Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study.

Salam Dhou,John H Lewis,Christopher Williams,Mohanad Alkhodari,Dan Ionascu

doi:10.3390/jimaging8020017

Abstract

A method for generating fluoroscopic (time-varying) volumetric images using patient-specific motion models derived from four-dimensional cone-beam CT (4D-CBCT) images was developed. 4D-CBCT images acquired immediately prior to treatment have the potential to accurately represent patient anatomy and respiration during treatment. Fluoroscopic 3D image estimation is performed in two steps: (1) deriving motion models and (2) optimization. To derive motion models, every phase in a 4D-CBCT set is registered to a reference phase chosen from the same set using deformable image registration (DIR). Principal components analysis (PCA) is used to reduce the dimensionality of the displacement vector fields (DVFs) resulting from DIR into a few vectors representing organ motion found in the DVFs. The PCA motion models are optimized iteratively by comparing a cone-beam CT (CBCT) projection to a simulated projection computed from both the motion model and a reference 4D-CBCT phase, resulting in a sequence of fluoroscopic 3D images. Patient datasets were used to evaluate the method by estimating the tumor location in the generated images compared to manually defined ground truth positions. Experimental results showed that the average tumor mean absolute error (MAE) along the superior–inferior (SI) direction and the 95th percentile in two patient datasets were 2.29 and 5.79 mm for patient 1, and 1.89 and 4.82 mm for patient 2. This study demonstrated the feasibility of deriving 4D-CBCT-based PCA motion models that have the potential to account for the 3D non-rigid patient motion and localize tumors and other patient anatomical structures on the day of treatment.

Highlights

Image-based motion modeling of patient anatomy during radiotherapy can be useful in accurately localizing tumors and other anatomical structures in the body [3,4,5,6,7]
Principal component analysis (PCA)-based motion modeling has proven its efficacy in representing the spatiotemporal relationship of the entire lung motion [8]
Because of their compactness and performance, PCA motion models are being used along with projection images captured at the day of treatment for generating time-varying volumetric images, often called fluoroscopic because they are produced in a continuous fashion similar to the images produced using the well-known fluoroscopy procedure [9,10,11,12,13,14,15,16]

Summary

Introduction

Principal component analysis (PCA)-based motion modeling has proven its efficacy in representing the spatiotemporal relationship of the entire lung motion [8]. Because 4DCT images are acquired at the time of treatment planning, which happens days or weeks before the treatment delivery day, PCA motion models derived from them may not accurately represent patient anatomy or motion patterns at the day of treatment delivery [14]. They may not account for tumor baseline shifts that are observed frequently in the clinic [18]

Methods

Results

Discussion

Conclusion