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AI and Conventional Methods for UCT Projection Data Estimation

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Abstract

A 2D Compact ultrasound computerized tomography (UCT) system is developed. Fully automatic post-processing tools involving signal and image processing are developed as well. Square of the amplitude values are used in transmission mode with natural 1.5 MHz frequency and rise time 10.4 ns and fall time 8.4 ns and duty cycle of 4.32%. The highest peak to corresponding trough values are considered as transmitting wave between transducers in direct line talk. Sensitivity analysis of methods to extract peak to the corresponding trough per transducer are discussed in this paper. Total five methods are tested. These methods are taken from broad categories: (a) Conventional and (b) Artificial Intelligence (AI) based methods. Conventional methods, namely: (a) simple gradient based peak detection, (b) Fourier based, (c) wavelet transform, are compared with AI based methods: (a) support vector machine (SVM), (b) artificial neural network (ANN). The classification step is performed as well to discard the signal which does not has a contribution to the transmission wave. It is found that AI methods have equally good as compared to conventional methods. Reconstruction error, KT 1error estimates, accuracy, F-Score, recall, precision, specificity and MCC are used. ANN and FFT methods are processing the UCT signal with the best recovery.

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References

  1. Abd Rahman, N. A., En Hong, L., Rahim, R., Rahim, H., Ahmad, N., Bunyamin, S., & Mansor, M. S. (2015). A Review: Tomography Systems in Medical and Industrial Processes. Jurnal Teknologi, 73, 1–11. https://doi.org/10.11113/jt.v73.4398

  2. Honarvar, F., & Varvani-Farahani, A. (2020). A review of ultrasonic testing applications in additive manufacturing: Defect evaluation, material characterization, and process control. Ultrasonics, 108, 106227. https://doi.org/10.1016/j.ultras.2020.106227

  3. Ibrahim, S., Yunus, M. A. M., Khairi, M. T. M., & Faramarzi, M. (2014). A review on ultrasonic process tomography system. Jurnal Teknologi, 70(3). https://doi.org/10.11113/jt.v70.3452

  4. WHO Study Group on Training in Diagnostic Ultrasound : Essentials Pennsylvania P. and S. (1996) : P., & Organization W. H. (1998). Training in diagnostic ultrasound : essentials, principles and standards : report of a WHO study group. Geneva PP - Geneva: World Health Organization. Retrieved from https://apps.who.int/iris/handle/10665/42093

  5. Goswami, M., Munshi, P., Khanna, A., & Saxena, A. (2015). Nonuniform Arrangement of Emitter-Receiver Pairs Arrangement and Compact Ultrasonic Tomography Setup. IEEE Sensors Journal, 15(2), 1198–1207. https://doi.org/10.1109/JSEN.2014.2361201

    Article  Google Scholar 

  6. Krautkrämer, J., & Krautkrämer, H. (2013). Ultrasonic testing of materials. Springer Science & Business Media.

  7. Chan, V., & Perlas, A. (2011). Basics of ultrasound imaging. In Atlas of ultrasound-guided procedures in interventional pain management (pp. 13–19). Springer.

  8. Fuentes, R., Mineo, C., Pierce, S. G., Worden, K., & Cross, E. J. (2019). A probabilistic compressive sensing framework with applications to ultrasound signal processing. Mechanical Systems and Signal Processing, 117, 383–402. https://doi.org/10.1016/j.ymssp.2018.07.036

  9. Parrilla, M., Anaya, J. J., & Fritsch, C. (1991). Digital signal processing techniques for high accuracy ultrasonic range measurements. IEEE Transactions on instrumentation and measurement, 40(4), 759–763. https://doi.org/10.1109/19.85348

    Article  Google Scholar 

  10. Kogan, S. (2008). Electronic noise and fluctuations in solids. Cambridge University Press.

    Google Scholar 

  11. Neal, S. P., & Thompson, D. O. (1990). The Measurement and Analysis of Acoustic Noise as a Random Variable BT - Review of Progress in Quantitative Nondestructive Evaluation. In D. O. Thompson & D. E. Chimenti (Eds.), (pp. 625–632). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4684-5772-8_78

  12. Zhang, G.-M., & Harvey, D. M. (2012). Contemporary ultrasonic signal processing approaches for nondestructive evaluation of multilayered structures. Nondestructive testing and evaluation, 27(1), 1–27.

    Article  Google Scholar 

  13. Hooge, F. N. (1994). 1/f noise sources. IEEE Transactions on Electron Devices, 41(11), 1926–1935. https://doi.org/10.1109/16.333808

    Article  Google Scholar 

  14. Zidelmal, Z., Amirou, A., Ould-Abdeslam, D., Moukadem, A., & Dieterlen, A. (2014). QRS detection using S-Transform and Shannon energy. Computer methods and programs in biomedicine, 116(1), 1–9. https://doi.org/10.1016/j.cmpb.2014.04.008

    Article  Google Scholar 

  15. Yang, G., Dai, J., Liu, X., Chen, M., & Wu, X. (2020). Spectral feature extraction based on continuous wavelet transform and image segmentation for peak detection. Analytical Methods, 12(2), 169–178.

    Article  Google Scholar 

  16. Jarman, K. H., Daly, D. S., Anderson, K. K., & Wahl, K. L. (2003). A new approach to automated peak detection. Chemometrics and intelligent laboratory systems, 69(1–2), 61–76. https://doi.org/10.1016/S0169-7439(03)00113-8

    Article  Google Scholar 

  17. Manikandan, M. S., & Soman, K. P. (2012). A novel method for detecting R-peaks in electrocardiogram (ECG) signal. Biomedical Signal Processing and Control, 7(2), 118–128. https://doi.org/10.1016/j.bspc.2011.03.004

    Article  Google Scholar 

  18. Lange, E., Gröpl, C., Reinert, K., Kohlbacher, O., & Hildebrandt, A. (2006). High-accuracy peak picking of proteomics data using wavelet techniques. In Biocomputing 2006 (pp. 243–254). World Scientific. https://doi.org/10.1142/9789812701626_0023

  19. Li, C., Huang, L., Duric, N., Zhang, H., & Rowe, C. (2009). An improved automatic time-of-flight picker for medical ultrasound tomography. Ultrasonics, 49(1), 61–72. https://doi.org/10.1016/j.ultras.2008.05.005

    Article  Google Scholar 

  20. Herter, S., Youssef, S., Becker, M. M., & Fischer, S. C. L. (2021). Machine Learning Based Preprocessing to Ensure Validity of Cross-Correlated Ultrasound Signals for Time-of-Flight Measurements. Journal of Nondestructive Evaluation, 40(1), 20. https://doi.org/10.1007/s10921-020-00745-7

    Article  Google Scholar 

  21. Andria, G., Attivissimo, F., & Giaquinto, N. (2001). Digital signal processing techniques for accurate ultrasonic sensor measurement. Measurement, 30(2), 105–114. https://doi.org/10.1016/S0263-2241(00)00059-2

    Article  Google Scholar 

  22. Iyer, S., Sinha, S. K., Tittmann, B. R., & Pedrick, M. K. (2012). Ultrasonic signal processing methods for detection of defects in concrete pipes. Automation in Construction, 22, 135–148. https://doi.org/10.1016/j.autcon.2011.06.012

  23. Yu, D., & Deng, L. (2011). Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP]. IEEE Signal Processing Magazine, 28(1), 145–154. https://doi.org/10.1109/MSP.2010.939038

    Article  Google Scholar 

  24. Vijaya, G., Kumar, V., & Verma, H. K. (1998). ANN-based QRS-complex analysis of ECG. Journal of Medical Engineering & Technology, 22(4), 160–167. https://doi.org/10.3109/03091909809032534

    Article  Google Scholar 

  25. Arbitrary Waveform Generators | Keysight. (n.d.). Retrieved July 17, 2021, from https://www.keysight.com/in/en/products/arbitrary-waveform-generators.html

  26. Arbitrary Waveform Generators | Tektronix. (n.d.). Retrieved July 17, 2021, from https://www.tek.com/arbitrary-waveform-generator

  27. Bhardwaj, A., Patel, K., Bhardwaj, M. C., & Fetfatsidis, K. A. (2014). Application of advanced non-contact ultrasound for composite material qualification. In ASNT Annual Conference 2014 (pp. 17–25).

  28. Khan, A. A. (2005). Digital signal processing fundamentals. Firewall Media.

  29. Sahidullah, M., & Saha, G. (2013). A Novel Windowing Technique for Efficient Computation of MFCC for Speaker Recognition. IEEE Signal Processing Letters, 20(2), 149–152. https://doi.org/10.1109/LSP.2012.2235067

    Article  Google Scholar 

  30. Kadah, Y. M., Farag, A. A., Zurada, J. M., Badawi, A. M., & Youssef, A. B. (1996). Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images. IEEE transactions on Medical Imaging, 15(4), 466–478. https://doi.org/10.1109/42.511750

    Article  Google Scholar 

  31. Wang, C. C., & Chang, C. D. (2010). SVD and SVM based approach for congestive heart failure detection from ECG signal. In The 40th International Conference on Computers & Indutrial Engineering (pp. 1–5). IEEE. https://doi.org/10.1109/ICCIE.2010.5668319

  32. Cao, C., & Wang, Z. (2018). IMCStacking: Cost-sensitive stacking learning with feature inverse mapping for imbalanced problems. Knowledge-Based Systems, 150, 27–37. https://doi.org/10.1016/j.knosys.2018.02.031

  33. Zhu, Q. (2020). On the performance of Matthews correlation coefficient (MCC) for imbalanced dataset. Pattern Recognition Letters, 136, 71–80. https://doi.org/10.1016/j.patrec.2020.03.030

  34. Shakya, S., & Munshi, P. (2015). Error analysis of tomographic reconstructions in the absence of projection data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2043), 20140394.

    Article  Google Scholar 

  35. Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 6. https://doi.org/10.1186/s12864-019-6413-7

    Article  Google Scholar 

  36. Khare, P., & Goswami, M. (2021). AI Algorithm for Mode Classification of PCF SPR Sensor Design. arXiv preprint arXiv:2107.06184.

  37. Chowdhury, M., & Sadek, A. W. (2012). Advantages and limitations of artificial intelligence. Artificial intelligence applications to critical transportation issues, 6(3), 360–375.

    Google Scholar 

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Acknowledgements

MG acknowledge the Science and Engineering Research Board (SERB), Government of India, for providing support with Grant No. ECR/2017/001432. AK like to acknowledge CSIR Research fellowship. We also acknowledge Mr. Utakarsh Vinayak Parkhi’s help in testing preliminary KCNN codes in MATLAB™. AK acknowledges Dr. Snehlata Shakya for her help in KT1 theorem discussions.

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  1. Divyadrishti Imaging Laboratory, Department of Physics, IIT Roorkee, Roorkee, India

    Ankur Kumar, Prasunika Khare & Mayank Goswami

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  1. Ankur Kumar
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  2. Prasunika Khare
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  3. Mayank Goswami
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Contributions

Ankur Kumar: Methodology, Investigation, data measurement, Writing and Analysis, non-AI methods. Prasunika Khare: AI methods and analysis, Mayank Goswami: Methodology, Investigation, Writing, Visualization, Supervision, Funding acquisition.

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Correspondence to Mayank Goswami.

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Kumar, A., Khare, P. & Goswami, M. AI and Conventional Methods for UCT Projection Data Estimation. J Sign Process Syst 94, 425–433 (2022). https://doi.org/10.1007/s11265-021-01697-5

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