English

Assessment of the Axial Resolution of a Compact Gamma Camera with Coded Aperture Collimator

Medical Physics 2024-01-22 v1

Abstract

Purpose: Handheld gamma cameras with coded aperture collimators are under investigation for intraoperative imaging in nuclear medicine. Coded apertures are a promising collimation technique for applications such as lymph node localization due to their high sensitivity and the possibility of 3D imaging. We evaluated the axial resolution and computational performance of two reconstruction methods. Methods: An experimental gamma camera was set up consisting of the pixelated semiconductor detector Timepix3 and MURA mask of rank 3131 with round holes of 0.080.08mm in diameter in a 0.110.11mm thick Tungsten sheet. A set of measurements was taken where a point-like gamma source was placed centrally at 2121 different positions within the range of 1212 to 100100mm. For each source position, the detector image was reconstructed in 0.50.5mm steps around the true source position, resulting in an image stack. The axial resolution was assessed by the full width at half maximum (FWHM) of the contrast-to-noise ratio (CNR) profile along the z-axis of the stack. Two reconstruction methods were compared: MURA Decoding and a 3D maximum likelihood expectation maximization algorithm (3D-MLEM). Results: While taking 4,4004{,}400 times longer in computation, 3D-MLEM yielded a smaller axial FWHM and a higher CNR. The axial resolution degraded from 5.35.3mm and 1.81.8mm at 1212mm to 42.242.2mm and 13.513.5mm at 100100mm for MURA Decoding and 3D-MLEM respectively. Conclusion: Our results show that the coded aperture enables the depth estimation of single point-like sources in the near field. Here, 3D-MLEM offered a better axial resolution but was computationally much slower than MURA Decoding, whose reconstruction time is compatible with real-time imaging.

Keywords

Cite

@article{arxiv.2401.10633,
  title  = {Assessment of the Axial Resolution of a Compact Gamma Camera with Coded Aperture Collimator},
  author = {Tobias Meißner and Laura Antonia Cerbone and Paolo Russo and Werner Nahm and Jürgen Hesser},
  journal= {arXiv preprint arXiv:2401.10633},
  year   = {2024}
}