Results for “Image Processing” — Page 22

DOE OSTI2018

Progress Update on Segmentation of Neutron Tomographic Images, 2018

The present report documents the techniques developed to segment fast-neutron tomographic images of objects consisting of assemblies of machined parts with distinct boundaries. This report satisfies the fiscal year 2018 technical deliverable for project OR16-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons,” to report on the extension of geometric shape-finding algorithms to three dimensions. The project has two overall goals. The first of these goals is to extend associated-particle fast-neutron transmission and, particularly, induced-reaction tomographic imaging algorithms to three dimensions. This aspect of the project is beyond the scope of this report. The second goal is to automatically segment the resultant tomographic images into constituent parts and then extract information about the parts, such as the class of shape and potentially the shape parameters. This report describes progress for the image segmentation part of the project. Imaging techniques have been developed as high-confidence methods for confirming the presence and configuration of special nuclear materials (SNM). This high confidence is achieved at the cost of revealing considerable information that may be undesirable to share with the operator of the equipment. One potential way to make use of the high-confidence of imaging methods without revealing imaging data is to employ automated analysis that can extract meaningful attributes of the SNM without showing imaging data to the operator. An essential step of this automated analysis is the segmentation of the image into its constituent parts. For example, a three-dimensional (3D) fast-neutron tomographic image of an assembly containing uranium could be reconstructed and then segmented into discrete parts. The properties of a constituent part identified as uranium would be of interest and could be extracted. For instance, the volume and density of the part would together determine its mass. Moreover, its shape might be expected to fall into a particular geometric class, such as a cube, cylinder, or sphere, and the parameters of the shape (such as side length, height, or diameter) would be expected to fall in a given range. In such an analysis, automated identification of the boundaries and properties of the uranium part is an essential step. A key goal of the present work is to infer the boundaries of objects in fast-neutron tomographic images via the general method of image segmentation. Development of this capability can be broken into more manageable steps, including 1. operator-guided segmentation of two-dimensional (2D) fast-neutron tomographic images, 2. operator-guided segmentation of 3D fast-neutron tomographic images, 3. development of automated segmentation algorithms, and 4. extraction of shape parameters from constituent volumes in 3D. In the first year, the project team reported progress on the first step. Last year, progress on the second step, the extension into 3D, was reported with application to simulated data. The present report describes additional progress on the second step, and progress toward the third step, automated segmentation algorithms. In particular, a multiphase level set approach for image segmentation in three dimensions has been applied to measured 3D data for the first time. Activities related to the fourth step will also be reported, but it will focus on estimation of 3D volumes rather than shape parameters. In this report, the steps before image segmentation will be described, and the multiphase level sets approach to image segmentation will be briefly reviewed. This will be followed by a discussion of the issues encountered when applying the level set technique to measured 3D transmission images, and then a description of the inclusion of induced-reaction data will be provided. Finally, segmentation results for various measured target assemblies will be presented and the image segmentation portion of this project will be summarized and future directions will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2017

Optimizing radiotherapy protocols using computer automata to model tumour cell death as a function of oxygen diffusion processes

The concept of hypofractionation is gaining momentum in radiation oncology centres, enabled by recent advances in radiotherapy apparatus. The gain of efficacy of this innovative treatment must be defined. We present a computer model based on translational murine data for in silico testing and optimization of various radiotherapy protocols with respect to tumour resistance and the microenvironment heterogeneity. This model combines automata approaches with image processing algorithms to simulate the cellular response of tumours exposed to ionizing radiation, modelling the alteration of oxygen permeabilization in blood vessels against repeated doses, and introducing mitotic catastrophe (as opposed to arbitrary delayed cell-death) as a means of modelling radiation-induced cell death. Published data describing cell death in vitro as well as tumour oxygenation in vivo are used to inform parameters. Our model is validated by comparing simulations to in vivo data obtained from the radiation treatment of mice transplanted with human prostate tumours. We then predict the efficacy of untested hypofractionation protocols, hypothesizing that tumour control can be optimized by adjusting daily radiation dosage as a function of the degree of hypoxia in the tumour environment. Further biological refinement of this tool will permit the rapid development of more sophisticated strategies for radiotherapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

The DECam Plane Survey: Optical Photometry of Two Billion Objects in the Southern Galactic Plane

The DECam Plane Survey is a five-band optical and near-infrared survey of the southern Galactic plane with the Dark Energy Camera at Cerro Tololo. The survey is designed to reach past the main-sequence turn-off of old populations at the distance of the Galactic center through a reddening $E(B-V)$ of 1.5 mag. Typical single-exposure depths are 23.7, 22.8, 22.3, 21.9, and 21.0 mag (AB) in the grizY bands, with seeing around $1^{\prime\prime} $. The footprint covers the Galactic plane with $| b| \lesssim 4^\circ $, $5^\circ \gt l\gt -120^\circ $. The survey pipeline simultaneously solves for the positions and fluxes of tens of thousands of sources in each image, delivering positions and fluxes of roughly two billion stars with better than 10 mmag precision. Most of these objects are highly reddened and deep in the Galactic disk, probing the structure and properties of the Milky Way and its interstellar medium. Furthermore, the fully-processed images and derived catalogs are publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning

Abstract Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions. Biomineralization in diatoms, unicellular algae that use silica to construct micron-scale cell walls with nanoscale features, is an attractive candidate for functional synthesis of materials for applications including photonics, sensing, filtration, and drug delivery. Therefore, controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field. In this work, we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning. An artificial neural network (NN) was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein (Thaps3_21880), resulting in 94% detection accuracy. Class activation maps visualized physical changes that allowed the NNs to separate diatom strains, subsequently establishing a specific gene that controls pores. A further NN was created to batch process image data, automatically recognize pores, and extract pore-related parameters. Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool, called CrossVis, and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Extraction of Vibration Data with Imaging

To date, the primary sensing technology used to measure the vibration response has been accelerometers and strain gages mounted directly to the structure and using either wired or, more recently, wireless telemetry. Cost issues with these sensors and the associated data acquisition systems typically limit the numbers that are deployed on in situ structures. Although there are a few structures with larger sensing counts that in some cases exceed over 1000 sensors, more typical numbers range from ten to one hundred sensors resulting in low spatial resolution when they are applied to physically large systems. When one considers that nuclear power plant structures usually have complex geometries, material properties, connectivity and boundary conditions, it is clear these current approaches to vibration measurements can only provide limited information about a system’s dynamics response characteristics. As an alternative, many non-contact measurement technologies have emerged, including point wise measurement methods such as Global Positioning System (GPS), microwave interferometry, and laser Doppler vibrometry (LDV), as well as simultaneous full-field measurement methods such as electronic speckle pattern interferometry, holography interferometry, and muon tomography, some of which can provide high spatial resolution measurements. Among these methods, digital video imaging techniques have emerged as a feasible solution for full-field vibration measurements that provide significantly more detailed dynamic response information because every pixel becomes a measurement point. Furthermore, recent advances in image processing and computer vision algorithms have been successfully used to process video data for experimental and operational modal analysis. Such full-field measurements have the potential to significantly improve many current structural assessment procedures including system identification (modal parameter estimation), structural health monitoring, load reconstruction, model validation, and model updating. Furthermore, more recent full-field imaging techniques can be accomplished with relatively low-cost, commercially-available off-the-shelf cameras. However, these measurement procedures have other limitations that must be considered such as the ability to only measure visibly accessible points on a structure and a more limited dynamic range and bandwidth than can be achieved with accelerometers or strain gages.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (a) does not require experimental or image preprocessing, (b) uses the raw RGB images at full resolution, and (c) requires very few samples for training (e.g., just 8 images for vein segmentation). Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools, validated using real-world physical measurements, and used to conduct a genome-wide association study to identify genes controlling the traits. In this way, the current work is designed to provide the plant phenotyping community with (a) methods for fast and accurate image-based feature extraction that require minimal training data and (b) a new population-scale dataset, including 68 different leaf phenotypes, for domain scientists and machine learning researchers. All of the few-shot learning code, data, and results are made publicly available.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Postprocessing Algorithm for Driving Conventional Scanning Tunneling Microscope at Fast Scan Rates

We present an image postprocessing framework for Scanning Tunneling Microscope (STM) to reduce the strong spurious oscillations and scan line noise at fast scan rates and preserve the features, allowing an order of magnitude increase in the scan rate without upgrading the hardware. The proposed method consists of two steps for large scale images and four steps for atomic scale images. For large scale images, we first apply for each line an image registration method to align the forward and backward scans of the same line. In the second step we apply a “rubber band” model which is solved by a novel Constrained Adaptive and Iterative Filtering Algorithm (CIAFA). The numerical results on measurement from copper(111) surface indicate the processed images are comparable in accuracy to data obtained with a slow scan rate, but are free of the scan drift error commonly seen in slow scan data. For atomic scale images, an additional first step to remove line-by-line strong background fluctuations and a fourth step of replacing the postprocessed image by its ranking map as the final atomic resolution image are required. The resulting image restores the lattice image that is nearly undetectable in the original fast scan data.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications

We introduce a physics guided data-driven method for image-based multi-material decomposition for dual-energy computed tomography (CT) scans. The method is demonstrated for CT scans of virtual human phantoms containing more than two types of tissues. The method is a physics-driven supervised learning technique. We take advantage of the mass attenuation coefficient of dense materials compared to that of muscle tissues to perform a preliminary extraction of the dense material from the images using unsupervised methods. We then perform supervised deep learning on the images processed by the extracted dense material to obtain the final multi-material tissue map. The method is demonstrated on simulated breast models with calcifications as the dense material placed amongst the muscle tissues. The physics-guided machine learning method accurately decomposes the various tissues from input images, achieving a normalized root-mean-squared error of 2.75%.

Gopalakrishnan Meena, Murali↗

DOE OSTI2022

A Combined Computer Vision and Deep Learning Approach for Rapid Drone-Based Optical Characterization of Parabolic Troughs

Optical accuracy is a primary driver of parabolic trough concentrating solar power (CSP) plant performance, but can be damaged by wind loads, gravity, error during installation, and regular plant operation. Collecting and analyzing optical measurements over an entire operating parabolic trough plant is difficult, given the large scale of typical installations. Distant Observer, a software tool developed at the National Renewable Energy Laboratory, uses images of the absorber tube reflected in the collector mirror to measure both surface slope in the parabolic mirror and offset of the absorber tube from the ideal focal point. This technology has been adapted for fast data collection using low-cost commercial drones, but until recently still required substantial human labor to process large amounts of data. A new method leveraging advanced deep learning and computer vision tools can drastically reduce the time required to process images. This new method addresses the primary analysis bottleneck, identifying featureless, reflective mirror corner points to a high degree of accuracy. Recent work has shown promising results using computer vision methods. The combined deep learning and computer vision approach presented here proved highly effective and has the potential to further automate data collection and analysis, making the tool more robust. The method presented in this paper automatically identified 74.3% of mirror corners within 2 pixels of their manually marked counterparts and 91.9% within 3 pixels. This level of accuracy is sufficient for practical Distant Observer analysis within a target uncertainty. A commercial drone collected video of over 100 parabolic trough modules at an operating CSP plant to demonstrate the deep learning and computer vision method's usefulness in processing large amounts of data. These troughs were successfully analyzed using Distant Observer, paired with the new deep learning and computer vision algorithm, and can provide plant operators and trough designers with valuable insight about plant performance, operating strategies, and plant-wide optical error trends.

computer vision↗

DOE OSTI2017

Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis

We introduce new dictionary learning methods for tensor-variate data of any order. We represent each data item as a sum of Kruskal decomposed dictionary atoms within the framework of beta-process factor analysis (BPFA). Our model is nonparametric and can infer the tensor-rank of each dictionary atom. This Kruskal-Factor Analysis (KFA) is a natural generalization of BPFA. We also extend KFA to a deep convolutional setting and develop online learning methods. We test our approach on image processing and classification tasks achieving state of the art results for 2D & 3D inpainting and Caltech 101. The experiments also show that atom-rank impacts both overcompleteness and sparsity.

Stevens, Andrew J.↗

DOE OSTI2015

Structure of the runaway electron loss during induced disruptions in TEXTOR

The loss of runaway electrons during an induced disruption is recorded by a synchrotron imaging technique using a fast infrared CCD camera. The loss is predominantly diffuse. During the “spiky-loss phase”, when the runaway beam moves close to the wall, a narrow channel between the runaway column and a scintillator probe is formed and lasts until the runaway beam is terminated. In some cases, the processed images show a stripe pattern at the plasma edge. A comparison between the MHD dominated disruptions and the MHD-free disruption is performed. A new mechanism of plasma disruptions with the runaway electron generation and a novel model which reproduces many characteristic features of the plasma beam evolution during a disruption is briefly described.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Interlaboratory comparison of secondary ion mass spectrometry analysis results from the 7th international technical nuclear forensics working group collaborative material exercise

A review and interlaboratory comparison of secondary ion mass spectrometry (SIMS) data obtained by laboratories participating in the International Technical Nuclear Forensics Working Group (ITWG) 7th Collaborative Material Exercise (CMX-7) has been conducted. The analyzed materials were two uranium compounds in powder form and two pieces of uranium metal. The instruments used in this comparison were a small-geometry (SG) SIMS, CAMECA IMS 7f from the Research Centre Rez, and a large-geometry (LG) SIMS, CAMECA IMS 1280 from Los Alamos National Laboratory. Despite the differences in instruments and analytical procedures, e.g., sample preparation, SIMS setups, and data post-processing, there was good agreement for the 234 U/ 238 U, 235 U/ 238 U, and 236 U/ 238 U ratios in the analyzed materials. The main difference was in the precision, which was, as expected, higher for the LG-SIMS. In addition, a comparison between the laboratories was also made for the image processing algorithms applied to raw data acquired in automated particle measurement (APM) mode. In conclusion, the result of this comparison has led to identification of best practices for setting up parameters of the APM software.

and nuclear chemistry↗

DOE OSTI2026

Methods development towards automated, physics-informed, quantitative quality control of TRISO-SiC

Tristructural-isotropic (TRISO) fuel particles have been developed as a high-performance fuel for use in high-temperature gas-cooled reactor (HTGR) systems due to their high efficiency and stability under both normal and off-normal conditions. Broader deployment of this technology in advanced nuclear applications may benefit from quantitative quality assurance and quality control (QA/QC) methods that directly link TRISO properties to downstream performance. A key TRISO property is the SiC layer microstructure, which influences fission product retention during irradiation. However, existing QA/QC for the TRISO-SiC microstructure comprises only a qualitative visual inspection; therefore, there is a clear opportunity for the development of quantitative methods for TRISO QA/QC. Here, to this end, previous work has demonstrated an image processing approach to grain boundary (GB) identification and subsequent extraction of microstructural metrics; however, extensive twinning within the SiC layer complicates such analyses because twin GBs significantly influence microstructural metrics but are not expected to contribute to fission product transport. This study presents the initial development, training, and testing of an ML-based image segmentation algorithm designed to identify and remove twin GBs from standard backscattered electron micrographs, providing an industrially applicable, quantitative, and physically meaningful QA/QC approach for the TRISO-SiC microstructure. Although pixel-wise performance metrics for the twin predictions are low, the change in grain area and the number of GB pixels after twin removal predicted by the ML workflow are within 1% of the true values calculated using crystallographic data. This suggests that the model is well capable of predicting overall twin boundary structures and grain morphology, and continued advancement of this approach could enable automated, scalable, and physics-informed QA/QC for TRISO-SiC microstructures, supporting the reliable qualification of coated particle fuels for next-generation reactor systems.

Computer vision↗

DOE OSTI2016

Kira: Processing Astronomy Imagery Using Big Data Technology

Scientific analyses commonly compose multiple single-process programs into a dataflow. An end-to-end dataflow of single-process programs is known as a many-task application. Typically, HPC tools are used to parallelize these analyses. In this work, we investigate an alternate approach that uses Apache Spark-a modern platform for data intensive computing-to parallelize many-task applications. We implement Kira, a flexible and distributed astronomy image processing toolkit, and its Source Extractor (Kira SE) application. Using Kira SE as a case study, we examine the programming flexibility, dataflow richness, scheduling capacity and performance of Apache Spark running on the Amazon EC2 cloud. By exploiting data locality, Kira SE achieves a 4.1× speedup over an equivalent C program when analyzing a 1TB dataset using 512 cores on the Amazon EC2 cloud. Furthermore, Kira SE on the Amazon EC2 cloud achieves a 1.8× speedup over the C program on the NERSC Edison supercomputer. A 128-core Amazon EC2 cloud deployment of Kira SE using Spark Streaming can achieve a second-scale latency with a sustained throughput of 800 MB/s. Our experience with Kira demonstrates that data intensive computing platforms like Apache Spark are a performant alternative for many-task scientific applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Bubble-Size Evolution during Polyurethane Foam Expansion

We are developing computational models to elucidate the expansion and dynamic filling process of a polyurethane (PMDI) foam used to encapsulate electronic components or to produce lightweight structural parts. The polyurethane of interest is a chemically blown foam, where carbon dioxide is produced via the reaction of water, a blowing agent, and isocyanate. Here, we take a careful look at the evolution of the bubble sizes during blowing. This information will help the development of subgrid models to predict bubble formation, growth, coalescence and collapse, drainage, and, hence, eventually the development of engineering models to predict foam expansion into a mold. Close-up views of bubbles at a transparent wall of a narrow, temperature-controlled channel are recorded during the foaming reaction and analyzed with image processing. Because these bubbles are pressed against the wall, the bubble sizes in the last frames after the expansion has stopped are compared to scanning electron microscope (SEM) images of the interior of some of the cured samples to determine if the presence of the wall significantly changes the bubble sizes. In addition, diffusing wave spectroscopy (DWS) is used to determine the average bubble sizes across the width of a similar channel as the bubbles change with time. DWS also gives information about microstructural changes as bubbles rearrange upon bubble collapse or coalescence. In this paper we conclude qualitatively that the bubble size distribution is heavily dependent on the formulation of foam being tested, temperature, the height in the foam bar, the proximity to a wall, and the degree of over-packing.

36 MATERIALS SCIENCE↗

DOE OSTI2016

MO-B-201-00: Motion Management in Current Stereotactic Body Radiation Therapy (SBRT) Practice

The motion management in stereotactic body radiation therapy (SBRT) is a key to success for a SBRT program, and still an on-going challenging task. A major factor is that moving structures behave differently than standing structures when examined by imaging modalities, and thus require special considerations and employments. Understanding the motion effects to these different imaging processes is a prerequisite for a decent motion management program. The commonly used motion control techniques to physically restrict tumor motion, if adopted correctly, effectively increase the conformity and accuracy of hypofractionated treatment. The effective application of such requires one to understand the mechanics of the application and the related physiology especially related to respiration. The image-guided radiation beam control, or tumor tracking, further realized the endeavor for precision-targeting. During tumor tracking, the respiratory motion is often constantly monitored by non-ionizing beam sources using the body surface as its surrogate. This then has to synchronize with the actual internal tumor motion. The latter is often accomplished by stereo X-ray imaging or similar techniques. With these advanced technologies, one may drastically reduce the treated volume and increase the clinicians’ confidence for a high fractional ablative radiation dose. However, the challenges in implementing the motion management may not be trivial and is dependent on each clinic case. This session of presentations is intended to provide an overview of the current techniques used in managing the tumor motion in SBRT, specifically for routine lung SBRT, proton based treatments, and newly-developed MR guided RT. Learning Objectives: Through this presentation, the audience will understand basic roles of commonly used imaging modalities for lung cancer studies; familiarize the major advantages and limitations of each discussed motion control methods; familiarize the major advantages and limitations of each discussed radiation beam control methodology and tumor tacking method; understand the key points in motion management for a high quality SBRT program.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-B-201-01: Overcoming the Challenges of Motion Management in Current Lung SBRT Practice

The motion management in stereotactic body radiation therapy (SBRT) is a key to success for a SBRT program, and still an on-going challenging task. A major factor is that moving structures behave differently than standing structures when examined by imaging modalities, and thus require special considerations and employments. Understanding the motion effects to these different imaging processes is a prerequisite for a decent motion management program. The commonly used motion control techniques to physically restrict tumor motion, if adopted correctly, effectively increase the conformity and accuracy of hypofractionated treatment. The effective application of such requires one to understand the mechanics of the application and the related physiology especially related to respiration. The image-guided radiation beam control, or tumor tracking, further realized the endeavor for precision-targeting. During tumor tracking, the respiratory motion is often constantly monitored by non-ionizing beam sources using the body surface as its surrogate. This then has to synchronize with the actual internal tumor motion. The latter is often accomplished by stereo X-ray imaging or similar techniques. With these advanced technologies, one may drastically reduce the treated volume and increase the clinicians’ confidence for a high fractional ablative radiation dose. However, the challenges in implementing the motion management may not be trivial and is dependent on each clinic case. This session of presentations is intended to provide an overview of the current techniques used in managing the tumor motion in SBRT, specifically for routine lung SBRT, proton based treatments, and newly-developed MR guided RT. Learning Objectives: Through this presentation, the audience will understand basic roles of commonly used imaging modalities for lung cancer studies; familiarize the major advantages and limitations of each discussed motion control methods; familiarize the major advantages and limitations of each discussed radiation beam control methodology and tumor tacking method; understand the key points in motion management for a high quality SBRT program.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-B-201-02: Motion Management for Proton Lung SBR

The motion management in stereotactic body radiation therapy (SBRT) is a key to success for a SBRT program, and still an on-going challenging task. A major factor is that moving structures behave differently than standing structures when examined by imaging modalities, and thus require special considerations and employments. Understanding the motion effects to these different imaging processes is a prerequisite for a decent motion management program. The commonly used motion control techniques to physically restrict tumor motion, if adopted correctly, effectively increase the conformity and accuracy of hypofractionated treatment. The effective application of such requires one to understand the mechanics of the application and the related physiology especially related to respiration. The image-guided radiation beam control, or tumor tracking, further realized the endeavor for precision-targeting. During tumor tracking, the respiratory motion is often constantly monitored by non-ionizing beam sources using the body surface as its surrogate. This then has to synchronize with the actual internal tumor motion. The latter is often accomplished by stereo X-ray imaging or similar techniques. With these advanced technologies, one may drastically reduce the treated volume and increase the clinicians’ confidence for a high fractional ablative radiation dose. However, the challenges in implementing the motion management may not be trivial and is dependent on each clinic case. This session of presentations is intended to provide an overview of the current techniques used in managing the tumor motion in SBRT, specifically for routine lung SBRT, proton based treatments, and newly-developed MR guided RT. Learning Objectives: Through this presentation, the audience will understand basic roles of commonly used imaging modalities for lung cancer studies; familiarize the major advantages and limitations of each discussed motion control methods; familiarize the major advantages and limitations of each discussed radiation beam control methodology and tumor tacking method; understand the key points in motion management for a high quality SBRT program.

60 APPLIED LIFE SCIENCES↗