We propose a pipeline for synthetic generation of personalized Computer Tomography (CT) images, with a radiation exposure evaluation and a lifetime attributable risk (LAR) assessment. We perform a patient-specific performance evaluation for a broad range of denoising algorithms (including the most popular deep learning denoising approaches, wavelets-based methods, methods based on Mumford−Shah denoising, etc.), focusing both on accessing the capability to reduce the patient-specific CT-induced LAR and on computational cost scalability. We introduce a parallel Probabilistic Mumford−Shah denoising model (PMS) and show that it markedly-outperforms the compared common denoising methods in denoising quality and cost scaling. In particular, we show that it allows an approximately 22-fold robust patient-specific LAR reduction for infants and a 10-fold LAR reduction for adults. Using a normal laptop, the proposed algorithm for PMS allows cheap and robust (with a multiscale structural similarity index >90%) denoising of very large 2D videos and 3D images (with over 107 voxels) that are subject to ultra-strong noise (Gaussian and non-Gaussian) for signal-to-noise ratios far below 1.0. The code is provided for open access.

Department of Mathematics VSB Ostrava Ludvika Podeste 1875 17 708 33 Ostrava Czech Republic

Faculty of Mathematics Technical University of Kaiserslautern 67663 Kaiserslautern Germany

Institute for Human Genetics University Medical Center of the Johannes Gutenberg University Mainz 55128 Mainz Germany

Institute of Computing Faculty of Informatics Universitá della Svizzera Italiana 6962 Viganello Switzerland

Institute of Occupational Medicine Faculty of Medicine GU Frankfurt 60590 Frankfurt am Main Germany

Institute of Pathophysiology University Medical Center of the Johannes Gutenberg University Mainz 55128 Mainz Germany

Institute of Physiology University Medical Center of the Johannes Gutenberg University Mainz 55128 Mainz Germany

Lung Radiology Center Berlin 10627 Berlin Germany

Zobrazit více v PubMed

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