Population of anatomically variable 4D XCAT adult phantoms for imaging research and optimization

Segars, W. Paul; Bond, Jennifer; Frush, Jack; Hon, Sylvia; Eckersley, Chris; Williams, Cameron H.; Feng, Jingnan; Tward, Daniel J.; Ratnanather, J. Tilak; Miller, Michael I.; Frush, D; Samei, Ehsan

doi:10.1118/1.4794178

Cited by 175 publications

(166 citation statements)

References 49 publications

(55 reference statements)

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“…In an effort to provide consistent datasets for algorithm validation, several researchers have made their ground‐truth models publicly available. Examples include the extended cardiac–torso (XCAT) phantom, (3) the point‐validated pixel‐based breathing thorax model (POPI model), (4) the DIR‐Lab Thoracic 4D CT model, 5 , 6 and those provided as part of the EMPIRE 10 challenge (7) . In an effort to improve the correlation of computer‐based phantoms with the actual anatomical changes seen over a course of radiation therapy, the current authors previously developed synthetic datasets derived from clinical images of real head and neck patients (8) .…”

Section: Introductionmentioning

confidence: 99%

Benchmarking of five commercial deformable image registration algorithms for head and neck patients

Pukala

Johnson

Shah

et al. 2016

J Applied Clin Med Phys

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Benchmarking is a process in which standardized tests are used to assess system performance. The data produced in the process are important for comparative purposes, particularly when considering the implementation and quality assurance of DIR algorithms. In this work, five commercial DIR algorithms (MIM, Velocity, RayStation, Pinnacle, and Eclipse) were benchmarked using a set of 10 virtual phantoms. The phantoms were previously developed based on CT data collected from real head and neck patients. Each phantom includes a start of treatment CT dataset, an end of treatment CT dataset, and the ground‐truth deformation vector field (DVF) which links them together. These virtual phantoms were imported into the commercial systems and registered through a deformable process. The resulting DVFs were compared to the ground‐truth DVF to determine the target registration error (TRE) at every voxel within the image set. Real treatment plans were also recalculated on each end of treatment CT dataset and the dose transferred according to both the ground‐truth and test DVFs. Dosimetric changes were assessed, and TRE was correlated with changes in the DVH of individual structures. In the first part of the study, results show mean TRE on the order of 0.5 mm to 3 mm for all phantoms and ROIs. In certain instances, however, misregistrations were encountered which produced mean and max errors up to 6.8 mm and 22 mm, respectively. In the second part of the study, dosimetric error was found to be strongly correlated with TRE in the brainstem, but weakly correlated with TRE in the spinal cord. Several interesting cases were assessed which highlight the interplay between the direction and magnitude of TRE and the dose distribution, including the slope of dosimetric gradients and the distance to critical structures. This information can be used to help clinicians better implement and test their algorithms, and also understand the strengths and weaknesses of a dose adaptive approach.PACS number(s): 87.57.nj, 87.55.dk, 87.55.Qr

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Section: Introductionmentioning

confidence: 99%

Benchmarking of five commercial deformable image registration algorithms for head and neck patients

Pukala

Johnson

Shah

et al. 2016

J Applied Clin Med Phys

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“…This was done by scaling the skin surfaces of the arms and legs as was similarly done in the creation of our adult population. 31 A good amount of the body's weight is carried in the arms and legs. These were added from templates and not patient specific, so they provided a good means for adjustment.…”

Section: E Fine Tuning Of the Modelsmentioning

confidence: 99%

“…The MC-LDDMM transform is simply applied to these models to define them in the new pediatric anatomies. 31 As in the original XCAT adult models, both cardiac and respiratory models are parameterized so a user can alter their patterns to simulate various normal or abnormal states.…”

Section: D Incorporation Of Cardiac and Respiratory Motionsmentioning

confidence: 99%

“…31 We first create an initial patient model by segmenting the CT data using ImageSegment, a graphical application developed in our laboratory. Segmentation was performed for the following structures: the body outline, skull, eyes, sternum, ribs, backbone, pelvis, scapulas, clavicles, left and right humerus, left and right femur, heart, lungs, liver, gallbladder, stomach, spleen, thyroid, kidneys, pancreas, intestines, prostate, testes, and bladder.…”

Section: A Patient Data and Creation Of Initial Target Modelsmentioning

confidence: 99%

“…31) based on patient CT data. This method is similar to other studies mentioned above that transform template phantoms to create new ones.…”

Section: Introductionmentioning

confidence: 99%

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A set of 4D pediatric XCAT reference phantoms for multimodality research

et al. 2014

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Purpose:The authors previously developed an adult population of 4D extended cardiac-torso (XCAT) phantoms for multimodality imaging research. In this work, the authors develop a reference set of 4D pediatric XCAT phantoms consisting of male and female anatomies at ages of newborn, 1, 5, 10, and 15 years. These models will serve as the foundation from which the authors will create a vast population of pediatric phantoms for optimizing pediatric CT imaging protocols. Methods: Each phantom was based on a unique set of CT data from a normal patient obtained from the Duke University database. The datasets were selected to best match the reference values for height and weight for the different ages and genders according to ICRP Publication 89. The major organs and structures were segmented from the CT data and used to create an initial pediatric model defined using nonuniform rational B-spline surfaces. The CT data covered the entire torso and part of the head. To complete the body, the authors manually added on the top of the head and the arms and legs using scaled versions of the XCAT adult models or additional models created from cadaver data. A multichannel large deformation diffeomorphic metric mapping algorithm was then used to calculate the transform from a template XCAT phantom (male or female 50th percentile adult) to the target pediatric model. The transform was applied to the template XCAT to fill in any unsegmented structures within the target phantom and to implement the 4D cardiac and respiratory models in the new anatomy. The masses of the organs in each phantom were matched to the reference values given in ICRP Publication 89. The new reference models were checked for anatomical accuracy via visual inspection. Results: The authors created a set of ten pediatric reference phantoms that have the same level of detail and functionality as the original XCAT phantom adults. Each consists of thousands of anatomical structures and includes parameterized models for the cardiac and respiratory motions. Based on patient data, the phantoms capture the anatomic variations of childhood, such as the development of bone in the skull, pelvis, and long bones, and the growth of the vertebrae and organs. The phantoms can be combined with existing simulation packages to generate realistic pediatric imaging data from different modalities. Conclusions: The development of patient-derived pediatric computational phantoms is useful in providing variable anatomies for simulation. Future work will expand this ten-phantom base to a host of pediatric phantoms representative of the public at large. This can provide a means to evaluate and improve pediatric imaging devices and to optimize CT protocols in terms of image quality and radiation dose.

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Variability in commercially available deformable image registration: A multi‐institution analysis using virtual head and neck phantoms

Kubli

Pukala

Shah

et al. 2021

J Applied Clin Med Phys

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Purpose: The purpose of this study was to evaluate the performance of three common deformable image registration (DIR) packages across algorithms and institutions. Methods and Materials:The Deformable Image Registration Evaluation Project (DIREP) provides ten virtual phantoms derived from computed tomography (CT) datasets of head-and-neck cancer patients over a single treatment course. Using the DIREP phantoms, DIR results from 35 institutions were submitted using either Velocity, MIM, or Eclipse. Submitted deformation vector fields (DVFs) were compared to ground-truth DVFs to calculate target registration error (TRE) for six regions of interest (ROIs). Statistical analysis was performed to determine the variability between each DIR software package and the variability of users within each algorithm.Results: Overall mean TRE was 2.04 ± 0.35 mm for Velocity, 1.10 ± 0.29 mm for MIM, and 2.35 ± 0.15 mm for Eclipse. The MIM mean TRE was significantly different than both Velocity and Eclipse for all ROIs. Velocity and Eclipse mean TREs were not significantly different except for when evaluating the registration of the cord or mandible. Significant differences between institutions were found for the MIM and Velocity platforms. However, these differences could be explained by variations in Velocity DIR parameters and MIM software versions.Conclusions: Average TRE was shown to be <3 mm for all three software platforms. However, maximum errors could be larger than 2 cm indicating that care should be exercised when using DIR. While MIM performed statistically better than the other packages, all evaluated algorithms had an average TRE better than the largest voxel dimension. For the phantoms studied here, significant differences between algorithm users were minimal suggesting that the algorithm used may have more impact on DIR accuracy than the particular registration technique employed. A significant difference in TRE was discovered between MIM versions showing that DIR QA should be performed after software upgrades as recommended by TG-132.

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Population of anatomically variable 4D XCAT adult phantoms for imaging research and optimization

Cited by 175 publications

References 49 publications

Benchmarking of five commercial deformable image registration algorithms for head and neck patients

Benchmarking of five commercial deformable image registration algorithms for head and neck patients

A set of 4D pediatric XCAT reference phantoms for multimodality research

Variability in commercially available deformable image registration: A multi‐institution analysis using virtual head and neck phantoms

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