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Computational models and methods for ultrasound tomography

Başlık çevirisi mevcut değil.

  1. Tez No: 400494
  2. Yazar: HÜSEYİN EMRE GÜVEN
  3. Danışmanlar: PROF. ERIC L. MILLER, PROF. ROBIN O. CLEVELAND
  4. Tez Türü: Doktora
  5. Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2012
  8. Dil: İngilizce
  9. Üniversite: Northeastern University
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 94

Özet

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Özet (Çeviri)

In this dissertation, computational methods are developed for modeling and reconstruction of ultrasound tomographic imaging. A fast method for computing the acoustic eld of ultrasound transducers is presented with application to rectangular elements that are cylindrically focused. Our motivation is the rapid calculation of imaging kernels for physics based diagnostic imaging where current methods are computationally too intensive. Here the surface integral de ning the acoustic eld from a baed piston is converted to a three-dimensional spatial convolution of the element surface and the Green's function. A three-dimensional version of the overlap-save method from digital signal processing is employed to obtain a fast computational algorithm based on spatial Fourier transforms. Further eciency is gained by using a separable approximation to the Green's function through singular value decomposition and increasing the e ective sampling rate by polyphase ltering. Secondly, ultrasound images are reconstructed from frequency-domain measurements of the scattered eld from an object with contrast in attenuation and sound speed. The case where the object has uniform but unknown contrast in these properties relative to the background is considered. Background clutter is taken into account by considering an exact scattering model for randomly located small scatterers that vary in sound speed. The resulting statistical characteristics of the interference is incorporated into the imaging solution, which includes applying a total-variation minimization based approach where the relative e ect of perturbation in sound speed to attenuation is included as a parameter. Convex optimization methods provide the basis for the reconstruction algorithm. Numerical data for inversion examples are generated by solving the discretized Lippman-Schwinger equation for the object and speckle-forming scatterers in the background. A statistical model based on the Born approximation is used for reconstruction of the object pro le. Results are presented for a two dimensional problem in terms of classi cation performance and compared to minimum-`2-norm reconstruction. Classi cation using the proposed method is shown to be robust down to a signal-to-clutter ratio of less than 1 dB.

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