Design and construction of a variable resolution cone-beam small animal mini-CT prototype for in vivo studies

Detection performance of pixelated lutetium-yttrium oxyorthosilicate (LYSO) scintillators for high-resolution photon-counting CT imaging

High-resolution photon-counting detector (PCD) computed tomography (CT) imaging is increasingly used for several applications. Recent technological advances in CT instrumentation have introduced various types of radiation detectors. Therefore, this work aims at evaluating the lutetium-yttrium oxyorthosilicate (LYSO) scintillator for use in PCD CT from a detector point of view. To do so, a mini-CT prototype was designed and constructed based on the pixelated LYSO blocks. The detector comprises four 10 × 10 linearly arranged LYSO blocks coupled with four position-sensitive photomultiplier tubes. The prototype utilizes a point gamma-ray source along with a cone-beam collimator. An in-home MATLAB-based data processing software package was also developed for storing the list-mode data, event positioning, and energy windowing. A set of experiments were conducted to assess the performance of the constructed energy-resolved LYSO:Ce detector for mini-CT imaging. The results show good crystal identification for all blocks with a maximum peak-to-valley ratio of 3.48. In addition, the findings confirm that the developed detector is position-sensitive. The 20% energy window provides an optimal performance by simultaneously providing good crystal identification and a scatter removal factor of 0.71. A 96% uniformity was also observed when the detector was irradiated with a uniform flood. The spatial resolution of the mini-CT prototype in the x- and y-directions was calculated to be 0.9 and 0.93 mm, respectively, corrected for a magnification factor of 2.5. It is concluded that the pixelated LYSO crystal is a promising alternative to the current detectors and would be the scintillator of choice for high-resolution PCD CT imaging tasks.

Estimation of optimized timely system matrix with improved image quality in iterative reconstruction algorithm: A simulation study

The system matrix (SM) being a main part of statistical image reconstruction algorithms establishes relationship between the object and projection space. The aim was to determine it in a short duration time, towards obtaining the best quality of contrast images. In this study, a new analytical method based on Cavalieri's principle as subdividing common regions has been proposed in which the precision of the amounts of estimated areas was improved by increasing the number of divisions (NOD), and consequently the total SM's time was increased. An important issue is the tradeoff between the NODs and computational time. For this purpose, a Monte Carlo simulated Jaszczak phantom study was performed by the Monte Carlo N-Particle transport code version 5 (MCNP5) in which the tomographic images of resolution and contrast phantoms were reconstructed by maximum likelihood expectation maximization (MLEM) algorithm, and the influence of NODs variations was investigated. The results show that the lowest and best quality have been obtained at the NODs of 0 and 8, respectively and in the optimum case, the SM's total time at NOD of 8 was 925 s, which was much lower than those of the conventional Monte Carlo simulations and experimental test.