Evaluation of 3D fluoroscopic image generation from a single planar treatment image on patient data with a modified XCAT phantom

Technical note: Performance evaluation of volumetric imaging based on motion modeling by principal component analysis

PURPOSE

To quantitatively evaluate the achievable performance of volumetric imaging based on lung motion modeling by principal component analysis (PCA).

METHODS

In volumetric imaging based on PCA, internal deformation was represented as a linear combination of the eigenvectors derived by PCA of the deformation vector fields evaluated from patient-specific four-dimensional-computed tomography (4DCT) datasets. The volumetric image was synthesized by warping the reference CT image with a deformation vector field which was evaluated using optimal principal component coefficients (PCs). Larger PCs were hypothesized to reproduce deformations larger than those included in the original 4DCT dataset. To evaluate the reproducibility of PCA-reconstructed volumetric images synthesized to be close to the ground truth as possible, mean absolute error (MAE), structure similarity index measure (SSIM) and discrepancy of diaphragm position were evaluated using 22 4DCT datasets of nine patients.

RESULTS

Mean MAE and SSIM values for the PCA-reconstructed volumetric images were approximately 80 HU and 0.88, respectively, regardless of the respiratory phase. In most test cases including the data of which motion range was exceeding that of the modeling data, the positional error of diaphragm was less than 5 mm. The results suggested that large deformations not included in the modeling 4DCT dataset could be reproduced. Furthermore, since the first PC correlated with the displacement of the diaphragm position, the first eigenvector became the dominant factor representing the respiration-associated deformations. However, other PCs did not necessarily change with the same trend as the first PC, and no correlation was observed between the coefficients. Hence, randomly allocating or sampling these PCs in expanded ranges may be applicable to reasonably generate an augmented dataset with various deformations.

CONCLUSIONS

Reasonable accuracy of image synthesis comparable to those in the previous research were shown by using clinical data. These results indicate the potential of PCA-based volumetric imaging for clinical applications.

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

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.

Real-time CT image generation based on voxel-by-voxel modeling of internal deformation by utilizing the displacement of fiducial markers

PURPOSE

To show the feasibility of real-time CT image generation technique utilizing internal fiducial markers that facilitate the evaluation of internal deformation.

METHODS

In the proposed method, a linear regression model that can derive internal deformation from the displacement of fiducial markers is built for each voxel in the training process before the treatment session. Marker displacement and internal deformation are derived from the four-dimensional computed tomography (4DCT) dataset. In the treatment session, the three-dimensional deformation vector field is derived according to the marker displacement, which is monitored by the real-time imaging system. The whole CT image can be synthesized by deforming the reference CT image with a deformation vector field in real-time. To show the feasibility of the technique, image synthesis accuracy and tumor localization accuracy were evaluated using the dataset generated by extended NURBS-Based Cardiac-Torso (XCAT) phantom and clinical 4DCT datasets from six patients, containing 10 CT datasets each. In the validation with XCAT phantom, motion range of the tumor in training data and validation data were about 10 and 15 mm, respectively, so as to simulate motion variation between 4DCT acquisition and treatment session. In the validation with patient 4DCT dataset, eight CT datasets from the 4DCT dataset were used in the training process. Two excluded inhale CT datasets can be regarded as the datasets with large deformations more than training dataset. CT images were generated for each respiratory phase using the corresponding marker displacement. Root mean squared error (RMSE), normalized RMSE (NRMSE), and structural similarity index measure (SSIM) between the original CT images and the synthesized CT images were evaluated as the quantitative indices of the accuracy of image synthesis. The accuracy of tumor localization was also evaluated.

RESULTS

In the validation with XCAT phantom, the mean NRMSE, SSIM, and three-dimensional tumor localization error were 7.5 ± 1.1%, 0.95 ± 0.02, and 0.4 ± 0.3 mm, respectively. In the validation with patient 4DCT dataset, the mean RMSE, NRMSE, SSIM, and three-dimensional tumor localization error in six patients were 73.7 ± 19.6 HU, 9.2 ± 2.6%, 0.88 ± 0.04, and 0.8 ± 0.6 mm, respectively. These results suggest that the accuracy of the proposed technique is adequate when the respiratory motion is within the range of the training dataset. In the evaluation with a marker displacement larger than that of the training dataset, the mean RMSE, NRMSE, and tumor localization error were about 100 HU, 13%, and <2.0 mm, respectively, except for one case having large motion variation. The performance of the proposed method was similar to those of previous studies. Processing time to generate the volumetric image was <100 ms.

CONCLUSION

We have shown the feasibility of the real-time CT image generation technique for volumetric imaging.

AAPM Task Group 264: The safe clinical implementation of MLC tracking in radiotherapy

The era of real-time radiotherapy is upon us. Robotic and gimbaled linac tracking are clinically established technologies with the clinical realization of couch tracking in development. Multileaf collimators (MLCs) are a standard equipment for most cancer radiotherapy systems, and therefore MLC tracking is a potentially widely available technology. MLC tracking has been the subject of theoretical and experimental research for decades and was first implemented for patient treatments in 2013. The AAPM Task Group 264 Safe Clinical Implementation of MLC Tracking in Radiotherapy Report was charged to proactively provide the broader radiation oncology community with (a) clinical implementation guidelines including hardware, software, and clinical indications for use, (b) commissioning and quality assurance recommendations based on early user experience, as well as guidelines on Failure Mode and Effects Analysis, and (c) a discussion of potential future developments. The deliverables from this report include: an explanation of MLC tracking and its historical development; terms and definitions relevant to MLC tracking; the clinical benefit of, clinical experience with and clinical implementation guidelines for MLC tracking; quality assurance guidelines, including example quality assurance worksheets; a clinical decision pathway, future outlook and overall recommendations.

Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy

To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent radiotherapy in the pre-CT era, only 2D radiographs were acquired, thus 3D dose distributions must be reconstructed from limited information. State-of-the-art methods achieve this by using 3D surrogate anatomies. These can however lack personalization and lead to coarse reconstructions. We present and validate a surrogate-free dose reconstruction method based on Machine Learning (ML). Abdominal planning CTs (n = 142) of recently-treated childhood cancer patients were gathered, their organs at risk were segmented, and 300 artificial Wilms' tumor plans were sampled automatically. Each artificial plan was automatically emulated on the 142 CTs, resulting in 42,600 3D dose distributions from which dose-volume metrics were derived. Anatomical features were extracted from digitally reconstructed radiographs simulated from the CTs to resemble historical radiographs. Further, patient and radiotherapy plan features typically available from historical treatment records were collected. An evolutionary ML algorithm was then used to link features to dose-volume metrics. Besides 5-fold cross validation, a further evaluation was done on an independent dataset of five CTs each associated with two clinical plans. Cross-validation resulted in mean absolute errors ≤ 0.6 Gy for organs completely inside or outside the field. For organs positioned at the edge of the field, mean absolute errors ≤ 1.7 Gy for [Formula: see text], ≤ 2.9 Gy for [Formula: see text], and ≤ 13% for [Formula: see text] and [Formula: see text], were obtained, without systematic bias. Similar results were found for the independent dataset. To conclude, we proposed a novel organ dose reconstruction method that uses ML models to predict dose-volume metric values given patient and plan features. Our approach is not only accurate, but also efficient, as the setup of a surrogate is no longer needed.

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

The aim of this paper is to quantify the day-to-day variations of motion models derived from pre-treatment 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.

Continuous generation of volumetric images during stereotactic body radiation therapy using periodic kV imaging and an external respiratory surrogate

We present a technique for continuous generation of volumetric images during SBRT using periodic kV imaging and an external respiratory surrogate signal to drive a patient-specific PCA motion model. Using the on-board imager, kV radiographs are acquired every 3 s and used to fit the parameters of a motion model so that it matches observed changes in internal patient anatomy. A multi-dimensional correlation model is established between the motion model parameters and the external surrogate position and velocity, enabling volumetric image reconstruction between kV imaging time points. Performance of the algorithm was evaluated using 10 realistic eXtended CArdiac-Torso (XCAT) digital phantoms including 3D anatomical respiratory deformation programmed with 3D tumor positions measured with orthogonal kV imaging of implanted fiducial gold markers. The clinically measured ground truth 3D tumor positions provided a dataset with realistic breathing irregularities, and the combination of periodic on-board kV imaging with recorded external respiratory surrogate signal was used for correlation modeling to account for any changes in internal-external correlation. The three-dimensional tumor positions are reconstructed with an average root mean square error (RMSE) of 1.47 mm, and an average 95th percentile 3D positional error of 2.80 mm compared with the clinically measured ground truth 3D tumor positions. This technique enables continuous 3D anatomical image generation based on periodic kV imaging of internal anatomy without the additional dose of continuous kV imaging. The 3D anatomical images produced using this method can be used for treatment verification and delivered dose computation in the presence of irregular respiratory motion.

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

PURPOSE

To develop and evaluate a method of reconstructing a patient- and treatment day- specific volumetric image and motion model from free-breathing cone-beam projections and respiratory surrogate measurements. This Motion-Compensated Simultaneous Algebraic Reconstruction Technique (MC-SART) generates and uses a motion model derived directly from the cone-beam projections, without requiring prior motion measurements from 4DCT, and can compensate for both inter- and intrabin deformations. The motion model can be used to generate images at arbitrary breathing points, which can be used for estimating volumetric images during treatment delivery.

METHODS

The MC-SART was formulated using simultaneous image reconstruction and motion model estimation. For image reconstruction, projections were first binned according to external surrogate measurements. Projections in each bin were used to reconstruct a set of volumetric images using MC-SART. The motion model was estimated based on deformable image registration between the reconstructed bins, and least squares fitting to model parameters. The model was used to compensate for motion in both projection and backprojection operations in the subsequent image reconstruction iterations. These updated images were then used to update the motion model, and the two steps were alternated between. The final output is a volumetric reference image and a motion model that can be used to generate images at any other time point from surrogate measurements.

RESULTS

A retrospective patient dataset consisting of eight lung cancer patients was used to evaluate the method. The absolute intensity differences in the lung regions compared to ground truth were 50.8 ± 43.9 HU in peak exhale phases (reference) and 80.8 ± 74.0 in peak inhale phases (generated). The 50th percentile of voxel registration error of all voxels in the lung regions with >5 mm amplitude was 1.3 mm. The MC-SART was also applied to measured patient cone-beam projections acquired with a linac-mounted CBCT system. Results from this patient data demonstrate the feasibility of MC-SART and showed qualitative image quality improvements compared to other state-of-the-art algorithms.

CONCLUSION

We have developed a simultaneous image reconstruction and motion model estimation method that uses Cone-beam computed tomography (CBCT) projections and respiratory surrogate measurements to reconstruct a high-quality reference image and motion model of a patient in treatment position. The method provided superior performance in both HU accuracy and positional accuracy compared to other existing methods. The resultant reference image and motion model can be combined with respiratory surrogate measurements to generate volumetric images representing patient anatomy at arbitrary time points.

Innovations in Computer Technologies Have Impacted Radiation Dosimetry Through Anatomically Realistic Phantoms and Fast Monte Carlo Simulations

Radiological physics principles have not changed in the past 60 y when computer technologies advanced exponentially. The research field of anatomical modeling for the purpose of radiation dose calculations has experienced an explosion in activity in the past two decades. Such an exciting advancement is due to the feasibility of creating three-dimensional geometric details of the human anatomy from tomographic imaging and of performing Monte Carlo radiation transport simulations on increasingly fast and cheap personal computers. The advent of a new type of high-performance computing hardware in recent years-graphics processing units-has made it feasible to carry out time-consuming Monte Carlo calculations at near real-time speeds. This paper introduces the history of three generations of computational human phantoms (the stylized medical internal radiation dosimetry-type phantoms, the voxelized tomographic phantoms, and the boundary representation deformable phantoms) and new development of the graphics processing unit-based Monte Carlo radiation dose calculations. Examples are given for research projects performed by my students in applying computational phantoms and a new Monte Carlo code, ARCHER, to problems in radiation protection, imaging, and radiotherapy. Finally, the paper discusses challenges and future opportunities for research.

3D delivered dose assessment using a 4DCT-based motion model

PURPOSE

The purpose of this work is to develop a clinically feasible method of calculating actual delivered dose distributions for patients who have significant respiratory motion during the course of stereotactic body radiation therapy (SBRT).

METHODS

A novel approach was proposed to calculate the actual delivered dose distribution for SBRT lung treatment. This approach can be specified in three steps. (1) At the treatment planning stage, a patient-specific motion model is created from planning 4DCT data. This model assumes that the displacement vector field (DVF) of any respiratory motion deformation can be described as a linear combination of some basis DVFs. (2) During the treatment procedure, 2D time-varying projection images (either kV or MV projections) are acquired, from which time-varying "fluoroscopic" 3D images of the patient are reconstructed using the motion model. The DVF of each timepoint in the time-varying reconstruction is an optimized linear combination of basis DVFs such that the 2D projection of the 3D volume at this timepoint matches the projection image. (3) 3D dose distribution is computed for each timepoint in the set of 3D reconstructed fluoroscopic images, from which the total effective 3D delivered dose is calculated by accumulating deformed dose distributions. This approach was first validated using two modified digital extended cardio-torso (XCAT) phantoms with lung tumors and different respiratory motions. The estimated doses were compared to the dose that would be calculated for routine 4DCT-based planning and to the actual delivered dose that was calculated using "ground truth" XCAT phantoms at all timepoints. The approach was also tested using one set of patient data, which demonstrated the application of our method in a clinical scenario.

RESULTS

For the first XCAT phantom that has a mostly regular breathing pattern, the errors in 95% volume dose (D95) are 0.11% and 0.83%, respectively for 3D fluoroscopic images reconstructed from kV and MV projections compared to the ground truth, which is clinically comparable to 4DCT (0.093%). For the second XCAT phantom that has an irregular breathing pattern, the errors are 0.81% and 1.75% for kV and MV reconstructions, both of which are better than that of 4DCT (4.01%). In the case of real patient, although it is impossible to obtain the actual delivered dose, the dose estimation is clinically reasonable and demonstrates differences between 4DCT and MV reconstruction-based dose estimates.

CONCLUSIONS

With the availability of kV or MV projection images, the proposed approach is able to assess delivered doses for all respiratory phases during treatment. Compared to the planning dose based on 4DCT, the dose estimation using reconstructed 3D fluoroscopic images was as good as 4DCT for regular respiratory pattern and was a better dose estimation for the irregular respiratory pattern.

A Technique for Generating Volumetric Cine-Magnetic Resonance Imaging

PURPOSE

The purpose of this study was to develop a techique to generate on-board volumetric cine-magnetic resonance imaging (VC-MRI) using patient prior images, motion modeling, and on-board 2-dimensional cine MRI.

METHODS AND MATERIALS

One phase of a 4-dimensional MRI acquired during patient simulation is used as patient prior images. Three major respiratory deformation patterns of the patient are extracted from 4-dimensional MRI based on principal-component analysis. The on-board VC-MRI at any instant is considered as a deformation of the prior MRI. The deformation field is represented as a linear combination of the 3 major deformation patterns. The coefficients of the deformation patterns are solved by the data fidelity constraint using the acquired on-board single 2-dimensional cine MRI. The method was evaluated using both digital extended-cardiac torso (XCAT) simulation of lung cancer patients and MRI data from 4 real liver cancer patients. The accuracy of the estimated VC-MRI was quantitatively evaluated using volume-percent-difference (VPD), center-of-mass-shift (COMS), and target tracking errors. Effects of acquisition orientation, region-of-interest (ROI) selection, patient breathing pattern change, and noise on the estimation accuracy were also evaluated.

RESULTS

Image subtraction of ground-truth with estimated on-board VC-MRI shows fewer differences than image subtraction of ground-truth with prior image. Agreement between normalized profiles in the estimated and ground-truth VC-MRI was achieved with less than 6% error for both XCAT and patient data. Among all XCAT scenarios, the VPD between ground-truth and estimated lesion volumes was, on average, 8.43 ± 1.52% and the COMS was, on average, 0.93 ± 0.58 mm across all time steps for estimation based on the ROI region in the sagittal cine images. Matching to ROI in the sagittal view achieved better accuracy when there was substantial breathing pattern change. The technique was robust against noise levels up to SNR = 20. For patient data, average tracking errors were less than 2 mm in all directions for all patients.

CONCLUSIONS

Preliminary studies demonstrated the feasibility of generating real-time VC-MRI for on-board localization of moving targets in radiation therapy.

An initial study on the estimation of time-varying volumetric treatment images and 3D tumor localization from single MV cine EPID images

PURPOSE

In this work the authors develop and investigate the feasibility of a method to estimate time-varying volumetric images from individual MV cine electronic portal image device (EPID) images.

METHODS

The authors adopt a two-step approach to time-varying volumetric image estimation from a single cine EPID image. In the first step, a patient-specific motion model is constructed from 4DCT. In the second step, parameters in the motion model are tuned according to the information in the EPID image. The patient-specific motion model is based on a compact representation of lung motion represented in displacement vector fields (DVFs). DVFs are calculated through deformable image registration (DIR) of a reference 4DCT phase image (typically peak-exhale) to a set of 4DCT images corresponding to different phases of a breathing cycle. The salient characteristics in the DVFs are captured in a compact representation through principal component analysis (PCA). PCA decouples the spatial and temporal components of the DVFs. Spatial information is represented in eigenvectors and the temporal information is represented by eigen-coefficients. To generate a new volumetric image, the eigen-coefficients are updated via cost function optimization based on digitally reconstructed radiographs and projection images. The updated eigen-coefficients are then multiplied with the eigenvectors to obtain updated DVFs that, in turn, give the volumetric image corresponding to the cine EPID image.

RESULTS

The algorithm was tested on (1) Eight digital eXtended CArdiac-Torso phantom datasets based on different irregular patient breathing patterns and (2) patient cine EPID images acquired during SBRT treatments. The root-mean-squared tumor localization error is (0.73 ± 0.63 mm) for the XCAT data and (0.90 ± 0.65 mm) for the patient data.

CONCLUSIONS

The authors introduced a novel method of estimating volumetric time-varying images from single cine EPID images and a PCA-based lung motion model. This is the first method to estimate volumetric time-varying images from single MV cine EPID images, and has the potential to provide volumetric information with no additional imaging dose to the patient.

3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models

3D fluoroscopic images represent volumetric patient anatomy during treatment with high spatial and temporal resolution. 3D fluoroscopic images estimated using motion models built using 4DCT images, taken days or weeks prior to treatment, do not reliably represent patient anatomy during treatment. In this study we developed and performed initial evaluation of techniques to develop patient-specific motion models from 4D cone-beam CT (4DCBCT) images, taken immediately before treatment, and used these models to estimate 3D fluoroscopic images based on 2D kV projections captured during treatment. We evaluate the accuracy of 3D fluoroscopic images by comparison to ground truth digital and physical phantom images. The performance of 4DCBCT-based and 4DCT-based motion models are compared in simulated clinical situations representing tumor baseline shift or initial patient positioning errors. The results of this study demonstrate the ability for 4DCBCT imaging to generate motion models that can account for changes that cannot be accounted for with 4DCT-based motion models. When simulating tumor baseline shift and patient positioning errors of up to 5 mm, the average tumor localization error and the 95th percentile error in six datasets were 1.20 and 2.2 mm, respectively, for 4DCBCT-based motion models. 4DCT-based motion models applied to the same six datasets resulted in average tumor localization error and the 95th percentile error of 4.18 and 5.4 mm, respectively. Analysis of voxel-wise intensity differences was also conducted for all experiments. In summary, this study demonstrates the feasibility of 4DCBCT-based 3D fluoroscopic image generation in digital and physical phantoms and shows the potential advantage of 4DCBCT-based 3D fluoroscopic image estimation when there are changes in anatomy between the time of 4DCT imaging and the time of treatment delivery.

Generation of fluoroscopic 3D images with a respiratory motion model based on an external surrogate signal

Respiratory motion during radiotherapy can cause uncertainties in definition of the target volume and in estimation of the dose delivered to the target and healthy tissue. In this paper, we generate volumetric images of the internal patient anatomy during treatment using only the motion of a surrogate signal. Pre-treatment four-dimensional CT imaging is used to create a patient-specific model correlating internal respiratory motion with the trajectory of an external surrogate placed on the chest. The performance of this model is assessed with digital and physical phantoms reproducing measured irregular patient breathing patterns. Ten patient breathing patterns are incorporated in a digital phantom. For each patient breathing pattern, the model is used to generate images over the course of thirty seconds. The tumor position predicted by the model is compared to ground truth information from the digital phantom. Over the ten patient breathing patterns, the average absolute error in the tumor centroid position predicted by the motion model is 1.4 mm. The corresponding error for one patient breathing pattern implemented in an anthropomorphic physical phantom was 0.6 mm. The global voxel intensity error was used to compare the full image to the ground truth and demonstrates good agreement between predicted and true images. The model also generates accurate predictions for breathing patterns with irregular phases or amplitudes.

An exponential growth of computational phantom research in radiation protection, imaging, and radiotherapy: a review of the fifty-year history

Radiation dose calculation using models of the human anatomy has been a subject of great interest to radiation protection, medical imaging, and radiotherapy. However, early pioneers of this field did not foresee the exponential growth of research activity as observed today. This review article walks the reader through the history of the research and development in this field of study which started some 50 years ago. This review identifies a clear progression of computational phantom complexity which can be denoted by three distinct generations. The first generation of stylized phantoms, representing a grouping of less than dozen models, was initially developed in the 1960s at Oak Ridge National Laboratory to calculate internal doses from nuclear medicine procedures. Despite their anatomical simplicity, these computational phantoms were the best tools available at the time for internal/external dosimetry, image evaluation, and treatment dose evaluations. A second generation of a large number of voxelized phantoms arose rapidly in the late 1980s as a result of the increased availability of tomographic medical imaging and computers. Surprisingly, the last decade saw the emergence of the third generation of phantoms which are based on advanced geometries called boundary representation (BREP) in the form of Non-Uniform Rational B-Splines (NURBS) or polygonal meshes. This new class of phantoms now consists of over 287 models including those used for non-ionizing radiation applications. This review article aims to provide the reader with a general understanding of how the field of computational phantoms came about and the technical challenges it faced at different times. This goal is achieved by defining basic geometry modeling techniques and by analyzing selected phantoms in terms of geometrical features and dosimetric problems to be solved. The rich historical information is summarized in four tables that are aided by highlights in the text on how some of the most well-known phantoms were developed and used in practice. Some of the information covered in this review has not been previously reported, for example, the CAM and CAF phantoms developed in 1970s for space radiation applications. The author also clarifies confusion about 'population-average' prospective dosimetry needed for radiological protection under the current ICRP radiation protection system and 'individualized' retrospective dosimetry often performed for medical physics studies. To illustrate the impact of computational phantoms, a section of this article is devoted to examples from the author's own research group. Finally the author explains an unexpected finding during the course of preparing for this article that the phantoms from the past 50 years followed a pattern of exponential growth. The review ends on a brief discussion of future research needs (a supplementary file '3DPhantoms.pdf' to figure 15 is available for download that will allow a reader to interactively visualize the phantoms in 3D).

A technique for estimating 4D-CBCT using prior knowledge and limited-angle projections

PURPOSE

To develop a technique to estimate onboard 4D-CBCT using prior information and limited-angle projections for potential 4D target verification of lung radiotherapy.

METHODS

Each phase of onboard 4D-CBCT is considered as a deformation from one selected phase (prior volume) of the planning 4D-CT. The deformation field maps (DFMs) are solved using a motion modeling and free-form deformation (MM-FD) technique. In the MM-FD technique, the DFMs are estimated using a motion model which is extracted from planning 4D-CT based on principal component analysis (PCA). The motion model parameters are optimized by matching the digitally reconstructed radiographs of the deformed volumes to the limited-angle onboard projections (data fidelity constraint). Afterward, the estimated DFMs are fine-tuned using a FD model based on data fidelity constraint and deformation energy minimization. The 4D digital extended-cardiac-torso phantom was used to evaluate the MM-FD technique. A lung patient with a 30 mm diameter lesion was simulated with various anatomical and respirational changes from planning 4D-CT to onboard volume, including changes of respiration amplitude, lesion size and lesion average-position, and phase shift between lesion and body respiratory cycle. The lesions were contoured in both the estimated and "ground-truth" onboard 4D-CBCT for comparison. 3D volume percentage-difference (VPD) and center-of-mass shift (COMS) were calculated to evaluate the estimation accuracy of three techniques: MM-FD, MM-only, and FD-only. Different onboard projection acquisition scenarios and projection noise levels were simulated to investigate their effects on the estimation accuracy.

RESULTS

For all simulated patient and projection acquisition scenarios, the mean VPD (±S.D.)∕COMS (±S.D.) between lesions in prior images and "ground-truth" onboard images were 136.11% (±42.76%)∕15.5 mm (±3.9 mm). Using orthogonal-view 15°-each scan angle, the mean VPD∕COMS between the lesion in estimated and "ground-truth" onboard images for MM-only, FD-only, and MM-FD techniques were 60.10% (±27.17%)∕4.9 mm (±3.0 mm), 96.07% (±31.48%)∕12.1 mm (±3.9 mm) and 11.45% (±9.37%)∕1.3 mm (±1.3 mm), respectively. For orthogonal-view 30°-each scan angle, the corresponding results were 59.16% (±26.66%)∕4.9 mm (±3.0 mm), 75.98% (±27.21%)∕9.9 mm (±4.0 mm), and 5.22% (±2.12%)∕0.5 mm (±0.4 mm). For single-view scan angles of 3°, 30°, and 60°, the results for MM-FD technique were 32.77% (±17.87%)∕3.2 mm (±2.2 mm), 24.57% (±18.18%)∕2.9 mm (±2.0 mm), and 10.48% (±9.50%)∕1.1 mm (±1.3 mm), respectively. For projection angular-sampling-intervals of 0.6°, 1.2°, and 2.5° with the orthogonal-view 30°-each scan angle, the MM-FD technique generated similar VPD (maximum deviation 2.91%) and COMS (maximum deviation 0.6 mm), while sparser sampling yielded larger VPD∕COMS. With equal number of projections, the estimation results using scattered 360° scan angle were slightly better than those using orthogonal-view 30°-each scan angle. The estimation accuracy of MM-FD technique declined as noise level increased.

CONCLUSIONS

The MM-FD technique substantially improves the estimation accuracy for onboard 4D-CBCT using prior planning 4D-CT and limited-angle projections, compared to the MM-only and FD-only techniques. It can potentially be used for the inter/intrafractional 4D-localization verification.