Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients.

Purpose: This study aimed to evaluate the clinical need for an automated decision-support software platform for adaptive radiation therapy (ART) of head and neck cancer (HNC) patients. Methods: We tested RTapp (SegAna), a new ART software platform for deciding when a treatment replan is needed, to investigate a set of 27 HNC patients' data retrospectively. For each fraction, the software estimated key components of ART such as daily dose distribution and cumulative doses received by targets and organs at risk (OARs) from daily 3D imaging in real-time. RTapp also included a prediction algorithm that analyzed dosimetric parameter (DP) trends against user-specified thresholds to proactively trigger adaptive re-planning up to four fractions ahead. The DPs evaluated for ART were based on treatment planning dose constraints. Warning (V95<95%) and adaptation (V95<93%) thresholds were set for PTVs, while OAR adaptation dosimetric endpoints of +10% (DE10) were set for all Dmax and Dmean DPs. Any threshold violation at end of treatment (EOT) triggered a review of the DP trends to determine the threshold-crossing fraction Fx when the violations occurred. The prediction model accuracy was determined as the difference between calculated and predicted DP values with 95% confidence intervals (CI95). Results: RTapp was able to address the needs of treatment adaptation. Specifically, we identified 18/27 studies (67%) for violating PTV coverage or parotid Dmean at EOT. Twelve PTVs had V95<95% (mean coverage decrease of -6.8 ± 2.9%) including six flagged for adaptation at median Fx = 6 (range, 1-16). Seventeen parotids were flagged for exceeding Dmean dose constraints with a median increase of +2.60 Gy (range, 0.99-6.31 Gy) at EOT, including nine with DP>DE10. The differences between predicted and calculated PTV V95 and parotid Dmean was up to 7.6% (mean ± CI95, -2.7 ± 4.1%) and 5 Gy (mean ± CI95, 0.3 ± 1.6 Gy), respectively. The most accurate predictions were obtained closest to the threshold-crossing fraction. For parotids, the results showed that Fx ranged between fractions 1 and 23, with a lack of specific trend demonstrating that the need for treatment adaptation may be verified for every fraction. Conclusion: Integrated in an ART clinical workflow, RTapp aids in predicting whether specific treatment would require adaptation up to four fractions ahead of time.

An adversarial machine learning framework and biomechanical model-guided approach for computing 3D lung tissue elasticity from end-expiration 3DCT.

PURPOSE: Lung elastography aims at measuring the lung parenchymal tissue elasticity for applications ranging from diagnostic purposes to biomechanically guided deformations. Characterizing the lung tissue elasticity requires four-dimensional (4D) lung motion as an input, which is currently estimated by deformably registering 4D computed tomography (4DCT) datasets. Since 4DCT imaging is widely used only in a radiotherapy treatment setup, there is a need to predict the elasticity distribution in the absence of 4D imaging for applications within and outside of radiotherapy domain. METHODS: In this paper, we present a machine learning-based method that predicts the three-dimensional (3D) lung tissue elasticity distribution for a given end-expiration 3DCT. The method to predict the lung tissue elasticity from an end-expiration 3DCT employed a deep neural network that predicts the tissue elasticity for the given CT dataset. For training and validation purposes, we employed five-dimensional CT (5DCT) datasets and a finite element biomechanical lung model. The 5DCT model was first used to generate end-expiration lung geometry, which was taken as the source lung geometry for biomechanical modeling. The deformation vector field pointing from end expiration to end inhalation was computed from the 5DCT model and taken as input in order to solve for the lung tissue elasticity. An inverse elasticity estimation process was employed, where we iteratively solved for the lung elasticity distribution until the model reproduced the ground-truth deformation vector field. The machine learning process uses a specific type of learning process, namely a constrained generalized adversarial neural network (cGAN) that learned the lung tissue elasticity in a supervised manner. The biomechanically estimated tissue elasticity together with the end-exhalation CT was the input for the supervised learning. The trained cGAN generated the elasticity from a given breath-hold CT image. The elasticity estimated was validated in two approaches. In the first approach, a L2-norm-based direct comparison was employed between the estimated elasticity and the ground-truth elasticity. In the second approach, we generated a synthetic four-dimensional CT (4DCT0 using a lung biomechanical model and the estimated elasticity and compared the deformations with the ground-truth 4D deformations using three image similarity metrics: mutual Information (MI), structured similarity index (SSIM), and normalized cross correlation (NCC). RESULTS: The results show that a cGAN-based machine learning approach was effective in computing the lung tissue elasticity given the end-expiration CT datasets. For the training data set, we obtained a learning accuracy of 0.44 ± 0.2 KPa. For the validation dataset, consisting of 13 4D datasets, we were able to obtain an accuracy of 0.87 ± 0.4 KPa. These results show that the cGAN-generated elasticity correlates well with that of the underlying ground-truth elasticity. We then integrated the estimated elasticity with the biomechanical model and applied the same boundary conditions in order to generate the end inhalation CT. The cGAN-generated images were very similar to that of the original end inhalation CT. The average value of the MI is 1.77 indicating the high local symmetricity between the ground truth and the cGAN elasticity-generated end inhalation CT data. The average value of the structural similarity for the 13 patients was observed to be 0.89 indicating the high structural integrity of the cGAN elasticity-generated end inhalation CT. Finally, the average NCC value of 0.97 indicates that potential variations in the contrast and brightness of the cGAN elasticity-generated end inhalation CT and the ground-truth end inhalation CT. CONCLUSION: The cGAN-generated lung tissue elasticity given an end-expiration CT image can be computed in near real time. Using the lung tissue elasticity along with a biomechanical model, 4D lung deformations can be generated from a given end-expiration CT image within clinically acceptable numerical accuracy.

A quantitative analysis of biomechanical lung model consistency using 5DCT datasets.

PURPOSE: Lung biomechanical models are important for understanding and characterizing lung anatomy and physiology. A key parameter of biomechanical modeling is the underlying tissue elasticity distribution. While human lung elasticity estimations do not have ground truths, model consistency checks can and should be employed to gauge the stability of the estimation techniques. This work proposes such a consistency check using a set of 10 subjects. METHODS: We hypothesize that lung dynamics will be stable over a 2-3 min time period and that this stability can be employed to check biomechanical estimation stability. For this purpose, two sets of 12 fast helical free breathing computed tomography scans (FHFBCT) were acquired back-to-back for each of the subjects. A published breathing motion model [five-dimensional CT (5DCT)] was generated from each set. Both of the models were used to generate two biomechanical modeling input sets: (a) The lung geometry at the end-exhalation, and (b) the voxel displacement map that mapped the end-exhalation lung geometry with the end-inhalation lung geometry. Finite element biomechanical lung models were instantiated using the end-exhalation lung geometries. The models included voxel-specific lung tissue elasticity values and were optimized using a gradient search approach until the biomechanical model-generated displacement maps matched those of the 5DCT voxel displacement maps. Finally, the two elasticity distributions associated with each of the patient 5DCTs were quantitatively compared. Because the end-exhalation geometries differed slightly between the two scan datasets, the elasticity distributions were deformably mapped to one of the exhalation datasets. RESULTS: For the 10 patients, on average, 90% of parenchymal voxels had <2 kPa Young's modulus difference between the two estimations, with a mean voxel difference of only 0.6 kPa. Similarly, 97% of the parenchymal voxels had <2 mm displacement difference between the two models with a mean difference of 0.48 mm. Furthermore, overlapping elasticity histograms for voxels between -600 and -900 HU (parenchymal tissues) showed that the histograms were consistent between the two estimations. CONCLUSION: In this paper, we demonstrated that biomechanical lung models can be consistently estimated when using motion-model based imaging datasets, even though the models were created from scans acquired at different breaths.

Systematic feasibility analysis of performing elastography using reduced dose CT lung image pairs.

PURPOSE: Elastography using computer tomography (CT) is a promising methodology that can provide patient-specific regional distributions of lung biomechanical properties. The purpose of this paper is to investigate the feasibility of performing elastography using simulated lower dose CT scans. METHODS: A cohort of eight patient CT image pairs were acquired with a tube current-time product of 40 mAs for estimating baseline lung elastography results. Synthetic low mAs CT scans were generated from the baseline scans to simulate the additional noise that would be present in acquisitions at 30, 25, and 20 mAs, respectively. For the simulated low mAs scans, exhalation and inhalation datasets were registered using an in-house optical flow deformable image registration algorithm. The registered deformation vector fields (DVFs) were taken to be ground truth for the elastography process. A model-based elasticity estimation was performed for each of the reduced mAs datasets, in which the goal was to optimize the elasticity distribution that best represented their respective DVFs. The estimated elasticity and the DVF distributions of the reduced mAs scans were then compared with the baseline elasticity results for quantitative accuracy purposes. RESULTS: The DVFs for the low mAs and baseline scans differed from each other by an average of 1.41 mm, which can be attributed to the noise added by the simulated reduction in mAs. However, the elastography results using the DVFs from the reduced mAs scans were similar from the baseline results, with an average elasticity difference of 0.65, 0.71, and 0.76 kPa, respectively. This illustrates that elastography can provide equivalent results using low-dose CT scans. CONCLUSIONS: Elastography can be performed equivalently using CT image pairs acquired with as low as 20 mAs. This expands the potential applications of CT-based elastography.

Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy.

Electron density maps must be accurately estimated to achieve valid dose calculation in MR-only radiotherapy. The goal of this study is to assess whether two deep learning models, the conditional generative adversarial network (cGAN) and the cycle-consistent generative adversarial network (cycleGAN), can generate accurate abdominal synthetic CT (sCT) images from 0.35T MR images for MR-only liver radiotherapy. A retrospective study was performed using CT images and 0.35T MR images of 12 patients with liver (n = 8) and non-liver abdominal (n = 4) cancer. CT images were deformably registered to the corresponding MR images to generate deformed CT (dCT) images for treatment planning. Both cGAN and cycleGAN were trained using MR and dCT transverse slices. Four-fold cross-validation testing was conducted to generate sCT images for all patients. The HU prediction accuracy was evaluated by voxel-wise similarity metric between each dCT and sCT image for all 12 patients. dCT-based and sCT-based dose distributions were compared using gamma and dose-volume histogram (DVH) metric analysis for 8 liver patients. sCTcycleGAN achieved the average mean absolute error (MAE) of 94.1 HU, while sCTcGAN achieved 89.8 HU. In both models, the average gamma passing rates within all volumes of interest were higher than 95% using a 2%, 2 mm criterion, and 99% using a 3%, 3 mm criterion. The average differences in the mean dose and DVH metrics were within ±0.6% for the planning target volume and within ±0.15% for evaluated organs in both models. Results: demonstrated that abdominal sCT images generated by both cGAN and cycleGAN achieved accurate dose calculation for 8 liver radiotherapy plans. sCTcGAN images had smaller average MAE and achieved better dose calculation accuracy than sCTcyleGAN images. More abdominal patients will be enrolled in the future to further evaluate the two models.

Feasibility of deriving a novel imaging biomarker based on patient-specific lung elasticity for characterizing the degree of COPD in lung SBRT patients.

OBJECTIVE:: Lung tissue elasticity is an effective spatial representation for Chronic Obstructive Pulmonary Disease phenotypes and pathophysiology. We investigated a novel imaging biomarker based on the voxel-by-voxel distribution of lung tissue elasticity. Our approach combines imaging and biomechanical modeling to characterize tissue elasticity. METHODS:: We acquired 4DCT images for 13 lung cancer patients with known COPD diagnoses based on GOLD 2017 criteria. Deformation vector fields (DVFs) from the deformable registration of end-inhalation and end-exhalation breathing phases were taken to be the ground-truth. A linear elastic biomechanical model was assembled from end-exhalation datasets with a density-guided initial elasticity distribution. The elasticity estimation was formulated as an iterative process, where the elasticity was optimized based on its ability to reconstruct the ground-truth. An imaging biomarker (denoted YM1-3) derived from the optimized elasticity distribution, was compared with the current gold standard, RA950 using confusion matrix and area under the receiver operating characteristic (AUROC) curve analysis. RESULTS:: The estimated elasticity had 90 % accuracy when representing the ground-truth DVFs. The YM1-3 biomarker had higher diagnostic accuracy (86% vs 71 %), higher sensitivity (0.875 vs 0.5), and a higher AUROC curve (0.917 vs 0.875) as compared to RA950. Along with acting as an effective spatial indicator of lung pathophysiology, the YM1-3 biomarker also proved to be a better indicator for diagnostic purposes than RA950. CONCLUSIONS:: Overall, the results suggest that, as a biomarker, lung tissue elasticity will lead to new end points for clinical trials and new targeted treatment for COPD subgroups. ADVANCES IN KNOWLEDGE:: The derivation of elasticity information directly from 4DCT imaging data is a novel method for performing lung elastography. The work demonstrates the need for a mechanics-based biomarker for representing lung pathophysiology.

Real Time Gabor-Domain Optical Coherence Microscopy for 3D Imaging.

Fast, robust, nondestructive 3D imaging is needed for the characterization of microscopic tissue structures across various clinical applications. A custom microelectromechanical system (MEMS)-based 2D scanner was developed to achieve, together with a multi-level GPU architecture, 55 kHz fast-axis A-scan acquisition in a Gabor-domain optical coherence microscopy (GD-OCM) custom instrument. GD-OCM yields high-definition micrometer-class volumetric images. A dynamic depth of focusing capability through a bio-inspired liquid lens-based microscope design, as in whales' eyes, was developed to enable the high definition instrument throughout a large field of view of 1 mm3 volume of imaging. Developing this technology is prime to enable integration within the workflow of clinical environments. Imaging at an invariant resolution of 2 µm has been achieved throughout a volume of 1 × 1 × 0.6 mm3, acquired in less than 2 minutes. Volumetric scans of human skin in vivo and an excised human cornea are presented.

GPU-Based Parallelized Solver for Large Scale Vascular Blood Flow Modeling and Simulations.

Cardio-vascular blood flow simulations are essential in understanding the blood flow behavior during normal and disease conditions. To date, such blood flow simulations have only been done at a macro scale level due to computational limitations. In this paper, we present a GPU based large scale solver that enables modeling the flow even in the smallest arteries. A mechanical equivalent of the circuit based flow modeling system is first developed to employ the GPU computing framework. Numerical studies were employed using a set of 10 million connected vascular elements. Run-time flow analysis were performed to simulate vascular blockages, as well as arterial cut-off. Our results showed that we can achieve ~100 FPS using a GTX 680m and ~40 FPS using a Tegra K1 computing platform.

Multi-Kinect v2 Camera Based Monitoring System for Radiotherapy Patient Safety.

3D kinect camera systems are essential for real-time imaging of 3D treatment space that consists of both the patient anatomy as well as the treatment equipment setup. In this paper, we present the technical details of a 3D treatment room monitoring system that employs a scalable number of calibrated and coregistered Kinect v2 cameras. The monitoring system tracks radiation gantry and treatment couch positions, and tracks the patient and immobilization accessories. The number and positions of the cameras were selected to avoid line-of-sight issues and to adequately cover the treatment setup. The cameras were calibrated with a calibration error of 0.1 mm. Our tracking system evaluation show that both gantry and patient motion could be acquired at a rate of 30 frames per second. The transformations between the cameras yielded a 3D treatment space accuracy of < 2 mm error in a radiotherapy setup within 500mm around the isocenter.

Systematic feasibility analysis of a quantitative elasticity estimation for breast anatomy using supine/prone patient postures.

PURPOSE: Breast elastography is a critical tool for improving the targeted radiotherapy treatment of breast tumors. Current breast radiotherapy imaging protocols only involve prone and supine CT scans. There is a lack of knowledge on the quantitative accuracy with which breast elasticity can be systematically measured using only prone and supine CT datasets. The purpose of this paper is to describe a quantitative elasticity estimation technique for breast anatomy using only these supine/prone patient postures. Using biomechanical, high-resolution breast geometry obtained from CT scans, a systematic assessment was performed in order to determine the feasibility of this methodology for clinically relevant elasticity distributions. METHODS: A model-guided inverse analysis approach is presented in this paper. A graphics processing unit (GPU)-based linear elastic biomechanical model was employed as a forward model for the inverse analysis with the breast geometry in a prone position. The elasticity estimation was performed using a gradient-based iterative optimization scheme and a fast-simulated annealing (FSA) algorithm. Numerical studies were conducted to systematically analyze the feasibility of elasticity estimation. For simulating gravity-induced breast deformation, the breast geometry was anchored at its base, resembling the chest-wall/breast tissue interface. Ground-truth elasticity distributions were assigned to the model, representing tumor presence within breast tissue. Model geometry resolution was varied to estimate its influence on convergence of the system. A priori information was approximated and utilized to record the effect on time and accuracy of convergence. The role of the FSA process was also recorded. A novel error metric that combined elasticity and displacement error was used to quantify the systematic feasibility study. For the authors' purposes, convergence was set to be obtained when each voxel of tissue was within 1 mm of ground-truth deformation. RESULTS: The authors' analyses showed that a ∼97% model convergence was systematically observed with no-a priori information. Varying the model geometry resolution showed no significant accuracy improvements. The GPU-based forward model enabled the inverse analysis to be completed within 10-70 min. Using a priori information about the underlying anatomy, the computation time decreased by as much as 50%, while accuracy improved from 96.81% to 98.26%. The use of FSA was observed to allow the iterative estimation methodology to converge more precisely. CONCLUSIONS: By utilizing a forward iterative approach to solve the inverse elasticity problem, this work indicates the feasibility and potential of the fast reconstruction of breast tissue elasticity using supine/prone patient postures.

Analytic Intermodel Consistent Modeling of Volumetric Human Lung Dynamics.

Human lung undergoes breathing-induced deformation in the form of inhalation and exhalation. Modeling the dynamics is numerically complicated by the lack of information on lung elastic behavior and fluid-structure interactions between air and the tissue. A mathematical method is developed to integrate deformation results from a deformable image registration (DIR) and physics-based modeling approaches in order to represent consistent volumetric lung dynamics. The computational fluid dynamics (CFD) simulation assumes the lung is a poro-elastic medium with spatially distributed elastic property. Simulation is performed on a 3D lung geometry reconstructed from four-dimensional computed tomography (4DCT) dataset of a human subject. The heterogeneous Young's modulus (YM) is estimated from a linear elastic deformation model with the same lung geometry and 4D lung DIR. The deformation obtained from the CFD is then coupled with the displacement obtained from the 4D lung DIR by means of the Tikhonov regularization (TR) algorithm. The numerical results include 4DCT registration, CFD, and optimal displacement data which collectively provide consistent estimate of the volumetric lung dynamics. The fusion method is validated by comparing the optimal displacement with the results obtained from the 4DCT registration.

Parallelized multi-graphics processing unit framework for high-speed Gabor-domain optical coherence microscopy.

Gabor-domain optical coherence microscopy (GD-OCM) is a volumetric high-resolution technique capable of acquiring three-dimensional (3-D) skin images with histological resolution. Real-time image processing is needed to enable GD-OCM imaging in a clinical setting. We present a parallelized and scalable multi-graphics processing unit (GPU) computing framework for real-time GD-OCM image processing. A parallelized control mechanism was developed to individually assign computation tasks to each of the GPUs. For each GPU, the optimal number of amplitude-scans (A-scans) to be processed in parallel was selected to maximize GPU memory usage and core throughput. We investigated five computing architectures for computational speed-up in processing 1000×1000 A-scans. The proposed parallelized multi-GPU computing framework enables processing at a computational speed faster than the GD-OCM image acquisition, thereby facilitating high-speed GD-OCM imaging in a clinical setting. Using two parallelized GPUs, the image processing of a 1×1×0.6 mm3 skin sample was performed in about 13 s, and the performance was benchmarked at 6.5 s with four GPUs. This work thus demonstrates that 3-D GD-OCM data may be displayed in real-time to the examiner using parallelized GPU processing.

4D-CT Lung registration using anatomy-based multi-level multi-resolution optical flow analysis and thin-plate splines.

PURPOSE: The accuracy of 4D-CT registration is limited by inconsistent Hounsfield unit (HU) values in the 4D-CT data from one respiratory phase to another and lower image contrast for lung substructures. This paper presents an optical flow and thin-plate spline (TPS)-based 4D-CT registration method to account for these limitations. METHODS: The use of unified HU values on multiple anatomy levels (e.g., the lung contour, blood vessels, and parenchyma) accounts for registration errors by inconsistent landmark HU value. While 3D multi-resolution optical flow analysis registers each anatomical level, TPS is employed for propagating the results from one anatomical level to another ultimately leading to the 4D-CT registration. 4D-CT registration was validated using target registration error (TRE), inverse consistency error (ICE) metrics, and a statistical image comparison using Gamma criteria of 1 % intensity difference in 2 mm(3) window range. RESULTS: Validation results showed that the proposed method was able to register CT lung datasets with TRE and ICE values <3 mm. In addition, the average number of voxel that failed the Gamma criteria was <3 %, which supports the clinical applicability of the propose registration mechanism. CONCLUSION: The proposed 4D-CT registration computes the volumetric lung deformations within clinically viable accuracy.

Multi-scale, multi-modal image integration for image-guided clinical interventions in the head and neck anatomy.

The aim of this paper is to enable model guided multi-scale and multi-modal image integration for the head and neck anatomy. The image modality used for this purpose includes multi-pose Magnetic Resonance Imaging (MRI), Mega Voltage CT, and hand-held Optical Coherence Tomography. A biomechanical model that incorporates subject-specific young's modulus and shear modulus properties is developed from multi-pose MRI, positioned in the treatment setup using Mega Voltage CT (MVCT), and actuated using multiple kinect surface cameras to mimic patient postures during Optical Coherence Microscopy (OCM) imaging. Two different 3D tracking mechanisms were employed for aligning the patient surface and the probe position to the MRI data. The results show the accuracy of the two tracking algorithms and the 3D head and neck deformation representing the multiple poses, the subject will take during the OCM imaging.

A client-server framework for 3D remote visualization of radiotherapy treatment space.

Radiotherapy is safely employed for treating wide variety of cancers. The radiotherapy workflow includes a precise positioning of the patient in the intended treatment position. While trained radiation therapists conduct patient positioning, consultation is occasionally required from other experts, including the radiation oncologist, dosimetrist, or medical physicist. In many circumstances, including rural clinics and developing countries, this expertise is not immediately available, so the patient positioning concerns of the treating therapists may not get addressed. In this paper, we present a framework to enable remotely located experts to virtually collaborate and be present inside the 3D treatment room when necessary. A multi-3D camera framework was used for acquiring the 3D treatment space. A client-server framework enabled the acquired 3D treatment room to be visualized in real-time. The computational tasks that would normally occur on the client side were offloaded to the server side to enable hardware flexibility on the client side. On the server side, a client specific real-time stereo rendering of the 3D treatment room was employed using a scalable multi graphics processing units (GPU) system. The rendered 3D images were then encoded using a GPU-based H.264 encoding for streaming. Results showed that for a stereo image size of 1280 × 960 pixels, experts with high-speed gigabit Ethernet connectivity were able to visualize the treatment space at approximately 81 frames per second. For experts remotely located and using a 100 Mbps network, the treatment space visualization occurred at 8-40 frames per second depending upon the network bandwidth. This work demonstrated the feasibility of remote real-time stereoscopic patient setup visualization, enabling expansion of high quality radiation therapy into challenging environments.

Computational fluid dynamics modeling of airflow inside lungs using heterogenous anisotropic lung tissue elastic properties.

The aim of this paper is to model the airflow inside lungs during breathing and its fluid-structure interaction with the lung tissues and the lung tumor using subject-specific elastic properties. The fluid-structure interaction technique simultaneously simulates flow within the airway and anisotropic deformation of the lung lobes. The three-dimensional (3D) lung geometry is reconstructed from the end-expiration 3D CT scan datasets of humans with lung cancer. The lung is modeled as a poro-elastic medium with anisotropic elastic property (non-linear Young's modulus) obtained from inverse lung elastography of 4D CT scans for the same patients. The predicted results include the 3D anisotropic lung deformation along with the airflow pattern inside the lungs. The effect is also presented of anisotropic elasticity on both the spatio-temporal volumetric lung displacement and the regional lung hysteresis.

Modeling Airflow Using Subject-Specific 4DCT-Based Deformable Volumetric Lung Models.

Lung radiotherapy is greatly benefitted when the tumor motion caused by breathing can be modeled. The aim of this paper is to present the importance of using anisotropic and subject-specific tissue elasticity for simulating the airflow inside the lungs. A computational-fluid-dynamics (CFD) based approach is presented to simulate airflow inside a subject-specific deformable lung for modeling lung tumor motion and the motion of the surrounding tissues during radiotherapy. A flow-structure interaction technique is employed that simultaneously models airflow and lung deformation. The lung is modeled as a poroelastic medium with subject-specific anisotropic poroelastic properties on a geometry, which was reconstructed from four-dimensional computed tomography (4DCT) scan datasets of humans with lung cancer. The results include the 3D anisotropic lung deformation for known airflow pattern inside the lungs. The effects of anisotropy are also presented on both the spatiotemporal volumetric lung displacement and the regional lung hysteresis.

Visualization of 3D volumetric lung dynamics for real-time external beam lung radiotherapy.

This paper reports on the usage of physics-based 3D volumetric lung dynamic models for visualizing and monitoring the radiation dose deposited on the lung of a human subject during lung radiotherapy. The dynamic model of each subject is computed from a 4D Computed Tomography (4DCT) imaging acquired before the treatment. The 3D lung deformation and the radiation dose deposited are computed using Graphics Processing Units (GPU). Additionally, using the dynamic lung model, the airflow inside the lungs during the treatment is also investigated. Results show the radiation dose deposited on the lung tumor as well as the surrounding tissues, the combination of which is patient-specific and varies from one treatment fraction to another.

Generating classes of 3D virtual mandibles for AR-based medical simulation.

INTRODUCTION: Simulation and modeling represent promising tools for several application domains from engineering to forensic science and medicine. Advances in 3D imaging technology convey paradigms such as augmented reality (AR) and mixed reality inside promising simulation tools for the training industry. METHODS: Motivated by the requirement for superimposing anatomically correct 3D models on a human patient simulator (HPS) and visualizing them in an AR environment, the purpose of this research effort was to develop and validate a method for scaling a source human mandible to a target human mandible within a 2 mm root mean square (RMS) error. RESULTS: Results show that, given a distance between 2 same landmarks on 2 different mandibles, a relative scaling factor may be computed. Using this scaling factor, results show that a 3D virtual mandible model can be made morphometrically equivalent to a real target-specific mandible within a 1.30 mm RMS error. CONCLUSION: The virtual mandible may be further used as a reference target for registering other anatomic models, such as the lungs, on the HPS. Such registration will be made possible by physical constraints among the mandible and the spinal column in the horizontal normal rest position.

Real-time simulation of 4D lung tumor radiotherapy using a breathing model.

In this paper, we present a real-time simulation and visualization framework that models a deformable surface lung model with tumor, simulates the tumor motion and predicts the amount of radiation doses that would be deposited in the moving lung tumor during the actual delivery of radiation. The model takes as input a subject-specific 4D Computed Tomography (4D CT) of lungs and computes a deformable lung surface model by estimating the deformation properties of the surface model using an inverse dynamics approach. Once computed, the deformable model is used to simulate and visualize lung tumor motion that would occur during radiation therapy accounting for variations in the breathing pattern. A radiation treatment plan for the lung tumor is developed using one of the 4D CT phases. During the simulation of radiation delivery, the dose on the lung tumor is computed for each beam independently.