ArticleMedical physics2025
Deep learning-based estimation of respiration-induced deformation from surface motion: A proof-of-concept study on 4D thoracic image synthesis.
Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- 'See' through the surface: surface-derived three-dimensional AI-driven real-time imaging solution for intra-treatment image guidance.Machine Learning. Health · 2026Article
- Article
- Contrast-Enhanced Mammography in Breast Lesion Assessment: Accuracy and Surgical Impact.Tomography (Ann Arbor, Mich.) · 2025Article
- Deep learning-based estimation of respiration-induced deformation from surface motion: A proof-of-concept study on 4D thoracic image synthesis.Medical physics · 2025Article
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Abstract
backgroundFour-dimension computed tomography (4D-CT) provides important respiration-related information for thoracic radiotherapy. Its quality is challenged by various respiratory patterns. Its acquisition gives rise to the risk of higher radiation exposure. Based on a continuously estimated deformation, a 4D synthesis by warping a high-quality volumetric image is a possible solution. PURPOSE: To propose a non-patient-specific cascaded ensemble model (CEM) to estimate respiration-induced thoracic tissue deformation from surface motion.
methodsThe CEM is cascaded by three deep learning-based models. By inputting the surface motion, CEM outputs a deformation vector field (DVF) inside thorax. In our work, the surface motion was simulated using the body contours derived from 4D-CT. The CEM was trained on our private database including 62 4D-CT sets, and was tested on a public database encompassing 80 4D-CT sets. To evaluate CEM, we employed the model output DVF to generate a few series of synthesized CTs, and compared them with the ground truth. CEM was also compared with other published works.
resultsCEM synthesized CT with an mRMSE (average root mean square error) of 61.06 ± 10.43HU (average ± standard deviation), an mSSIM (average structural similarity index measure) of 0.990 ± 0.004, and an mMAE (average mean absolute error) of 26.80 ± 5.65HU. Compared with other works, CEM showed the best result.
conclusionsThe results demonstrated the effectiveness of CEM on estimating tissue DVF inside thorax. CEM requires no patient-specific breathing data sampling and no additional training before treatment. It shows potential for broad applications.
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