UltRAP‐Net: Reverse Approximation of Tissue Properties in Ultrasound Imaging
Medical ultrasound (US) has been widely used in clinical practices due to its merits of being low cost, real time, and radiation free. However, its capability to reveal the underlying tissue properties remains underexplored. A physics‐constrained learning framework is studied to reversely approximat...
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400865 |
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| author | Yingqi Li Ka‐Wai Kwok Magdalena Wysocki Nassir Navab Zhongliang Jiang |
| author_facet | Yingqi Li Ka‐Wai Kwok Magdalena Wysocki Nassir Navab Zhongliang Jiang |
| author_sort | Yingqi Li |
| collection | DOAJ |
| description | Medical ultrasound (US) has been widely used in clinical practices due to its merits of being low cost, real time, and radiation free. However, its capability to reveal the underlying tissue properties remains underexplored. A physics‐constrained learning framework is studied to reversely approximate tissue property representations from multiple B‐mode images acquired with varying dynamic ranges. First, an extractor network is used to generate property maps, that is, attenuation coefficient α, reflection coefficient β, border probability ρb, scattering density ρs, scattering amplitude ϕ, and one perturbation p map characterizing the variations caused by varying dynamic range. The α − ϕ maps are loosely regularized by rendering them forward to realistic US images using ray‐tracing simulator. To further enforce the physics constraints, a ranking loss is introduced to align the disparity between two estimated p maps with the discrepancy between two distinct inputs. Due to the missing ground truth α − ϕ maps, alternatively, the method is validated by evaluating the consistency between the feature maps inferred from distinct images. The results demonstrate that the proposed method can robustly extract consistent intermediate maps from images. Furthermore, one potential downstream application is showcased to perform realistic US augmentation by introducing specific noise into the physics‐inspired α − ϕ maps. |
| format | Article |
| id | doaj-art-768eb89632bc4ad492ecc261b6596e2c |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-768eb89632bc4ad492ecc261b6596e2c2025-08-21T11:05:47ZengWileyAdvanced Intelligent Systems2640-45672025-08-0178n/an/a10.1002/aisy.202400865UltRAP‐Net: Reverse Approximation of Tissue Properties in Ultrasound ImagingYingqi Li0Ka‐Wai Kwok1Magdalena Wysocki2Nassir Navab3Zhongliang Jiang4Department of Mechanical Engineering The University of Hong Kong Hong Kong 999077 P. R. ChinaDepartment of Mechanical Engineering The University of Hong Kong Hong Kong 999077 P. R. ChinaChair for Computer‐Aided Medical Procedures and Augmented Reality Technical University of Munich 85748 Munich GermanyChair for Computer‐Aided Medical Procedures and Augmented Reality Technical University of Munich 85748 Munich GermanyChair for Computer‐Aided Medical Procedures and Augmented Reality Technical University of Munich 85748 Munich GermanyMedical ultrasound (US) has been widely used in clinical practices due to its merits of being low cost, real time, and radiation free. However, its capability to reveal the underlying tissue properties remains underexplored. A physics‐constrained learning framework is studied to reversely approximate tissue property representations from multiple B‐mode images acquired with varying dynamic ranges. First, an extractor network is used to generate property maps, that is, attenuation coefficient α, reflection coefficient β, border probability ρb, scattering density ρs, scattering amplitude ϕ, and one perturbation p map characterizing the variations caused by varying dynamic range. The α − ϕ maps are loosely regularized by rendering them forward to realistic US images using ray‐tracing simulator. To further enforce the physics constraints, a ranking loss is introduced to align the disparity between two estimated p maps with the discrepancy between two distinct inputs. Due to the missing ground truth α − ϕ maps, alternatively, the method is validated by evaluating the consistency between the feature maps inferred from distinct images. The results demonstrate that the proposed method can robustly extract consistent intermediate maps from images. Furthermore, one potential downstream application is showcased to perform realistic US augmentation by introducing specific noise into the physics‐inspired α − ϕ maps.https://doi.org/10.1002/aisy.202400865robotic ultrasoundsultrasound augmentationsultrasound image analyses |
| spellingShingle | Yingqi Li Ka‐Wai Kwok Magdalena Wysocki Nassir Navab Zhongliang Jiang UltRAP‐Net: Reverse Approximation of Tissue Properties in Ultrasound Imaging Advanced Intelligent Systems robotic ultrasounds ultrasound augmentations ultrasound image analyses |
| title | UltRAP‐Net: Reverse Approximation of Tissue Properties in Ultrasound Imaging |
| title_full | UltRAP‐Net: Reverse Approximation of Tissue Properties in Ultrasound Imaging |
| title_fullStr | UltRAP‐Net: Reverse Approximation of Tissue Properties in Ultrasound Imaging |
| title_full_unstemmed | UltRAP‐Net: Reverse Approximation of Tissue Properties in Ultrasound Imaging |
| title_short | UltRAP‐Net: Reverse Approximation of Tissue Properties in Ultrasound Imaging |
| title_sort | ultrap net reverse approximation of tissue properties in ultrasound imaging |
| topic | robotic ultrasounds ultrasound augmentations ultrasound image analyses |
| url | https://doi.org/10.1002/aisy.202400865 |
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