These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

113 related articles for article (PubMed ID: 30221260)

  • 1. A supervoxel based random forest synthesis framework for bidirectional MR/CT synthesis.
    Zhao C; Carass A; Lee J; Jog A; Prince JL
    Simul Synth Med Imaging; 2017 Sep; 10557():33-40. PubMed ID: 30221260
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Multiatlas Fusion with a Hybrid CT Number Correction Technique for Subject-Specific Pseudo-CT Estimation in the Context of MRI-Only Radiation Therapy.
    Boukellouz W; Moussaoui A; Taleb-Ahmed A; Boydev C
    J Med Imaging Radiat Sci; 2019 Sep; 50(3):425-440. PubMed ID: 31128942
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms.
    Ger RB; Yang J; Ding Y; Jacobsen MC; Fuller CD; Howell RM; Li H; Jason Stafford R; Zhou S; Court LE
    Med Phys; 2017 Oct; 44(10):5153-5161. PubMed ID: 28622410
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Supervoxel based method for multi-atlas segmentation of brain MR images.
    Huo J; Wu J; Cao J; Wang G
    Neuroimage; 2018 Jul; 175():201-214. PubMed ID: 29625235
    [TBL] [Abstract][Full Text] [Related]  

  • 5. MR-based synthetic CT generation using a deep convolutional neural network method.
    Han X
    Med Phys; 2017 Apr; 44(4):1408-1419. PubMed ID: 28192624
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Reliability-based robust multi-atlas label fusion for brain MRI segmentation.
    Sun L; Zu C; Shao W; Guang J; Zhang D; Liu M
    Artif Intell Med; 2019 May; 96():12-24. PubMed ID: 31164205
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Multi-atlas-based CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning.
    Lee J; Carass A; Jog A; Zhao C; Prince JL
    Proc SPIE Int Soc Opt Eng; 2017 Feb; 10133():. PubMed ID: 29142336
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs.
    Tang F; Liang S; Zhong T; Huang X; Deng X; Zhang Y; Zhou L
    Eur Radiol; 2020 Feb; 30(2):823-832. PubMed ID: 31650265
    [TBL] [Abstract][Full Text] [Related]  

  • 9. MR to CT Registration of Brains using Image Synthesis.
    Roy S; Carass A; Jog A; Prince JL; Lee J
    Proc SPIE Int Soc Opt Eng; 2014 Mar; 9034():. PubMed ID: 25057341
    [TBL] [Abstract][Full Text] [Related]  

  • 10. New method to assess the registration of CT-MR images of the head.
    Pappas IP; Puja M; Styner M; Liu J; Caversaccio M
    Injury; 2004 Jun; 35 Suppl 1():S-A105-12. PubMed ID: 15183711
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Dosimetric characterization of MRI-only treatment planning for brain tumors in atlas-based pseudo-CT images generated from standard T1-weighted MR images.
    Demol B; Boydev C; Korhonen J; Reynaert N
    Med Phys; 2016 Dec; 43(12):6557. PubMed ID: 27908187
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A segmentation of brain MRI images utilizing intensity and contextual information by Markov random field.
    Chen M; Yan Q; Qin M
    Comput Assist Surg (Abingdon); 2017 Dec; 22(sup1):200-211. PubMed ID: 29072503
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Tissue segmentation-based electron density mapping for MR-only radiotherapy treatment planning of brain using conventional T1-weighted MR images.
    Yu H; Oliver M; Leszczynski K; Lee Y; Karam I; Sahgal A
    J Appl Clin Med Phys; 2019 Aug; 20(8):11-20. PubMed ID: 31257709
    [TBL] [Abstract][Full Text] [Related]  

  • 14. MR image-based synthetic CT for IMRT prostate treatment planning and CBCT image-guided localization.
    Chen S; Quan H; Qin A; Yee S; Yan D
    J Appl Clin Med Phys; 2016 May; 17(3):236-245. PubMed ID: 27167281
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging.
    Fu J; Yang Y; Singhrao K; Ruan D; Chu FI; Low DA; Lewis JH
    Med Phys; 2019 Sep; 46(9):3788-3798. PubMed ID: 31220353
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Fast Patch-Based Pseudo-CT Synthesis from T1-Weighted MR Images for PET/MR Attenuation Correction in Brain Studies.
    Torrado-Carvajal A; Herraiz JL; Alcain E; Montemayor AS; Garcia-CaƱamaque L; Hernandez-Tamames JA; Rozenholc Y; Malpica N
    J Nucl Med; 2016 Jan; 57(1):136-43. PubMed ID: 26493204
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning.
    Arabi H; Koutsouvelis N; Rouzaud M; Miralbell R; Zaidi H
    Phys Med Biol; 2016 Sep; 61(17):6531-52. PubMed ID: 27524504
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Multiatlas approach with local registration goodness weighting for MRI-based electron density mapping of head and neck anatomy.
    Farjam R; Tyagi N; Veeraraghavan H; Apte A; Zakian K; Hunt MA; Deasy JO
    Med Phys; 2017 Jul; 44(7):3706-3717. PubMed ID: 28444772
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Accelerating multi-modal image registration using a supervoxel-based variational framework.
    Lafitte L; Zachiu C; Kerkmeijer LGW; Ries M; Denis de Senneville B
    Phys Med Biol; 2018 Nov; 63(23):235009. PubMed ID: 30468684
    [TBL] [Abstract][Full Text] [Related]  

  • 20. PET attenuation correction using synthetic CT from ultrashort echo-time MR imaging.
    Roy S; Wang WT; Carass A; Prince JL; Butman JA; Pham DL
    J Nucl Med; 2014 Dec; 55(12):2071-7. PubMed ID: 25413135
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.