ArticleIEEE transactions on radiation and plasma medical sciences2025
Finite Element Method-Based Hybrid MRI/CBCT Generation to Improve Liver Stereotactic Body Radiation Therapy Targets Localization Accuracy.
Article in IEEE transactions on radiation and plasma medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- PhysMorph: A biomechanical and image-guided deep learning framework for real-time multi-modal liver image registration.Physics and imaging in radiation oncology · 2026Article
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10 authors.
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Abstract
Cone-beam CT (CBCT) is commonly used in treatment imaging, but its limited soft tissue contrast presents challenges for liver tumor localization. As a result, indirect localization methods relying on the liver's boundary are commonly utilized, which have limited accuracy for tumor localization. Onboard MRI offers superior soft tissue contrast but is limited by the cost. To address this, we devised a method to generate onboard virtual MRI by integrating pretreatment MRI with onboard CBCT, enhancing liver stereotactic body radiation therapy (SBRT) tumor localization accuracy. We employed a finite element method (FEM) for deformable mapping, deforming prior liver MR images onto CBCT geometry to create a virtual MRI. This hybrid virtual-MRI/CBCT (hMRI-CBCT) approach was evaluated in a pilot study involving 48 patients. The hMRI-CBCT demonstrated superb soft-tissue contrast with clear tumor visualization. Registration accuracy of hMRI-CBCT to planning CT significantly surpasses the onboard CBCT to planning CT registration, particularly for tumors not near the liver boundary, with an average error reduction of 1.53 ± 2.16 mm. Our study demonstrated that hybrid MRI/CBCT can apparently reduce localization errors in liver SBRT, potentially improving tumor control and reducing toxicities, and opening avenues for further margin reduction and dose escalation.
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