High-resolution monolithic LYSO detector with 6-layer depth-of-interaction for clinical PET

Phys Med Biol. 2021 Jul 30;66(15). doi: 10.1088/1361-6560/ac1459.


The system spatial resolution of whole-body positron emission tomography (PET) is limited to around 2 mm due to positron physics and the large diameter of the bore. To stay below this 'physics'-limit a scintillation detector with an intrinsic spatial resolution of around 1.3 mm is needed. Currently used detector technology consists of arrays of 2.6-5 mm segmented scintillator pixels which are the dominant factor contributing to the system resolution. Pixelated detectors using smaller pixels exist but face major drawbacks in sensitivity, timing, energy resolution and cost. Monolithic continuous detectors, where the spatial resolution is determined by the shape of the light distribution on the photodetector array, are a promising alternative. Without having the drawbacks of pixelated detectors, monolithic ones can also provide depth-of-interaction (DOI) information. In this work we present a monolithic detector design aiming to serve high-resolution clinical PET systems while maintaining high sensitivity. A 50 × 50 × 16 mm3Lutetium-Yttrium oxyorthosilicate scintillation crystal with silicon photomultiplier (SiPM) backside readout is calibrated in singles mode by a collimated beam obtaining a reference dataset for the event positioning. A mean nearest neighbour (MNN) algorithm and an artificial neural network for positioning are compared. The targeted intrinsic detector resolution of 1.3 mm needed to reach a 2 mm resolution on system level was accomplished with both algorithms. The neural network achieved a mean spatial resolution of 1.14 mm FWHM for the whole detector and 1.02 mm in the centre (30 × 30 mm2). The MNN algorithm performed slightly worse with 1.17 mm for the whole detector and 1.13 mm in the centre. The intrinsic DOI information will also result in uniform system spatial resolution over the full field of view.

Keywords: PET; TB-PET; high-resolution; monolithic detector; nearest neighbour; neural networks.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Neural Networks, Computer
  • Physical Phenomena
  • Positron-Emission Tomography*