Finite Element Method-Based Hybrid MRI/CBCT Generation to Improve Liver Stereotactic Body Radiation Therapy Targets Localization Accuracy

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.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Radiation and Plasma Medical Sciences
Published
2024-09-23
DOI
https://doi.org/10.1109/trpms.2024.3466184
Citations
1
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.40

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Finite Element Method-Based Hybrid MRI/CBCT Generation to Improve Liver Stereotactic Body Radiation Therapy Targets Localization Accuracy

D A Guo, Jason K. Molitoris, Zhuoran Jiang, Ke Lü et al.
1 citations
IEEE Transactions on Radiation and Plasma Medical Sciences
Advanced MRI Techniques and Applications
0.40
article

Finite Element Method-Based Hybrid MRI/CBCT Generation to Improve Liver Stereotactic Body Radiation Therapy Targets Localization Accuracy

D A Guo, Jason K. Molitoris, Zhuoran Jiang, Ke Lü, Phuoc T. Tran, Zeyu Zhang, Lei Ren, F Yin, Mark Chen, Hualiang Zhong
article en
1 citations

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.

IEEE Transactions on Radiation and Plasma Medical SciencesVol. 9(3)
University of Maryland, Baltimore (US), University of Maryland Medical System (US), Duke University (US), Medical College of Wisconsin (US)
Varian Medical Systems, National Institutes of Health
Openalex Percentile: Top 35%
Advanced MRI Techniques and Applications
0.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.