175 related articles for article (PubMed ID: 21225425)
1. PET functional volume delineation: a robustness and repeatability study.
Hatt M; Cheze Le Rest C; Albarghach N; Pradier O; Visvikis D
Eur J Nucl Med Mol Imaging; 2011 Apr; 38(4):663-72. PubMed ID: 21225425
[TBL] [Abstract][Full Text] [Related]
2. A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET.
Hatt M; Cheze le Rest C; Turzo A; Roux C; Visvikis D
IEEE Trans Med Imaging; 2009 Jun; 28(6):881-93. PubMed ID: 19150782
[TBL] [Abstract][Full Text] [Related]
3. Repeatability of
Pfaehler E; Beukinga RJ; de Jong JR; Slart RHJA; Slump CH; Dierckx RAJO; Boellaard R
Med Phys; 2019 Feb; 46(2):665-678. PubMed ID: 30506687
[TBL] [Abstract][Full Text] [Related]
4. PET based volume segmentation with emphasis on the iterative TrueX algorithm.
Knäusl B; Hirtl A; Dobrozemsky G; Bergmann H; Kletter K; Dudczak R; Georg D
Z Med Phys; 2012 Feb; 22(1):29-39. PubMed ID: 21251804
[TBL] [Abstract][Full Text] [Related]
5. SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET.
Lapuyade-Lahorgue J; Visvikis D; Pradier O; Cheze Le Rest C; Hatt M
Med Phys; 2015 Oct; 42(10):5720-34. PubMed ID: 26429246
[TBL] [Abstract][Full Text] [Related]
6. Instrumentation factors affecting variance and bias of quantifying tracer uptake with PET/CT.
Doot RK; Scheuermann JS; Christian PE; Karp JS; Kinahan PE
Med Phys; 2010 Nov; 37(11):6035-46. PubMed ID: 21158315
[TBL] [Abstract][Full Text] [Related]
7. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications.
Hatt M; Cheze le Rest C; Descourt P; Dekker A; De Ruysscher D; Oellers M; Lambin P; Pradier O; Visvikis D
Int J Radiat Oncol Biol Phys; 2010 May; 77(1):301-8. PubMed ID: 20116934
[TBL] [Abstract][Full Text] [Related]
8. Multi-centre calibration of an adaptive thresholding method for PET-based delineation of tumour volumes in radiotherapy planning of lung cancer.
Schaefer A; Nestle U; Kremp S; Hellwig D; Grgic A; Buchholz HG; Mischke W; Gromoll C; Dennert P; Plotkin M; Senftleben S; Thorwarth D; Tosch M; Wahl A; Wengenmair H; Rübe C; Kirsch CM
Nuklearmedizin; 2012; 51(3):101-10. PubMed ID: 22446512
[TBL] [Abstract][Full Text] [Related]
9. Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.
Altazi BA; Zhang GG; Fernandez DC; Montejo ME; Hunt D; Werner J; Biagioli MC; Moros EG
J Appl Clin Med Phys; 2017 Nov; 18(6):32-48. PubMed ID: 28891217
[TBL] [Abstract][Full Text] [Related]
10. Feasibility of a semi-automated contrast-oriented algorithm for tumor segmentation in retrospectively gated PET images: phantom and clinical validation.
Carles M; Fechter T; Nemer U; Nanko N; Mix M; Nestle U; Schaefer A
Phys Med Biol; 2015 Dec; 60(24):9227-51. PubMed ID: 26576926
[TBL] [Abstract][Full Text] [Related]
11. A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET.
Belhassen S; Zaidi H
Med Phys; 2010 Mar; 37(3):1309-24. PubMed ID: 20384268
[TBL] [Abstract][Full Text] [Related]
12. Influence of different contributions of scatter and attenuation on the threshold values in contrast-based algorithms for volume segmentation.
Matheoud R; Della Monica P; Secco C; Loi G; Krengli M; Inglese E; Brambilla M
Phys Med; 2011 Jan; 27(1):44-51. PubMed ID: 20399128
[TBL] [Abstract][Full Text] [Related]
13. Influence of PET reconstruction parameters on the TrueX algorithm. A combined phantom and patient study.
Knäusl B; Rausch IF; Bergmann H; Dudczak R; Hirtl A; Georg D
Nuklearmedizin; 2013; 52(1):28-35. PubMed ID: 23348719
[TBL] [Abstract][Full Text] [Related]
14. A graph-theoretic approach for segmentation of PET images.
Bağci U; Yao J; Caban J; Turkbey E; Aras O; Mollura DJ
Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():8479-82. PubMed ID: 22256316
[TBL] [Abstract][Full Text] [Related]
15. A Dirichlet process mixture model for automatic (18)F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions.
Giri MG; Cavedon C; Mazzarotto R; Ferdeghini M
Med Phys; 2016 May; 43(5):2491. PubMed ID: 27147360
[TBL] [Abstract][Full Text] [Related]
16. Detection of sub-centimeter lesions using digital TOF-PET/CT system combined with Bayesian penalized likelihood reconstruction algorithm.
Miwa K; Wagatsuma K; Nemoto R; Masubuchi M; Kamitaka Y; Yamao T; Hiratsuka S; Yamaguchi M; Yoshii T; Kobayashi R; Miyaji N; Ishii K
Ann Nucl Med; 2020 Oct; 34(10):762-771. PubMed ID: 32623569
[TBL] [Abstract][Full Text] [Related]
17. Clinical validation of fully 3-D versus 2.5-D RAMLA reconstruction on the Philips-ADAC CPET PET scanner.
Chiang S; Cardi C; Matej S; Zhuang H; Newberg A; Alavi A; Karp JS
Nucl Med Commun; 2004 Nov; 25(11):1103-7. PubMed ID: 15577588
[TBL] [Abstract][Full Text] [Related]
18. Optimization of PET reconstruction algorithm, SUV thresholding algorithm and PET acquisition time in clinical 11C-acetate PET/CT.
Strandberg S; Hashemi A; Axelsson J; Riklund K
PLoS One; 2018; 13(12):e0209169. PubMed ID: 30543705
[TBL] [Abstract][Full Text] [Related]
19. How to harmonize SUVs obtained by hybrid PET/CT scanners with and without point spread function correction.
Ferretti A; Chondrogiannis S; Rampin L; Bellan E; Marzola MC; Grassetto G; Gusella S; Maffione AM; Gava M; Rubello D
Phys Med Biol; 2018 Nov; 63(23):235010. PubMed ID: 30474620
[TBL] [Abstract][Full Text] [Related]
20. Physical performance of the new hybrid PET∕CT Discovery-690.
Bettinardi V; Presotto L; Rapisarda E; Picchio M; Gianolli L; Gilardi MC
Med Phys; 2011 Oct; 38(10):5394-411. PubMed ID: 21992359
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
