1002 related articles for article (PubMed ID: 32449519)
1. An adversarial machine learning framework and biomechanical model-guided approach for computing 3D lung tissue elasticity from end-expiration 3DCT.
Santhanam AP; Stiehl B; Lauria M; Hasse K; Barjaktarevic I; Goldin J; Low DA
Med Phys; 2021 Feb; 48(2):667-675. PubMed ID: 32449519
[TBL] [Abstract][Full Text] [Related]
2. Estimation and validation of patient-specific high-resolution lung elasticity derived from 4DCT.
Hasse K; O'Connell D; Min Y; Neylon J; Low DA; Santhanam A
Med Phys; 2018 Feb; 45(2):666-677. PubMed ID: 29172237
[TBL] [Abstract][Full Text] [Related]
3. A quantitative analysis of biomechanical lung model consistency using 5DCT datasets.
Stiehl B; Lauria M; O'Connell D; Hasse K; Barjaktarevic IZ; Lee P; Low DA; Santhanam AP
Med Phys; 2020 Nov; 47(11):5555-5567. PubMed ID: 32521048
[TBL] [Abstract][Full Text] [Related]
4. 4D-Precise: Learning-based 3D motion estimation and high temporal resolution 4DCT reconstruction from treatment 2D+t X-ray projections.
Zakeri A; Hokmabadi A; Nix MG; Gooya A; Wijesinghe I; Taylor ZA
Comput Methods Programs Biomed; 2024 Jun; 250():108158. PubMed ID: 38604010
[TBL] [Abstract][Full Text] [Related]
5. Scalable quorum-based deep neural networks with adversarial learning for automated lung lobe segmentation in fast helical free-breathing CTs.
Stiehl B; Lauria M; Singhrao K; Goldin J; Barjaktarevic I; Low D; Santhanam A
Int J Comput Assist Radiol Surg; 2021 Oct; 16(10):1775-1784. PubMed ID: 34378122
[TBL] [Abstract][Full Text] [Related]
6. Lung tumor segmentation in 4D CT images using motion convolutional neural networks.
Momin S; Lei Y; Tian Z; Wang T; Roper J; Kesarwala AH; Higgins K; Bradley JD; Liu T; Yang X
Med Phys; 2021 Nov; 48(11):7141-7153. PubMed ID: 34469001
[TBL] [Abstract][Full Text] [Related]
7. Feasibility of deriving a novel imaging biomarker based on patient-specific lung elasticity for characterizing the degree of COPD in lung SBRT patients.
Hasse K; Neylon J; Min Y; O'Connell D; Lee P; Low DA; Santhanam AP
Br J Radiol; 2019 Feb; 92(1094):20180296. PubMed ID: 30281329
[TBL] [Abstract][Full Text] [Related]
8. A neural network approach for fast, automated quantification of DIR performance.
Neylon J; Min Y; Low DA; Santhanam A
Med Phys; 2017 Aug; 44(8):4126-4138. PubMed ID: 28477340
[TBL] [Abstract][Full Text] [Related]
9. Synthetic 4DCT(MRI) lung phantom generation for 4D radiotherapy and image guidance investigations.
Duetschler A; Bauman G; Bieri O; Cattin PC; Ehrbar S; Engin-Deniz G; Giger A; Josipovic M; Jud C; Krieger M; Nguyen D; Persson GF; Salomir R; Weber DC; Lomax AJ; Zhang Y
Med Phys; 2022 May; 49(5):2890-2903. PubMed ID: 35239984
[TBL] [Abstract][Full Text] [Related]
10. U-net-based deformation vector field estimation for motion-compensated 4D-CBCT reconstruction.
Huang X; Zhang Y; Chen L; Wang J
Med Phys; 2020 Jul; 47(7):3000-3012. PubMed ID: 32198934
[TBL] [Abstract][Full Text] [Related]
11. Generation of virtual lung single-photon emission computed tomography/CT fusion images for functional avoidance radiotherapy planning using machine learning algorithms.
Jang BS; Chang JH; Park AJ; Wu HG
J Med Imaging Radiat Oncol; 2019 Apr; 63(2):229-235. PubMed ID: 30874371
[TBL] [Abstract][Full Text] [Related]
12. A Method for Assessing Ground-Truth Accuracy of the 5DCT Technique.
Dou TH; Thomas DH; O'Connell DP; Lamb JM; Lee P; Low DA
Int J Radiat Oncol Biol Phys; 2015 Nov; 93(4):925-33. PubMed ID: 26530763
[TBL] [Abstract][Full Text] [Related]
13. Real-time CT image generation based on voxel-by-voxel modeling of internal deformation by utilizing the displacement of fiducial markers.
Hayashi R; Miyazaki K; Takao S; Yokokawa K; Tanaka S; Matsuura T; Taguchi H; Katoh N; Shimizu S; Umegaki K; Miyamoto N
Med Phys; 2021 Sep; 48(9):5311-5326. PubMed ID: 34260755
[TBL] [Abstract][Full Text] [Related]
14. LungRegNet: An unsupervised deformable image registration method for 4D-CT lung.
Fu Y; Lei Y; Wang T; Higgins K; Bradley JD; Curran WJ; Liu T; Yang X
Med Phys; 2020 Apr; 47(4):1763-1774. PubMed ID: 32017141
[TBL] [Abstract][Full Text] [Related]
15. RMSim: controlled respiratory motion simulation on static patient scans.
Lee D; Yorke E; Zarepisheh M; Nadeem S; Hu YC
Phys Med Biol; 2023 Feb; 68(4):. PubMed ID: 36652721
[No Abstract] [Full Text] [Related]
16. A model that predicts a real-time tumour surface using intra-treatment skin surface and end-of-expiration and end-of-inhalation planning CT images.
Wei Z; Huang X; Sun A; Peng L; Lou Z; Hu Z; Wang H; Xing L; Yu J; Qian J
Br J Radiol; 2024 May; 97(1157):980-992. PubMed ID: 38547402
[TBL] [Abstract][Full Text] [Related]
17. Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN).
Chang Y; Lafata K; Segars WP; Yin FF; Ren L
Phys Med Biol; 2020 Mar; 65(6):065009. PubMed ID: 32023555
[TBL] [Abstract][Full Text] [Related]
18. Self-contained deep learning-based boosting of 4D cone-beam CT reconstruction.
Madesta F; Sentker T; Gauer T; Werner R
Med Phys; 2020 Nov; 47(11):5619-5631. PubMed ID: 33063329
[TBL] [Abstract][Full Text] [Related]
19. Comparison of planned dose on different CT image sets to four-dimensional Monte Carlo dose recalculation using the patient's actual breathing trace for lung stereotactic body radiation therapy.
Freislederer P; von Münchow A; Kamp F; Heinz C; Gerum S; Corradini S; Söhn M; Reiner M; Roeder F; Floca R; Alber M; Belka C; Parodi K
Med Phys; 2019 Jul; 46(7):3268-3277. PubMed ID: 31074510
[TBL] [Abstract][Full Text] [Related]
20. Few-shot learning for deformable image registration in 4DCT images.
Chi W; Xiang Z; Guo F
Br J Radiol; 2022 Jan; 95(1129):20210819. PubMed ID: 34662242
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
