Ultrasound Imaging Publications

Deep Learning-Based Inpainting for Sparse Arrays in Ultrafast Ultrasound Imaging

R. Viñals; J-P. Thiran

IEEE Transactions on Computational Imaging. 2025. p. 1 – 16. DOI : 10.1109/tci.2025.3648531.

Inverse Problems in Pulse-Echo Ultrasound: Exploiting Coherence for Image Enhancement and Quantitative Imaging

S. D. Beuret / J-P. Thiran (Dir.)

Lausanne, EPFL, 2024.

An Ultrasound Imaging-Based Guidance System for Childbirth Monitoring – Integrating Statistical Modelling and Deep Learning Methods for Fetal Head 3D Reconstruction

S. M. Marcadent / J-P. Thiran; D. Desseauve (Dir.)

Lausanne, EPFL, 2026.

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (4/6)

2024.

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (1/6)

2024.

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (5/6)

2024.

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (2/6)

2024.

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (3/6)

2024.

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (6/6)

2024.

Imaging from Echoes: On Inverse Problems in Ultrasound

A. G. J. Besson / J-P. Thiran; Y. Wiaux (Dir.)

Lausanne, EPFL, 2018.

Sparse regularization methods in ultrafast ultrasound imaging

A. G. J. Besson; R. Carrillo; M. Zhang; D. Friboulet; O. Bernard et al.

2016. 2016 European Signal Processing Conference (EUSIPCO), Budapest, Hungary, September, 2016. p. 552 – 556. DOI : 10.1109/EUSIPCO.2016.7760309.

Extension of ultrasound Fourier slice Imaging theory to sectorial acquisition

M. Zhang; A. G. J. Besson; R. Carrillo; F. Varray; H. Liebgott et al.

2015. IEEE International Ultrasonics Symposium, Taipei, Taiwan, October 21-24, 2015. p. 1 – 4. DOI : 10.1109/Ultsym.2015.0185.

Beamforming-deconvolution: A novel concept of deconvolution for ultrasound imaging

Z. Chen; A. G. J. Besson; J-P. Thiran; Y. Wiaux

2017. International BASP Frontiers workshop 2017, Villars sur Ollon, Switzerland, January 29-February 3, 2017.

Windowed Radon Transform and Tensor Rank-1 Decomposition for Adaptive Beamforming in Ultrafast Ultrasound

S. Beuret; J-P. Thiran

IEEE Transactions on Medical Imaging (T-MI). 2024. Vol. 43, num. 1, p. 135 – 148. DOI : 10.1109/TMI.2023.3295657.

Semi-Supervised Segmentation of Ultrasound Images Based on Patch Representation and Continuous Min Cut

A. Ciurte; X. Bresson; O. Cuisenaire; N. Houhou; S. Nedevschi et al.

Plos One. 2014. Vol. 9, num. 7, p. e100972. DOI : 10.1371/journal.pone.0100972.

On Problem Formulation, Efficient Modeling and Deep Neural Networks for High-Quality Ultrasound Imaging

D. Perdios; A. Besson; F. Martinez; M. Vonlanthen; M. Arditi et al.

2019. 53rd Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, Mar 20-22, 2019. DOI : 10.1109/CISS.2019.8692870.

Ultrasound Imaging: From Physical Modeling to Deep Learning

D. Perdios / J-P. Thiran (Dir.)

Lausanne, EPFL, 2021.

A sparse regularization approach for ultrafast ultrasound imaging

R. Carrillo; A. G. J. Besson; M. Zhang; D. Friboulet; Y. Wiaux et al.

2015. IEEE International Ultrasonics Symposium, Taipei, Taiwan, October 21-24, 2015. DOI : 10.1109/Ultsym.2015.0184.

Sampling at the rate of innovation for ultrasound imaging and localization

E. Bezzam

2018.