Faculty Collaboration Database

Utilization of a hybrid finite-element based registration method to quantify heterogeneous tumor response for adaptive treatment for lung cancer patients. Phys Med Biol 2018 Mar 21;63(6):065017

Date

02/27/2018

Pubmed ID

29480158

Pubmed Central ID

PMC6242271

DOI

10.1088/1361-6560/aab235

Scopus ID

2-s2.0-85044828538 (requires institutional sign-in at Scopus site)   10 Citations

Abstract

Tumor response to radiation treatment (RT) can be evaluated from changes in metabolic activity between two positron emission tomography (PET) images. Activity changes at individual voxels in pre-treatment PET images (PET1), however, cannot be derived until their associated PET-CT (CT1) images are appropriately registered to during-treatment PET-CT (CT2) images. This study aimed to investigate the feasibility of using deformable image registration (DIR) techniques to quantify radiation-induced metabolic changes on PET images. Five patients with non-small-cell lung cancer (NSCLC) treated with adaptive radiotherapy were considered. PET-CTs were acquired two weeks before RT and 18 fractions after the start of RT. DIR was performed from CT1 to CT2 using B-Spline and diffeomorphic Demons algorithms. The resultant displacements in the tumor region were then corrected using a hybrid finite element method (FEM). Bitmap masks generated from gross tumor volumes (GTVs) in PET1 were deformed using the four different displacement vector fields (DVFs). The conservation of total lesion glycolysis (TLG) in GTVs was used as a criterion to evaluate the quality of these registrations. The deformed masks were united to form a large mask which was then partitioned into multiple layers from center to border. The averages of SUV changes over all the layers were 1.0  ±  1.3, 1.0  ±  1.2, 0.8  ±  1.3, 1.1  ±  1.5 for the B-Spline, B-Spline  +  FEM, Demons and Demons  +  FEM algorithms, respectively. TLG changes before and after mapping using B-Spline, Demons, hybrid-B-Spline, and hybrid-Demons registrations were 20.2%, 28.3%, 8.7%, and 2.2% on average, respectively. Compared to image intensity-based DIR algorithms, the hybrid FEM modeling technique is better in preserving TLG and could be useful for evaluation of tumor response for patients with regressing tumors.

Author List

Sharifi H, Zhang H, Bagher-Ebadian H, Lu W, Ajlouni MI, Jin JY, Kong FS, Chetty IJ, Zhong H

Author

Hualiang Zhong PhD Associate Professor in the Radiation Oncology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Algorithms
Carcinoma, Non-Small-Cell Lung
Clinical Trials, Phase II as Topic
Finite Element Analysis
Humans
Image Processing, Computer-Assisted
Lung Neoplasms
Positron Emission Tomography Computed Tomography
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted
Randomized Controlled Trials as Topic
Tumor Burden