Singh, Imraj; Denker, Alexander; Jin, Bangti; Thielemans, Kris; Arridge, Simon; (2024) Investigating Intensity Normalisation for PET Reconstruction with Supervised Deep Learning. In: Proceedings of the IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector (RTSD) Conference 2023. (pp. pp. 1-2). IEEE (In press).

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Abstract

Deep learning methods have shown great promise in the field of Positron Emission Tomography (PET) reconstruction, but the successful application of these methods depends heavily on the intensity scale of the images. Normalisation is a crucial step that aims to adjust the intensity of network inputs to make them more uniform and comparable across samples, acquisition times, and activity levels. In this work, we study the influence of different linear intensity normalisation approaches. We focus on two popular deep learning based image reconstruction methods: an unrolled algorithm (Learned Primal-Dual) and a post-processing method (OSEMConvNet). Results on the out-ofdistribution test dataset demonstrate that the choice of intensity normalisation significantly impacts on generalisability of these methods. Normalisation using the mean of acquisition data or corrected acquisition data led to improved peak-signal-to-noiseratio (PSNR) and data-consistency (KLDIV). Through evaluation of lesion-specific metrics of contrast recovery coefficients (CRC) and standard deviation (STD) an increase in CRC and STD is observed. These findings highlight the importance of carefully selecting an appropriate normalisation method for supervised deep learning-based PET reconstruction applications.

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