{'fraction_non_alphanumeric': 0.01903553299492386, 'fraction_numerical': 0.0, 'mean_word_length': 5.261904761904762, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Evaluation of image quality of a deep learning image reconstruction algorithm

The iterative reconstruction methods ASiR and ASiR-V have been accepted by hundreds of sites as their standard of care for a variety of protocols and applications. While the reduction in noise has been significant some readers have a preference for the classic image appearance. To maintain the classic image appearance of FBP at the same dose levels used for the standard of care with ASiR-V we introduce, Deep Learning Image Reconstruction (DLIR), a technique using artificial neural networks. This paper demonstrates that DLIR can maintain or improve upon the performance of the conventional iterative reconstruction algorithm (ASiR-V) in terms of low contrast detectability, noise, and spatial resolution.