TABLE I.

Challenges and machine learning methods in PET detector

Position (x,y,z)
Monolithic detectors:
Challenge: Determine position-of-interaction in the scintillator from the distribution of scintillation light measured by the photodetectors
Algorithm: Train a machine learning model to map the charge collected by each photodetector (input) to position-of-interaction (output). Training data acquired with a narrow beam of photons or with simulated data.
Estimate position (2D and 3D) with artificial neural network (ANN) [16, 20–24].	Estimate position with gradient tree boosting algorithm [25–27].		Estimate 2D position in quasi-monolithic detector with convolutional neural network (CNN) [28].		Estimate position using a library of reference signals and k nearest neighbors (k-NN) [29–31].
Pixelated detectors:
Challenge: Determine and recover inter-crystal scattering (photon interacts in two or more crystals)
Algorithm: Train a machine learning model to map the charge collected bv each photodetector (input) or charge to position-of-interaction (output). Training data acquired with a narrow beam of photons or with simulated data.
Identify inter-crystal scatter in multi-layer DOI detector with support vector machine (SVM) [35].			Identify trues from triplet coincidences caused by inter-crystal scatter using artifical neural network (ANN) [36].
Timing (TOF)
Challenge: Determine time-of-flight with ~hundreds psec precision from noisy photodetector signals.
Algorithm: Train a machine learning model to estimate timing from digitized detector waveforms. Training data acquired experimentally with known source-to-detector distances.
Use ANN to estimate timing pick-off from digitized waveforms [38].		Use CNN to estimate time-of-flight directly from set of coincidence waveforms [39].		Estimate time-of-flight from library of digitized waveforms and k-NN algorithm [43].