Point-of-Care Ultrasound (POCUS) imaging can help efficient resource utilization by reducing the secondary care referrals, and work as an extension in physical examination. Recently, many methods were proposed to reduce the size and power consumption of the system while improving the visual quality, but hand-held POCUS devices still have inferior image contrast and spatial resolution compared to the high-end ultrasound systems. To address this, here we propose an efficient solution for contrast and resolution enhancement of hand-held POCUS images using unsupervised deep learning. In contrast to the existing CycleGAN approaches that have difficulty in improving both contrast and image resolutions, the proposed method mitigate the problem by decomposing the contrast transfer and resolution improvement through CycleGAN and self-supervised learning. Experimental results confirmed that our method is superior to the conventional approaches.

1. Contrast and Resolution Improvement of
POCUS Using Self-Consistent CycleGAN
Shujaat Khan, Jaeyoung Huh, and Jong Chul Ye
Department of Bio and Brain Engineering, Korea Advanced Institute of Science
and Technology (KAIST), 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Korea.
{shujaat,woori93,jong.ye}@kaist.ac.kr
https://bispl.weebly.com/
In conjunction with

2. Contents
I. Introduction
I. Motivation
II. Problem formulation
II. Proposed method
I. Divide and conquer
II. Self-consistent cycleGAN
III. Network architecture
IV. Dataset
III. Results
I. Qualitative results
II. Quantitative results
III. Comparative results
IV. Conclusion
2

3. Applications Needs
 Efficacy
 Portability
 Low Cost
 Accessibility
Possibilities
Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN 3
Introduction: motivation
• Can save the treatment cost about 33% by reducing the referral
to secondary care.
• Expected to become an in-demand electric gadget for personal
and tele-health monitoring

4. low-contrast/low-resolution high-contrast/high-resolution
Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN 4
Introduction: problem formulation

5. low-contrast/low-resolution high-contrast/high-resolution
high-contrast/low-resolution
Contrast
enhancement
Network
Super-resolution
Network
Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN 5
Proposed method: divide and conquer

6. - fixed weights
high-contrast/high-resolution
high-contrast/low-resolution
Unsupervised training Supervised training
:high-contrast low-resolution
:low-contrast/ low-resolution
Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN 6
Proposed method: self-consistent cycleGAN

7. Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN 7
Proposed method: network architecture
The proposed method was implemented using Python with TensorFlow on an Nvidia GEFORCE GTX 1080 Ti GPU.
For network training Adam optimizer was used and learning rate linearly changed from 5 × 10−4
→ 1 × 10−4
in 200 epochs.

8. Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN 8
Proposed method: Dataset
LOW-QUALITY INPUT IMAGES HIGH-QUALITY TARGET IMAGES
Scanner
NPUS050 portable US system.
NOPROBLEM MEDICAL Co., China
L3–12 linear array,
E-CUBE 12R US Alpinion Co., Korea
Description
• 200 in vivo (carotid/thyroid regions of
2 healthy volunteers)
• 200 ATS-539 phantom images
• 320 in vivo (carotid/thyroid regions of 8
healthy volunteers)
• 192 ATS-539 phantom images.
• Filtered by NLLR[1] and DeepDeconv [2]
Training • 157 in vivo and 168 phantom All
Testing • 43 in vivo and 32 phantom None
[1] Zhu, Lei, et al, IEEE CVPR 2017 [2] Khan et al, IEEE TUFFC, 2021

9. Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN 9
Results: qualitative evaluation
Reconstruction results on in vivo and phantom data
Sample
results
from
different
regions
of
phantom
and
anatomical
regions.

10. Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN 10
Results: quantitative evaluation
• For the simulated low quality images that are generated from 2×
axially and 2× laterally sub-sampled images of high-end images,
the PSNR and SSIM metrics show 13.58 dB and 0.63 units gain.
• On average, the proposed method achieves 14.96 dB, 2.38, 0.86
04 units CR, CNR and GCNR, which is 21.77%, 30.06%, and 44.4
2% higher, respectively, than those of the input images.
• On average the reconstruction time for a single image is around
13.18 (milliseconds), and it could further reduce by optimized i
mplementation.

11. Axial
depth(mm)
36
27
18
9
0
45
Phantom
in
vivo
Lateral length(mm)
0 20
10 30 38.2
(a) Input image (b) histogram equalization (c) standard CycleGAN (d) our method
A B
C
Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN 11
Results: comparative evaluation

12. Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN 12
Conclusion
• The proposed method can directly process unpaired image domai
n data to generate high-quality noise and artifact free images from
low-quality images.
• In contrast to the existing approaches, we decomposed the contra
st enhancement and resolution improvement into two steps and sh
owed improved performance without spurious artifacts.
• Even though proposed method shows noticeable gain in image qua
lity, the results are still not ideal and require a comprehensive clini
cal comparison.
• However, experimental results on in vivo and phantom data sugges
t that the proposed schemes may substantially help in designing lo
w-powered, high quality pocus systems.

13. Thank You