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Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition
- 1. Workshop: Deep Learning for Biomedical Image Reconstruction Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition Boah Kim, Junyoung Kim, Wontaek seo, Choul Woo Shin, Jong Chul Ye
- 2. Contents ISBI2020 Workshop Presentation 2 Boah Kim 1. Introduction 2. Theory 3. Experiment 4. Conclusion Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition
- 3. Introduction Bone subtraction on chest radiography Chest radiography (CR) ISBI2020 Workshop Presentation 3 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition • Fundamental imaging method to diagnose disease (ex. lung cancer, arterial stenosis, pleural abnormalities) • Often overlap subtle lesions with normal structure → Hard to be detected due to low sensitivity of the lesions Standard CR images Kuhlman, Janet E., et al. Radiographics, 2006 Bone subtraction • Enhance the visibility of soft tissues • Detect abnormal nodules with high accuracy
- 4. Introduction Dual-energy subtraction ISBI2020 Workshop Presentation 4 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition MacMahon, Heber, et al. Journal of thoracic imaging, 2008 Standard radiograph Dual energy soft tissue Dual energy bone Employ differences of attenuation coefficients of body tissues • Principle: Bone contained calcium absorbs more photons at lower energy than soft tissues. • Be used to generate bone-subtracted soft tissue images • Superior sensitivity for the detection of calcification within a pulmonary nodule
- 5. Introduction Dual-energy subtraction ISBI2020 Workshop Presentation 5 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition MacMahon, Heber, et al. Journal of thoracic imaging, 2008 Standard radiograph Dual energy soft tissue Dual energy bone Challenge • Computationally expensive and sensitive to compute weight of bone subtraction - Estimate thickness of bone & soft tissue on images - Use of nonlinear polynomial approximation algorithms
- 6. Introduction Dual-energy subtraction ISBI2020 Workshop Presentation 6 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition Challenge • Computationally expensive and sensitive to compute weight of bone subtraction - Estimate thickness of bone & soft tissue on images - Use of nonlinear polynomial approximation algorithms Multi-energy subtraction Based on eigenvalue decomposition (EVD) Can be used for application of deep-learning-based multi-energy image synthesis Multi-energy chest radiography
- 7. Contents ISBI2020 Workshop Presentation 7 Boah Kim 1. Introduction 2. Theory 3. Experiment 4. Conclusion Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition
- 8. Theory Chest radiography Principle ISBI2020 Workshop Presentation 8 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition 𝐼𝐼 = 𝜇𝜇𝑏𝑏𝑙𝑙𝑏𝑏 + 𝜇𝜇𝑠𝑠𝑙𝑙𝑠𝑠 + 𝑤𝑤 𝜇𝜇𝑏𝑏 𝜇𝜇𝑠𝑠 X-ray Source X-ray Film • Obtained by attenuation coefficients 𝜇𝜇 and thickness 𝑙𝑙 of tissues Chest radiography image, 𝐼𝐼 Bone Soft tissue 𝑙𝑙𝑏𝑏 𝑙𝑙𝑠𝑠
- 9. Theory Chest radiography Principle ISBI2020 Workshop Presentation 9 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition 𝐼𝐼 = 𝜇𝜇𝑏𝑏𝑙𝑙𝑏𝑏 + 𝜇𝜇𝑠𝑠𝑙𝑙𝑠𝑠 + 𝑤𝑤 X-ray Source X-ray Film • Obtained by attenuation coefficients 𝜇𝜇 and thickness 𝑙𝑙 of tissues Chest radiography image, 𝐼𝐼 Bone Soft tissue 𝑙𝑙𝑏𝑏 𝑙𝑙𝑠𝑠 Different attenuation coefficient values according to the X-ray energy levels 𝜇𝜇𝑏𝑏 𝜇𝜇𝑠𝑠 Kim, D-H., et al. Journal of Instrumentation, 2013
- 10. Theory Multi-energy chest radiography Images obtained by different X-ray energy levels ISBI2020 Workshop Presentation 10 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition • Can be represented with a matrix form with 𝜇𝜇 and 𝑙𝑙 Multi-energy chest radiography image, 𝐈𝐈 𝐼𝐼1 𝐼𝐼2 𝐼𝐼3 𝐼𝐼𝑁𝑁 𝐈𝐈 = 𝐼𝐼1 𝐼𝐼2 ⋮ 𝐼𝐼𝑁𝑁 = 𝜇𝜇𝑏𝑏 1 𝜇𝜇𝑠𝑠 1 𝜇𝜇𝑏𝑏 2 𝜇𝜇𝑠𝑠 2 ⋮ ⋮ 𝜇𝜇𝑏𝑏 4 𝜇𝜇𝑠𝑠 4 𝑙𝑙𝑏𝑏 𝑙𝑙𝑠𝑠 + 𝑤𝑤 = 𝝁𝝁𝑏𝑏𝑙𝑙𝑏𝑏 + 𝝁𝝁𝑠𝑠𝑙𝑙𝑠𝑠 = 𝐈𝐈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 + 𝐈𝐈𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠
- 11. Multi-energy chest radiography image, 𝐈𝐈 𝐼𝐼1 𝐼𝐼2 𝐼𝐼3 𝐼𝐼𝑁𝑁 𝐈𝐈 = 𝐼𝐼1 𝐼𝐼2 ⋮ 𝐼𝐼𝑁𝑁 = 𝜇𝜇𝑏𝑏 1 𝜇𝜇𝑠𝑠 1 𝜇𝜇𝑏𝑏 2 𝜇𝜇𝑠𝑠 2 ⋮ ⋮ 𝜇𝜇𝑏𝑏 4 𝜇𝜇𝑠𝑠 4 𝑙𝑙𝑏𝑏 𝑙𝑙𝑠𝑠 + 𝑤𝑤 = 𝝁𝝁𝑏𝑏𝑙𝑙𝑏𝑏 + 𝝁𝝁𝑠𝑠𝑙𝑙𝑠𝑠 + w = 𝐈𝐈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 + 𝐈𝐈𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 + w Theory Multi-energy chest radiography Images obtained by different X-ray energy levels ISBI2020 Workshop Presentation 11 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition If 𝐈𝐈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 is estimated, 𝐈𝐈𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 can be obtained. • Can be represented with a matrix form with 𝜇𝜇 and 𝑙𝑙
- 12. Theory Bone removal method Estimation of bone & soft tissue images from multi-energy data ISBI2020 Workshop Presentation 12 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition • Assumption Multi-energy CR images are all aligned. • Two steps 1. Estimation of bone image - use eigenvalue decomposition 2. Subtraction of bone to get soft tissue images - use correlation Flow chart of our proposed method
- 13. Theory Bone removal method 1st: Estimation of bone image ISBI2020 Workshop Presentation 13 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition
- 14. Theory Bone removal method 1st: Estimation of bone image ISBI2020 Workshop Presentation 14 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition 1) Get a matrix 𝐘𝐘 with MxM kernel 𝐘𝐘 = 𝐀𝐀𝐀𝐀 + 𝑤𝑤 𝐀𝐀 : Attenuation coefficient matrix 𝐗𝐗 : path length matrix on the kernel
- 15. Theory Bone removal method 1st: Estimation of bone image ISBI2020 Workshop Presentation 15 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition 1) Get a matrix 𝐘𝐘 with MxM kernel 2) Find 𝐀𝐀𝐀𝐀 by cost function - With the optimal point 𝐗𝐗∗ = 𝐀𝐀T 𝐀𝐀 −𝟏𝟏 𝐀𝐀T 𝐘𝐘, min 𝐘𝐘 − 𝐀𝐀𝐀𝐀 2 𝐘𝐘 = 𝐀𝐀𝐀𝐀 + 𝑤𝑤 𝐀𝐀 : Attenuation coefficient matrix 𝐗𝐗 : path length matrix on the kernel 𝐘𝐘 − 𝐀𝐀𝐗𝐗∗ 2 = 𝐘𝐘 2 − P𝐀𝐀𝐘𝐘 2 = 𝐀𝐀 𝐀𝐀T 𝐀𝐀 −𝟏𝟏 𝐀𝐀T = 𝐮𝐮𝐮𝐮T - Assume 𝐀𝐀T 𝐀𝐀 = 𝐼𝐼 - Consider only 𝐮𝐮 𝐮𝐮 𝐈𝐈 = 𝐼𝐼1 𝐼𝐼2 ⋮ 𝐼𝐼𝑁𝑁 = 𝜇𝜇𝑏𝑏 1 𝜇𝜇𝑠𝑠 1 𝜇𝜇𝑏𝑏 2 𝜇𝜇𝑠𝑠 2 ⋮ ⋮ 𝜇𝜇𝑏𝑏 4 𝜇𝜇𝑠𝑠 4 𝑙𝑙𝑏𝑏 𝑙𝑙𝑠𝑠 + 𝑤𝑤
- 16. Theory Bone removal method 1st: Estimation of bone image ISBI2020 Workshop Presentation 16 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition 1) Get a matrix 𝐘𝐘 with MxM kernel 2) Find 𝐀𝐀𝐀𝐀 by cost function 𝐘𝐘 = 𝐀𝐀𝐀𝐀 + 𝑤𝑤 𝐀𝐀 : Attenuation coefficient matrix 𝐗𝐗 : path length matrix on the kernel min 𝐘𝐘 − 𝐀𝐀𝐀𝐀 2 = min 𝐘𝐘 2 − P𝐀𝐀𝐘𝐘 2 = max P𝐀𝐀𝐘𝐘 2 = max 𝐮𝐮 𝐮𝐮𝐮𝐮T 𝐘𝐘 2 = max 𝐮𝐮 𝐮𝐮T 𝐘𝐘𝐘𝐘T 𝐮𝐮
- 17. Theory Bone removal method 1st: Estimation of bone image ISBI2020 Workshop Presentation 17 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition 1) Get a matrix 𝐘𝐘 with MxM kernel 2) Find 𝐀𝐀𝐀𝐀 by cost function 𝐘𝐘 = 𝐀𝐀𝐀𝐀 + 𝑤𝑤 𝐀𝐀 : Attenuation coefficient matrix 𝐗𝐗 : path length matrix on the kernel min 𝐘𝐘 − 𝐀𝐀𝐀𝐀 2 = min 𝐘𝐘 2 − P𝐀𝐀𝐘𝐘 2 = max P𝐀𝐀𝐘𝐘 2 = max 𝐮𝐮 𝐮𝐮𝐮𝐮T 𝐘𝐘 2 = max 𝐮𝐮 𝐮𝐮T 𝐘𝐘𝐘𝐘T 𝐮𝐮 Covariance of 𝐘𝐘
- 18. Theory Bone removal method 1st: Estimation of bone image ISBI2020 Workshop Presentation 18 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition 3) Perform eigen decomposition 𝐘𝐘𝐘𝐘T = 𝐔𝐔𝐔𝐔𝐔𝐔T - One of eigenvectors 𝐮𝐮𝐛𝐛 provides bone information.
- 19. Theory Bone removal method 1st: Estimation of bone image ISBI2020 Workshop Presentation 19 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition 3) Perform eigen decomposition 𝐘𝐘𝐘𝐘T = 𝐔𝐔𝐔𝐔𝐔𝐔T - One of eigenvectors 𝐮𝐮𝐛𝐛 provides bone information. 4) Estimate bone image by 𝐮𝐮𝐛𝐛 𝐈𝐈𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃 = 𝐮𝐮𝐛𝐛𝐮𝐮𝐛𝐛 T 𝐘𝐘
- 20. Theory Bone removal method 2nd: Estimation of soft tissue image ISBI2020 Workshop Presentation 20 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition 𝐈𝐈𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 = 𝐈𝐈 − 𝐈𝐈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 − 𝑤𝑤 = 𝐈𝐈 − 𝛼𝛼𝐈𝐈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 Approximate noise 𝑤𝑤 min 𝛼𝛼 Corr < 𝐈𝐈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 , 𝐈𝐈𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 > = min 𝛼𝛼 𝐈𝐈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 T (𝐈𝐈 −𝛼𝛼𝐈𝐈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏) 𝐈𝐈 −𝛼𝛼𝐈𝐈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 • Subtract bone from CR data • Compute 𝛼𝛼 by correlation
- 21. Contents ISBI2020 Workshop Presentation 21 Boah Kim 1. Introduction 2. Method 3. Experiment 4. Conclusion Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition
- 22. Experiment Simulated chest radiography ISBI2020 Workshop Presentation 22 Boah Kim • Provided by DRTECH Corporation • Data with multi-energy levels : [60, 120] kVp images with 10kVp interval Dataset Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition Simulation CR data with multi-energy levels 60kVp 70kVp 80kVp 90kVp 100kVp 110kVp 120kVp
- 23. Experiment Bone removal results from our proposed method ISBI2020 Workshop Presentation 23 Boah Kim Results Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition Result: Bone Result: Soft tissue Data: 60kVp Data: 120kVp
- 24. Experiment Application to deep learning ISBI2020 Workshop Presentation 24 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition
- 25. Experiment Application to deep learning GAN-based multi-energy image generation ISBI2020 Workshop Presentation 25 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition • Synthesize the image of unknown energies by CollaGAN • Proposed method could be applicable even with dual-energy data Flow chart of CollaGAN Lee, et al. CVPR, 2019
- 26. Experiment Application to deep learning GAN-based multi-energy image generation ISBI2020 Workshop Presentation 26 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition • Synthesize the image of unknown energies by CollaGAN • Proposed method could be applicable even with dual-energy data Multi-energy image generation Flow chart of CollaGAN Lee, et al. CVPR, 2019
- 27. Experiment Bone removal results with CollaGAN ISBI2020 Workshop Presentation 27 Boah Kim Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition CollaGAN Result: 110kVp Result: Soft tissue Data: 60kVp Data: 120kVp Application to deep learning
- 28. Contents ISBI2020 Workshop Presentation 28 Boah Kim 1. Introduction 2. Method 3. Experiment 4. Conclusion Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition
- 29. Conclusion Propose a novel bone subtraction method using multi-energy chest radiography eigenvalue decomposition ISBI2020 Workshop Presentation 29 Boah Kim Demonstrate the effectiveness of our method with the simulated multi-energy chest radiography data Can be used in deep learning application of X-ray data in various ways Multi-energy Bone Subtraction in Chest Radiography by Eigenvalue Decomposition
- 30. Acknowledgement • This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) [2016-0-00562(R0124-16-0002), Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly] • This work was also supported by the Industrial Strategic technology development program (10072064, Development of Novel Artificial Intelligence Technologies To Assist Imaging Diagnosis of Pulmonary, Hepatic, and Cardiac Diseases and Their Integration into Commercial Clinical PACS Platforms) funded by the Ministry of Trade Industry and Energy (MI, Korea). Medical Image Synthesis with Improved Cycle-GAN: CT from CECT ISBI2020 Workshop Presentation 30 Boah Kim
- 31. Workshop: Deep Learning for Biomedical Image Reconstruction Medical Image Synthesis with Improved Cycle-GAN: CT from CECT ISBI2020 Workshop Presentation 31 Boah Kim Thank you.