A Deep Learning Method for Motion Artifact Correction in Intravascular Photoacoustic Image Sequence

Sun, Zaihong; Du, Jiang; Yao, Yue; Meng, Qi; Sun, Haitao

doi:10.1109/tmi.2022.3202910

Cited by 8 publications

(6 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, PSNR is a comprehensive measure of image quality, while SSIM is a local measure of contrast, brightness, and structural similarity, which can objectively measure the degree of difference between the model reconstructed image and the original image. These model evaluation measures are calculated according to Equations ( 4)- (6).…”

Section: Quantitative Assessment Metrics For Image Reconstruction Modelsmentioning

confidence: 99%

“…PAI combines the advantages of optical imaging and ultrasound imaging, and has unique advantages in imaging depth, spatial resolution, and tissue imaging. It is able to provide abundant tissue functional and structural information, widely used in biomedical fields [4][5][6]. In a variety of clinical applications, PAI has shown significant potential and effectiveness [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images

Jiang,

He,

Chen

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Photoacoustic imaging (PAI) is an emerging imaging technique that offers real-time, non-invasive, and radiation-free measurements of optical tissue properties. However, image quality degradation due to factors such as non-ideal signal detection hampers its clinical applicability. To address this challenge, this paper proposes an algorithm for super-resolution reconstruction and segmentation based on deep learning. The proposed enhanced deep super-resolution minimalistic network (EDSR-M) not only mitigates the shortcomings of the original algorithm regarding computational complexity and parameter count but also employs residual learning and attention mechanisms to extract image features and enhance image details, thereby achieving high-quality reconstruction of PAI. DeepLabV3+ is used to segment the images before and after reconstruction to verify the network reconstruction performance. The experimental results demonstrate average improvements of 19.76% in peak-signal-to-noise ratio (PSNR) and 4.80% in structural similarity index (SSIM) for the reconstructed images compared to those of their pre-reconstructed counterparts. Additionally, mean accuracy, mean intersection and union ratio (IoU), and mean boundary F1 score (BFScore) for segmentation showed enhancements of 8.27%, 6.20%, and 6.28%, respectively. The proposed algorithm enhances the effect and texture features of PAI and makes the overall structure of the image restoration more complete.

show abstract

Section: Quantitative Assessment Metrics For Image Reconstruction Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images

Jiang,

He,

Chen

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Chen et al 175 introduced a CNN approach with three convolutional layers to address motion artifacts and pixel dislocation in in vivo rat brain images. Zheng et al 176 proposed MAC-Net, a network based on VGG16 GAN 134 and spatial transformer networks (STN), 177 to suppress motion artifacts in IVPA. Both methods demonstrated successful improvement in image quality.…”

Section: Overcoming Other Specified Issuesmentioning

confidence: 99%

Recent advances in deep-learning-enhanced photoacoustic imaging

et al. 2023

View full text Add to dashboard Cite

Photoacoustic imaging (PAI), recognized as a promising biomedical imaging modality for preclinical and clinical studies, uniquely combines the advantages of optical and ultrasound imaging. Despite PAI's great potential to provide valuable biological information, its wide application has been hindered by technical limitations, such as hardware restrictions or lack of the biometric information required for image reconstruction. We first analyze the limitations of PAI and categorize them by seven key challenges: limited detection, low-dosage light delivery, inaccurate quantification, limited numerical reconstruction, tissue heterogeneity, imperfect image segmentation/classification, and others. Then, because deep learning (DL) has increasingly demonstrated its ability to overcome the physical limitations of imaging modalities, we review DL studies from the past five years that address each of the seven challenges in PAI. Finally, we discuss the promise of future research directions in DL-enhanced PAI.

show abstract

“…Deep learning approaches have shown promise in addressing these challenges. For example, motion artifact correction-Net 115 corrects motion artifacts in intravascular PA data by learning correlations from simulated training data. It uses a convolutional network to correct motion frame-by-frame while preserving structures.…”

Section: Pa Plus Deep Learningmentioning

confidence: 99%

Photoacoustic imaging plus X: a review

Jiang,

Zhu,

Tong

et al. 2023

J. Biomed. Opt.

View full text Add to dashboard Cite

. Significance Photoacoustic (PA) imaging (PAI) represents an emerging modality within the realm of biomedical imaging technology. It seamlessly blends the wealth of optical contrast with the remarkable depth of penetration offered by ultrasound. These distinctive features of PAI hold tremendous potential for various applications, including early cancer detection, functional imaging, hybrid imaging, monitoring ablation therapy, and providing guidance during surgical procedures. The synergy between PAI and other cutting-edge technologies not only enhances its capabilities but also propels it toward broader clinical applicability. Aim The integration of PAI with advanced technology for PA signal detection, signal processing, image reconstruction, hybrid imaging, and clinical applications has significantly bolstered the capabilities of PAI. This review endeavor contributes to a deeper comprehension of how the synergy between PAI and other advanced technologies can lead to improved applications. Approach An examination of the evolving research frontiers in PAI, integrated with other advanced technologies, reveals six key categories named “PAI plus X.” These categories encompass a range of topics, including but not limited to PAI plus treatment, PAI plus circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. Results After conducting a comprehensive review of the existing literature and research on PAI integrated with other technologies, various proposals have emerged to advance the development of PAI plus X. These proposals aim to enhance system hardware, improve imaging quality, and address clinical challenges effectively. Conclusions The progression of innovative and sophisticated approaches within each category of PAI plus X is positioned to drive significant advancements in both the development of PAI technology and its clinical applications. Furthermore, PAI not only has the potential to integrate with the above-mentioned technologies but also to broaden its applications even further.

show abstract

A Deep Learning Method for Motion Artifact Correction in Intravascular Photoacoustic Image Sequence

Cited by 8 publications

References 48 publications

Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images

Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images

Recent advances in deep-learning-enhanced photoacoustic imaging

Photoacoustic imaging plus X: a review

Contact Info

Product

Resources

About