Continuous single-ended depth-of-interaction measurement using highly multiplexed signals and artificial neural...

Area of Science:

• Medical Imaging
• Nuclear Medicine
• Detector Physics

Background:

• Positron Emission Tomography (PET) systems require sophisticated detectors for accurate imaging.
• Estimating the depth-of-interaction (DOI) is crucial for improving PET image resolution and reducing parallax errors.
• Traditional DOI estimation methods often necessitate a large number of readout channels, increasing system complexity and cost.

Purpose of the Study:

• To develop a cost-effective continuous depth-of-interaction (cDOI) measurement technique for PET systems.
• To significantly reduce the number of readout channels in scintillation detectors while preserving DOI accuracy.
• To implement a signal multiplexing strategy combined with artificial neural networks (ANNs) for enhanced DOI estimation.

Main Methods:

• A high-pass filter-based signal multiplexing technique was employed to reduce 64 signals from an 8x8 silicon photomultiplier (SiPM) array to four channels.
• Artificial neural networks (ANNs) were utilized to demultiplex the signals and recover original energy information.
• Two DOI estimation strategies were explored: single-step estimation directly from multiplexed signals and a two-stage cascade estimation involving demultiplexing first.

Main Results:

• Recovered signal amplitudes using ANN-based demultiplexing showed a strong correlation with ground truth (R²=0.98 at 125 MHz sampling rate).
• Both single-step and two-stage DOI estimation methods achieved high accuracy, with average DOI resolutions of 4.86 mm and 5.11 mm, respectively.
• The proposed technique successfully validated DOI measurements at various depths (2-18 mm).

Conclusions:

• The novel signal multiplexing technique effectively reduces readout channel requirements for cDOI PET detectors.
• ANN-based demultiplexing enables accurate energy estimation from multiplexed signals.
• This approach offers a significant advancement in developing more cost-effective and high-performance cDOI PET imaging systems.