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Open Access Publications from the University of California

Scholarly Works (12 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2022)

The measurement of the elastic properties of human tissue, or elastography, allows for the quantitative assessment of tissue functionality. Patients undergoing radiation therapy often present with lung disease, such as COPD, which is known to cause degradation of tissue elasticity. The reliance of normal lung function on these elastic properties is well established, underscoring the importance of function-preserving efforts in radiotherapy. Regional elasticity distributions may be used to identify regions of parenchymal tissue that contribute significantly to lung function. This knowledge can, in turn, aid in functional lung sparing efforts during the treatment planning process. Previous elastography efforts have been performed using a well-validated biomechanical model in combination with model-based CT images. Recent studies also suggest that biomechanical modeling and elasticity estimation may prove to have useful applications outside of the radiotherapy domain. These tools could potentially help improve the evaluation of patient candidacy and outcome prediction for various lung interventions, such as lung volume reduction surgery (LVRS) or bronchoscopic lung volume reduction (BLVR) procedures. The first aim of the dissertation was to develop and improve current CT-based elastography algorithms and tools. This aim was addressed by carrying out a consistency study where elastography was performed using separate image datasets generated from scans acquired at different points in the breathing trace for each patient. As part of a feasibility study to test the hypothesis that additional elasticity information could be obtained using large deformation image pairs, an elastography method was developed for use with breath-hold CT images acquired at the forced breathing stages of residual volume (RV) and total lung capacity (TLC). The second aim was to investigate the applications of biomechanical modeling and elastography for other function-preserving treatment interventions. This work involved the development of a quorum-based machine learning approach to perform lobar segmentation for lobe identification in lung intervention simulations and lobe-wise analyses. A framework for simulating a lobectomy procedure by incorporating elasticity information and biomechanical modeling was also constructed. Using this framework, the feasibility of using the resulting predicted post-intervention lung geometry for approximating pulmonary function test (PFT) values was investigated. The third aim of this proposal was to develop and employ a machine learning application for elasticity estimation from single end-exhalation breath-hold CT scans. A conditional generative adversarial (cGAN) neural network was built and validated for elasticity estimation. We further investigated the effects of the imaging dose used during the acquisition of CT image data on the accuracy of the proposed machine learning implementation. The expanded use of biomechanical modeling and elastography within the radiotherapy context has the potential to improve functional avoidance efforts for patients presenting with co-morbidities affecting lung function prior to treatment. Additionally, there is an opportunity to extend these tools toward improving and informing other lung intervention efforts. Finally, the ability to take advantage of CT-based elastography methods through machine learning in scenarios where model-based CT is not typically available could expand the scope of CT-based elastography in both the radiotherapy domain and for the expanded use in other lung intervention workflows. The tools and applications presented in this dissertation aim to highlight and expand the benefits of CT-based biomechanical modeling and elastography within the radiotherapy domain and for use with other lung intervention procedures.

    Cover page: Biomechanical Modeling Applications for Function-Preserving Lung Interventions
    • Thesis
    • Peer Reviewed
    UCLA Electronic Theses and Dissertations (2022)

    Diseases affecting the respiratory system are among the most common and deadliest worldwide. The ability to characterize the patient-specific, physical properties of the respiratory system could improve treatments and interventions that aim to combat these diseases. With the unique capabilities of modern CT technology, we can formulate novel approaches to capture and characterize breathing motion. The development of fast-helical free-breathing CT (FHFBCT), along with the 5DCT motion modeling protocol, have allowed us to acquire CT images with dynamic information, free of the artifacts typically observed in 4DCT approaches. Though this protocol was developed for radiation oncology, its value stems far beyond. The goal of this dissertation is to use FHFBCT in several applications that provide guidance or improvement to technologies involved in the treatment of patients with respiratory diseases such as lung cancer and chronic obstructive pulmonary disease (COPD). First, we applied FHFBCT and motion modeling within its primary area of CT simulation for radiotherapy. We studied the use of the respiratory bellows and the entire patient skin surface as respiratory surrogates for CT simulation using FHFBCT data and found that the center abdomen would be a reliable skin surface-based surrogate for diaphragm motion. We then investigated an application of FHFBCT in the radiotherapy workflow outside of CT simulation. We demonstrated the feasibility of using FHFBCT and 5DCT motion modeling in cone-beam CT (CBCT) reconstruction to provide accurate motion compensation. Finally, we proposed the use of dynamic breathing information from FHFBCT beyond radiotherapy altogether, into areas such as surgical planning, pulmonology, and diagnostics. We developed an automatic, triangulation meshing technique for airway segmentations extracted from FHFBCT data to enable computational fluid dynamics (CFD) simulations with free-breathing data. We also calculated ventilation from FHFBCTs and characterized its heterogeneity across lobes of patients with COPD of varying severities. We performed a similar study using elasticity, a measure of tissue stiffness obtained through a CT-based elastography approach, instead of ventilation, to propose an additional biomarker for lung function. The work in this dissertation provides a demonstration of the unique advantages of breathing dynamics derived from FHFBCT. We hope that this work encourages others to research the studied topics as well as continue to implement FHFBCT and motion modeling to other areas of medicine to improve diagnostics and treatments for patients suffering from respiratory illnesses.

      Cover page: Applications of Breathing Motion Dynamics Derived from Fast-Helical Free-Breathing CT
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2016)

      Purpose

      Breast elastography is a critical tool for improving the targeted radiotherapy treatment of breast tumors. Current breast radiotherapy imaging protocols only involve prone and supine CT scans. There is a lack of knowledge on the quantitative accuracy with which breast elasticity can be systematically measured using only prone and supine CT datasets. The purpose of this paper is to describe a quantitative elasticity estimation technique for breast anatomy using only these supine/prone patient postures. Using biomechanical, high-resolution breast geometry obtained from CT scans, a systematic assessment was performed in order to determine the feasibility of this methodology for clinically relevant elasticity distributions.

      Methods

      A model-guided inverse analysis approach is presented in this paper. A graphics processing unit (GPU)-based linear elastic biomechanical model was employed as a forward model for the inverse analysis with the breast geometry in a prone position. The elasticity estimation was performed using a gradient-based iterative optimization scheme and a fast-simulated annealing (FSA) algorithm. Numerical studies were conducted to systematically analyze the feasibility of elasticity estimation. For simulating gravity-induced breast deformation, the breast geometry was anchored at its base, resembling the chest-wall/breast tissue interface. Ground-truth elasticity distributions were assigned to the model, representing tumor presence within breast tissue. Model geometry resolution was varied to estimate its influence on convergence of the system. A priori information was approximated and utilized to record the effect on time and accuracy of convergence. The role of the FSA process was also recorded. A novel error metric that combined elasticity and displacement error was used to quantify the systematic feasibility study. For the authors' purposes, convergence was set to be obtained when each voxel of tissue was within 1 mm of ground-truth deformation.

      Results

      The authors' analyses showed that a ∼97% model convergence was systematically observed with no-a priori information. Varying the model geometry resolution showed no significant accuracy improvements. The GPU-based forward model enabled the inverse analysis to be completed within 10-70 min. Using a priori information about the underlying anatomy, the computation time decreased by as much as 50%, while accuracy improved from 96.81% to 98.26%. The use of FSA was observed to allow the iterative estimation methodology to converge more precisely.

      Conclusions

      By utilizing a forward iterative approach to solve the inverse elasticity problem, this work indicates the feasibility and potential of the fast reconstruction of breast tissue elasticity using supine/prone patient postures.
        Cover page: Systematic feasibility analysis of a quantitative elasticity estimation for breast anatomy using supine/prone patient postures
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2019)

        Objective:

        Lung tissue elasticity is an effective spatial representation for Chronic Obstructive Pulmonary Disease phenotypes and pathophysiology. We investigated a novel imaging biomarker based on the voxel-by-voxel distribution of lung tissue elasticity. Our approach combines imaging and biomechanical modeling to characterize tissue elasticity.

        Methods:

        We acquired 4DCT images for 13 lung cancer patients with known COPD diagnoses based on GOLD 2017 criteria. Deformation vector fields (DVFs) from the deformable registration of end-inhalation and end-exhalation breathing phases were taken to be the ground-truth. A linear elastic biomechanical model was assembled from end-exhalation datasets with a density-guided initial elasticity distribution. The elasticity estimation was formulated as an iterative process, where the elasticity was optimized based on its ability to reconstruct the ground-truth. An imaging biomarker (denoted YM1-3) derived from the optimized elasticity distribution, was compared with the current gold standard, RA950 using confusion matrix and area under the receiver operating characteristic (AUROC) curve analysis.

        Results:

        The estimated elasticity had 90 % accuracy when representing the ground-truth DVFs. The YM1-3 biomarker had higher diagnostic accuracy (86% vs 71 %), higher sensitivity (0.875 vs 0.5), and a higher AUROC curve (0.917 vs 0.875) as compared to RA950. Along with acting as an effective spatial indicator of lung pathophysiology, the YM1-3 biomarker also proved to be a better indicator for diagnostic purposes than RA950.

        Conclusions:

        Overall, the results suggest that, as a biomarker, lung tissue elasticity will lead to new end points for clinical trials and new targeted treatment for COPD subgroups.

        Advances in knowledge:

        The derivation of elasticity information directly from 4DCT imaging data is a novel method for performing lung elastography. The work demonstrates the need for a mechanics-based biomarker for representing lung pathophysiology.
          Cover page: Feasibility of deriving a novel imaging biomarker based on patient-specific lung elasticity for characterizing the degree of COPD in lung SBRT patients
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2012)
          Lung radiotherapy is greatly benefitted when the tumor motion caused by breathing can be modeled. The aim of this paper is to present the importance of using anisotropic and subject-specific tissue elasticity for simulating the airflow inside the lungs. A computational-fluid-dynamics (CFD) based approach is presented to simulate airflow inside a subject-specific deformable lung for modeling lung tumor motion and the motion of the surrounding tissues during radiotherapy. A flow-structure interaction technique is employed that simultaneously models airflow and lung deformation. The lung is modeled as a poroelastic medium with subject-specific anisotropic poroelastic properties on a geometry, which was reconstructed from four-dimensional computed tomography (4DCT) scan datasets of humans with lung cancer. The results include the 3D anisotropic lung deformation for known airflow pattern inside the lungs. The effects of anisotropy are also presented on both the spatiotemporal volumetric lung displacement and the regional lung hysteresis.
            Cover page: Modeling Airflow Using Subject-Specific 4DCT-Based Deformable Volumetric Lung Models
            • Article
            • Peer Reviewed
            UCLA Previously Published Works (2020)

            Purpose

            To develop and demonstrate the efficacy of a novel head-and-neck multimodality image registration technique using deep-learning-based cross-modality synthesis.

            Methods and materials

            Twenty-five head-and-neck patients received magnetic resonance (MR) and computed tomography (CT) (CTaligned ) scans on the same day with the same immobilization. Fivefold cross validation was used with all of the MR-CT pairs to train a neural network to generate synthetic CTs from MR images. Twenty-four of 25 patients also had a separate CT without immobilization (CTnon-aligned ) and were used for testing. CTnon-aligned 's were deformed to the synthetic CT, and compared to CTnon-aligned registered to MR. The same registrations were performed from MR to CTnon-aligned and from synthetic CT to CTnon-aligned . All registrations used B-splines for modeling the deformation, and mutual information for the objective. Results were evaluated using the 95% Hausdorff distance among spinal cord contours, landmark error, inverse consistency, and Jacobian determinant of the estimated deformation fields.

            Results

            When large initial rigid misalignment is present, registering CT to MRI-derived synthetic CT aligns the cord better than a direct registration. The average landmark error decreased from 9.8 ± 3.1 mm in MR→CTnon-aligned to 6.0 ± 2.1 mm in CTsynth →CTnon-aligned deformable registrations. In the CT to MR direction, the landmark error decreased from 10.0 ± 4.3 mm in CTnon-aligned →MR deformable registrations to 6.6 ± 2.0 mm in CTnon-aligned →CTsynth deformable registrations. The Jacobian determinant had an average value of 0.98. The proposed method also demonstrated improved inverse consistency over the direct method.

            Conclusions

            We showed that using a deep learning-derived synthetic CT in lieu of an MR for MR→CT and CT→MR deformable registration offers superior results to direct multimodal registration.
              Cover page: Multimodality image registration in the head-and-neck using a deep learning-derived synthetic CT as a bridge.
              • Article
              • Peer Reviewed
              UCLA Previously Published Works (2013)
              Radiotherapy is safely employed for treating wide variety of cancers. The radiotherapy workflow includes a precise positioning of the patient in the intended treatment position. While trained radiation therapists conduct patient positioning, consultation is occasionally required from other experts, including the radiation oncologist, dosimetrist, or medical physicist. In many circumstances, including rural clinics and developing countries, this expertise is not immediately available, so the patient positioning concerns of the treating therapists may not get addressed. In this paper, we present a framework to enable remotely located experts to virtually collaborate and be present inside the 3D treatment room when necessary. A multi-3D camera framework was used for acquiring the 3D treatment space. A client-server framework enabled the acquired 3D treatment room to be visualized in real-time. The computational tasks that would normally occur on the client side were offloaded to the server side to enable hardware flexibility on the client side. On the server side, a client specific real-time stereo rendering of the 3D treatment room was employed using a scalable multi graphics processing units (GPU) system. The rendered 3D images were then encoded using a GPU-based H.264 encoding for streaming. Results showed that for a stereo image size of 1280 × 960 pixels, experts with high-speed gigabit Ethernet connectivity were able to visualize the treatment space at approximately 81 frames per second. For experts remotely located and using a 100 Mbps network, the treatment space visualization occurred at 8-40 frames per second depending upon the network bandwidth. This work demonstrated the feasibility of remote real-time stereoscopic patient setup visualization, enabling expansion of high quality radiation therapy into challenging environments.
                Cover page: A client–server framework for 3D remote visualization of radiotherapy treatment space
                • Article
                • Peer Reviewed
                UCLA Previously Published Works (2021)
                PURPOSE: To examine the use of multiple fast-helical free breathing computed tomography (FHFBCT) scans for ventilation measurement. METHODS: Ten patients were scanned 25 times in alternating directions using a FHFBCT protocol. Simultaneously, an abdominal pneumatic bellows was used as a real-time breathing surrogate. Regions-of-interest (ROIs) were selected from the upper right lungs of each patient for analysis. The ROIs were first registered using a published registration technique (pTV). A subsequent follow-up registration employed an objective function with two terms, a ventilation-adjusted Hounsfield Unit difference and a conservation-of-mass term labeled ΔΓ that denoted the difference between the deformation Jacobian and the tissue density ratio. The ventilations were calculated voxel-by-voxel as the slope of a first-order fit of the Jacobian as a function of the breathing amplitude. RESULTS: The ventilations of the 10 patients showed different patterns and magnitudes. The average ventilation calculated from the deformation vector fields (DVFs) of the pTV and secondary registration was nearly identical, but the standard deviation of the voxel-to-voxel differences was approximately 0.1. The mean of the 90th percentile values of ΔΓ was reduced from 0.153 to 0.079 between the pTV and secondary registration, implying first that the secondary registration improved the conservation-of-mass criterion by almost 50% and that on average the correspondence between the Jacobian and density ratios as demonstrated by ΔΓ was less than 0.1. This improvement occurred in spite of the average of the 90th percentile changes in the DVF magnitudes being only 0.58 mm. CONCLUSIONS: This work introduces the use of multiple free-breathing CT scans for free-breathing ventilation measurements. The approach has some benefits over the traditional use of 4-dimensional CT (4DCT) or breath-hold scans. The benefit over 4DCT is that FHFBCT does not have sorting artifacts. The benefits over breath-hold scans include the relatively small motion induced by quiet respiration versus deep-inspiration breath hold and the potential for characterizing dynamic breathing processes that disappear during breath hold.
                  Cover page: Ventilation measurements using fast-helical free-breathing computed tomography.
                  • Article
                  • Peer Reviewed
                  UCLA Previously Published Works (2018)
                  OBJECTIVE:To evaluate variations in intra- and interfractional tumour motion, and the effect on internal target volume (ITV) contour accuracy, using deformable image registration of real-time two-dimensional-sagittal cine-mode MRI acquired during lung stereotactic body radiation therapy (SBRT) treatments. METHODS:Five lung tumour patients underwent free-breathing SBRT treatments on the ViewRay system, with dose prescribed to a planning target volume (defined as a 3-6 mm expansion of the 4DCT-ITV). Sagittal slice cine-MR images (3.5 × 3.5 mm2 pixels) were acquired through the centre of the tumour at 4 frames per second throughout the treatments (3-4 fractions of 21-32 min). Tumour gross tumour volumes (GTVs) were contoured on the first frame of the MR cine and tracked for the first 20 min of each treatment using offline optical-flow based deformable registration implemented on a GPU cluster. A ground truth ITV (MR-ITV20 min) was formed by taking the union of tracked GTV contours. Pseudo-ITVs were generated from unions of the GTV contours tracked over 10 s segments of image data (MR-ITV10 s). RESULTS:Differences were observed in the magnitude of median tumour displacement between days of treatments. MR-ITV10 s areas were as small as 46% of the MR-ITV20 min. CONCLUSION:An ITV offers a "snapshot" of breathing motion for the brief period of time the tumour is imaged on a specific day. Real-time MRI over prolonged periods of time and over multiple treatment fractions shows that ITV size varies. Further work is required to investigate the dosimetric effect of these results. Advances in knowledge: Five lung tumour patients underwent free-breathing MRI-guided SBRT treatments, and their tumours tracked using deformable registration of cine-mode MRI. The results indicate that variability of both intra- and interfractional breathing amplitude should be taken into account during planning of lung radiotherapy.
                    Cover page: Initial clinical observations of intra- and interfractional motion variation in MR-guided lung SBRT.
                    • Article
                    • Peer Reviewed
                    UCLA Previously Published Works (2016)

                    Purpose

                    Radiation therapy simulations solely based on MRI have advantages compared to CT-based approaches. One feature readily available from computed tomography (CT) that would need to be reproduced with MR is the ability to compute digitally reconstructed radiographs (DRRs) for comparison against on-board radiographs commonly used for patient positioning. In this study, the authors generate MR-based bone images using a single ultrashort echo time (UTE) pulse sequence and quantify their 3D and 2D image registration accuracy to CT and radiographic images for treatments in the cranium.

                    Methods

                    Seven brain cancer patients were scanned at 1.5 T using a radial UTE sequence. The sequence acquired two images at two different echo times. The two images were processed using an in-house software to generate the UTE bone images. The resultant bone images were rigidly registered to simulation CT data and the registration error was determined using manually annotated landmarks as references. DRRs were created based on UTE-MRI and registered to simulated on-board images (OBIs) and actual clinical 2D oblique images from ExacTrac™.

                    Results

                    UTE-MRI resulted in well visualized cranial, facial, and vertebral bones that quantitatively matched the bones in the CT images with geometric measurement errors of less than 1 mm. The registration error between DRRs generated from 3D UTE-MRI and the simulated 2D OBIs or the clinical oblique x-ray images was also less than 1 mm for all patients.

                    Conclusions

                    UTE-MRI-based DRRs appear to be promising for daily patient setup of brain cancer radiotherapy with kV on-board imaging.
                      Cover page: Accuracy of UTE-MRI-based patient setup for brain cancer radiation therapy.
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