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The Effect of Loss Functions on Denoising and Reconstructing Sinograms Based on Deep Learning Methodologies

Recent advancements in machine learning and, more specifically, deep learning methodologies showed promising results in Computed tomography (CT) imaging, either by enhancing existing reconstruction methodologies by cleaning the signal from the collected data or used for the complete reconstruction of the images directly from raw data. The loss function used for the training of these deep learning methodologies can significantly impact the training process. This paper compares different deep learning loss functions in the framework of denoising and recon-structing sinograms based on Single-photon emission computed tomography (SPECT) imaging. The loss functions evaluated in the proposed work are Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Logarithmic Error (MSLE), Huber, Peak Signal-to-Noise ratio (PSRN) and Structural Similarity Index (SSIM). These loss functions were used to train an identical proposed convolutional-based deep learning methodology to denoise sinograms constructed using software phantoms and compared the results. Furthermore, a study is constructed to measure the reconstructed accuracy of the denoised sinograms and compare the results using the ordered subset expectation maximisation (OSEM) method. Based on our analysis, some guidelines are derived for selecting a loss function in denoising data used for inverse problems such as sinogram reconstruction for medical image applications.