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Research Article

Real-time estimation of lesion depth and control of radiofrequency ablation within ex vivo animal tissues using a neural network

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Pages 1104-1113 | Received 18 Jul 2017, Accepted 09 Dec 2017, Published online: 04 Jan 2018

References

  • Chu KF, Dupuy DE. (2014). Thermal ablation of tumours: biological mechanisms and advances in therapy. Nat Rev Cancer 14:199–208.
  • Goldberg SN, Gazelle GS, Dawson SL, et al. (1995). Tissue ablation with radiofrequency: effect of probe size, gauge, duration, and temperature on lesion volume. Acad Radiol 2:399–404.
  • Larina IV, Larin KV, Esenaliev RO. (2005). Real-time optoacoustic monitoring of temperature in tissues. J Phys D Appl Phys 38:2633.
  • Wi H, McEwan AL, Lam V, et al. (2015). Real‐time conductivity imaging of temperature and tissue property changes during radiofrequency ablation: an ex vivo model using weighted frequency difference. Bioelectromagnetics 36:277–86.
  • Zhou Z, Wu S, Wang CY, et al. (2015). Monitoring radiofrequency ablation using real-time ultrasound Nakagami imaging combined with frequency and temporal compounding techniques. PLoS One 10:e0118030.
  • Cherepenin VA, Karpov AY, Korjenevsky AV, et al. (2002). Three-dimensional EIT imaging of breast tissues: system design and clinical testing. IEEE Trans Med Imaging 21:662–7.
  • Javaherian A, Soleimani M, Moeller K. (2016). A fast time-difference inverse solver for 3D EIT with application to lung imaging. Med Biol Eng Comput 54:1243–55.
  • Martin S, Choi CT. (2017). A post-processing method for three-dimensional electrical impedance tomography. Sci Rep 7:7212.
  • Dean-Ben XL, Buehler A, Ntziachristos V, Razansky D. (2012). Accurate model-based reconstruction algorithm for three-dimensional optoacoustic tomography. IEEE Trans Med Imaging 31:1922–8.
  • Pang GA, Bay E, Dean-Ben X, Razansky D. (2015). Three‐dimensional optoacoustic monitoring of lesion formation in real time during radiofrequency catheter ablation. J Cardiovasc Electrophysiol 26:339–45.
  • Adler A. (2004). Accounting for erroneous electrode data in electrical impedance tomography. Physiol Meas 25:227.
  • Graham BM, Adler A. (2007). Electrode placement configurations for 3D EIT. Physiol Meas 28:S29.
  • Oh TI, Kim TE, Yoon S, et al. (2012). Flexible electrode belt for EIT using nanofiber web dry electrodes. Physiol Meas 33:1603.
  • Tompson J, Schlachter K, Sprechmann P, Perlin K. (2016). Accelerating Eulerian fluid simulation with convolutional networks. Proceedings of the 34th International Conference on Machine Learning; Pre-print on 2016 Jul 13.
  • Baymani M, Effati S, Niazmand H, Kerayechian A. (2015). Artificial neural network method for solving the Navier–Stokes equations. Neural Comput Appl 26:765–73.
  • Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. (2013). Mitosis detection in breast cancer histology images with deep neural networks. International Conference on Medical Image Computing and Computer-assisted Intervention 2013 Sep 22 (pp. 411–18), Springer, Berlin, Heidelberg.
  • Esteva A, Kuprel B, Novoa RA, et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–18.
  • Li Q, Cai W, Wang X, Zhou Y, Feng DD, Chen M. (2014). Medical image classification with convolutional neural network. In: International Conference on Control, Automation, Robotics & Vision (ICARCV), 2014 13th IEEE International Conference, 2014 Dec 10 (pp. 844–8).
  • Scudamore CH, Lee SI, Patterson EJ, et al. (1999). Radiofrequency ablation followed by resection of malignant liver tumors. Am J Surg 177:411–17.
  • Patterson EJ, Scudamore CH, Owen DA, et al. (1998). Radiofrequency ablation of porcine liver in vivo: effects of blood flow and treatment time on lesion size. Ann Surg 227:559.
  • Lardo AC, McVeigh ER, Jumrussirikul P, et al. (2000). Visualization and temporal/spatial characterization of cardiac radiofrequency ablation lesions using magnetic resonance imaging. Circulation 102:698–705.
  • Dreiseitl S, Ohno-Machado L. (2002). Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 35:352–9.
  • Nielsen MA. (2015). Neural networks and deep learning. San Francisco, CA: Determination Press.
  • Kingma D, Ba J. (2014). Adam: a method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations, 2014 Dec 22.
  • Glorot X, Bengio Y. (2010). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics 2010 Mar 31 (pp. 249–56).
  • Glorot X, Bordes A, Bengio Y. (2011). Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics 2011 Jun 14 (pp. 315–23).
  • Hecht-Nielsen R. (1988). Theory of the backpropagation neural network. Neural Netw 1:445–8.
  • Jain AK, Mao J, Mohiuddin KM. (1996). Artificial neural networks: a tutorial. Computer 29:31–44.
  • Funahashi KI, Nakamura Y. (1993). Approximation of dynamical systems by continuous time recurrent neural networks. Neural Netw 6:801–6.

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