J. Webster Stayman ([email protected]) Fully 3D 2015 (May 31-June 4, 2015) Task-Based Optimization of Source-Detector Orbits in Interventional Cone-beam CT J. Webster Stayman, Grace Gang, and Jeffrey Siewerdsen Biomedical Engineering Johns Hopkins University Acknowledgements AIAI Laboratory Advanced Imaging Algorithms and Instrumentation Lab aiai.jhu.edu [email protected] I-STAR Laboratory Imaging for Surgery, Therapy, and Radiology istar.jhu.edu [email protected] Faculty and Scientists Clinicians Students Tharindu De Silva John Carey Qian Cao Grace Gang Gary Gallia Hao Dang Aswin Mathews A Jay Khanna Sarah Ouadah Amir Pourmorteza Martin Radvany Sureerat Reaungamornrat Jeffrey Siewerdsen Doug Reh Steven Tilley II Alejandro Sisniega Marc Sussman Ali Uneri Shiyu Xu Jennifer Xu Wojciech Zbijewski Thomas Yi Funding NIH U01EB014964, NIH R21EB014964, NIH KL2TR001077, NIH R01CA112163 This work was supported, in part, by the above grants. The contents of this presentation are solely the responsibility of the authors and do not necessarily represent the official view of Johns Hopkins or the NIH. AIAI Laboratory (aiai.jhu.edu) and I-STAR Laboratory (istar.jhu.edu), Dept of Biomedical Engineering, Johns Hopkins University 1 J. Webster Stayman ([email protected]) Fully 3D 2015 (May 31-June 4, 2015) Task-Driven Interventional Imaging Conventionally Ignored by Interventional Devices Conventional Interventional Imaging Intraoperative CT Preoperative Planning Diagnostic Flat-Panel Detector Image Data Imaging Task-Driven Trajectory Task Traditional ? Definition X-ray Circular Source Trajectory Patient- and Task-Driven Prior Information Intraoperative CT about Patient and Task Optimization Framework Anatomical Patient Model Optimal Patient Imaging Parameters Volume Task (W*) Imaging System Model Imaging Observer Parameters Data Image Imaging Task (W) Acquisition Formation Model Performance Performance Adjust Imaging Parameters for Increased Performance G. Gang, J. W. Stayman, T. Ehtiati, J. H. Siewerdsen, “Task-driven image acquisition and reconstruction in cone-beam CT,” Physics in Medicine and Biology, 60 3129-3150 (March 2015). AIAI Laboratory (aiai.jhu.edu) and I-STAR Laboratory (istar.jhu.edu), Dept of Biomedical Engineering, Johns Hopkins University 2 J. Webster Stayman ([email protected]) Fully 3D 2015 (May 31-June 4, 2015) Performance Prediction for Penalized-Likelihood Reconstruction Detectability Index – Non-prewhitening observer: Spatial Resolution Noise Imaging Task Spatial Resolution (MTF) 1 2 3 Consider local Fourier approximation of MTF and NPS: T ADAye Theory F j Empirical MTF j T F ADARy ej ej T Noise Power Spectrum (NPS) F ADAye j NPS j 2 T F ADARye jj e yb DA exp , J A Fessler and W Leslie Rogers, “Spatial resolution properties of penalized-likelihood image reconstruction methods: Space-invariant tomographs,” IEEE Trans. Im. Proc., 5(9):1346-58, Sep. 1996. Performance Prediction as an Acquisition Design Objective Detectability Index – Non-prewhitening observer: Spatial Resolution Noise Imaging Task Acquisition Design Objective: Consider local Fourier approximation of MTF and NPS: ˆ ˆ 2 , arg max d ';, WTask , , J. W. Stayman and J. H. Siewerdsen, “Task-Based Trajectories in Iteratively Reconstructed Interventional Cone-Beam CT,” Int'l Mtg. Fully 3D Image Recon. in Radiology and Nuc. Med., Lake Tahoe, (June 16-21, 2013). AIAI Laboratory (aiai.jhu.edu) and I-STAR Laboratory (istar.jhu.edu), Dept of Biomedical Engineering, Johns Hopkins University 3 J. Webster Stayman ([email protected]) Fully 3D 2015 (May 31-June 4, 2015) Orbit Parameterization / Optimization Orbit specified by a low-dimensional parameterization: … Single location, single task optimization: ˆ ˆ ˆ 2 , arg max d ';, WTask , 200 150 100 50 200 150 100 50 0.047 0.047 Multiple-location and/or multiple-task optimization: 500 450 0.048 ˆ ˆ ˆ 2 20.048 2 , arg max mind ' ,,, ;WWW ,200 d ' ; ,..., d ' ; 12 Task(1) Task (2) L Task ( L ) , 400 0.049 0.049 Solve using a nonlinear, nonconvex optimization strategy: 350 0.05 0.05 150 CMA-ES (Covariance Matrix Adaptation Evolution Strategy) 300 0.051 0.051 Hansen N, Müller SD, Koumoutsakos P (2003). Reducing the time complexity of the derandomized evolution strategy with covariance 250 matrix adaptation (CMA-ES). Evolutionary Computation, 11(1) pp. 1–18. 100 200 0.052 0.052 150 0.053 0.053 50 100 0.054 0.054 50 0.055 0.055 200 150 100 50 200 150 100 50 0.047 0.047 500 450 0.048 0.048 200 400 0.049 Optimization for a Simple0.049 Object 350 0.05 0.05 Location #1 150 Orbit #1 300 Object: 0.051 0.051 250 10 cm cylinder, = 0.05 mm-1 100 200 0.052 Optimization:0.052 150 0.053 90.053 orbital bases 50 100 0.054 q Iter #50(1/1) Time:2.6e+03 s -500.054 ° ≤ ≤ 50°, 0° ≤ ≤ 360° 0.055 50 CMA 0.055-ES (pop=40) 50 0.055 0.055 Location #1 0.054 0.054 50 s 100 Time:2.6e+03 #50(1/1) Iter 0.053 0.053 150 0.052 0.052 100 Fluence200 of rays through Location #1 0.051 250 0.051 40 300 150 0.05 0.05 20 350 0.049 0.049 0 400 200 (degrees) 0.048 -20 0.048 450 -40 0.047 500 0.047 50 100 150 200 50 100 150 200 0.055 0 100 200 3000.055 50 0.054 q (degrees) 0.054 100 50 0.053 0.053 150 0.052 0.052 200 100 250 0.051 0.051 300 0.05 150 0.05 350 0.049 0.049 AIAI Laboratory (aiai.jhu.edu)400 and 200 0.048 0.048 I-STAR Laboratory (istar.jhu.edu),450 500 0.047 0.047 Dept of Biomedical Engineering,50 100 150 200 50 100 150 200 Johns Hopkins University 4 200 150 100 50 200 150 100 50 0.047 0.047 500 450 0.048 0.048 200 400 0.049 0.049 350 0.05 0.05 150 300 0.051 0.051 250 100 200 0.052 J. Webster Stayman ([email protected]) Fully 3D 2015 (May 31-0.052 June 4, 2015) 150 0.053 0.053 50 100 0.054 0.054 50 0.055 0.055 200 150 100 50 200 150 100 50 0.047 0.047 Iter #50(1/1) Time:2.6e+03 s Iter #50(1/1) Time:2.7e+03 500 s 0.055 0.055 0.055 0.055 450 0.048 50 0.048 50 0.054 0.054 0.054 0.054 200 50 50100 400 100 0.049 Simple Object – Location0.049 -dependence 0.053 0.053 0.053 0.053 150 150 350 0.052 0.052 0.052 0.052 0.05 0.05 100 100200 Location #1 200 150 300 Orbit0.051 #1 250 0.051 0.051 250Orbit #2 0.051 0.051 0.051 300 250 300 150 0.05 150 0.05 0.05 0.05 100 350 200 350 0.052 0.049 0.049 0.049 0.0490.052 400 400 200 200 150 0.053 0.048 0.048 0.048 0.0480.053 450 450 50 100 0.047 500 0.047 0.047 500 0.047 0.054 50 100 150 200 50 50100 150 100200 150 200 Iter #50(1/1)50 100 Time:2.7e+03150 200 s 0.054 0.055 50 0.055 0.055 0.055 Location 0.055#2 0.055 50 50 50 0.055 0.055 0.054 Location #1 Location #2 0.0540.054 Location #1 0.054Location #2 0.054 0.054 100 50 100 50 50 s Time:2.7e+03 100 #50(1/1) Iter 0.053 0.0530.053 0.053 0.053 0.053 150 150 150 0.052 0.0520.052 0.052 0.052 0.052 200 200 100 200 100 100 250 0.051 250 0.0510.051 0.051 250 0.051 0.051 -4.63396079e+00 -3.24941049e+00 -2.09240068e+01 300 300 300 0.05 150 150 0.050.05 150 0.05 0.05 0.05 200 200 200 100 350 350 100 100 350 200 150 100 50 200 150 100 0 50 0 0 0.047 0.047 0.049 0.049 0.049 0.049 -100 0.049 500 0.049 -100 -100 -200 400 400 -200 -200 400 200 200 200 200 0.048200 2000.048 0.048 0.048 200 0.048 450 0.048 0 200 200 450 450 0 0 450 0.048 0 0.048 0 0 -200 -200 -200 -200 -200 -200 Fluence through Location #1 Fluence through Location #2 200 Fluence through Location #1 Fluence through Location #2 Φ Φ -4.63396079e+00 Φ Φ 500 500 -3.24941049e+00 -2.09240068e+01 500 50 50 0.047 50 0.047 0.047 0.047 0.047 0.047 400 50 100 150 200 50 100 50150 100 150200 200 50 100 150 200 50 100 150 200 50 100 150 200 40 4040 4040 4040 30 0.049 30 0.0550.049 30 0.055 20 2020 2020 2020 10 10 10 0 0 0 0 0 50 0 0 350 -10 -10 0.054-10 0.054 -20 -20 -20 0.05 0.05 -20 -20 -20150 -20 -30 -30 -30 100 50 300 -40 -40-40 -40-40 -40-40 -50 -50 0.053-50 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350 0.053 0 100 200 300 0 100 200 300 0 150 100 200 300 0 100 200 300 0.051 0.051 250 Θ Θ Θ 0.052 Θ 0.052 2.2x d’ 200 2.5x d’ 100 100 200 0.052 250 0.0510.052 0.051 300 150 0.05 150 0.05 0.053 0.053 50 350 0.049 100 0.049 0.054 400 0.054 200 50 0.048 0.048 450 0.055 0.055 500 0.047 200 150 100 50 200 150 100 50 0.047 50 100 150 200 50 100 150 200 0.047 0.047 500 450 0.048 0.048 200 400 0.049 Simple Object – Task-dependence0.049 350 0.05 0.05 High-Frequency Symmetric Task Asymmetric Line Pair Task 150 300 0.051 0.051 Orbit #1 250 Orbit #2 100 200 0.052 0.052 150 0.053 0.053 50 100 0.054 Iter #50(1/1) Time:2.6e+03 s 0.054 0.055 50 0.055 50 0.055 0.055 0.054 0.054 0.06 s Time:2.6e+03 #50(1/1) Iter 50 100 0.06 0.053 0.053 150 0.052 0.052 0.055 200 100 0.055 0.051 250 0.051 300 150 0.05 0.05 0.05 0.05 350 0.049 0.049 400 200 0.045 0.048 0.048 0.045 450 50 50 40 0.047 500 0.047 30 50 100 150 200 50 100 150 200 20 0.055 0.055 10 0 0 -10 50 -20 0.054 0.054 -30 -40 100 50 -50 -50 0 50 100 150 200 250 300 350 0.053 0 0.05350 100 150 200 250 300 350 150 0.052 0.052 200 100 250 0.051 0.051 300 0.05 150 0.05 350 0.049 0.049 AIAI Laboratory (aiai.jhu.edu)400 and 200 0.048 0.048 I-STAR Laboratory (istar.jhu.edu),450 500 0.047 0.047 Dept of Biomedical Engineering,50 100 150 200 50 100 150 200 Johns Hopkins University 5 J.