Browsing by Subject "Therapy"

Introduction: RECA (Radiotherapy Enhanced with Cherenkov photo- Activation) is a novel treatment that induces a synergistic therapeutic effect by combining conventional radiation therapy with phototherapy using the anti-cancer and potentially immunogenic drug, psoralen. This work presents a novel method to investigate the therapeutic effect of RECA using rat brain slices and the agarose- based tissue equivalent material. Methods: 4T1 mCherry Firefly Luciferase mouse breast cancer cells are placed on the brain slice after exposed to psoralen solution. Taking fluorescent imaging of the brain slices every day after irradiation, an independent luciferase imaging was taken after the fifth fluorescence imaging. Using different imaging processing and analysis method to identify the cells. Result: Four analyzing method give different result about the fluorescence signal or luminescence signal. The overall trend of the fluorescence signal is rising over day, reaches the lowest point at 48 hours after irradiation. Control group (no radiation and no Cherenkov lights) has the lowest signal compared with other groups. The signal of brain slices with 4T1 cells exposed to psoralen solution is lower than that of brain slices without psoralen exposition. Conclusion: This work shows that rat brain slice can be used to simulate in vivo environment in exploring the therapeutic effect of RECA. Future work should focus on improving the image analyze method to better identify cells and noises.

Purpose: To develop a heuristic greedy algorithm to generate automatic multi-leaf collimator (MLC) sequencing for spatial fractioned radiation therapy (SFRT) using 3D dynamic conformal arc (DCA).Methods and materials: One late stage lung cancer patient with simulated sphere target grid was included in this study. N_t spheres were equally spaced within the gross target volume (GTV). The sphere targets are 1.5 cm in diameter, 4.3 cm spacing for 6,9,10, and 12 targets scenarios, and 2.8cm spacing for one special 10 targets scenario. Optimization was designed to complete within one coplanar arc from 180° to 0° in a clockwise direction with 2° as the angle interval. The problem is formalized as finding optimal MLC sequencing to cover N_t targets with K control points (CPs) for each arc. The state of each target’s MLC opening at each CP is binary. The original NP-hard problem can be approximated to a feasible subproblem by the greedy approximation on each control point and using the heuristic approach for the initial point. The algorithm focuses on the normalized relative dose relationship as the object function during the optimization. The dose matrix for each step was rasterized and grouped based on Monte Carlo simulation as the pre-calculation process. The physical speed limitation of the MLC motion was considered in the optimization to achieve a realistic and deliverable final MLC sequencing solution. Four grid arrays (6, 9, 10, and 12 targets respectively) were tested for plan quality. The arc collimator angle was planned with both 0 and 30 degrees for comparison. Prescription was set to 20 Gy to one fraction. The delivered dose will be normalized to equalize the minimum target dose to the prescription dose. Key dosimetric endpoints including target mean dose, D5, and D95, were reported. Results: The complexity of this algorithm has been reduced by a factor of \frac{2^K}{2\left(K-1\right)}. The D95 deviations of all targets as the main focus object were within 2.88% in four grid arrays with 0°/30° collimator rotation angles, 4.3 cm spacing for 6,9,10 and 12 targets scenarios, and 2.8 cm spacing for one special 10 targets scenario. For all scenarios with 4.3 cm spacing, the mean valley-to-peak ratios were under 0.45 and were within the constraint that the dose of the other part of the tumor is no more than 45% of the max normalized D95 delivered target dose during the algorithm optimization. Conclusion: This algorithm is a feasible and practical method with high efficiency while delivering the prescription dose to small target volume for late stage cancer palliative management. The proposed solution provides decent coverage to the tumor volume as well as the valley-to-peak ratio. It provides a competitive alternative solution to the standard alloy grid delivery technique.

Purpose: To develop an automated program that can generate the optimal beams for whole breast radiation therapy (WBRT).

Methods and Materials: A total of twenty patients receiving WBRT were included in this study. The computed tomography (CT) simulation images and structures of all 20 patients were used to develop and validate the program. All patients had the breast planning target volume (PTV) contour drawn by physicians and radio-opaque catheters placed on the skin during CT simulation. First, an initial beam was calculated based on the CT images, the radio-opaque catheters, and the breast PTV contour. The beam includes five main parameters: the gantry angles, the isocenter location, the field size, the collimator angles, and the initial multi-leaf collimator (MLC) shape.

To optimize the beam parameters, a geometry-based objective function was constructed to optimize target coverage and organ-at-risk (OAR) sparing. The objective function is the weighted sum of the square of the relative volumes of the PTV outside the field and the ipsilateral lung inside the field. Due to the curvature of the chest wall, a portion of the ipsilateral lung will be included in the irradiated volume. The balance between PTV coverage and OAR sparing is embodied by the relative weight of the lung volume in the objective function, which was trained and validated from the clinical plans of the twenty patients. Two different optimization schemes were developed to minimize the objective function: the exhaustive search and the local search. The search was conducted in a 2-dimensional grid with the gantry angle (1° increments) and the isocenter location (1 mm increments) as two axes and the initial beam as the origin point. For the exhaustive search, the ranges of the gantry angle and the isocenter location are ±12° and ±21 mm. The local search does not require a search range. The beam with the minimal objective function value in the grid is considered optimal. The optimal beam was transferred to an in-house automatic fluence optimization program developed specifically for WBRT. The automatic plans were compared with the manually generated clinical plans for target coverage, dose conformity and homogeneity, and OAR dose.

Results: The calculation time of the beam optimization was under one minute for all cases. The local search (~15 s) took less time than the exhaustive search (~45 s), and the two methods produced the same result for the same patient. The automatic plans have overall comparable plan quality to the clinical plans, which usually take 1 to 4 hours to make. Generally, the PTV coverage is improved while the dose to the ipsilateral lung and the heart is similar. The breast PTV Eval V95% of all cases are above 95%, and the mean V95% (97.7%) is increased compared with the clinical plans (96.8%). The ipsilateral lung V16Gy is reduced for 14 out of 20 cases, and the mean V16Gy is decreased in the automatic plans (12.6% vs. 13.6%). The average heart mean dose is slightly increased in the automatic plans (2.06% vs. 1.99%).

Conclusion: Optimal beams for WBRT can be automatically generated in one minute given the patient’s simulation CT images and structures. The automated beam setup program offers a valuable tool for WBRT planning, as it provides clinically relevant solutions based on previous clinical practice as well as patient specific anatomy.

Cone-beam computed tomography (CBCT) is an X-ray-based imaging modality widely used in medical practices. Due to the ionizing imaging dose induced by CBCT, many studies were conducted to reduce the number of projections (sparse sampling) to lower the imaging dose while maintaining good image quality and fast reconstruction speed. Conventionally, a CBCT volume is reconstructed analytically with the Feldkamp Davis Kress (FDK) algorithm that backprojects filtered projections according to projection angles. However, the FDK algorithm requires a dense angular sampling that satisfies the Shannon-Nyquist theorem. The FDK algorithm reconstructs CBCT with a high speed but requires relatively high patient imaging dose. The iterative methods like algebraic reconstruction technique (ART) and compressed sensing (CS) methods are investigated to reduce patient imaging dose. These iterative methods update estimated images iteratively and the CS methods apply penalty terms to award desired features. Yet these methods are limited by the iterative design with substantially increased computation time and consumption of computation power. Scholars have also conducted research on bypassing the limit of Shannon-Nyquist theorem by interpolating densely sampled CBCT projections from sparsely sampled projections. However, blurred structures in reconstructed images remain to be a concern for analytical interpolation methods. As such, previous research indicates that it is hard to achieve the three goals of lowered patient imaging dose, good image quality, and fast reconstruction speed all at once.

As deep learning (DL) gained popularity in fields like computer vision and data science, scholars also applied DL techniques in medical image processing. Studies on DL-based CT image reconstruction have yielded encouraging results, but GPU memory limitation made it challenging to apply DL techniques on CBCT reconstruction.

In this dissertation, we hypothesize that the image quality of CBCT reconstructed from under-sampled projections (low-dose) using deep learning techniques can be comparable to that of CBCT reconstructed from fully sampled projections for treatment verification in radiation therapy. This dissertation proposes that by applying DL techniques in pre-processing, reconstruction, and post-processing stages, the challenge of improving CBCT image quality with low imaging dose and fast reconstruction speed can be mitigated.

The dissertation proposed a geometry-guided deep learning (GDL) technique, which is as the first technique to perform end-to-end CBCT reconstruction from sparsely sampled projections and demonstrated its feasibility for CBCT reconstruction from real patient projection data. In this study, we have found that incorporating geometry information into the DL technique can effectively reduce the model size, mitigating the memory limitation in CBCT reconstruction. The novel GDL technique is composed of a GDL reconstruction module and a post-processing module. The GDL reconstruction module learns and performs projection-to-image domain transformation by replacing the traditional single fully connected layer with an array of small fully connected layers in the network architecture based on the projection geometry. The additional deep learning post-processing module further improves image quality after reconstruction.

This dissertation further optimizes the number of beamlets used in the GDL technique through a geometry-guided multi-beamlet deep learning (GMDL) technique. In addition to connecting each pixel in the projection domain to beamlet points along the central beamlet in the image domain as GDL does, these smaller fully connected layers in GMDL connect each pixel to beamlets peripheral to the central beamlet based on the CT projection geometry. Due to the limitation of GPU VRAM, the proposed technique is demonstrated through low-dose CT image reconstruction and is compared with the GDL technique and a large fully connected layer-based reconstruction method.

In addition, the dissertation also investigates deep learning-based CBCT projection interpolation and proposes a patient-independent deep learning projection interpolation technique for CBCT reconstruction. Different from previous studies that interpolate phantom or simulated data, the proposed technique is demonstrated to work on real patient projection data with unevenly distributed projection angles. The proposed technique re-slices the stack of interpolated projections axially, and each acquired slice is processed by a deep residual U-Net (DRU) model to augment the slice’s image quality. The resulting slices are reassembled into a stack of densely-sampled projections to be reconstructed into a CBCT volume. A second DRU model further post-processes the reconstructed CBCT volume to improve the image quality.

In summary, a geometry-guided deep learning (GDL) technique was proposed as the first deep learning technique for end-to-end CBCT reconstruction from sparsely sampled real patient projection data. The geometry-guided multi-beamlet deep learning (GMDL) technique further optimizes the number of beamlets based on the GDL technique. A patient-independent deep learning projection interpolation technique was also proposed for the pre-processing and post-processing stage of CBCT reconstruction.

In conclusion, the work presented in this dissertation demonstrates the feasibility of improving CBCT image quality with low imaging dose and fast reconstruction speed. The techniques developed in this dissertation also have great potential for clinical applications to enhance CBCT imaging for radiation therapy.

Background: Cervical cancer is a common gynecological malignancy among women worldwide. Among the primary modalities for treating cervical cancer, radiation therapy occupies a central role. Using Cone-Beam Computed Tomography (CBCT) scans obtained prior to treatment for target registration and alignment holds critical significance for precision radiation therapy. Accurately contouring targets and critical-organs-at risk (OARs) is the most time-consuming task for radiation oncologists. The OAR contouring in CBCT plays a crucial role in the radiotherapy of cervical cancer. Specifically, the location and volume of the rectum and bladder can significantly impact the precision of cervical cancer treatment, as the patients need to drink certain amount of water to fill the bladder prior to the treatment for target localization. The resulting change in position of rectum and bladder may lead to alterations in the target dose. Further, changes in radiation dose to these two OARs can directly affect the severity of the acute and late radiation induced damage. Therefore, the OAR contouring not only allows for better localization before each radiotherapy session, but also provides valuable reference for clinicians when they need to adjust the treatment plan.Purpose: The objective of this study is to evaluate the capabilities of four deep-learning models for contouring OARs in CBCT images of cervical cancer patients. Materials and Methods: The study dataset comprising 40 sets of CBCT images were collected from the Fujian Provincial Cancer Hospital in China. Two experienced radiation oncologists meticulously delineated 10 groups of OARs (Body, Bladder, Bone Marrow, Bowel Bag, Femoral Head L, Femoral Head R, Femoral Head and Neck L, Femoral Head and Neck R, Rectum, Spinal Canal) on the CBCT images as reference/ground truth. Subsequently, the 24 sets of CBCT reference were used to train the CBCT model, and the unedited CBCT images of the remaining 16 sets were used for comparing with their reference to test the four models. The only difference between these four models is the adoption of different neural network structures. They are classic U-Net, Flex U-Net, Attention U-Net (ATT), and SegResNet respectively. The evaluation of contouring quality for the four models was performed using the metrics such as 95 percentile Hausdorff Distance (HD95), Dice Similarity Coefficient (DICE), Average Symmetric Surface Distance (ASSD), Maximum Symmetric Surface Distance (MSSD), and Relative Absolute Volume Difference (RAVD), respectively. Results: The average DICE was 0.86 for bladder contouring among four models. The average DICE for rectum on CBCT image was 0.84 for four models. Conclusion: According to the quantitative analysis, classic U-Net neural network architecture with minor adjustments can obtain competitive segmentation on CBCT images.

Purpose: To investigate and characterize a novel 3D dosimetry system consisting of ClearView radiochromic gel dosimeters and a state-of-the-art telecentric optical CT scanner: The Duke Large Field of View Optical-CT Scanner (DLOS). Methods and Materials: ClearView radiochromic dosimeters (Modus QA) are gellan gum based radiochromic dosimeters containing a water-soluble tetrazolium salt which reduces into an insoluble formazan dye (with associated color change) under ionizing radiation. Initial spectrophotometric studies investigated linearity of dose response on small volumes of ClearView in optical cuvettes. Simple, single beam benchmark radiation therapy treatments (central axis photon, lateral photon, and electron deliveries) were delivered to 10 and 15-cm diameter ClearView dosimeters. Additionally, a “stacking pyramid” delivery was developed consisting of overlaid fields of increasing size, delivered down the central axis of each dosimeter, with a 1x1 cm2 small field at the center. The treatments were modeled with a commissioned Eclipse treatment planning system. Dosimeters were scanned with the DLOS, submerged in a refractive index (RI) matching fluid (ca. 10% propylene glycol) both pre- and post-irradiation (within 24h) and 3D reconstructions of the change in linear-optical-attenuation was determined using in-house software and 3D Slicer. Percent depth-dose (PDD), cross plane and in-plane profiles, and relative 3D gamma analysis were performed and compared to the commissioned Eclipse dose, which served as ground truth. All experiments followed a standardized workflow for consistency. Results: Linearity of dose response was confirmed in the cuvette study with excellent agreement (R2 ≥ 0.9986) at two wavelengths (520-and 632 nm) at 3 post-irradiation time points: 21 hours, 6 and 10 days. Dosimeter reconstructions were performed at 1mm³ resolution in full 3D. Relative dose profiles of all irradiations, in both 15-and 10 cm dosimeters, show good agreement in comparison to Eclipse dose calculations, with root mean square errors (RMSE) 0.00107-0.006649, and R2 ≥ 0.9808. Relative 3D gamma analysis was performed at 7%4mm, 5%3mm, 3%3mm, 3%2mm, and 2%2mm for all deliveries on both 10-and 15-cm dosimeters. 15-cm benchmark irradiations passed with ≥ 94% at 2%2mm, ≥ 90% at 3%3mm, and ≥ 90% at 2%2mm, for the central axis, left lateral, and electron deliveries, respectively. 10-cm benchmark irradiations passed with ≥ 93% at 3%2mm, ≥ 91% at 3%3mm, and ≥ 90 at 3%2mm, for the central axis, left lateral, and electron deliveries, respectively. 15-cm stacking field irradiations passed with ≥ 94% at 3%2mm, and 10-cm stacking field irradiations passed with ≥ 96% at 2%2mm. Regions of known artifacts were excluded from gamma analysis (jar base, neck, wall). Some artifacts remain unaccounted for (e.g., ring and cupping artifacts). Conclusion: This work presents the first use of a telecentric optical-CT scanner with ClearView. The system shows substantive promise for a new, comprehensive 3D dosimetry system, and this effort lays the groundwork for further, more specialized applications. Both the benchmark irradiations and stacked field deliveries performed exceptionally well in the various gamma analyses and investigation of the profiles, even in the presence of artifacts that were not completely accounted for in the data analysis. General differences between the 15-cm and 10-cm dosimeters are not made abundantly clear with the small sample size of this work, but both seem viable. There seems to be no difference between photon and electron deliveries, and both are viable with the system given the experiments performed.

Purpose: Total body irradiation (TBI) is to deplete patient’s bone marrow and suppress the immune system by delivering uniform dose to patient’s whole body with a relatively low dose rate. The widely used total body irradiation (TBI) protocol in many institutions is to extend the source to surface distance (SSD) to over 400 cm in a large treatment room. The TBI techniques currently used at Duke University Medical Center is anteroposterior (AP)/posteroanterior (PA) technique and bilateral technique. Though bilateral technique TBI is executed with simpler treatment planning and setups in a more comfortable position, it could not provide adequate shielding to lungs and kidneys using blocks like AP/PA TBI technique. However, the whole process of block fabricating and verification is labor-consuming and time-costing. This project aims to develop a better AP/PA TBI treatment method in recumbent position, which provides better sparing for lungs and kidneys in any treatment room.

Methods: In this study, we considered different treatment techniques (three-dimensional conformal radiation therapy (3DCRT) and intensity-modulated radiation therapy (IMRT)), different treatment position (on the floor or on the couch), and different setups (gantry rotation and platform movement). TBI treatment plans were simulated in Eclipse treatment planning system by using both water equivalent phantom and patient CT image. Prescription for the treatment plans was 200 cGy per fraction with 4 fractions. The dose homogeneity should within ±10% of the prescription dose. Dose constraints for kidney and lung are 25% of the prescription dose. In 3DCRT TBI, we applied multi-leaf collimators (MLCs) for OARs shielding and used boost field to provide adequate dose to lungs and kidneys. For IMRT TBI, an iterative optimization algorithm was generated for increasing dose uniformity. By using IMRT, dynamic multi-leaf collimators (DMLCs) provided shielding for kidneys and lungs, which were considered in fluence map optimization. Volume dose and dose profiles were used to analyze the dose uniformity. Measurements with solid water phantom in treatment room were performed to verify the simulation results. IMRT QA with portal imager was performed for phantom.

Results: 3DCRT could not ensure the dose homogeneity and dose deliver accuracy at the same time. To ensure the dose homogeneity in 3DCRT TBI, patient/platform position should be changed between field or applying customized wedge to compensate the inverse square law. For IMRT, the optimization algorithm has excellent performance for both phantom and patients. The dose homogeneity in the mid-plane of both phantom and patients were less than ±5% of the prescription dose after a few iterations. Lungs and kidneys could receive around 25% prescription dose. The simulation and measurement results agree with each other. No additional physical compensators or partial transmission blocks were needed. Portal dose and predict dose perfectly agreed with each other. CR film worked well in positioning. Surface dose enhancement under blocked field was observed.

Conclusion: IMRT technique performed much better than 3DCRT in TBI treatment. In this study, we develop an AP/PA recumbent position IMRT TBI technique that could be used in any linac room. This technique can ensure high dose homogeneity, provide better sparing to lungs and kidneys, and reduce the complexity of TBI treatment planning without the need of labor-intensive compensators and partial transmission blocks.

The aim of the study is to develop a geometrically adaptive and statistically robust plan quality inference method. A knowledge-based plan quality inference method is proposed in this study. It references to similar plans in the history database for patient-specific plan quality evaluation. Similar plans are retrieved using a novel plan similarity metric, and dosimetric statistical inferences are obtained from the selected similar plans. Two plan quality metrics—dosimetric result probability (DRP) and dose deviation index (DDI)—are proposed to quantify plan quality amongst prior similar plans. 927 clinical approved head-and-neck treatment plans with two planning targets were exported and used as the historical database. Eight organs-at-risk (OARs), including brainstem, spinal cord, larynx, mandible, pharynx, oral cavity, left parotid and right parotid were analyzed in this study. Statistical analysis is performed to validate the similarity of the selected reference plans. 12 sub-optimal plans identified by DRP were re-planned to validate the capability of the proposed methods in identifying inferior plans, To demonstrate the potential of our proposed method as a plan quality data analytics tool, a population-wise analysis was conducted on all retrieved plans sorted every two years. A ready-to-use stand-along application was also developed to streamline the evaluation process.

After replanning, left and right parotid median dose are reduced by 31.7\% and 18.2\% respectively. 83\% of these cases would not be identified as suboptimal without the proposed similarity plan selection. The population plan quality analysis reveals that the average parotid sparing has increased by 21.7\% from 2005 to 2018. Notably, the increasing dose sparing over time in retrospective plan quality analysis is strongly correlated with the increasing dose prescription ratios to the two planning targets, revealing the collective trend in planning conventions.

The proposed similar plan retrieval and analysis methodology has been proven to be predictive of the current plan quality. Therefore, the proposed workflow can potentially be applied in the clinics as a real-time plan quality assurance tool. The proposed metrics can also serve the purpose of plan quality analytics in finding connections and historical trends in the clinical treatment planning workflow.

The outcomes for both (i) radiation therapy and (ii) preclinical small animal radio- biology studies are dependent on the delivery of a known quantity of radiation to a specific and intentional location. Adverse effects can result from these procedures if the dose to the target is too high or low, and can also result from an incorrect spatial distribution in which nearby normal healthy tissue can be undesirably damaged by poor radiation delivery techniques. Thus, in mice and humans alike, the spatial dose distributions from radiation sources should be well characterized in terms of the absolute dose quantity, and with pin-point accuracy. When dealing with the steep spatial dose gradients consequential to either (i) high dose rate (HDR) brachytherapy or (ii) within the small organs and tissue inhomogeneities of mice, obtaining accurate and highly precise dose results can be very challenging, considering commercially available radiation detection tools, such as ion chambers, are often too large for in-vivo use.

In this dissertation two tools are developed and applied for both clinical and preclinical radiation measurement. The first tool is a novel radiation detector for acquiring physical measurements, fabricated from an inorganic nano-crystalline scintillator that has been fixed on an optical fiber terminus. This dosimeter allows for the measurement of point doses to sub-millimeter resolution, and has the ability to be placed in-vivo in humans and small animals. Real-time data is displayed to the user to provide instant quality assurance and dose-rate information. The second tool utilizes an open source Monte Carlo particle transport code, and was applied for small animal dosimetry studies to calculate organ doses and recommend new techniques of dose prescription in mice, as well as to characterize dose to the murine bone marrow compartment with micron-scale resolution.

Hardware design changes were implemented to reduce the overall fiber diameter to <0.9 mm for the nano-crystalline scintillator based fiber optic detector (NanoFOD) system. Lower limits of device sensitivity were found to be approximately 0.05 cGy/s. Herein, this detector was demonstrated to perform quality assurance of clinical 192Ir HDR brachytherapy procedures, providing comparable dose measurements as thermo-luminescent dosimeters and accuracy within 20% of the treatment planning software (TPS) for 27 treatments conducted, with an inter-quartile range ratio to the TPS dose value of (1.02-0.94=0.08). After removing contaminant signals (Cerenkov and diode background), calibration of the detector enabled accurate dose measurements for vaginal applicator brachytherapy procedures. For 192Ir use, energy response changed by a factor of 2.25 over the SDD values of 3 to 9 cm; however a cap made of 0.2 mm thickness silver reduced energy dependence to a factor of 1.25 over the same SDD range, but had the consequence of reducing overall sensitivity by 33%.

For preclinical measurements, dose accuracy of the NanoFOD was within 1.3% of MOSFET measured dose values in a cylindrical mouse phantom at 225 kV for x-ray irradiation at angles of 0, 90, 180, and 270˝. The NanoFOD exhibited small changes in angular sensitivity, with a coefficient of variation (COV) of 3.6% at 120 kV and 1% at 225 kV. When the NanoFOD was placed alongside a MOSFET in the liver of a sacrificed mouse and treatment was delivered at 225 kV with 0.3 mm Cu filter, the dose difference was only 1.09% with use of the 4x4 cm collimator, and -0.03% with no collimation. Additionally, the NanoFOD utilized a scintillator of 11 µm thickness to measure small x-ray fields for microbeam radiation therapy (MRT) applications, and achieved 2.7% dose accuracy of the microbeam peak in comparison to radiochromic film. Modest differences between the full-width at half maximum measured lateral dimension of the MRT system were observed between the NanoFOD (420 µm) and radiochromic film (320 µm), but these differences have been explained mostly as an artifact due to the geometry used and volumetric effects in the scintillator material. Characterization of the energy dependence for the yttrium-oxide based scintillator material was performed in the range of 40-320 kV (2 mm Al filtration), and the maximum device sensitivity was achieved at 100 kV. Tissue maximum ratio data measurements were carried out on a small animal x-ray irradiator system at 320 kV and demonstrated an average difference of 0.9% as compared to a MOSFET dosimeter in the range of 2.5 to 33 cm depth in tissue equivalent plastic blocks. Irradiation of the NanoFOD fiber and scintillator material on a 137Cs gamma irradiator to 1600 Gy did not produce any measurable change in light output, suggesting that the NanoFOD system may be re-used without the need for replacement or recalibration over its lifetime.

For small animal irradiator systems, researchers can deliver a given dose to a target organ by controlling exposure time. Currently, researchers calculate this exposure time by dividing the total dose that they wish to deliver by a single provided dose rate value. This method is independent of the target organ. Studies conducted here used Monte Carlo particle transport codes to justify a new method of dose prescription in mice, that considers organ specific doses. Monte Carlo simulations were performed in the Geant4 Application for Tomographic Emission (GATE) toolkit using a MOBY mouse whole-body phantom. The non-homogeneous phantom was comprised of 256x256x800 voxels of size 0.145x0.145x0.145 mm3. Differences of up to 20-30% in dose to soft-tissue target organs was demonstrated, and methods for alleviating these errors were suggested during whole body radiation of mice by utilizing organ specific and x-ray tube filter specific dose rates for all irradiations.

Monte Carlo analysis was used on 1 µm resolution CT images of a mouse femur and a mouse vertebra to calculate the dose gradients within the bone marrow (BM) compartment of mice based on different radiation beam qualities relevant to x-ray and isotope type irradiators. Results and findings indicated that soft x-ray beams (160 kV at 0.62 mm Cu HVL and 320 kV at 1 mm Cu HVL) lead to substantially higher dose to BM within close proximity to mineral bone (within about 60 µm) as compared to hard x-ray beams (320 kV at 4 mm Cu HVL) and isotope based gamma irradiators (137Cs). The average dose increases to the BM in the vertebra for these four aforementioned radiation beam qualities were found to be 31%, 17%, 8%, and 1%, respectively. Both in-vitro and in-vivo experimental studies confirmed these simulation results, demonstrating that the 320 kV, 1 mm Cu HVL beam caused statistically significant increased killing to the BM cells at 6 Gy dose levels in comparison to both the 320 kV, 4 mm Cu HVL and the 662 keV, 137Cs beams.

Cone-beam Computed Tomography (CBCT) has been widely used in image-guided radiation therapy for target localization. 3D CBCT has been developed for localizing static targets, while 4D CBCT has been developed for localizing moving targets. Although CBCT has been used as the gold standard in current clinical practice, it has several major limitations: (1). High imaging dose to the normal tissue from repeated 3D/4D CBCT scans. Low dose CBCT reconstruction is changeling because of streak artifacts caused by limited projection and increased noise by low exposure. Previous methods such as compressed sensing method can successfully remove streak artifacts and reduce noise. However, the reconstructed images are blurred, especially at edge regions due to the uniform image gradient penalty, which will affect the accuracy of patient positioning for target localization. (2). Poor soft tissue contrast due to the inherent nature of x-ray based imaging as well as several CBCT artifacts such as scatter and beam hardening. As a result, the accuracy of using CBCT to localize tumors in the abdominal region is extremely limited. To address these limitations, we propose to use prior images and deep learning techniques to enhance the edge information and soft tissue contrast of 3D/4D CBCT images. The specific aims include: 1) Establish a novel prior contour based TV (PCTV) method to enhance the edge information in compressed sensing reconstruction for CBCT. 2) Establish hybrid PCTV and deep learning methods to achieve accurate and fast low dose CBCT reconstruction.3) Establish deep learning methods to generate virtual on-board multi-modality images to enhance soft tissue contrast in CBCT. The results from this research will be highly relevant to the clinical application of on-board image for head and neck, lung and liver patient treatment to improve target localization while reducing radiation dose of 3D/4D CBCT scan.

To address the first limitation of current clinical CBCT, a novel prior contour based TV (PCTV) method was developed in this dissertation to enhance the edge information in compressed sensing reconstruction. Specifically, the edge information is extracted from the prior planning-CT via edge detection. Prior CT is first registered with on-board CBCT reconstructed with TV method through rigid or deformable registration. The edge contour in prior-CT is then mapped to CBCT and used as the weight map for TV regularization to enhance edge information in CBCT reconstruction. The proposed PCTV method was evaluated using extended-cardiac-torso (XCAT) phantom, physical CatPhan phantom and brain patient data. Results were compared with both TV and edge-preserving TV (EPTV) methods which are commonly used for limited projection CBCT reconstruction. Relative error was used to calculate image pixel value difference and edge cross correlation was defined as the similarity of edge information between reconstructed images and ground truth in the quantitative evaluation. Compared to TV and EPTV, PCTV enhanced the edge information of bone, lung vessels and tumor in XCAT reconstruction and complex bony structures in brain patient CBCT. In XCAT study using 45 half-fan CBCT projections, compared with ground truth, relative errors were 1.5%, 0.7% and 0.3% and edge cross correlations were 0.66, 0.72 and 0.78 for TV, EPTV and PCTV, respectively. PCTV is more robust to the projection number reduction. Although edge enhancement was reduced slightly with noisy projections, PCTV was still superior to other methods. PCTV can maintain resolution while reducing the noise in the low mAs CatPhan reconstruction. Low contrast edges were preserved better with PCTV compared with TV and EPTV.

The first technique developed in this dissertation demonstrates that PCTV preserved edge information as well as reduced streak artifacts and noise in low dose CBCT reconstruction. PCTV is superior to TV and EPTV methods in edge enhancement, which can potentially improve the localization accuracy in radiation therapy. However, the accuracy of edge enhancement in PCTV is affected by the registration errors and anatomical changes from prior to on-board images, especially when deformation exists. The next section of the dissertation describes the development of the hybrid-PCTV to further improve the accuracy and robustness of PCTV. Similar to PCTV method, planning-CT is used as prior images and deformably registered with on-board CBCT reconstructed by the edge preserving TV (EPTV) method. Edges derived from planning CT are deformed based on the registered deformation vector fields to generate on-board edges for edge enhancement in PCTV reconstruction. Reference CBCT is reconstructed from the simulated projections of the deformed planning-CT. Image similarity map is then calculated between the reference and on-board CBCT using structural similarity index (SSIM) method to estimate local registration accuracy. The hybrid-PCTV method enhances the edge information based on a weighted edge map that combines edges from both PCTV and EPTV methods. Higher weighting is given to PCTV edges at regions with high registration accuracy and to EPTV edges at regions with low registration accuracy. The hybrid-PCTV method was evaluated using both digital extended-cardiac-torso (XCAT) phantom and lung patient data. In XCAT study, breathing amplitude change, tumor shrinkage and new tumor were simulated from CT to CBCT. In the patient study, both simulated and real projections of lung patients were used for reconstruction. Results were compared with both EPTV and PCTV methods. EPTV led to blurring bony structures due to missing edge information, and PCTV led to blurring tumor edges due to inaccurate edge information caused by errors in the deformable registration. In contrast, hybrid-PCTV enhanced edges of both bone and tumor. In XCAT study using 30 half-fan CBCT projections, compared with ground truth, relative errors were 1.3%, 1.1%, and 0.9% and edge cross-correlation were 0.66, 0.68 and 0.71 for EPTV, PCTV and hybrid-PCTV, respectively. Moreover, in the lung patient data, hybrid-PCTV avoided the wrong edge enhancement in the PCTV method while maintaining enhancements of the correct edges. Overall, hybrid-PCTV further improved the robustness and accuracy of PCTV by accounting for uncertainties in deformable registration and anatomical changes between prior and onboard images. The accurate edge enhancement in hybrid-PCTV will be valuable for target localization in radiation therapy.

In the next section, a technique for predicting daily on-board edge deformation using deep convolutional neural networks (CNN) is described to bypass deformable registration to improve the PCTV reconstruction efficiency. Edge deformation was predicted by deep learning model including a supervised and an unsupervised convolutional neural network (CNN) learning model. In the supervised model, deformation vector field (DVF) registered from CT to full-sampled CBCTs and retrospectively under-sampled low-dose CBCT were obtained on the first treatment day to train the model, which was then updated with following days’ data. In contrast, no ground truth DVF was needed for the unsupervised model and image pair of planning CT and CBCT were used as input to fine-tune the model. The model predicts DVF for low-dose CBCT acquired on the following day to generate on-board contours for PCTV reconstruction. This method was evaluated using lung SBRT patient data. In the intra-patient evaluation study, the first n-1 day’s CBCTs were used for CNN training to predict nth day edge information (n=2, 3, 4, 5). In addition, 10 lung SBRT patient data were obtained for the inter-patient study. The unsupervised model was trained on 9 of 10 patient data with transferring learning and to predict the while DVF for the other patient. 45 half-fan projections covering 360˚ from nth day CBCT was used for reconstruction and results from Edge-preserving (EPTV), PCTV and PCTV-CNN were compared. The cross-correlations between predicted and reference edge maps were about 0.74 for intra-patient study using the supervised CNN model. When using the unsupervised CNN mode, the cross-correlations of the predicted edge map were about 0.88 for both intra-patient and inter-patient. PCTV-CNN enhanced bone edges in CBCT compared to EPTV and achieved comparable image quality as PCTV while avoiding the user dependent and time-consuming deformable registration process. These results demonstrated the feasibility to use CNN to predict daily deformation of on-board edge information for PCTV based low-dose CBCT reconstruction. Thus, PCTV-CNN has a great potential for enhancing the edge sharpness with high efficiency for low dose 3D CBCT or 4D CBCT to improve the precision of on-board target localization and adaptive radiotherapy.

In the last part of this dissertation, prior images such as high-quality planning CT with deformation was used to generate on-board CT/CBCT image to improve soft tissue contrast for CBCT. The whole deformation vector field (DVF) was predicted using the unsupervised CNN model fine-tuned on liver SBRT patient. The on-board virtual CT in the liver region is obtained by deforming the prior planning CT using the finite element model (FEM) based on the deformation of livers surfaces from planning CT to CBCT. The deformed CT is embedded in the liver region to improve soft tissue contrast for tumor localization in the liver, while on-board CBCT is used for the region outside the liver to verify the positions of healthy tissues close to the tumor. In the current study, we mainly investigated the feasibility to use deep learning to generate accurate liver contours in CBCT. The method was evaluated using 15 SBRT liver patients’ data including planning CT and daily CBCT. Image sets of 14 patients were obtained to train the model while the other one was used to test. Deformed CT with DVF generated from Velocity and predicted DVF were compared using image similarity matrix including mutual information (MI), cross correlation (CC) and structural similarity index measurement (SSIM). The MI, CC and SSIM between predicted deformed CT and first-day on-board CBCT were 1.28, 0.98 and 0.91 for a new patient, respectively. All similarity evaluation results demonstrated the unsupervised CNN model can predict DVF to deform CT equivalently with Velocity. Therefore, it is feasible to apply deep CNN deformation model for fast on-board virtual image generation to improve the precision of the treatment of low contrast soft tissue tumors.

In conclusion, the works presented in this dissertation aim to use prior image and deep learning to improve the image quality of the on-board low dose 3D/4D CBCT by enhancing the edge sharpness and soft tissue contrast. The goals of this dissertation research are to: 1) establish a novel prior contour based TV (PCTV) method to enhance the edge information in compressed sensing reconstruction for CBCT; 2) establish hybrid PCTV to improve the accuracy and robustness of PCTV when deformable registration needed 3) implement deep learning methods to bypass deformable registration to automate and accelerate the low dose CBCT reconstruction; 4) establish deep learning methods to generate virtual on-board multi-modality images to enhance soft tissue contrast in CBCT. Results demonstrated that 1) edge sharpness can be improved for low dose 3D/4D CBCT using prior contour based TV method; 2) virtual image generated by fusing CBCT and deformed CT can improve the soft tissue contrast for the liver patient and 3) deep learning can be applied to improve the efficiency and automation in image deformable registration. Imaging augmentation including high and low contrast improvement using these techniques can improve the precision of dose delivery for image-guided radiation therapy, which might path the way to be applied in the clinic to improve the patient care.

Surface guided radiation therapy (SGRT) is an emerging technology that uses non-ionizing methods for patient positioning and motion tracking during radiotherapy delivery. However, the use of radiotherapy boluses, which are tissue-equivalent materials placed on the skin to increase surface dose, has been shown to interfere with SGRT systems due to reflections from the bolus surface. This thesis presents the development and validation of an opaque and non-glossy radiotherapy bolus called the "Surface Guidance Optimized" (SGO), which is a variation of the previously developed transparent Clearsight bolus.The Surface Guidance Optimized bolus was rendered opaque by adding 0.6% titanium dioxide and given a matte finish using matte release paper. Spectroscopy measurements confirmed optimal opaqueness, while gloss meter readings verified a non-glossy surface. The bolus density was quantified to be 0.853 g/cm3 using water displacement and CT methods. Dosimetric characterization through direct surface dose measurements and Monte Carlo simulations demonstrated the SGO bolus mimics the dose deposition of water-equivalent materials when accounting for density differences. Compatibility testing with the AlignRT SGRT system showed the bolus allowed accurate surface reconstruction and submillimeter tracking (within 0.4 mm) under different lighting conditions. Overall, the SGO bolus mitigates issues of transparency and glossiness that interfered with SGRT systems, while maintaining desirable dosimetric properties for clinical use as a radiotherapy bolus compatible with modern surface guided techniques.

Abstract

Purpose

Single-isocenter multi-target (SIMT) Volumetric Modulated Arc Therapy (VMAT) technique can produce highly conformal dose distributions and short treatment delivery times for the treatment of multiple brain metastases. SIMT radiosurgery using VMAT is primarily limited to linear accelerators utilizing 2.5mm leaf width MLCs. We explore feasibility of applying this technique to linear accelerators utilizing MLCs with leaf width of 5mm to broaden the applicability of SIMT radiosurgery using VMAT to include the greater number of linear accelerators with standard 5mm MLCs.

Methods

Twenty patients with 3-10 intracranial brain metastases originally treated with 2.5 mm leaf width MLCs were re-planned using standard 5mm leaf width MLCs and the same treatment geometry (3-5 VMAT arcs). Conformity index, low (V30%), and moderate (V50%) isodose spill were selected for analysis. V12Gy was also analyzed for single fraction cases. We tested the effects of several strategies to mitigate degradations of dose quality values when 5 mm leaf width MLCs were used; these included duplicating each VMAT arc with altered collimator angles by 10°, 15°, and 90°, and adding 1-2 VMAT arcs, with all arcs equally spaced.

Results

Wider MLCs caused small changes in total MUs (5827±2334 vs 5572±2220, p=.006), and Conformity Index (CI) (2.22%±0.05%, p=.045), but produced more substantial increases in brain V30%[%] and V50%[%] (27.75%±0.16% and 20.04%±0.13% respectively, p < .001 for both), and V12Gy[cc] (16.91%±0.12%, p < .001).

Conclusion

SIMT radiosurgery delivered via VMAT using 5mm leaf width MLCs can achieve similar CI compared to that using 2.5mm leaf width MLCs but with moderately increased isodose spill, which can be only partially mitigated by increasing the number of VMAT arcs.

Purpose: Using a simulation study 1) to estimate the effect of setup uncertainty on optimal dosimetric margin by analyzing dose distribution and biological effect of LINAC-based stereotactic radiosurgery (SRS) with a dynamic conformal arc (DCA) technique; 2) to find the suitable prescription percentage isodose surface (%IDS) for a given the setup error and dosimetric margin to reach an optimal dose distribution and favorable biological effect.

Methods and materials: In this project, SRS treatment plans were made based on Rando head phantom’s computed tomography (CT) scans. Photon beam with 6 megavoltage (MV) energy was used to deliver 20 Gy prescription dose in one fraction. Plans were simulated in a commercial treatment planning system (Brainlab iPlan RT Dose ver. 4.5.4), using four non-coplanar DCAs with total gantry angles of 480 degrees. A single sphere brain lesion with 4 different diameters (5 mm, 10 mm, 20 mm, and 30 mm) was simulated at the center of the head phantom. Five plans each with different dosimetric margins (-1 mm, 0 mm, 1 mm, 2 mm, and 3 mm) were generated for each planning target volume (PTV), which is equal to the volume of the simulated lesion with a uniformly expanded margin. For each plan, the isocenter position was shifted to 15 different locations (in three orthogonal directions each with 0 mm, 0.5 mm, 1.0 mm, 1.5 mm, and 2.0 mm shift) to imitate the potential setup errors in a fixed multileaf collimator (MLC) shape. To evaluate the plan quality, three dosimetric parameters: Conformity Index (CI), Heterogeneity Index (HI), Gradient Index (GI), and three biological effect parameters: generalized equivalent uniform dose (gEUD)-based Tumor Control Probability (TCP), Normal Tissue Complication Probability (NTCP), and biological objective function p+= TCP x (1-NTCP) were calculated after normalizing the dose-volume histogram for each plan to different %IDSs, ranging from 50%~98% with 1% increment.

Results: With up to a 2 mm setup error, a smaller dosimetric margin results in a smaller GI with lower p+. A larger dosimetric margin results in a larger GI. Compared to 0 mm and -1 mm dosimetric margins, a 1 mm dosimetric margin could result in a much higher p+. Compared to 2 mm and 3 mm dosimetric margins, a 1 mm dosimetric margin could result in a smaller GI while achieving an equivalent p+ in a certain range of %IDS. For a given 2 mm setup error and 1 mm dosimetric margin, an optimal %IDS range could be given by considering CI smaller than 2.5, small GI, and high p+. The %IDS ranges optimized in this simulation study for each PTV were: around 70%IDS (5 mm diameter); around 80%IDS (10 mm diameter); 63%~70%IDS (20 mm diameter); 66%~79%IDS (30 mm diameter). For the 5 mm diameter PTV, the constraint of CI smaller than 2.5 was not satisfied, compromising the dose conformity to achieve a high tumor control probability. For a given 1.5 mm setup error and a 2 mm dosimetric margin, the %IDS ranges for different PTV sizes were: 53%~68%IDS (5 mm diameter); 58%~70%IDS (10 mm diameter); 68%~75%IDS (20 mm diameter); 65%~77%IDS (30 mm diameter). For PTVs with both 5 mm and 10 mm diameters, the constraint of CI smaller than 2.5 was not satisfied, compromising the dose conformity to achieve a high tumor control probability.

Conclusion: This simulation study estimated the effect of setup uncertainty on optimal dosimetric margin for the LINAC-based SRS with the DCA technique. It also recommended the suitable prescription percentage isodose surface (%IDS) for a given setup error and dosimetric margin to reach an optimal dose distribution and favorable biological effect. With 1 mm dosimetric margin and a suitable selection of %IDS between 63%~80% based on PTV size, proper target conformity, TCP and NTCP can still be reached even with up to 2 mm of setup error.

Purpose/Objective(s): The standard total skin irradiation (TSI) procedure for patients with Mycosis fungoides at our clinic is the Stanford technique where dual electron beams are directed toward patient standing at an extended source to skin distance (SSD) of 300 cm. Patients rotate along the cranial-caudal axis in 6 directions to get full coverage to skin surface. The purposes of this study are to validate the commissioning dosimetric data using Monte Carlo (MC) systems, and to investigate the effect of scattering filter on the standard stand-up technique with a single beam.

Materials/Methods: The first MC system is the EGSnrc environment with BEAMnrc and DOSXYZnrc packages, which has been the standard MC simulation system used in the radiation therapy field. In this study, extended SSD with electron beam was tested, which is not a common use of EGSnrc. The second system is the VirtuaLinac, a recently developed cloud-based application from Varian for research purpose based on GEANT4 platform. For both MC systems, the same phase space files which have been previously validated were used. At each direction, a dual-field electron beam with jaws opening of 36 × 36 cm2 and gantry angle at ±19° degrees from horizontal direction was used. The following quantities were studied and compared with the measurements during commissioning: for each field/direction at the treatment SSD, the percentage depth dose (PDD), the profiles at the depth of maximum, and the absolute dosimetric output on a flat solid water phantom; the composite dose distribution on a cylindrical phantom of 30 cm diameter. For the investigation part, the materials (Cu, Fe, Au, Zn, Ag) were chosen because of their stability and availability. The thickness ranges from 0.05 mm to 0.55 mm, depending on characteristics of materials. The extended source to skin distance (SSD) from 250 cm to 350 cm were studied. For each material, we vary the thickness and SSD, to evaluate following quantities: percent depth dose (PDD), profiles and output at dmax, and compared them with the standard dual beams at treatment SSD.

Results: For the dual-field electron beam from one direction, the average(maximum) difference in profiles between EGSnrc/VirtuaLinac and measurement were -5.5% (-8.7%) and 0.9% (2.0%). Both dmax (1.1 cm) and R50 (2.1 cm) in PDD of both MC systems agreed well with the measurements within 1 mm. The X-ray contamination at 15 cm depth was 0.5%/0.6% for EGSnrc/ VirtuaLinac, compared with the measured value of 0.8%. The output was -2.4%/-3.2% difference for EGSnrc/VirtuaLinac when compared with measurement. When radiation from all six directions are combined on a cylindrical phantom, the ratio of output at the surface from 6 directions to a single direction, defined as B-factor, is 3.1 from both MC systems and the measurement. The dmax also shifted toward the surface at 0.15 cm. The X-ray contamination of all fields was 1.2 % and 1.3% for EGSnrc and VirtuaLinac, compared with 2% in the measurements. For the investigation part, no material shows acceptable profile flatness (±10% within the central 160 cm) at 250 cm SSD. At 300 cm SSD, Au (0.1 mm), Ag (0.25 mm), and Cu (0.45 mm) are acceptable. Zn (0.45 mm) requires 325 cm SSD to meet the requirement. For these 4 configurations, the dmax is 0.87-0.99 cm, similar to dual beam (0.97 cm); R50 is 1.85-1.91 cm, compared with dual beam of 2.06 cm; the output ranges from 0.025-0.029, lower than the dual beam (0.080). The composite fields for 4 configurations, the dmax is 0.1 cm, compared with dual beam (0.16 cm). The surface dose is 97%, similar to dual beam (96%). B-factor is 3.3-3.4, compared with dual beam (3.1). The maximum x-ray contamination is 3%, slightly higher than dual beam (2%).

Conclusions: The results from both Monte Carlo systems in general agree well with the measurement for the validation part. Furthermore, MC results suggest the stand-up TSI technique can be implemented using a single beam if the customized filter is used. In addition to those measurable quantities, the Monte Carlo simulation can provide further information such as the full dose distribution of the patient phantom, thus become the foundation for investigations for future technique optimizations.

Accurate target localization is critical for liver and lung cancers due to uncertainties caused by respiratory motions. On-board four-dimensional (4D) or real-time verification of the tumor before and during Stereotactic Body Radiation Therapy (SBRT) is necessary because SBRT uses high fractional doses, tight Planning Target Volume (PTV) margins and a long treatment time. Current imaging of moving targets for on-board localization cannot image full volumetric information in real-time. The purposes of this dissertation research are to do the following. (1) Develop a real-time quasi-cine CBCT reconstruction method for on-board CBCT-guided target verification; (2) Develop a volumetric cine MRI (VC-MRI) technique for on-board MRI-guided target verification using an MRI-guided radiotherapy machine; (3) Develop an on-board 4D MRI technique for on-board MRI-guided target verification using kV projections from a conventional linear accelerator (LINAC); (4) Accelerate VC-MRI through undersampling acquisition while maintaining image quality; and (5) Improve geometric accuracy of VC-MRI through novel undersampling acquisition and deformation models.

A technique for 4D CBCT estimation was previously developed using a deformation field map (DFM)-based strategy. In the previous method, each phase of the 4D CBCT was generated by deforming a prior CT volume acquired during simulation. The DFM was solved by a global motion model (GMM) extracted by a global Principal Component Analysis (PCA) from prior 4D-CT and free-form deformation (FD) technique, using a data fidelity constraint. However, this technique has limitations in both reconstruction time (~5 minutes) and accuracy. In the new proposed study of this dissertation, a quasi-cine CBCT estimation technique was developed to address these issues for real-time application. Specifically, a new structural PCA method was developed to build a structural motion model (SMM) instead of GMM by accounting for potential relative motion pattern changes between different anatomical structures from simulation to treatment. The motion model extracted from planning 4D CT was divided into two structures: tumor and body excluding tumor, and the parameters of both structures were optimized together. Weighted free-form deformation (WFD) was employed afterwards to introduce flexibility in adjusting the weightings of different structures in the data fidelity constraint based on clinical interests. As such, the localization accuracy could be substantially improved with extremely limited angle kV projections. The technique was evaluated by simulating a 30 mm diameter lesion in a computerized patient model (XCAT) with various anatomical and respiratory changes from planning 4D CT to on-board volume. The estimation accuracy was evaluated by the volume percent difference (VPD)/center-of-mass-shift (COMS) between lesions in the estimated and "ground-truth" on-board quasi-cine CBCT. Different on-board projection acquisition scenarios and projection noise levels were simulated to investigate their effects on the estimation accuracy. The method was also evaluated against three lung patients.

The SMM-WFD method achieved substantially better accuracy than the GMM-FD method for CBCT estimation using extremely small scan angles or projections. Using orthogonal 15° scanning angles, the VPD/COMS were 3.47 ± 2.94% and 0.23 ± 0.22 mm for SMM-WFD and 25.23 ± 19.01% and 2.58 ± 2.54 mm for GMM-FD among all eight XCAT scenarios. Compared to GMM-FD, SMM-WFD was more robust against reduction of the scanning angles down to orthogonal 10° with VPD/COMS of 6.21 ± 5.61% and 0.39 ± 0.49 mm, and more robust against reduction of projection numbers down to only 8 projections in total for both orthogonal-view 30° and orthogonal-view 15° scan angles. SMM-WFD method was also more robust than the GMM-FD method against increasing levels of noise in the projection images. Additionally, the SMM-WFD technique provided better tumor estimation for all three lung patients compared to the GMM-FD technique.

The first technique developed in this dissertation showed that compared to the GMM-FD technique, the SMM-WFD technique can substantially improve the CBCT estimation accuracy using extremely small scan angles and low number of projections to provide fast low dose 4D target verification.

The next section of the dissertation describes ways in which volumetric cine MRI (VC-MRI) was developed for MRI-guided radiation therapy. Currently, there are no ways to image real-time MRI in three-dimensions (3D) for on-board MRI-guided radiotherapy. In the first subsection of the second section of this dissertation, a novel technique was developed to generate, for the first time, real-time 3D VC-MRI using patient prior images, motion modeling and on-board two-dimensional (2D) cine MRI.

One phase of a 4D MRI acquired during patient simulation is used as patient prior images. Three major respiratory deformation patterns of the patient are extracted from 4D MRI based on PCA. 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 2D cine MRI. The method was evaluated using both 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 VPD, 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.

Image subtraction of ground-truth with estimated on-board VC-MRI showed 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 Signal-to-Noise Ratio (SNR) = 20. For patient data, average tracking errors were less than 2 mm in all directions for all patients. The feasibility of generating real-time VC-MRI for on-board localization of moving targets was demonstrated.

Next, a technique was developed to explore the feasibility of using an on-board kV imaging system and patient prior MRI knowledge to generate on-board quasi-cine volumetric MRI for target localization. Very few clinics have MRI-guided radiotherapy units, but most clinics have kV imaging capabilities with a conventional LINAC. The technique developed in this section of the dissertation aims to utilize conventional LINAC imaging capabilities, along with prior patient 4D MRI to estimate on-board 4D MRI for MRI-guided radiotherapy. Prior 4D MRI volumes were separated into end-of-expiration (EOE) phase (MRIprior) and all other phases. MRIprior was used to generate a synthetic CT at EOE phase (sCTprior). On-board quasi-cine 3D or 4D MRI at each respiratory phase was considered a deformation of MRIprior. The Deformation Field Map (DFM) was estimated by matching Digitally Reconstructed Radiographs (DRRs) of the deformed sCTprior to on-board kV projections using a MM-FD deformation optimization algorithm. The on-board 4D MRI method was evaluated using both XCAT simulation and real patient data. The accuracy of the estimated MRI was quantitatively evaluated using VPD, Volume Dice Coefficient (VDC) and COMS. Effects of scan angle and number of projections were also evaluated.

In the XCAT study, VPD/VDC/COMS among all XCAT scenarios were 10.16±1.31%/0.95±0.01/0.88±0.15mm using orthogonal-view 30° scan angles with 102 projections. The on-board 4D MRI method was robust against various scan angles and projection numbers evaluated. In the patient study, estimated on-board 4D MRI was generated successfully when compared to the 'reference on-board 4D MRI' for the liver patient case. Preliminary results for this novel technique demonstrated the potential for MRI-based image guidance for liver SBRT using only a kV imaging system on a conventional LINAC.

The last section of the dissertation aims to accelerate the VC-MRI technique developed and improve the estimation accuracy by using novel undersampling and deformation models. VC-MRI was accelerated by using undersampled 2D cine MRI to provide real-time 3D guidance. Undersampled Cartesian and radial k-space acquisition strategies were investigated. The effects of k-space sampling percentage (SP) and distribution, tumor sizes and noise on the VC-MRI estimation were studied. The accelerated VC-MRI estimation was evaluated using XCAT simulation of lung cancer patients and data from liver cancer patients. VPD and COMS of the tumor volumes and tumor tracking errors were calculated.

For XCAT, VPD/COMS were 11.93  ±  2.37%/0.90  ±  0.27 mm and 11.53  ±  1.47%/0.85  ±  0.20 mm among all scenarios with Cartesian sampling (SP  =  10%) and radial sampling (21 spokes, SP  =  5.2%), respectively. When tumor size decreased, higher sampling rate achieved more accurate VC-MRI than lower sampling rate. VC-MRI was robust against noise levels up to SNR  =  20. For patient data, the tumor tracking errors in superior-inferior, anterior-posterior and lateral directions were 0.46  ±  0.20 mm, 0.56  ±  0.17 mm and 0.23  ±  0.16 mm, respectively, for Cartesian-based sampling with SP  =  20% and 0.60  ±  0.19 mm, 0.56  ±  0.22 mm and 0.42  ±  0.15 mm, respectively, for radial-based sampling with SP  =  8% (32 spokes).

Results from this method showed that VC-MRI could be accelerated from a single undersampled on-board 2D cine MRI. Phantom and patient studies showed that the temporal resolution of VC-MRI can potentially be improved by 5-10 times using a 2D cine image acquired with 10-20% k-space sampling.

Lastly, VC-MRI accuracy was improved by using multi-slice undersampled cine images, patient prior 4D MRI, motion modeling and free-form deformation. In this study, free-form deformation (FD) was introduced to correct for errors in the MM when large anatomical changes exist. Multiple-slice undersampled on-board 2D-cine images were used by reconstructing 10% of total k-space using a novel k-t SLR reconstruction method, which uses both the spatial and temporal resolution of the k-space data to reconstruct the 2D cine MRIs. The method was evaluated using XCAT simulation of lung cancer patients with various anatomical and respirational changes from prior 4D MRI to onboard volume. The accuracy was evaluated using VPD, VDC and COMS of the estimated tumor volume. Effects of ROI selection, 2D-cine slice orientation, slice number and slice location on the estimation accuracy were evaluated.

VC-MRI estimated using 10 undersampled sagittal 2D cine MRIs achieved VPD/VDC/COMS of 9.77±3.71%/0.95±0.02/0.75±0.26mm among all scenarios based on estimation with ROI MM and ROI FD. The FD optimization improved estimation significantly for scenarios with anatomical changes. Using ROI FD achieved better estimation than global FD. Changing the multi-slice orientation to axial, coronal, and axial/sagittal orthogonal reduced the accuracy of VC-MRI. Estimation using slices sampled uniformly through the tumor achieved better accuracy than using slices sampled non-uniformly.

In conclusion, the work presented in his dissertation builds upon previous research and develops novel solutions for generating real-time volumetric cine images for both CBCT and MRI. The completed research dissertation presents the following: (1) develops a quasi-real-time cine CBCT reconstruction method using structural PCA and weighted free-form deformation, (2) develops a VC-MRI technique using motion modeling and single slice 2D cine acquisition, (3) develops an on-board 4D-MRI technique using limited on-board kV projections from a conventional LINAC and deformation models, (4) accelerates VC-MRI through undersampling acquisition while maintaining image quality, and (5) improve geometric accuracy of VC-MRI through novel undersampling acquisition and deformation models.