Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning
Abstract Contrast-free ultrasound quantitative microvasculature imaging shows promise in several applications, including the assessment of benign and malignant lesions. However, motion represents one of the major challenges in imaging tumor microvessels in organs that are prone to physiological moti...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-10-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-77610-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846112670826102784 |
|---|---|
| author | Manali Saini Mostafa Fatemi Azra Alizad |
| author_facet | Manali Saini Mostafa Fatemi Azra Alizad |
| author_sort | Manali Saini |
| collection | DOAJ |
| description | Abstract Contrast-free ultrasound quantitative microvasculature imaging shows promise in several applications, including the assessment of benign and malignant lesions. However, motion represents one of the major challenges in imaging tumor microvessels in organs that are prone to physiological motions. This study aims at addressing potential microvessel image degradation in in vivo human thyroid due to its proximity to carotid artery. The pulsation of the carotid artery induces inter-frame motion that significantly degrades microvasculature images, resulting in diagnostic errors. The main objective of this study is to reduce inter-frame motion artifacts in high-frame-rate ultrasound imaging to achieve a more accurate visualization of tumor microvessel features. We propose a low-complex deep learning network comprising depth-wise separable convolutional layers and hybrid adaptive and squeeze-and-excite attention mechanisms to correct inter-frame motion in high-frame-rate images. Rigorous validation using phantom and in-vivo data with simulated inter-frame motion indicates average improvements of 35% in Pearson correlation coefficients (PCCs) between motion corrected and reference data with respect to that of motion corrupted data. Further, reconstruction of microvasculature images using motion-corrected frames demonstrates PCC improvement from 31 to 35%. Another thorough validation using in-vivo thyroid data with physiological inter-frame motion demonstrates average improvement of 20% in PCC and 40% in mean inter-frame correlation. Finally, comparison with the conventional image registration method indicates the suitability of proposed network for real-time inter-frame motion correction with 5000 times reduction in motion corrected frame prediction latency. |
| format | Article |
| id | doaj-art-f55f6f37b0c740d9bb5e720932a664ee |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f55f6f37b0c740d9bb5e720932a664ee2024-12-22T12:25:46ZengNature PortfolioScientific Reports2045-23222024-10-0114111510.1038/s41598-024-77610-4Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learningManali Saini0Mostafa Fatemi1Azra Alizad2Department of Radiology, Mayo Clinic College of Medicine and ScienceDepartment of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and ScienceDepartment of Radiology, Mayo Clinic College of Medicine and ScienceAbstract Contrast-free ultrasound quantitative microvasculature imaging shows promise in several applications, including the assessment of benign and malignant lesions. However, motion represents one of the major challenges in imaging tumor microvessels in organs that are prone to physiological motions. This study aims at addressing potential microvessel image degradation in in vivo human thyroid due to its proximity to carotid artery. The pulsation of the carotid artery induces inter-frame motion that significantly degrades microvasculature images, resulting in diagnostic errors. The main objective of this study is to reduce inter-frame motion artifacts in high-frame-rate ultrasound imaging to achieve a more accurate visualization of tumor microvessel features. We propose a low-complex deep learning network comprising depth-wise separable convolutional layers and hybrid adaptive and squeeze-and-excite attention mechanisms to correct inter-frame motion in high-frame-rate images. Rigorous validation using phantom and in-vivo data with simulated inter-frame motion indicates average improvements of 35% in Pearson correlation coefficients (PCCs) between motion corrected and reference data with respect to that of motion corrupted data. Further, reconstruction of microvasculature images using motion-corrected frames demonstrates PCC improvement from 31 to 35%. Another thorough validation using in-vivo thyroid data with physiological inter-frame motion demonstrates average improvement of 20% in PCC and 40% in mean inter-frame correlation. Finally, comparison with the conventional image registration method indicates the suitability of proposed network for real-time inter-frame motion correction with 5000 times reduction in motion corrected frame prediction latency.https://doi.org/10.1038/s41598-024-77610-4Inter-frame motion correctionHigh frame rateContrast-free ultrasound microvessel imagingDeep learning |
| spellingShingle | Manali Saini Mostafa Fatemi Azra Alizad Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning Scientific Reports Inter-frame motion correction High frame rate Contrast-free ultrasound microvessel imaging Deep learning |
| title | Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning |
| title_full | Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning |
| title_fullStr | Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning |
| title_full_unstemmed | Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning |
| title_short | Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning |
| title_sort | fast inter frame motion correction in contrast free ultrasound quantitative microvasculature imaging using deep learning |
| topic | Inter-frame motion correction High frame rate Contrast-free ultrasound microvessel imaging Deep learning |
| url | https://doi.org/10.1038/s41598-024-77610-4 |
| work_keys_str_mv | AT manalisaini fastinterframemotioncorrectionincontrastfreeultrasoundquantitativemicrovasculatureimagingusingdeeplearning AT mostafafatemi fastinterframemotioncorrectionincontrastfreeultrasoundquantitativemicrovasculatureimagingusingdeeplearning AT azraalizad fastinterframemotioncorrectionincontrastfreeultrasoundquantitativemicrovasculatureimagingusingdeeplearning |