Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27120%2F22%3A10250373" target="_blank" >RIV/61989100:27120/22:10250373 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2313-433X/8/6/156" target="_blank" >https://www.mdpi.com/2313-433X/8/6/156</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/jimaging8060156" target="_blank" >10.3390/jimaging8060156</a>

Result language

angličtina

Original language name

Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

Original language description

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 &gt;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.

Czech name

—

Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

—

OECD FORD branch

10103 - Statistics and probability

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Imaging

ISSN

2313-433X

e-ISSN

2313-433X

Volume of the periodical

8

Issue of the periodical within the volume

6

Country of publishing house

CH - SWITZERLAND

Number of pages

25

Pages from-to

"nestrankovano"

UT code for WoS article

000817351400001

EID of the result in the Scopus database

2-s2.0-85131675100