Deep learning inversion with supervision: A rapid and cascaded imaging technique
J Tong, M Lin, X Wang, J Li, J Ren, L Liang, Y Liu - Ultrasonics, 2022 - Elsevier
Abstract Machine learning has been demonstrated to be extremely promising in solving
inverse problems, but deep learning algorithms require enormous training samples to obtain …
inverse problems, but deep learning algorithms require enormous training samples to obtain …
Localization of high-concentration microbubbles for ultrasound localization microscopy by self-supervised deep learning
Ultrasound localization microscopy (ULM) has been developed to significantly improve the
spatial resolution of ultrasound imaging by localizing the microbubbles (MBs). However …
spatial resolution of ultrasound imaging by localizing the microbubbles (MBs). However …
Benchmarking Supervised and Self-Supervised Learning Methods in A Large Ultrasound Muti-task Images Dataset
P Liu, J Zhang, X Wu, S Liu, Y Wang… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
Deep learning in ultrasound (US) imaging aims to construct foundational models that
accurately reflect the modality's unique characteristics. Nevertheless, the limited datasets …
accurately reflect the modality's unique characteristics. Nevertheless, the limited datasets …
Physics and Deep Learning in Computational Wave Imaging
Computational wave imaging (CWI) extracts hidden structure and physical properties of a
volume of material by analyzing wave signals that traverse that volume. Applications include …
volume of material by analyzing wave signals that traverse that volume. Applications include …
Characterization of direct localization algorithms for ultrasound super-resolution imaging in a multibubble environment: A numerical and experimental study
Localization plays a significant role in the production of ultrasound localization microscopy
images. For instance, detecting more microbubbles reduces the time of acquisition, while …
images. For instance, detecting more microbubbles reduces the time of acquisition, while …
Quantitative Microwave Imaging using Deep Learning Network Guided by Plane Wave Equation
R Sharma, O Yurduseven - IEEE Transactions on Radar …, 2024 - ieeexplore.ieee.org
Accurately characterizing material properties, particularly the spatial distribution of
permittivity, is crucial across diverse domains such as medical imaging, nondestructive …
permittivity, is crucial across diverse domains such as medical imaging, nondestructive …
Encoder-decoder deep learning network for simultaneous reconstruction of fluorescence yield and lifetime distributions
A time-domain fluorescence molecular tomography in reflective geometry (TD-rFMT) has
been proposed to circumvent the penetration limit and reconstruct fluorescence distribution …
been proposed to circumvent the penetration limit and reconstruct fluorescence distribution …
Ultrasound Image Reconstruction by Self-Supervised Deep Neural Network A Study on Coherent Compounding Strategy
Sparse regularization (SR) techniques solve the inverse problem of beamforming and can
reconstruct high-quality ultrasound image from single plane wave (PW) transmission, but …
reconstruct high-quality ultrasound image from single plane wave (PW) transmission, but …
Recovery of full synthetic transmit aperture dataset with well-preserved phase information by self-supervised deep learning
Thanks to two-way dynamic focusing, synthetic transmit aperture (STA) imaging can obtain
images with high spatial resolution. However, STA imaging suffers from low signal-to-noise …
images with high spatial resolution. However, STA imaging suffers from low signal-to-noise …