A hybrid approach for fusing 4D‐MRI temporal information with 3D‐CT for the study of lung and lung tumor motion

• Published in: Medical physics (Lancaster) Vol. 42; no. 8; pp. 4484 - 4496
• Main Authors: Yang, Y. X., Teo, S.‐K., Van Reeth, E., Tan, C. H., Tham, I. W. K., Poh, C. L.
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01-08-2015
• Subjects: 60 APPLIED LIFE SCIENCES; Analysis of motion; Bioengineering; Biological material, e.g. blood, urine; Haemocytometers; biomedical MRI; BIOMEDICAL RADIOGRAPHY; cancer; Computed tomography; Computer Science; Computer Simulation; Computerised tomographs; computerised tomography; COMPUTERIZED TOMOGRAPHY; DATASETS; Datasets as Topic; Digital computing or data processing equipment or methods, specially adapted for specific applications; DIR; ERRORS; Feasibility Studies; feature extraction; FEM; Field emission microscopy; Finite Element Analysis; Finite element calculations; FINITE ELEMENT METHOD; Finite element methods; Image data processing or generation, in general; image fusion; image motion analysis; image registration; image resolution; Imaging; Imaging, Three-Dimensional - methods; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging; Life Sciences; lung; Lung - diagnostic imaging; Lung - pathology; lung cancer; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; LUNGS; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; medical image processing; Medical image spatial resolution; Medical Imaging; Motion; MRI: anatomic, functional, spectral, diffusion; Multimodal Imaging - methods; NEOPLASMS; NMR IMAGING; PATIENTS; Pneumodyamics, respiration; pneumodynamics; RADIATION PROTECTION AND DOSIMETRY; radiation therapy; RADIOTHERAPY; Registration; RESPIRATION; SIMULATION; SPATIAL RESOLUTION; Three dimensional image processing; Tissues; Tomography, X-Ray Computed - methods; tumours
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4923167

Purpose:
Accurate visualization of lung motion is important in many clinical applications, such as radiotherapy of lung cancer. Advancement in imaging modalities [e.g., computed tomography (CT) and MRI] has allowed dynamic imaging of lung and lung tumor motion. However, each imaging modality has its advantages and disadvantages. The study presented in this paper aims at generating synthetic 4D‐CT dataset for lung cancer patients by combining both continuous three‐dimensional (3D) motion captured by 4D‐MRI and the high spatial resolution captured by CT using the authors’ proposed approach.
Methods:
A novel hybrid approach based on deformable image registration (DIR) and finite element method simulation was developed to fuse a static 3D‐CT volume (acquired under breath‐hold) and the 3D motion information extracted from 4D‐MRI dataset, creating a synthetic 4D‐CT dataset.
Results:
The study focuses on imaging of lung and lung tumor. Comparing the synthetic 4D‐CT dataset with the acquired 4D‐CT dataset of six lung cancer patients based on 420 landmarks, accurate results (average error <2 mm) were achieved using the authors’ proposed approach. Their hybrid approach achieved a 40% error reduction (based on landmarks assessment) over using only DIR techniques.
Conclusions:
The synthetic 4D‐CT dataset generated has high spatial resolution, has excellent lung details, and is able to show movement of lung and lung tumor over multiple breathing cycles.