4 NEQ, DQE and Ideal Observer . 11 1.4 Hypothesis and Outline of the Thesis . 11 2 Spectral Analysis of Mammographic Images Using a Multitaper Method 13 2.1 Introduction . 13 2.2 Methods . 15 2.2.1 Noise-power spectrum (NPS) Estimation Technique . 15 2.2.2 Figures of Merit for Comparison . 19 2.2.3 Power Spectrum Applications . 19 2.3 Results . 21 2.3.1 Projection Image with Uniform Phantom . 21 2.3.2 QC Images from a Clinical System . 25 2.3.3 Projection Image with Anatomic Noise . 27 v 2.3.4 Clinical Images with Anatomic Noise . 27 2.4 Disscussion and Conclusions . 30 3 Constrained Paired-View Technique for Tomosynthesis Reconstruction 31 3.1 Introduction . 31 3.2 Materials and Methods . 32 3.2.1 Simulation of Breast Tomosynthesis System . 32 3.2.2 Incorporation of Background Structure . 33 3.2.3 Simulation of Microcalcification Array . 34 3.3 Reconstruction of Tomosynthesis . 35 3.3.1 Simultaneous Algebraic Reconstruction Technique (SART) . 35 3.3.2 Maximum Likelihood (ML) Algorithm . 36 3.3.3 Paired-view Normalization and Projection Sequences . 36 3.4 Figures of Merit (FOM) . 38 3.4.1 Contrast of Attenuation Coefficient . 38 3.4.2 SDNR . 38 3.4.3 Model Observer and Detectability Index . 39 3.4.4 Artifact Spread Function (ASF) . 40 3.5 Results . 40 3.5.1 Results of Phantom A with Uniform Background . 40 3.5.2 Results of Phantom B and C with Simulated Background . 43 3.5.3 Results of Microcalcification Array . 43 3.5.4 Results of Clinical Examples . 44 3.6 Discussion . 49 3.7 Conclusions . 49 4 Dose Reduction by Patch-based Denoising in Tomosynthesis Reconstruction 51 4.1 Introduction . 51 4.2 Methods . 52 4.2.1 Patch-based Denoising Algorithm . 52 4.2.2 Image Acquisition and Reconstruction . 53 4.3 Results . 53 4.3.1 Biopsy Phantom . 53 4.3.2 Clinical Mammograms . 54 4.3.3 Clinical DBT Exam, Projection Image . 55 4.3.4 Simulation of Uniform Phantoms . 57 4.3.5 Reconstructed Slices of Clinical DBT Exam . 57 4.4 Discussion and Conclusions . 58 5 Summary and Future Work 60 5.1 Summary . 60 5.2 Future Work . 60 5.3 Local Spectral Adaptive Multitaper Method (LSAMTM) . 61 5.3.1 Summary of Multitaper Method (MTM) . 62 vi 5.3.2 Choosing Number of Tapers Adaptively . 62 5.3.3 Bilateral Filtered LSAMTM . 63 5.3.4 Simulated X-ray Projections and Clinical QC Images . 63 5.3.5 Preliminary Results of LSAMTM . 63 5.4 Reconstruction by Blob-based Voxels . 67 5.4.1 Breast Volume Representation by Blobs . 68 5.4.2 Projection Matrix . 69 5.4.3 Fast Ray Tracing Algorithm . 69 5.4.4 Results of Reconstruction by Blob-based Voxels . 71 Bibliography 73 vii List of Tables 2.1 Alternative methods for spectrum estimation . 15 2.2 Comparison of WOAP and MB performance in power spectrum estimation . 22 2.3 Paired t-test of WOAP and MB estimation in Bias and Variance . 24 3.1 Sequence of projection views applied in reconstruction . 38 viii List of Figures 1.1 X-ray linear attenuation coefficients of fat and breast tissue . 5 1.2 Image acquisition geometry for tomosynthesis . 7 2.1 The effect of ROI size in the windowed overlapping NPS measurement method . 17 2.2 Flow chart of multitaper method . 18 2.3 A 2D projection view of a simulated breast phantom . 21 2.4 Comparison of NPS measurement in simulated images with coloured noise . 22 2.5 Comparison of the bias in NPS measurement by WOAP and multitaper methods . 23 2.6 Comparison of the variance in NPS measurement by WOAP and multitaper methods . 23 2.7 Comparison of NPS measurement in simulated images of coloured noise with peak . 24 2.8 Comparison of the bandwidth in NPS estimation by WOAP and MTM . ..