Time resolved inclusion investigation for continuous wave light


Creative Commons License

KAZANCI H. Ö.

OPTICAL AND QUANTUM ELECTRONICS, cilt.50, sa.3, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 50 Sayı: 3
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1007/s11082-018-1396-1
  • Dergi Adı: OPTICAL AND QUANTUM ELECTRONICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Time resolved input angle forward model sensitivity weight matrix, Programmable Delay Chip (PDC) circuit, TOMOGRAPHY
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Time resolved inclusion investigation method has been tested based on the Monte Carlo Modeling of Light (MCML) Transport. Seven layers head structure has been built. Tissue layers are hair 100 micrometer, epidermis 100 micrometer, dermis 1 mm, skull 5 mm, cerebrospinal fluid (CSF) 5 mm, gray matter 5 mm, and up to 3 cm overly thick white matter. MCML photon fluencies have been used to generate depth dependent forward model sensitivity weight matrix. Forward model weight matrix has been built according to back reflected diffuse optical tomography (DOT) device model geometry. The new, triangulated input angle photon penetration model which is correlated to time resolved mode for continuous wave light has been used first. 1 source and 299 detectors were placed on the 3 cm by 3 cm grid model. Number of z Nz, and number of x Nx grid coordinates are 299 by 299. This work was done to prepare for Programmable Delay Chip (PDC) circuit included analog current input digital voltage output analog to digital converter (ADC) with integrator circuit topology. By the help of PDC circuit, continuous wave photons which cross from different depth layers will be collected in different integrating time intervals by the capability of PDC circuit. In the simulation model, blood inclusion was buried into skull in approximately 1.8 mm depth. As long as employing the measurement capability of PDC circuit, different depth level voxels would be scanned. In this work with the presumable existence of PDC data, an image reconstruction algorithm has been developed. The inverse problem solution has been done by pseudoinverse matrix inverse problem solution method. Forward model sensitivity weight matrix functions have been calculated and used in image reconstruction algorithm.