Szczegóły publikacji
Opis bibliograficzny
Bias correction for non-stationary noise filtering in MRI / Tomasz PIĘCIAK, Iñaki Rabanillo-Viloria, Santiago Aja-Fernández // W: ISBI 2018 [Dokument elektroniczny] : 2018 IEEE International Symposium on Biomedical Imaging : April 4–7, 2018, Washington, D. C., USA. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2018. — (Proceedings (International Symposium on Biomedical Imaging) ; ISSN 1945-7928). — Dod. ISBN: 978-1-5386-3636-7. — e-ISBN: 978-1-5386-3635-0. — S. 307–310. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 310, Abstr. — T. Pięciak – dod. afiliacja: Universidad de Valladolid, Spain
Autorzy (3)
- AGHPięciak Tomasz
- Rabanillo-Viloria Iñaki
- Aja-Fernández Santiago
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 115147 |
|---|---|
| Data dodania do BaDAP | 2018-07-18 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ISBI.2018.8363580 |
| Rok publikacji | 2018 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | 2018 IEEE 15th International Symposium on Biomedical Imaging |
| Czasopismo/seria | Proceedings (International Symposium on Biomedical Imaging) |
Abstract
The aggregation of non-stationary distributed magnetic resonance imaging (MRI) samples results in a systematic bias that should be corrected prior to any further numerical processing, such as quantitative analysis. In this paper, we analytically derive two formulas to compensate the bias from aggregated non-stationary non-central chi (nc-χ) distributed random variables. As a proof-of-concept, we reformulate the unbiased non-local means (UNLM) filtering scheme to handle non-stationary nc-χ and particularly non-stationary Rician distributed MR data. The proposals are validated over synthetic and real parallel accelerated MR reconstructions leading to a considerable reduction of inherent bias component over the state-of-the-art UNLM approach.