Time Resolved Diffuse Optical Tomography model


Creative Commons License

Kazancı H. Ö.

OPTIK, cilt.162, ss.133-139, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 162
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1016/j.ijleo.2018.02.076
  • Dergi Adı: OPTIK
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.133-139
  • Anahtar Kelimeler: Time Resolved Diffuse Optical Tomography (TRDOT), Time dependent diffusion equation, OF-FLIGHT, TISSUE
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Time Resolved Diffuse Optical Tomography (TRDOT) modality is presented. Images were reconstructed for four different scenarios. 64-source and 64-detector bifurcated positions were used for simulation model. Inclusion was buried in different coordinate locations. Time-dependent diffusion equation for photon-tissue interactions were used to create the forward model. TRDOT devices have pulsed-laser source and photo-multiplier tube (PMT) photodetectors. Solid-state diode pumped pulsed-lasers and driving units constitute source. Solid-state lasers are made of Titanium Sapphire (TiSa). These laser sources are difficult to use, modify, maintain, and they are expensive. Instead of using expensive pulsed laser sources, cheap electronic-based pulsed-laser driving circuit will be implemented. Pseudomorphic high electron mobility transistors (pHEMTs) as switching elements will be used at both pulsed-laser and photodiode current readout sides. Hence, this work is taking its motivation from the next-generation electronic-based device instrument, creating the time dependent forward model as its focus. In this work, it was seen that selection of time intervals are important parameters to reconstruct the hidden images inside the homogeneous tissue. Inclusion was buried inside the homogeneous tissue model, and corresponding time modes have been selected to extract the hidden inclusion. In this work, time modes were analyzed for the TRDOT device. It has been realized that different time mode forward model weight functions should be used for different depth layers to reconstruct the hidden inclusion correctly. Hence, different time mode forward model weight matrix functions were generated from time 20 picoseconds to 400 picoseconds by 20 picoseconds steps, and from time 2 picoseconds to 40 picoseconds by 2 picoseconds steps. In the mathematical inverse problem solution algorithm, these time clusters were used to create two different weight matrixes.