Browsing by Subject "Phantom"

Computerized breast phantoms have been popular for low-cost alternatives to collecting clinical data by combing them with highly realistic simulation tools. Image segmentation of three-dimensional breast computed tomography (bCT) data is one method to create such phantoms, but requires multiple image processing steps to accurately classify the tissues within the breast. One key step in our segmentation routine is the use of a bilateral filter to smooth homogeneous regions, preserve edges and thin structures, and reduce the sensitivity of the voxel classification to noise corruption. In previous work, the well-known process of bilateral filtering was completed on the entire bCT volume with the primary goal of reducing the noise in the entire volume. In order to improve on this method, knowledge of the varying bCT noise in each slice was used to adaptively increase or decrease the filtering effect as a function of distance to the chest wall. Not only does this adaptive bilateral filter yield thinner structures in the segmentation result but is adaptive on a case-by-case basis, allowing for easy implementation with future virtual phantom generations.

Project 1: Chest Phantom Development for Chest X-ray Radiation Protection Surveys

Purpose: Develop an acrylic phantom to accurately represent an average adult’s chest for use in radiographic chest unit radiation protection surveys.

Materials and Methods: 6 sheets of 3.81 cm thick acrylic were cut and assembled to form a 30.5 x 30.5 x 20.3 cm hollow box phantom. The acrylic served as tissue equivalent material and the hollow center simulated lungs in a human patient. Six sheets of 1 mm thick aluminum were cut to line the inner walls of the acrylic phantom to potentially boost scatter radiation. Three phantoms underwent posterior-anterior (PA) and lateral chest protocol radiographic scans: the acrylic phantom (with and without the aluminum lining), a 3 gallon water bottle filled with water, and an adult male anthropomorphic phantom. The phantoms were set up as though they were adult patients and scanned with automatic exposure control. Scatter radiation was measured with ion chamber survey meters at 4 points within the room for each phantom and protocol. The scatter data from the acrylic phantom and water bottle were compared to the anthropomorphic phantom to determine which one more accurately represented an adult patient.

Results: For the PA protocol, the average percent difference in measurements between the acrylic phantom and anthropomorphic phantom was 33.3±28.8% with the aluminum lining and 33.0±21.2% without the lining. The percent difference between the water bottle and anthropomorphic phantom was 66.5±42.0%. For the lateral protocol, the average percent difference in measurements between the acrylic phantom and anthropomorphic phantom was 157.6±5.6% with the aluminum lining and 143.0±17.6% without the lining. The percent difference between the water bottle and anthropomorphic phantom was 78.3±22.8%.

Conclusions: The acrylic phantom provided a more accurate comparison to the anthropomorphic phantom than the water bottle for the PA protocol. For the lateral protocol, neither the acrylic phantom nor water bottle provided an adequate comparison to the anthropomorphic phantom.

Project 2: Internal Beta Dosimetry of an Iodine-131 Labelled Elastin-Like Polypeptide

Purpose: Develop a model and simulation to better understand the dosimetry of an I-131 labeled elastin-like polypeptide (ELP) brachytherapy technique.

Materials and Methods: To develop the model, an average scenario based on mouse trials was explored. A 125 mg tumor was approximated as a sphere, with the I-131 ELP injected into its center. The ELP solidifies into a spherical depot – approximately 1/3 the volume of the tumor – and becomes a permanent brachytherapy source. The injected activity of I-131 was 1.25 mCi. I-131 primarily emits β radiation with an average energy of 182 keV, therefore it was determined that all such emissions were confined within the bounds of the tumor. Gamma emissions associated with I-131 were ignored as they were determined to have enough energy to escape the bounds of the tumor without any interaction. This model was implemented into a simulation using the Monte Carlo program FLUKA. From this simulation, the absorbed dose to the tumor and ELP depot, along with the dose profile, was calculated.

Results: The tumor received an absorbed dose of 72.3 Gy while the ELP received 1.14×10^3 Gy. From the dose profile, it was determined that 99% of the absorbed dose to the tumor was highly localized to a 0.3 mm region surrounding the ELP depot.

Conclusions: The model and simulation provided a better understanding of the dosimetry underlying the novel ELP brachytherapy technique. Results obtained demonstrated that the ELP method delivers doses that are comparable to current conventional brachytherapy techniques.

Project 3: I-131 Beta Detection Using a Scintillating Nanoparticle Detector

Purpose: Determine if a scintillating nanocrystal fiber optic detector (nano-FOD) could detect β emissions from I-131.

Materials and Methods: The nano-FOD’s β response was tested using a source vial containing 101 mCi of I-131 in 2 mL of stabilizing solution. A glass vial containing the I-131 was placed inside a lead pig for shielding. A 1 mm diameter hole was drilled through the tops of the vial and pig to allow insertion of the nano-FOD. Measurements were taken every day over a 17 day period by repeatedly submerging the nano-FOD in the I-131 solution and recording the voltage signal it produced. The activity at the time of measurement was calculated based on the time and date of data acquisition. The net signal and signal-to-noise ratio (SNR) were then calculated and plotted as functions of I-131 concentration.

Results: The nano-FOD produced a measurable response when exposed to the β emissions of I-131. The net signal and SNR both demonstrated a linear correlation with the concentration of I-131.

Conclusions: The nano-FOD was demonstrated to be capable of β detection with a linear correlation to activity. If the signals measured can be calibrated to radiation exposure, then the nano-FOD has promising applications as a novel β detector.

Virtual imaging trials (VITs) are a growing part of medical imaging research. VITs are a powerful alternative to the current gold-standard for determining or verifying the efficacy of new technology in healthcare: the clinical trial. Prohibitively high expenses, multi-site standardization of protocols, and risks to the health of the trial’s patient population are all challenges associated with the clinical trial; conversely, these challenges highlight the strengths of virtualization, particularly with regard to evaluating medical imaging technologies.Virtual imaging requires a combination of virtual subjects, physics-based imaging simulation platforms, and virtual pathologies. Currently, most computational phantom organs and pathologies are segmented or generated from clinical CT images. With this approach, most computational organs and pathologies are necessarily static, comprising only a single instantaneous representation. Further, this static-anatomy–static-pathology approach does not address the underlying physiological constraints acting on the organs or their pathologies—making some imaging exams (e.g., perfusion, coronary angiography) difficult to simulate robustly. It also does not provide a clear path toward including anatomical and physiological (functional) detail at sub-CT resolution. This project aims to integrate high-resolution, dynamic features into computational human models. The focus is primarily an advanced model known as XCAT. These additions include healthy and progressive-disease anatomy and physiology, micron-level–resolution coronary artery lesions, and an array of pathologies. In particular, we focus on the physiology needed for CT perfusion studies, dynamic lesions, or coronary artery disease (CAD), and means to integrate each of these features into XCAT via custom software. The outcome is further to demonstrate the utility of each of these advances with representative simulated imaging. Chapter 1 presents a method using clinical information and physiological theory to develop a mathematical model that produces the liver vasculature within a given XCAT. The model can be used to simulate contrast perfusion by taking into account contrast position and concentration at an initial time t and the spatial extent of the contrast in the liver vasculature at subsequent times. The mathematical method enables the simulation of hepatic contrast perfusion in the presence or absence of abnormalities (e.g., focal or diffuse disease) for arbitrary imaging protocols, contrast concentrations, and virtual patient body habitus. The vessel growing method further generalizes to vascular models of other organs as it is based on a parameterized approach, allowing for flexible repurposing of the developed tool. Chapter 2 presents a method for using cardiac plaque histology and morphology data acquired at micron-level resolution to generate new, novel plaques informed by a large, original patient cohort. A methodology for curating and validating the anatomical and physiological realism was further applied to the synthesized plaques to ensure realism. This method was integrated with the XCAT heart and coronary artery models to allow simulated imaging of a wide variety of coronary artery plaques in varied orientations and with unique material distribution and composition. Generation of 200 unique plaques has been optimized to take as little as 5 seconds with GPU acceleration. This work enables future studies to optimize current and emerging CT imaging methods used to detect, diagnose, and treat coronary artery disease. Chapter 3 focuses on small-scale modeling of the internal structure of the bones of the chest. The internal structure of the bones appears as a diffuse but recognizable texture under medical imaging and corresponds to a complex physical structure tuned to meet the physical purpose of the bone (e.g., weight-bearing, protective structure, etc.). The project aimed to address the limitations of prior texture-based modelling by creating mathematically based fine bone structures. The method was used to generate realistic bone structures, defined as polygon meshes, with accurate morphological and topological detail for 45 chest bones for each XCAT phantom. This new method defines the spatial extent of the complementary bone–marrow structures that are the root cause of the characteristic image texture 1-4 and provides a transition from using image-informed characteristic power law textures to a ground-truth model with exact morphology—which we additionally paired with the DukeSim CT simulator5 and XCAT phantoms6 to produce radiography and CT images with physics-based bone textures. This work enables CT acquisition parameter optimization studies that can inform clinical image assessment of osteoporosis and bone fractures. Chapter 4 proposes a new model of lesion morphology and insertion and was created with the intent to be informed and validated by—rather than constrained by—imaging data. It additionally includes the new incorporation of biological data, intended to provide dynamic computational lung lesion models for use in CT simulation applications. Each chapter includes a section presenting an example application of the respective tools in virtual medical imaging. Chapter 5 concludes this work with a brief summary of the content and is followed by Appendices A–D. The appendices are organized by topic and contain a visual demonstration of the work in a series of high-resolution, full-page images.

Developing new technologies for clinical usage requires systematic and rigorous validation and optimization for various patient scenarios to ensure robustness and accuracy. Clinical trials on real patients are always considered the gold standard for evaluating newly-developed technologies. However, it is often challenging to achieve due to the high cost, difficulties in patient recruitment, ethical issues especially considering the extra imaging dose, lack of ground-truth patient data, insufficient number of patients for data-demanding studies, etc. As a result, anthropomorphic phantoms are utilized to substitute patients for the testing, evaluation, and optimization of various novel radiotherapy and medical imaging techniques before the patient studies. Physical phantoms can be used as a preliminary tool for evaluating some techniques, but they tend to be expensive, over-simplified, and unflexible to simulate various patient scenarios. By contrast, digital phantoms are cost-effective, providing a ground truth with known anatomy and physiological functions, being flexible in simulating different patient scenarios, and efficiently generating a large amount of data. Thus, digital phantoms are more likely to be a powerful tool for diverse technique assessments and substitute for patients in virtual clinical trials.The 4D-extended cardiac-torso (XCAT) phantom is a digital phantom that simulates diverse human anatomies (e.g., genders, heights, tumor sizes etc.) with cardiac and respiratory motions, and it has been widely used in medical imaging and radiotherapy research. Although being widespread, the 4D-XCAT phantom has several limitations. First, it lacks intra-organ anatomical details and textures. The absence of the heterogeneous anatomical textures would degrade its translatability from phantom to patient studies for various research in radiation therapy, such as imaging reconstruction, image registration, segmentation, treatment planning, etc. Second, the breathing motion pattern in the 4D-XCAT phantom is simplified to an approximate linear model. Therefore, the current phantom is not adequate for research that involves lung function, motion management, 4D imaging or treatment optimization, etc. Third, the XCAT phantom has not included onboard imaging artifacts. As a result, the current phantom is not adequate for evaluating treatment delivery techniques related to motion management, onboard localization, etc. This dissertation aims to address these limitations by developing an eXtended Modular ANthropomorphic (XMAN) phantom based on 4D-XCAT using generative adversarial network (GAN)-based deep learning techniques. Structure-wise, this dissertation includes the first chapter for introduction, the second chapter for specific aims addressing the three limitations of the 4D-XCAT phantom, and three chapters (chapters 3, 4, and 5) with one limitation discussed at each one of them. Finally, the dissertation work is summarized in chapter 6. In chapter 3, we proposed to synthesize anthropomorphic anatomical textures in the chest region of the XCAT phantom. A dual-discriminator conditional-GAN (D-CGAN) model was developed. For training purposes, we generated organ maps to mimic XCAT images and train the model to synthesize realistic anatomical textures in the organ maps to simulate real CT or CBCT images. The organ maps were generated by segmenting the organs and gross tumor volumes from the actual patient images and assigning unique HU values to each structure. The D-CGAN was uniquely designed with two discriminators, one trained for the body and the other for the tumor. The D-CGAN model was trained separately using 62 CT and 63 CBCT images from lung SBRT patients. Then, various XCAT phantoms were input to the D-CGAN model to generate multi-contrast textured XCAT (MT-XCAT), including CT and CBCT contrast. The MT-XCAT phantoms were evaluated by comparing the intensity histogram features. The visual examination demonstrated that the MT-XCAT phantoms presented similar general contrast and anatomical textures as CT and CBCT images. The mean HU of the MT-XCAT-CT and MT-XCAT-CBCT were -140.35 ± 336.48 and -185.72 ± 350.32, compared with that of real CT (-149.79 ± 346.37) and CBCT ((-245.41 ± 371.66). The study demonstrated that realistic MT-XCAT phantoms were generated using the proposed method. In chapter 4, we proposed to synthesize realistic and controllable respiratory motions in the XCAT phantoms by developing a GAN-based deep learning technique. A motion generation model was developed based on BicycleGAN with a novel 4D generator. Input with the end-of-inhale (EOI) phase images and a Gaussian perturbation, the model generates inter-phase deformable-vector-fields (DVFs), which are composed and applied to the input to generate 4D images. The model was trained and validated using 71 4D-CT images from lung cancer patients. During testing, the EOI phase images of the 4D-XCAT phantom are input to the well-trained motion generation model to generate 4D-XCAT with realistic respiratory motions. By tuning the input Gaussian perturbation, 4D-XCAT phantoms with different breathing amplitudes are generated. A separate respiratory motion amplitude control model was built using decision tree regression to predict the input perturbation needed for a specific motion amplitude. This model was developed using 300 4D-XCAT phantoms generated from 6 original 4D-XCAT phantoms of different sizes with 50 different perturbations for each size. For evaluating the generated 4D images, Dice coefficients for lungs and lung volume variation during respiration were compared between the simulated images and reference images. Deformation energy was calculated to evaluate the generated DVF. DVFs and ventilation maps of the simulated 4D-CT were compared with the reference 4D-CTs using cross-correlation and Spearman’s correlation. Additionally, a comparison of DVFs and ventilation maps among the original 4D-XCAT, the generated 4D-XCAT, and reference patient 4D-CTs were made to show the improvement of motion realism by the model. The amplitude control error was calculated as the absolute error between the generated and desired breathing amplitude. Results show that the maximum deviation of lung volume during respiration was 5.8% and the Dice coefficient for lungs reached at least 0.95 by comparing the simulated and reference 4D-CTs. The generated DVFs comparable deformation energy levels. The cross-correlation of DVFs achieved 0.89 ± 0.10/ 0.86 ± 0.12/ 0.95 ± 0.04 along the x/ y/ z directions in the testing patient group. The cross-correlation of ventilation maps derived achieved 0.80 ± 0.05/ 0.67 ± 0.09/ 0.68 ± 0.13, and the Spearman's correlation achieved 0.70 ± 0.05/ 0.60 ± 0.09/ 0.53 ± 0.01, respectively, in the training/validation/testing patient groups. The generated 4D-XCAT phantoms presented similar deformation energy as patient data while maintaining the original XCAT phantom (Dice=0.95, maximum lung volume variation=4%). The respiratory motion amplitude error was controlled to be less than 0.5 mm. The results demonstrated that realistic and controllable respiratory motion in the 4D-XCAT phantom was synthesized using the proposed method. In chapter 5, we proposed to synthesize realistic CBCT imaging artifacts in the XCAT phantoms. An artifact simulation model is developed using CT images and image acquisition conditions as inputs and is trained to generate CBCT images with the conditions-specific imaging artifacts. Then, XCAT phantoms are input to the model with user-desired image acquisition conditions to simulate CBCT artifacts in the XCAT phantoms. The model was trained in a two-step scheme: (1) simulate cone and under-sampling artifacts for different projection numbers (450, 150, or 100); (2) simulate scatter and beam-hardening artifacts. Cone-beam projections of the CT volumes were generated by ray-tracing and used to reconstruct CBCTs using FDK-backprojection. These CBCT volumes were used as the output for the first-step model and the input for second-step model. The second-step model outputs corresponding CBCTs reconstructed using Monte-Carlo (MC) projections. The model was trained and validated with 14 patients with 10-fold cross-validation and tested by one independent patient and 5 XCAT phantoms. In addition to qualitative evaluation, we quantitatively evaluated the model performance by peak-signal-to-noise-ratio (PSNR), structural similarity index (SSIM), cross-correlation, and NPS (noise power spectrum) correlation coefficient (NCC). For the under-sampling and cone artifacts simulation, the PSNR reached 38.58/37.24/36.92, and the SSIM reached 0.987/0.978/0.966 for the testing patient with 450/225/100 projections, respectively. The cross-correlation and NCC achieved at least 0.99 for all projection numbers. Qualitatively, the model successfully simulated the streak artifact due to under-sampling and cone artifact in the XCAT phantom. For scatter simulation, the PSNR reached 28.05, and SSIM reached 0.904 for the testing patient. The cross-correlation was 0.927, and NCC was 0.984. The results demonstrated the feasibility of synthesizing CBCT artifacts in the 4D-XCAT phantom using the proposed method. In summary, the results demonstrate that an eXtended Modular ANthropomorphic (XMAN) phantom has been built based on the 4D-XCAT phantom with the following important featurs: (1) Realistic anthropomorphic anatomical textures for CT and CBCT in the chest region. This feature can significantly enhance the translatability from phantom to patient studies, better preparing the phantoms for a wide variety of virtual clinical trials in radiation therapy, such as imaging reconstruction, image registration, segmentation, treatment planning, etc. (2) Realistic and controllable respiratory motion pattern. This feature is valuable for investigating 4D imaging sorting and reconstruction techniques, validating ventilation map calculations, functional image analysis, developing motion-robust treatment planning, etc. (3) Realistic onboard CBCT imaging artifacts. This feature is valuable for evaluating and optimizing the imaging protocol and various image processing and treatment techniques, such as CBCT artifact correction, CBCT based target localization, radiomics analysis, and plan adaptation.

Purpose: To assess the quantitative potential of breast tomosynthesis by estimating the percent density of voxelized anthropomorphic breast phantoms.

Methods and Materials:A Siemens breast tomosynthesis system was modeled using Monte Carlo methods and a voxelized anthropomorphic breast phantom. The images generated by the simulation were reconstructed using Siemens filtered back-projection software. The non-uniform background due to scatter, heel effect, and limited angular sampling was estimated by simulating and subtracting images of a uniform 100% fatty breast phantom. To estimate the density of each slice, the total number of fatty and glandular voxels was calculated both before and after applying a thresholding algorithm to classify voxels as fat vs. glandular. Finally, the estimated density of the reconstructed slice was compared to the known percent density of the corresponding slice from the voxelized phantom. This percent density estimation comparison was done for a 35%- and a 60%-dense 5cm breast phantom.

Results: Without thresholding, overall density estimation errors for the central eleven slices were 4.97% and 2.55% for the 35% and 60% dense phantoms, respectively. After thresholding to classify voxels as fat vs. glandular, errors for central eleven were 7.99% and 6.26%, respectively. Voxel to voxel matching of the phantom vs. reconstructed slice demonstrated 75.69% and 75.25% respectively of voxels were correctly classified.

Conclusion: The errors in slice density estimation were <8% for both the phantoms thus implying that quantification of breast density using tomosynthesis is possible. However, limitations of the acquisition and reconstruction process continue to pose challenges in density estimation leading to potential voxel to voxel errors that warrant further investigation.

Virtual imaging trials (VITs) provide the opportunity to conduct medical imaging experiments otherwise not feasible through patient images. The reliability of these virtual trials is directly dependent upon their ability to replicate clinical imaging experiments. The combined effect of various key simulation parameters on the closeness of virtual images to experimental images has not yet been explicitly quantified, which this sensitivity study aimed to address. To do so, a physical phantom, Mercury 3.0 (Sun Nuclear), was scanned using a clinical scanner (Siemens Force). Meanwhile, utilizing a validated CT simulator (DukeSim), a computational version of the Mercury 3.0 phantom was virtually imaged, emulating the same scanner model and imaging acquisition settings. The simulations were performed with varied parameters for the x-ray source, phantom model, and detector characteristics, evaluating their impact on the realism of the final reconstructed virtual images. Simulations were explicitly conducted and evaluated various source and detector subsampling (1 – 5 per side), phantom voxel resolution (0.1 mm – 0.5mm), anode heel severity (0% - 40% over anode-cathode axis), aluminum filtration (0.9cm - 1.1cm), and pixel-to-pixel detector crosstalk (0 – 10.5%, 0 – 15% per dimension). The real and simulated projections were then reconstructed, employing a vendor-specific reconstruction software (Siemens ReconCT), with identical reconstruction settings. The real and simulated images were then compared in terms of modulation transfer function (MTF), noise magnitude, noise power spectrum (NPS), and CT number accuracy. When the optimal simulation parameters were selected, the simulated images closely replicated real images (0.80% relative error in f50air metric). The error in the f50 measurements were highly sensitive to the variation of source and detector subsampling and phantom voxel size. The relative error in the noise magnitude measurements were not highly sensitive to the variation of source and detector subsampling or phantom voxel size but were sensitive to the modeling of the anode heel effect severity. The error in the nNPS measurements were not highly sensitive to the variation of source and detector subsampling, phantom voxel size, degree of anode heel severity, aluminum filtration, or detector cross talk. Finally, the error in the CT number accuracy measurements were not highly sensitive to the variation of source and detector subsampling, phantom voxel size, aluminum filtration, or degree of detector cross talk, but were sensitive to the modeling of anode heel severity. Through this study, the effects of various key simulation parameters on the realism of scanner-specific simulations were assessed. Certain simulation parameters, such as source and detector subsampling, and degree of anode heel severity, exert greater influence on simulation realism than others, thus they should be prioritized when exploring novel modeling avenues.