750 related articles for article (PubMed ID: 28205306)
1. Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data.
Beichel RR; Smith BJ; Bauer C; Ulrich EJ; Ahmadvand P; Budzevich MM; Gillies RJ; Goldgof D; Grkovski M; Hamarneh G; Huang Q; Kinahan PE; Laymon CM; Mountz JM; Muzi JP; Muzi M; Nehmeh S; Oborski MJ; Tan Y; Zhao B; Sunderland JJ; Buatti JM
Med Phys; 2017 Feb; 44(2):479-496. PubMed ID: 28205306
[TBL] [Abstract][Full Text] [Related]
2. Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model.
Montgomery DW; Amira A; Zaidi H
Med Phys; 2007 Feb; 34(2):722-36. PubMed ID: 17388190
[TBL] [Abstract][Full Text] [Related]
3. Phantom validation of coregistration of PET and CT for image-guided radiotherapy.
Lavely WC; Scarfone C; Cevikalp H; Li R; Byrne DW; Cmelak AJ; Dawant B; Price RR; Hallahan DE; Fitzpatrick JM
Med Phys; 2004 May; 31(5):1083-92. PubMed ID: 15191296
[TBL] [Abstract][Full Text] [Related]
4. Tumor delineation using PET in head and neck cancers: threshold contouring and lesion volumes.
Ford EC; Kinahan PE; Hanlon L; Alessio A; Rajendran J; Schwartz DL; Phillips M
Med Phys; 2006 Nov; 33(11):4280-8. PubMed ID: 17153406
[TBL] [Abstract][Full Text] [Related]
5. The use of zeolites to generate PET phantoms for the validation of quantification strategies in oncology.
Zito F; De Bernardi E; Soffientini C; Canzi C; Casati R; Gerundini P; Baselli G
Med Phys; 2012 Sep; 39(9):5353-61. PubMed ID: 22957603
[TBL] [Abstract][Full Text] [Related]
6. Combining multiple FDG-PET radiotherapy target segmentation methods to reduce the effect of variable performance of individual segmentation methods.
McGurk RJ; Bowsher J; Lee JA; Das SK
Med Phys; 2013 Apr; 40(4):042501. PubMed ID: 23556917
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.
Dutta K; Laforest R; Luo J; Jha AK; Shoghi KI
Med Phys; 2024 Jun; 51(6):4324-4339. PubMed ID: 38710222
[TBL] [Abstract][Full Text] [Related]
10. Evaluation of strategies towards harmonization of FDG PET/CT studies in multicentre trials: comparison of scanner validation phantoms and data analysis procedures.
Makris NE; Huisman MC; Kinahan PE; Lammertsma AA; Boellaard R
Eur J Nucl Med Mol Imaging; 2013 Oct; 40(10):1507-15. PubMed ID: 23754762
[TBL] [Abstract][Full Text] [Related]
11. An algorithm for PET tumor volume and activity quantification: without specifying camera's point spread function (PSF).
Bhatt R; Adjouadi M; Goryawala M; Gulec SA; McGoron AJ
Med Phys; 2012 Jul; 39(7):4187-202. PubMed ID: 22830752
[TBL] [Abstract][Full Text] [Related]
12. Investigation of 18F-FDG 3D mode PET image quality versus acquisition time.
Brown C; Dempsey MF; Gillen G; Elliott AT
Nucl Med Commun; 2010 Mar; 31(3):254-9. PubMed ID: 20032801
[TBL] [Abstract][Full Text] [Related]
13. A gel tumour phantom for assessment of the accuracy of manual and automatic delineation of gross tumour volume from FDG-PET/CT.
Skretting A; Evensen JF; Løndalen AM; Bogsrud TV; Glomset OK; Eilertsen K
Acta Oncol; 2013 Apr; 52(3):636-44. PubMed ID: 23075421
[TBL] [Abstract][Full Text] [Related]
14. A region growing method for tumor volume segmentation on PET images for rectal and anal cancer patients.
Day E; Betler J; Parda D; Reitz B; Kirichenko A; Mohammadi S; Miften M
Med Phys; 2009 Oct; 36(10):4349-58. PubMed ID: 19928065
[TBL] [Abstract][Full Text] [Related]
15. Radiomics prognostic analysis of PET/CT images in a multicenter head and neck cancer cohort: investigating ComBat strategies, sub-volume characterization, and automatic segmentation.
Xu H; Abdallah N; Marion JM; Chauvet P; Tauber C; Carlier T; Lu L; Hatt M
Eur J Nucl Med Mol Imaging; 2023 May; 50(6):1720-1734. PubMed ID: 36690882
[TBL] [Abstract][Full Text] [Related]
16. Comparison of 2-dimensional and 3-dimensional acquisition for 18F-FDG PET oncology studies performed on an LSO-based scanner.
Lodge MA; Badawi RD; Gilbert R; Dibos PE; Line BR
J Nucl Med; 2006 Jan; 47(1):23-31. PubMed ID: 16391183
[TBL] [Abstract][Full Text] [Related]
17. The effect of acquisition time on visual and semi-quantitative analysis of F-18 FDG-PET studies in patients with head and neck cancer.
Goethals I; D'Asseler Y; Dobbeleir A; Deblaere K; Ham H
Nucl Med Commun; 2010 Mar; 31(3):227-31. PubMed ID: 20023593
[TBL] [Abstract][Full Text] [Related]
18. New standards for phantom image quality and SUV harmonization range for multicenter oncology PET studies.
Akamatsu G; Shimada N; Matsumoto K; Daisaki H; Suzuki K; Watabe H; Oda K; Senda M; Terauchi T; Tateishi U
Ann Nucl Med; 2022 Feb; 36(2):144-161. PubMed ID: 35029817
[TBL] [Abstract][Full Text] [Related]
19. Robust Radiomics feature quantification using semiautomatic volumetric segmentation.
Parmar C; Rios Velazquez E; Leijenaar R; Jermoumi M; Carvalho S; Mak RH; Mitra S; Shankar BU; Kikinis R; Haibe-Kains B; Lambin P; Aerts HJ
PLoS One; 2014; 9(7):e102107. PubMed ID: 25025374
[TBL] [Abstract][Full Text] [Related]
20. Correction of an image size difference between positron emission tomography (PET) and computed tomography (CT) improves image fusion of dedicated PET and CT.
Vogel WV; van Dalen JA; Schinagl DA; Kaanders JH; Huisman H; Corstens FH; Oyen WJ
Nucl Med Commun; 2006 Jun; 27(6):515-9. PubMed ID: 16710106
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
