Reconstruction of a high‐quality volumetric image and a respiratory motion model from patient CBCT projections

Guo, Minghao; Chee, Geraldine; O’Connell, D; Dhou, Salam; Fu, Jie; Singhrao, Kamal; Ionascu, Dan; Ruan, Dan; Lee, Percy; Low, Daniel A.; Zhao, Jun; Lewis, John H.

doi:10.1002/mp.13595

Cited by 16 publications

(11 citation statements)

References 53 publications

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“…Planning for SBRT is typically performed using 4-dimesional CT (4DCT) images, which are often captured days or weeks before treatment. In many motion modeling approaches, these planning images are used for building patient-specific motion models [5,[8][9][10][11]. These models are often generated by applying principal component analysis (PCA) to a set of deformation vector fields (DVFs) between the phases of the 4DCT.…”

Section: Introductionmentioning

confidence: 99%

Quantifying day-to-day variations in 4DCBCT-based PCA motion models

Dhou

Lewis²,

Cai³

et al. 2020

Biomed. Phys. Eng. Express

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The aim of this paper is to quantify the day-to-day variations of motion models derived from pretreatment 4-dimensional cone beam CT (4DCBCT) fractions for lung cancer stereotactic body radiotherapy (SBRT) patients. Motion models are built by (1) applying deformable image registration (DIR) on each 4DCBCT image with respect to a reference image from that day, resulting in a set of displacement vector fields (DVFs), and (2) applying principal component analysis (PCA) on the DVFs to obtain principal components representing a motion model. Variations were quantified by comparing the PCA eigenvectors of the motion model built from the first day of treatment to the corresponding eigenvectors of the other motion models built from each successive day of treatment. Three metrics were used to quantify the variations: root mean squared (RMS) difference in the vectors, directional similarity, and an introduced metric called the Euclidean Model Norm (EMN). EMN quantifies the degree to which a motion model derived from the first fraction can represent the motion models of subsequent fractions. Twenty-one 4DCBCT scans from five SBRT patient treatments were used in this retrospective study. Experimental results demonstrated that the first two eigenvectors of motion models across all fractions have smaller RMS (0.00017), larger directional similarity (0.528), and larger EMN (0.678) than the last three eigenvectors (RMS: 0.00025, directional similarity: 0.041, and EMN: 0.212). The study concluded that, while the motion model eigenvectors varied from fraction to fraction, the first few eigenvectors were shown to be more stable across treatment fractions than others. This supports the notion that a pre-treatment motion model built from the first few PCA eigenvectors may remain valid throughout a treatment course. Future work is necessary to quantify how day-to-day variations in these models will affect motion reconstruction accuracy for specific clinical tasks.

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Section: Introductionmentioning

confidence: 99%

Quantifying day-to-day variations in 4DCBCT-based PCA motion models

Dhou

Lewis²,

Cai³

et al. 2020

Biomed. Phys. Eng. Express

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“…By way of example only, we mention here the lung motion model originally developed by Low and colleagues [49], [50]. This model has recently been applied in conjunction with an iterative projection algorithm for a motion-compensated simultaneous algebraic reconstruction technique (MC-SART) of cone beam CT (CBCT) [51], [52]. There is, however, a fundamental conceptual difference between our approach and the MC-SART algorithm.…”

Section: F Development Of a Motion-adapted Iterative Reconstruction A...mentioning

confidence: 99%

Developments in Mathematical Algorithms and Computational Tools for Proton CT and Particle Therapy Treatment Planning

Censor¹,

Schubert²,

Schulte³

2021

Preprint

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We summarize recent results and ongoing activities in mathematical algorithms and computer science methods related to proton computed tomography (pCT) and intensitymodulated particle therapy (IMPT) treatment planning. Proton therapy necessitates a high level of delivery accuracy to exploit the selective targeting imparted by the Bragg peak. For this purpose, pCT utilizes the proton beam itself to create images. The technique works by sending a low-intensity beam of protons through the patient and measuring the position, direction, and energy loss of each exiting proton. The pCT technique allows reconstruction of the volumetric distribution of the relative stopping power (RSP) of the patient tissues for use in treatment planning and pre-treatment range verification. We have investigated new ways to make the reconstruction both efficient and accurate. Better accuracy of RSP also enables more robust inverse approaches to IMPT. For IMPT, we developed a framework for performing intensity-modulation of the proton pencil beams. We expect that these developments will lead to additional project work in the years to come, which requires a regular exchange between experts in the fields of mathematics, computer science, and medical physics. We have initiated such an exchange by organizing annual workshops on pCT and IMPT algorithm and technology developments. This report is, admittedly, tilted toward our interdisciplinary work and methods. We offer a comprehensive overview of results, problems, and challenges in pCT and IMPT with the aim of making other scientists wanting to tackle such issues and to strengthen their interdisciplinary collaboration by bringing together cutting-edge know-how from medicine, computer science, physics, and mathematics to bear on medical physics problems at hand.

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“…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]. There are many approaches proposed for image-based motion modeling.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Reconstruction of a high‐quality volumetric image and a respiratory motion model from patient CBCT projections

Cited by 16 publications

References 53 publications

Quantifying day-to-day variations in 4DCBCT-based PCA motion models

Quantifying day-to-day variations in 4DCBCT-based PCA motion models

Developments in Mathematical Algorithms and Computational Tools for Proton CT and Particle Therapy Treatment Planning

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

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