148 related articles for article (PubMed ID: 32378776)
1. Automated detection of left ventricle in arterial input function images for inline perfusion mapping using deep learning: A study of 15,000 patients.
Xue H; Tseng E; Knott KD; Kotecha T; Brown L; Plein S; Fontana M; Moon JC; Kellman P
Magn Reson Med; 2020 Nov; 84(5):2788-2800. PubMed ID: 32378776
[TBL] [Abstract][Full Text] [Related]
2. Evaluation of an automated method for arterial input function detection for first-pass myocardial perfusion cardiovascular magnetic resonance.
Jacobs M; Benovoy M; Chang LC; Arai AE; Hsu LY
J Cardiovasc Magn Reson; 2016 Apr; 18():17. PubMed ID: 27055445
[TBL] [Abstract][Full Text] [Related]
3. Automatic in-line quantitative myocardial perfusion mapping: Processing algorithm and implementation.
Xue H; Brown LAE; Nielles-Vallespin S; Plein S; Kellman P
Magn Reson Med; 2020 Feb; 83(2):712-730. PubMed ID: 31441550
[TBL] [Abstract][Full Text] [Related]
4. Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning.
Xue H; Davies RH; Brown LAE; Knott KD; Kotecha T; Fontana M; Plein S; Moon JC; Kellman P
Radiol Artif Intell; 2020 Oct; 2(6):e200009. PubMed ID: 33330849
[TBL] [Abstract][Full Text] [Related]
5. Automatic arterial input function selection in CT and MR perfusion datasets using deep convolutional neural networks.
Winder A; d'Esterre CD; Menon BK; Fiehler J; Forkert ND
Med Phys; 2020 Sep; 47(9):4199-4211. PubMed ID: 32583617
[TBL] [Abstract][Full Text] [Related]
6. Automated segmentation of biventricular contours in tissue phase mapping using deep learning.
Shen D; Pathrose A; Sarnari R; Blake A; Berhane H; Baraboo JJ; Carr JC; Markl M; Kim D
NMR Biomed; 2021 Dec; 34(12):e4606. PubMed ID: 34476863
[TBL] [Abstract][Full Text] [Related]
7. Automatic left ventricle segmentation from cardiac magnetic resonance images using a capsule network.
He Y; Qin W; Wu Y; Zhang M; Yang Y; Liu X; Zheng H; Liang D; Hu Z
J Xray Sci Technol; 2020; 28(3):541-553. PubMed ID: 32176675
[TBL] [Abstract][Full Text] [Related]
8. Landmark Detection in Cardiac MRI by Using a Convolutional Neural Network.
Xue H; Artico J; Fontana M; Moon JC; Davies RH; Kellman P
Radiol Artif Intell; 2021 Sep; 3(5):e200197. PubMed ID: 34617022
[TBL] [Abstract][Full Text] [Related]
9. Convolutional neural network-based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images.
Zabihollahy F; White JA; Ukwatta E
Med Phys; 2019 Apr; 46(4):1740-1751. PubMed ID: 30734937
[TBL] [Abstract][Full Text] [Related]
10. Deep-Learning-Based Preprocessing for Quantitative Myocardial Perfusion MRI.
Scannell CM; Veta M; Villa ADM; Sammut EC; Lee J; Breeuwer M; Chiribiri A
J Magn Reson Imaging; 2020 Jun; 51(6):1689-1696. PubMed ID: 31710769
[TBL] [Abstract][Full Text] [Related]
11. Arterial Input Function (AIF) Correction Using AIF Plus Tissue Inputs with a Bi-LSTM Network.
Huang Q; Le J; Joshi S; Mendes J; Adluru G; DiBella E
Tomography; 2024 Apr; 10(5):660-673. PubMed ID: 38787011
[No Abstract] [Full Text] [Related]
12. Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network.
Penso M; Moccia S; Scafuri S; Muscogiuri G; Pontone G; Pepi M; Caiani EG
Comput Methods Programs Biomed; 2021 Jun; 204():106059. PubMed ID: 33812305
[TBL] [Abstract][Full Text] [Related]
13. Cine MRI analysis by deep learning of optical flow: Adding the temporal dimension.
Yan W; Wang Y; van der Geest RJ; Tao Q
Comput Biol Med; 2019 Aug; 111():103356. PubMed ID: 31323604
[TBL] [Abstract][Full Text] [Related]
14. Reliable segmentation of 2D cardiac magnetic resonance perfusion image sequences using time as the 3rd dimension.
Sandfort V; Jacobs M; Arai AE; Hsu LY
Eur Radiol; 2021 Jun; 31(6):3941-3950. PubMed ID: 33247342
[TBL] [Abstract][Full Text] [Related]
15. Automatic model-based contour detection of left ventricle myocardium from cardiac CT images.
Sugiura T; Takeguchi T; Sakata Y; Nitta S; Okazaki T; Matsumoto N; Fujisawa Y
Int J Comput Assist Radiol Surg; 2013 Jan; 8(1):145-55. PubMed ID: 22547333
[TBL] [Abstract][Full Text] [Related]
16. Deep Learning-based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study.
Tao Q; Yan W; Wang Y; Paiman EHM; Shamonin DP; Garg P; Plein S; Huang L; Xia L; Sramko M; Tintera J; de Roos A; Lamb HJ; van der Geest RJ
Radiology; 2019 Jan; 290(1):81-88. PubMed ID: 30299231
[TBL] [Abstract][Full Text] [Related]
17. Fully automated segmentation of left ventricular scar from 3D late gadolinium enhancement magnetic resonance imaging using a cascaded multi-planar U-Net (CMPU-Net).
Zabihollahy F; Rajchl M; White JA; Ukwatta E
Med Phys; 2020 Apr; 47(4):1645-1655. PubMed ID: 31955415
[TBL] [Abstract][Full Text] [Related]
18. Automated segmentation of the left ventricle from MR cine imaging based on deep learning architecture.
Qin W; Wu Y; Li S; Chen Y; Yang Y; Liu X; Zheng H; Liang D; Hu Z
Biomed Phys Eng Express; 2020 Feb; 6(2):025009. PubMed ID: 33438635
[TBL] [Abstract][Full Text] [Related]
19. Automatic left ventricle segmentation in volumetric SPECT data set by variational level set.
Hosntalab M; Babapour-Mofrad F; Monshizadeh N; Amoui M
Int J Comput Assist Radiol Surg; 2012 Nov; 7(6):837-43. PubMed ID: 22696199
[TBL] [Abstract][Full Text] [Related]
20. Fully automated intracardiac 4D flow MRI post-processing using deep learning for biventricular segmentation.
Corrado PA; Wentland AL; Starekova J; Dhyani A; Goss KN; Wieben O
Eur Radiol; 2022 Aug; 32(8):5669-5678. PubMed ID: 35175379
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
