Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems
Abstract Elastography is a medical imaging technique used to measure the elasticity of tissues by comparing ultrasound signals before and after a light compression. The lateral resolution of ultrasound is much inferior to the axial resolution. Current elastography methods generally require both axia...
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| Format: | Article |
| Language: | English |
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Wiley
2023-06-01
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| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202300439 |
| _version_ | 1797796987955838976 |
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| author | Chun‐Teh Chen Grace X. Gu |
| author_facet | Chun‐Teh Chen Grace X. Gu |
| author_sort | Chun‐Teh Chen |
| collection | DOAJ |
| description | Abstract Elastography is a medical imaging technique used to measure the elasticity of tissues by comparing ultrasound signals before and after a light compression. The lateral resolution of ultrasound is much inferior to the axial resolution. Current elastography methods generally require both axial and lateral displacement components, making them less effective for clinical applications. Additionally, these methods often rely on the assumption of material incompressibility, which can lead to inaccurate elasticity reconstruction as no materials are truly incompressible. To address these challenges, a new physics‐informed deep‐learning method for elastography is proposed. This new method integrates a displacement network and an elasticity network to reconstruct the Young's modulus field of a heterogeneous object based on only a measured axial displacement field. It also allows for the removal of the assumption of material incompressibility, enabling the reconstruction of both Young's modulus and Poisson's ratio fields simultaneously. The authors demonstrate that using multiple measurements can mitigate the potential error introduced by the “eggshell” effect, in which the presence of stiff material prevents the generation of strain in soft material. These improvements make this new method a valuable tool for a wide range of applications in medical imaging, materials characterization, and beyond. |
| first_indexed | 2024-03-13T03:41:20Z |
| format | Article |
| id | doaj.art-353f083e85874295bf5304798dc12019 |
| institution | Directory Open Access Journal |
| issn | 2198-3844 |
| language | English |
| last_indexed | 2024-03-13T03:41:20Z |
| publishDate | 2023-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj.art-353f083e85874295bf5304798dc120192023-06-23T07:34:34ZengWileyAdvanced Science2198-38442023-06-011018n/an/a10.1002/advs.202300439Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed ProblemsChun‐Teh Chen0Grace X. Gu1Department of Materials Science and Engineering University of California Berkeley CA 94720 USADepartment of Mechanical Engineering University of California Berkeley CA 94720 USAAbstract Elastography is a medical imaging technique used to measure the elasticity of tissues by comparing ultrasound signals before and after a light compression. The lateral resolution of ultrasound is much inferior to the axial resolution. Current elastography methods generally require both axial and lateral displacement components, making them less effective for clinical applications. Additionally, these methods often rely on the assumption of material incompressibility, which can lead to inaccurate elasticity reconstruction as no materials are truly incompressible. To address these challenges, a new physics‐informed deep‐learning method for elastography is proposed. This new method integrates a displacement network and an elasticity network to reconstruct the Young's modulus field of a heterogeneous object based on only a measured axial displacement field. It also allows for the removal of the assumption of material incompressibility, enabling the reconstruction of both Young's modulus and Poisson's ratio fields simultaneously. The authors demonstrate that using multiple measurements can mitigate the potential error introduced by the “eggshell” effect, in which the presence of stiff material prevents the generation of strain in soft material. These improvements make this new method a valuable tool for a wide range of applications in medical imaging, materials characterization, and beyond.https://doi.org/10.1002/advs.202300439artificial intelligencecomputational methodselastographyphysics‐informed machine learning |
| spellingShingle | Chun‐Teh Chen Grace X. Gu Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems Advanced Science artificial intelligence computational methods elastography physics‐informed machine learning |
| title | Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems |
| title_full | Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems |
| title_fullStr | Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems |
| title_full_unstemmed | Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems |
| title_short | Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems |
| title_sort | physics informed deep learning for elasticity forward inverse and mixed problems |
| topic | artificial intelligence computational methods elastography physics‐informed machine learning |
| url | https://doi.org/10.1002/advs.202300439 |
| work_keys_str_mv | AT chuntehchen physicsinformeddeeplearningforelasticityforwardinverseandmixedproblems AT gracexgu physicsinformeddeeplearningforelasticityforwardinverseandmixedproblems |