Experimental Assessment of PCA and DT Classification for Streamlined Position Reconstruction in Anger Cameras

In this article, we present a study of machine learning (ML) algorithms to simplify the computation of the planar scintillation coordinates in Anger Cameras for emission tomography applications. Two ML-based techniques for data inference and one technique to speed-up the training procedure are explored within the framework of a multimodal SPECT scanner. First, the use of principal component analysis (PCA), a dimensionality reduction algorithm, is explored to reduce the computational complexity of the maximum-likelihood statistical estimation method. The analysis indicates a similar to 3-fold reduction of computational complexity for typical Anger Camera architectures (with 72 channels). Second, the estimation of the scintillation coordinates is formulated as a classification problem, addressed by means of a decision tree (DT) classifier. No degradation of the achievable intrinsic spatial resolution (similar to 1.2-mm FWHM) of the detection module was observed when applying PCA (reducing from 72 to 25 components). The DT classifier was trained on experimental data obtained using a parallel-hole collimator: again no degradation of spatial resolution is observed and the computation cost is reduced by more than two orders of magnitude. Finally, in order to overcome the limits of a cumbersome training procedure involving the translation of the collimator, data augmentation was successfully leveraged for the generation of artificial data.

Automated summary

In this article, the authors study machine learning algorithms to simplify the computation of planar scintillation coordinates in Anger Cameras for emission tomography applications. They explore the use of principal component analysis (PCA) and decision tree (DT) classifiers to reduce computational complexity without degrading spatial resolution.