A webinar on "Physics-Informed Deep Learning for Efficient B-Mode Ultrasound Imaging" organized by Center for Professional Training (C.P.T.) National University of Computer and Emerging Sciences (NUCES), Karachi.
3. About Me
I did my Ph.D. under the mentorship of Prof. Jong Chul Ye at Bio Ima
ge Signal Processing & Learning (BISPL) Lab, Dept. of Bio and Brain E
ngineeing KAIST.
My research interests include medical imaging, bioinformatics and ma
chine learning. I had a pleasure of working with highly talented peopl
e, and I managed to publish multiple national and international paten
ts, and number of good papers in reputable conference and journals i
ncluding MICCAI, ISBI, IUS, IEEE-TMI, IEEE-TUFFC, IEEE/ACM-TCBB and
IEEE-TNNLS, etc.
Based on my academic performance in 2019 I was awarded with BISP
L's best researcher award.
shujaat@kaist.ac.kr sites.google.com/view/shujaatkhan Shujaat Khan
4. Contents
I. Ultrasound Imaging (overview)
II. Deep learning for ultrasound image artifact removal
III. Physics-informed deep learning for ultrasound image artifact
removal
IV. Conclusion
4
5. I. Ultrasound Imaging (overview)
basics of ultrasound data acquisition and delay and sum (DAS) beamformer
6. Application Needs
Better Image quality
Reduce the number of measurement
- Ultra-fast US
- Portable US
- 3 dimensional US
Ultrasound Imaging Physics (overview) 6
Introduction
9. II. Deep learning for ultrasound image artifact
removal
Deep learning for paired and unpaired datasets, geometry of deep learning,
data-driven cycleGAN
14. Deep learning for ultrasound image artifact removal 14
Theory
The objective is to learn optimal path forA B
Zhu, Jun-Yan et al, IEEE ICCV, 2017
15. III. Physics-informed deep learning for
ultrasound image artifact removal
Image-domain learning for ultrasound artifact removal using deep neural network
Khan et al, IEEE TUFFC, 2021
16. Physics-informed deep learning for ultrasound image artifact removal 16
Variational formulation of cycleGAN
Consider an inverse problem, where a measurement 𝑦𝑦 ∈ Y from an unobserved image
𝑥𝑥 ∈ X is given by
Geometric view of unsupervised learning
To estimate 𝑥𝑥 ∈ X , here, the goal of optimal transport is to find 𝐺𝐺𝜃𝜃 and 𝐹𝐹𝜙𝜙 that minimize
the Wasserstein-1 distance between 𝜇𝜇 and 𝜇𝜇𝜃𝜃, and 𝜈𝜈 and 𝜈𝜈𝜙𝜙, respectively.
Sim, B. et al, SIAM JIS, 2020
17. Physics-informed deep learning for ultrasound image artifact removal 17
Optimal transport driven CycleGAN
According to Sim, B. et al, SIAM JIS, 2020, the primal form
of cycleGAN can be represented by a dual formulation
When the duality gap vanishes, we have
Generic CycleGAN
Physics-informed OT-CycleGAN
Deconvolution
model
Speckle
removal
model
Khan et al, IEEE TUFFC, 2021
By incorporating prior
knowledge
27. 27
Conclusion
• Herein, some novel deep learning-based approaches are presented for the re
construction of high-quality B-mode ultrasound image.
• Unlike black-box approaches, proposed approach was derived based on the ri
gorous formulation using optimal transport theory and physics-informed deep
learning.
• The proposed methods can be applied for various US image enhancement ap
plications, providing an important platform for ultrasound artifact removal.