This document describes a study that used recurrent neural networks to predict multileaf collimator (MLC) position errors in radiation therapy. It presents the following:
1) The study aimed to predict MLC position and velocity errors before treatment using radiation treatment plans and an artificial neural network (RNN: LSTM).
2) The method involved converting treatment plans to expected MLC positions, using RNN to predict actual positions from parameters including expected position, and generating a probability error function.
3) An LSTM network was trained on MLC log files to predict actual leaf positions from inputs of expected position and other parameters over time. Validation was done using separate test log files.
Role of immobilisation and devices in radiotherapySwarnita Sahu
This document discusses the importance of immobilization devices in radiotherapy. Immobilization helps to accurately position the target, minimize dose to surrounding tissues, reduce setup time and patient apprehension. Various types of immobilization devices are described, from simple straps and wedges to customized thermoplastic masks and body molds. Newer techniques like stereotactic radiotherapy require very precise immobilization to deliver high fractional doses safely. Overall, accurate immobilization is critical to ensure radiation is delivered safely and effectively to tumors while sparing surrounding healthy tissues.
This document discusses various methods of modifying radiation beams used in radiation therapy. It describes four main types of beam modification: shielding, compensation, wedge filtration, and flattening. Shielding is used to protect critical structures by devices like blocks, compensators help achieve uniform dose for irregular surfaces, wedge filters tilt the dose distribution, and flattening filters even out the natural beam profile. The document outlines different beam modifying devices and materials like wedges, compensators, bolus, and blocks. It discusses factors that must be considered for beam modification like scatter and attenuation. Overall, the document provides an overview of common techniques and devices used to modify radiation beams for optimal dose distribution in radiation therapy.
This document discusses radiotherapy and CT simulation procedures. It begins with an introduction to radiotherapy and how it uses radiation to destroy cancer cells. It then describes the linear accelerator machine commonly used to deliver targeted radiation treatments. Finally, it outlines the mould room procedure for creating customized thermoplastic masks to immobilize patients, ensuring accurate radiation delivery to the treatment site.
The document describes the CyberKnife robotic radiosurgery system. It provides sub-millimeter accuracy for treating tumors throughout the body with precise radiation beams. Key features include its robotic ability to track and correct for tumor movement during treatment in real-time without needing invasive head/body frames. It has treated over 16,000 patients worldwide for conditions like brain, lung, prostate and spine tumors.
Radiation emergencies and preparedness in radiotherapyDeepjyoti saha
In a Radiotherapy Department where cancer patients are being treated with high energy photons,gamma rays,electrons; all the radiation workers should be alert regarding radiation accidents & how to face the situation.
introduction to Intensity modulated radiation therapyRahim Gohar
IMRT is an advanced form of radiation therapy that uses modulated beam intensities to more precisely target tumors while reducing radiation exposure to surrounding healthy tissues. It involves inverse planning where dose and volume constraints for the tumor and organs at risk are specified upfront. The treatment planning system then optimizes beam fluence maps through multiple iterations to achieve the desired dose distribution. Final treatment delivery involves either segmented or dynamic modulation of the multileaf collimator to sculpt the radiation beam according to the optimal fluence map calculated during planning.
PERFORMANCE EVALUATION OF COMPUTED TOMOGRAPHY (CT) SCANNERSBhuvi palaniswamy
This document discusses performance evaluation tests for computed tomography (CT) scanners. It can be broadly classified into electromechanical tests, x-ray generator (electrical) tests, image quality tests, radiation dose tests, and general tests related to CT number. Electromechanical tests evaluate the scan localization laser lights, table movement accuracy, and gantry tilt. X-ray generator tests check the accelerating voltage, milliampere linearity, and radiation output reproducibility. Image quality tests evaluate low-contrast resolution, high-contrast resolution, and noise. Radiation dose tests measure the computed tomography dose index and multiple scan average dose using phantoms.
Proper patient immobilization is crucial for accurate radiation therapy. Immobilization devices reduce setup errors and patient movement, allowing higher controlled doses to be delivered precisely to the target volume while sparing surrounding healthy tissues. Common immobilization methods include masks, molds, frames and boards for different body sites like head/neck, brain, thorax, breast and pelvis. Newer techniques like intensity-modulated radiation therapy allow improved immobilization for smaller treatment volumes and higher cure rates.
Role of immobilisation and devices in radiotherapySwarnita Sahu
This document discusses the importance of immobilization devices in radiotherapy. Immobilization helps to accurately position the target, minimize dose to surrounding tissues, reduce setup time and patient apprehension. Various types of immobilization devices are described, from simple straps and wedges to customized thermoplastic masks and body molds. Newer techniques like stereotactic radiotherapy require very precise immobilization to deliver high fractional doses safely. Overall, accurate immobilization is critical to ensure radiation is delivered safely and effectively to tumors while sparing surrounding healthy tissues.
This document discusses various methods of modifying radiation beams used in radiation therapy. It describes four main types of beam modification: shielding, compensation, wedge filtration, and flattening. Shielding is used to protect critical structures by devices like blocks, compensators help achieve uniform dose for irregular surfaces, wedge filters tilt the dose distribution, and flattening filters even out the natural beam profile. The document outlines different beam modifying devices and materials like wedges, compensators, bolus, and blocks. It discusses factors that must be considered for beam modification like scatter and attenuation. Overall, the document provides an overview of common techniques and devices used to modify radiation beams for optimal dose distribution in radiation therapy.
This document discusses radiotherapy and CT simulation procedures. It begins with an introduction to radiotherapy and how it uses radiation to destroy cancer cells. It then describes the linear accelerator machine commonly used to deliver targeted radiation treatments. Finally, it outlines the mould room procedure for creating customized thermoplastic masks to immobilize patients, ensuring accurate radiation delivery to the treatment site.
The document describes the CyberKnife robotic radiosurgery system. It provides sub-millimeter accuracy for treating tumors throughout the body with precise radiation beams. Key features include its robotic ability to track and correct for tumor movement during treatment in real-time without needing invasive head/body frames. It has treated over 16,000 patients worldwide for conditions like brain, lung, prostate and spine tumors.
Radiation emergencies and preparedness in radiotherapyDeepjyoti saha
In a Radiotherapy Department where cancer patients are being treated with high energy photons,gamma rays,electrons; all the radiation workers should be alert regarding radiation accidents & how to face the situation.
introduction to Intensity modulated radiation therapyRahim Gohar
IMRT is an advanced form of radiation therapy that uses modulated beam intensities to more precisely target tumors while reducing radiation exposure to surrounding healthy tissues. It involves inverse planning where dose and volume constraints for the tumor and organs at risk are specified upfront. The treatment planning system then optimizes beam fluence maps through multiple iterations to achieve the desired dose distribution. Final treatment delivery involves either segmented or dynamic modulation of the multileaf collimator to sculpt the radiation beam according to the optimal fluence map calculated during planning.
PERFORMANCE EVALUATION OF COMPUTED TOMOGRAPHY (CT) SCANNERSBhuvi palaniswamy
This document discusses performance evaluation tests for computed tomography (CT) scanners. It can be broadly classified into electromechanical tests, x-ray generator (electrical) tests, image quality tests, radiation dose tests, and general tests related to CT number. Electromechanical tests evaluate the scan localization laser lights, table movement accuracy, and gantry tilt. X-ray generator tests check the accelerating voltage, milliampere linearity, and radiation output reproducibility. Image quality tests evaluate low-contrast resolution, high-contrast resolution, and noise. Radiation dose tests measure the computed tomography dose index and multiple scan average dose using phantoms.
Proper patient immobilization is crucial for accurate radiation therapy. Immobilization devices reduce setup errors and patient movement, allowing higher controlled doses to be delivered precisely to the target volume while sparing surrounding healthy tissues. Common immobilization methods include masks, molds, frames and boards for different body sites like head/neck, brain, thorax, breast and pelvis. Newer techniques like intensity-modulated radiation therapy allow improved immobilization for smaller treatment volumes and higher cure rates.
This document provides an overview of planning systems in radiotherapy and discusses various topics related to clinical treatment planning using computerized treatment planning systems. It begins with an introduction to the author and their experience with different treatment planning systems. It then covers definitions and concepts important for clinical treatment planning such as volumes, dose specifications, patient data acquisition, beam combinations, and dose statistics. The document also discusses virtual simulation, image fusion, treatment aids, oblique incidence corrections, and portal imaging. It provides details on the hardware, calculations algorithms, and commissioning of computerized treatment planning systems. In summary, the document offers a comprehensive review of clinical treatment planning processes and considerations for computerized treatment planning systems.
Beam directed radiotherapy aims to deliver a homogenous tumor dose while minimizing radiation to normal tissues. It involves careful patient positioning, immobilization, tumor localization, field selection, dose calculations, and verification. Key steps include using positioning aids and molds to reproducibly position the patient, imaging such as CT to delineate the tumor volume, contouring to define external body outlines, and dose calculations and verification to ensure accurate delivery.
Immobilization devices are crucial for accurate radiation therapy by reducing setup errors and patient movement. Proper immobilization allows higher radiation doses to be delivered safely to the target volume while sparing surrounding healthy tissues. For the head and neck, aquaplast masks and thermoplastic shells molded to the patient are commonly used. Indexing bars and shoulder straps further improve reproducibility. For the thorax and breast, vacuum cushions and breast boards are utilized, while pelvic regions employ partial body casts or vacuum cushions extending from the lower thorax to mid-thighs. Advances in immobilization now enable smaller treatment volumes and higher controlled doses for improved patient outcomes.
CT simulators are essential for precise radiotherapy treatment planning. They use CT scanning to create a virtual 3D representation of the patient's anatomy. This allows clinicians to accurately localize tumors and delineate organs at risk. The CT images provide excellent soft tissue contrast and electron density data needed for treatment planning. CT simulators also facilitate patient positioning and immobilization during both simulation and treatment through reference marking and immobilization devices. Overall, CT simulation enables more accurate target localization and definition compared to conventional simulators, resulting in better optimized treatment plans.
Lars Leksell invented stereotactic radiosurgery in 1951 using an orthovoltage X-ray tube. In 1967, he invented the Gamma Knife which used 179 cobalt-60 sources. Pioneers like Lawrence, Kjellberg, and Steiner expanded radiosurgery's use of particle beams and gamma knives. By the 1980s, LINACs were being adapted for radiosurgery. In 1987, the first Gamma Knife was installed in the US at the University of Pittsburgh. Radiosurgery continued to be refined with techniques like optical tracking for increased flexibility.
The document discusses various immobilization devices used in radiotherapy to precisely position patients and minimize movement during treatment. It describes common immobilization methods for the head and neck, including aquaplast masks and bite blocks. For the thorax and breast, vacuum bags and breast boards are used. Belly boards are discussed for immobilizing the pelvis during prone treatments. The purpose of immobilization is to accurately target the treatment area while sparing surrounding healthy tissues from radiation exposure.
Clinical Radiotherapy Planning basics for beginnersDina Barakat
1) External beam radiotherapy involves delivering high-energy x-ray beams from outside the patient's body to treat tumors. The ICRU recommends doses be within ±7-5% of the prescribed dose to the target.
2) Treatment planning involves acquiring patient images like CT scans, outlining the tumor and organs at risk, determining beam geometry, and calculating dose distributions.
3) Virtual simulation uses digitally reconstructed radiographs from CT images to plan beam placement, replacing conventional simulation using x-rays. This allows direct use of patient anatomy in planning.
This document discusses various modern radiation therapy techniques including IMRT, IGRT, MVCBCT, and KVCBCT. It provides background on 2D and 3D conformal radiation therapy. IMRT uses intensity modulated beams and inverse planning to improve dose distribution. IGRT uses imaging before and during treatment for precise targeting. MVCBCT and KVCBCT provide volumetric imaging using megavoltage and kilovoltage sources, with KVCBCT offering better soft tissue contrast. Errors in patient positioning can be detected and corrected using these image-guided techniques.
This document discusses brachytherapy, a type of radiation therapy where radioactive material is placed directly inside the body near the tumor being treated. It begins by explaining the two major categories of radiation therapy: external-beam therapy where a machine emits radiation from outside the body, and brachytherapy where radioactive sources are placed inside the body. It then provides details on brachytherapy, including how it works from inside the body compared to external beam therapy, common radiation sources used, and the typical procedure involving planning, applicator insertion, treatment delivery, and removal of sources.
Beam modification devices are used in radiotherapy to modify the spatial distribution of radiation within the patient. The main types of beam modification are shielding to eliminate dose to some areas, compensation to allow for irregular surfaces and tissues, wedge filtration to modify isodose curves, and flattening filters to modify the natural beam profile. Beam modification devices can alter the dose distribution due to effects of primary radiation attenuation and scattering. Common beam modification devices include shielding blocks, compensators, wedges, and multileaf collimators.
This document discusses several key concepts in radiation oncology:
1. The Law of Bergonie and Tribondeau states that radiosensitivity varies with cell maturation and metabolism, with stem cells being most radiosensitive. Younger and more metabolically active tissues are also more radiosensitive.
2. Factors like linear energy transfer (LET), relative biological effectiveness (RBE), fractionation, and oxygenation level impact radiation response. High LET radiation like alpha particles is more effective due to dense ionization.
3. The oxygen enhancement ratio (OER) compares radiation doses needed under hypoxic vs aerated conditions. It is typically 2.5-3.0 for X-rays but 1 for high
This document discusses digital subtraction angiography (DSA), including its history, equipment, and applications. DSA involves acquiring digital fluoroscopic images before and after injecting contrast material, and using computer subtraction to remove bone structures and leave an image of blood vessels. It originated in the 1970s and allows for real-time angiography with improved vessel contrast compared to conventional techniques. Key components of DSA systems include an x-ray unit, image intensifier, computer, and software for image processing functions like subtraction, enhancement, and roadmapping.
This document discusses intensity-modulated radiation therapy (IMRT). It begins by defining IMRT as a radiation therapy technique that delivers a nonuniform radiation fluence from different beam positions to optimize the dose distribution. The principle of IMRT is to treat a patient from multiple directions with beams of varying fluence. IMRT planning uses inverse planning to optimize the dose distribution. Delivery techniques include step-and-shoot IMRT using a multileaf collimator, dynamic MLC IMRT, tomotherapy, and volumetric modulated arc therapy. The goals of IMRT include improved target dose uniformity and avoidance of critical structures.
This document discusses various techniques used for treatment verification in radiation therapy. It describes electronic portal imaging devices (EPID) which can be used for daily treatment localization and verification through portal images with little additional dose. Cone beam computed tomography (CBCT) is also discussed, which provides volumetric CT images with submillimeter resolution, allowing verification of patient positioning before treatment. Both EPID and CBCT help ensure the correct radiation dose is delivered to the intended target volume.
Brachytherapy involves placing radioactive sources inside or near a tumor to deliver radiation. It has advantages over external beam radiation in better targeting the tumor while sparing surrounding healthy tissue. The document discusses the history of brachytherapy and the types of sources, implants, and machines used. It also covers dosimetry systems for gynecological cancers like cervical cancer, which commonly uses intracavitary implants of radioactive sources in an applicator. Interstitial brachytherapy directly implants radioactive sources in the tumor. Remote afterloading machines allow safely implanting and removing radioactive sources.
This document provides information on teletherapy machines used to treat cancer with radiation. It discusses cobalt-60 teletherapy machines and linear accelerators. Cobalt-60 machines use a radioactive cobalt-60 source to generate gamma rays for treatment. Linear accelerators use microwave energy to accelerate electrons, which are then used to generate x-rays or electron beams for treatment. Both types of machines aim focused radiation beams at tumors while minimizing dose to surrounding healthy tissue using collimators and other targeting mechanisms. Linear accelerators have advantages over cobalt machines like more sharply defined beam edges and ability to vary dose rates.
Brachytherapy involves placing radioactive sources close to or inside the treatment area. The document discusses the history and discovery of radium and its use in early brachytherapy treatments. It then describes various brachytherapy techniques including interstitial, intracavitary, surface mould therapy and different remote afterloading systems. Key brachytherapy sources such as Cs-137, Ir-192, Co-60 and their properties are also outlined. Measurement units for source strength including air kerma rate and exposure rate constants are defined.
A summary of recent innovations in radiation oncology focussing on the priniciples of different techniques and their application. An overview of clinical results has also been given
2009 HEP Science Network Requirements Workshop Final Reportbutest
The document summarizes the proceedings of a workshop organized by the Energy Sciences Network (ESnet) and the Office of High Energy Physics (HEP) to characterize the networking requirements of HEP science programs over the next 10 years. Key points discussed include:
- The HEP community has large, distributed data needs that will continue growing with projects like the LHC. More LHC Tier-3 sites and Tier-2 to Tier-2 traffic are anticipated.
- The two LHC Tier-1 sites in the US predict needing 40-50Gbps capacity in 2-5 years and 100-200Gbps in 5-10 years to support HEP traffic.
- There are
Thermatic simulation platform for nano materials design in kistKIST
This slides introduce the web based thematic materials design platform developed in the Computational Science Center at KIST. This platform is to provide an easy-to-use materials simulation environment where people can perform various advanced simulations using the workflows very similar to those of the real experiment. These platforms were designed to reduce the entrance barrier to the complicated materials simulation using the high performance cluster computer. We are anticipating that these platforms will become robust R&D tool to design novel (nano) materials.
This document provides an overview of planning systems in radiotherapy and discusses various topics related to clinical treatment planning using computerized treatment planning systems. It begins with an introduction to the author and their experience with different treatment planning systems. It then covers definitions and concepts important for clinical treatment planning such as volumes, dose specifications, patient data acquisition, beam combinations, and dose statistics. The document also discusses virtual simulation, image fusion, treatment aids, oblique incidence corrections, and portal imaging. It provides details on the hardware, calculations algorithms, and commissioning of computerized treatment planning systems. In summary, the document offers a comprehensive review of clinical treatment planning processes and considerations for computerized treatment planning systems.
Beam directed radiotherapy aims to deliver a homogenous tumor dose while minimizing radiation to normal tissues. It involves careful patient positioning, immobilization, tumor localization, field selection, dose calculations, and verification. Key steps include using positioning aids and molds to reproducibly position the patient, imaging such as CT to delineate the tumor volume, contouring to define external body outlines, and dose calculations and verification to ensure accurate delivery.
Immobilization devices are crucial for accurate radiation therapy by reducing setup errors and patient movement. Proper immobilization allows higher radiation doses to be delivered safely to the target volume while sparing surrounding healthy tissues. For the head and neck, aquaplast masks and thermoplastic shells molded to the patient are commonly used. Indexing bars and shoulder straps further improve reproducibility. For the thorax and breast, vacuum cushions and breast boards are utilized, while pelvic regions employ partial body casts or vacuum cushions extending from the lower thorax to mid-thighs. Advances in immobilization now enable smaller treatment volumes and higher controlled doses for improved patient outcomes.
CT simulators are essential for precise radiotherapy treatment planning. They use CT scanning to create a virtual 3D representation of the patient's anatomy. This allows clinicians to accurately localize tumors and delineate organs at risk. The CT images provide excellent soft tissue contrast and electron density data needed for treatment planning. CT simulators also facilitate patient positioning and immobilization during both simulation and treatment through reference marking and immobilization devices. Overall, CT simulation enables more accurate target localization and definition compared to conventional simulators, resulting in better optimized treatment plans.
Lars Leksell invented stereotactic radiosurgery in 1951 using an orthovoltage X-ray tube. In 1967, he invented the Gamma Knife which used 179 cobalt-60 sources. Pioneers like Lawrence, Kjellberg, and Steiner expanded radiosurgery's use of particle beams and gamma knives. By the 1980s, LINACs were being adapted for radiosurgery. In 1987, the first Gamma Knife was installed in the US at the University of Pittsburgh. Radiosurgery continued to be refined with techniques like optical tracking for increased flexibility.
The document discusses various immobilization devices used in radiotherapy to precisely position patients and minimize movement during treatment. It describes common immobilization methods for the head and neck, including aquaplast masks and bite blocks. For the thorax and breast, vacuum bags and breast boards are used. Belly boards are discussed for immobilizing the pelvis during prone treatments. The purpose of immobilization is to accurately target the treatment area while sparing surrounding healthy tissues from radiation exposure.
Clinical Radiotherapy Planning basics for beginnersDina Barakat
1) External beam radiotherapy involves delivering high-energy x-ray beams from outside the patient's body to treat tumors. The ICRU recommends doses be within ±7-5% of the prescribed dose to the target.
2) Treatment planning involves acquiring patient images like CT scans, outlining the tumor and organs at risk, determining beam geometry, and calculating dose distributions.
3) Virtual simulation uses digitally reconstructed radiographs from CT images to plan beam placement, replacing conventional simulation using x-rays. This allows direct use of patient anatomy in planning.
This document discusses various modern radiation therapy techniques including IMRT, IGRT, MVCBCT, and KVCBCT. It provides background on 2D and 3D conformal radiation therapy. IMRT uses intensity modulated beams and inverse planning to improve dose distribution. IGRT uses imaging before and during treatment for precise targeting. MVCBCT and KVCBCT provide volumetric imaging using megavoltage and kilovoltage sources, with KVCBCT offering better soft tissue contrast. Errors in patient positioning can be detected and corrected using these image-guided techniques.
This document discusses brachytherapy, a type of radiation therapy where radioactive material is placed directly inside the body near the tumor being treated. It begins by explaining the two major categories of radiation therapy: external-beam therapy where a machine emits radiation from outside the body, and brachytherapy where radioactive sources are placed inside the body. It then provides details on brachytherapy, including how it works from inside the body compared to external beam therapy, common radiation sources used, and the typical procedure involving planning, applicator insertion, treatment delivery, and removal of sources.
Beam modification devices are used in radiotherapy to modify the spatial distribution of radiation within the patient. The main types of beam modification are shielding to eliminate dose to some areas, compensation to allow for irregular surfaces and tissues, wedge filtration to modify isodose curves, and flattening filters to modify the natural beam profile. Beam modification devices can alter the dose distribution due to effects of primary radiation attenuation and scattering. Common beam modification devices include shielding blocks, compensators, wedges, and multileaf collimators.
This document discusses several key concepts in radiation oncology:
1. The Law of Bergonie and Tribondeau states that radiosensitivity varies with cell maturation and metabolism, with stem cells being most radiosensitive. Younger and more metabolically active tissues are also more radiosensitive.
2. Factors like linear energy transfer (LET), relative biological effectiveness (RBE), fractionation, and oxygenation level impact radiation response. High LET radiation like alpha particles is more effective due to dense ionization.
3. The oxygen enhancement ratio (OER) compares radiation doses needed under hypoxic vs aerated conditions. It is typically 2.5-3.0 for X-rays but 1 for high
This document discusses digital subtraction angiography (DSA), including its history, equipment, and applications. DSA involves acquiring digital fluoroscopic images before and after injecting contrast material, and using computer subtraction to remove bone structures and leave an image of blood vessels. It originated in the 1970s and allows for real-time angiography with improved vessel contrast compared to conventional techniques. Key components of DSA systems include an x-ray unit, image intensifier, computer, and software for image processing functions like subtraction, enhancement, and roadmapping.
This document discusses intensity-modulated radiation therapy (IMRT). It begins by defining IMRT as a radiation therapy technique that delivers a nonuniform radiation fluence from different beam positions to optimize the dose distribution. The principle of IMRT is to treat a patient from multiple directions with beams of varying fluence. IMRT planning uses inverse planning to optimize the dose distribution. Delivery techniques include step-and-shoot IMRT using a multileaf collimator, dynamic MLC IMRT, tomotherapy, and volumetric modulated arc therapy. The goals of IMRT include improved target dose uniformity and avoidance of critical structures.
This document discusses various techniques used for treatment verification in radiation therapy. It describes electronic portal imaging devices (EPID) which can be used for daily treatment localization and verification through portal images with little additional dose. Cone beam computed tomography (CBCT) is also discussed, which provides volumetric CT images with submillimeter resolution, allowing verification of patient positioning before treatment. Both EPID and CBCT help ensure the correct radiation dose is delivered to the intended target volume.
Brachytherapy involves placing radioactive sources inside or near a tumor to deliver radiation. It has advantages over external beam radiation in better targeting the tumor while sparing surrounding healthy tissue. The document discusses the history of brachytherapy and the types of sources, implants, and machines used. It also covers dosimetry systems for gynecological cancers like cervical cancer, which commonly uses intracavitary implants of radioactive sources in an applicator. Interstitial brachytherapy directly implants radioactive sources in the tumor. Remote afterloading machines allow safely implanting and removing radioactive sources.
This document provides information on teletherapy machines used to treat cancer with radiation. It discusses cobalt-60 teletherapy machines and linear accelerators. Cobalt-60 machines use a radioactive cobalt-60 source to generate gamma rays for treatment. Linear accelerators use microwave energy to accelerate electrons, which are then used to generate x-rays or electron beams for treatment. Both types of machines aim focused radiation beams at tumors while minimizing dose to surrounding healthy tissue using collimators and other targeting mechanisms. Linear accelerators have advantages over cobalt machines like more sharply defined beam edges and ability to vary dose rates.
Brachytherapy involves placing radioactive sources close to or inside the treatment area. The document discusses the history and discovery of radium and its use in early brachytherapy treatments. It then describes various brachytherapy techniques including interstitial, intracavitary, surface mould therapy and different remote afterloading systems. Key brachytherapy sources such as Cs-137, Ir-192, Co-60 and their properties are also outlined. Measurement units for source strength including air kerma rate and exposure rate constants are defined.
A summary of recent innovations in radiation oncology focussing on the priniciples of different techniques and their application. An overview of clinical results has also been given
2009 HEP Science Network Requirements Workshop Final Reportbutest
The document summarizes the proceedings of a workshop organized by the Energy Sciences Network (ESnet) and the Office of High Energy Physics (HEP) to characterize the networking requirements of HEP science programs over the next 10 years. Key points discussed include:
- The HEP community has large, distributed data needs that will continue growing with projects like the LHC. More LHC Tier-3 sites and Tier-2 to Tier-2 traffic are anticipated.
- The two LHC Tier-1 sites in the US predict needing 40-50Gbps capacity in 2-5 years and 100-200Gbps in 5-10 years to support HEP traffic.
- There are
Thermatic simulation platform for nano materials design in kistKIST
This slides introduce the web based thematic materials design platform developed in the Computational Science Center at KIST. This platform is to provide an easy-to-use materials simulation environment where people can perform various advanced simulations using the workflows very similar to those of the real experiment. These platforms were designed to reduce the entrance barrier to the complicated materials simulation using the high performance cluster computer. We are anticipating that these platforms will become robust R&D tool to design novel (nano) materials.
NIST-JARVIS infrastructure for Improved Materials DesignKAMAL CHOUDHARY
The document describes the NIST-JARVIS infrastructure for materials design using computational methods. It provides electronic structure databases containing properties of thousands of materials calculated using DFT. Tools include JARVIS-DFT for electronic structure calculations, ALIGNN for developing machine learning models to predict material properties from structure, and AtomVision for analyzing microscopy images. The infrastructure aims to accelerate materials discovery and design through automation, collaboration and open access to computational data and tools.
Friday, October 15th, 2021, Sapporo, Hokkaido, Japan.
Hokkaido University ICReDD - Faculty of Medicine Joint Symposium
https://www.icredd.hokudai.ac.jp/event/5993
ICReDD (Institute for Chemical Reaction Design and Discovery)
https://www.icredd.hokudai.ac.jp
Presentation on machine learning and materials science at Computing in Engineering Forum 2018, Machine Ground Interaction Consortium (MaGIC) 2018, Wisconsin, Madison, December 4, 2018
This document summarizes the state of atom probe tomography (APT) as applied to electronic materials. APT allows for 3D atomic-scale imaging and analysis of materials composition with subnanometer spatial resolution and high sensitivity. Specimen preparation, especially using focused ion beam techniques, has enabled APT analysis of a wide range of electronic materials including semiconductor devices. APT provides unique capabilities for characterizing nanoscale structures in electronic materials such as buried interfaces and dopant profiles. The applications of APT to electronic materials are growing rapidly as the technique becomes more widely used.
(PhD Dissertation Defense) Theoretical and Numerical Investigations on Crysta...James D.B. Wang, PhD
This document summarizes Di-Bao Wang's Ph.D. dissertation on theoretical and numerical investigations of crystalline solid materials and their application in simulating laser-assisted nano-imprinting. The dissertation improves the time-history kernel method for modeling thermal motion in crystalline solids by developing a more efficient inverse Laplace transform approach. It also develops an absorbing boundary layer method to accelerate neighbor list updating for molecular dynamics simulations. These enhanced computational methods are applied to study laser-assisted nanoimprinting processes at the nanoscale.
Graphs, Environments, and Machine Learning for Materials Scienceaimsnist
This document summarizes key points from a workshop on machine learning for materials science held on August 1, 2019. It discusses how graphs are a natural representation for materials as they can capture local atomic environments and periodicity in crystals. A graph network framework called MEGNet is presented that achieves state-of-the-art performance for molecular and crystal property prediction. MEGNet models outperform previous methods on standard benchmarks and allow for transfer learning across properties. The workshop also covered practical considerations for training deep learning models and applications beyond bulk crystals.
Materials Design in the Age of Deep Learning and Quantum ComputationKAMAL CHOUDHARY
The document discusses recent developments in materials design using deep learning (DL) and quantum computation. It introduces several DL and quantum computation models developed at NIST including:
- ALIGNN, a graph neural network model that predicts materials properties from crystal structure.
- AtomVision, a DL framework for analyzing scanning probe microscopy and electron microscopy images of materials.
- AtomQC, which uses variational quantum algorithms like VQE to perform quantum simulations of materials on quantum computers.
The models have been applied to datasets containing thousands of materials to predict properties and analyze experimental images. Future work aims to integrate DL and quantum computation for accelerated materials discovery and design.
Multivariate dimensionality reduction in cross-correlation analysis ivanokitov
1. Dimensionality reduction techniques like PCA can be used to optimize master event templates for cross-correlation based seismic event detection and location. 2. The document explores using various dimensionality reduction methods such as PCA, IPCA, and SSD on both real and synthetic seismic data to minimize the number of templates needed. 3. Representing seismic data as hypercomplex numbers or tensors can allow dimensionality reduction techniques to utilize the full multidimensional information from seismic arrays for improved master event design.
The document summarizes the NNPDF3.1 global analysis which provides an updated determination of parton distribution functions (PDFs) from experimental data. Key points include:
1) NNPDF3.1 includes new high-precision measurements from the LHC as well as NNLO QCD calculations, allowing more data to be included. It also fits the charm PDF rather than assuming it is purely perturbative.
2) The new data provides stronger constraints on PDFs, particularly the gluon and down quark, significantly reducing their uncertainties. It also shows good agreement with the previous NNPDF3.0 analysis.
3) For the first time, NNPDF3.1 includes LHC
This document summarizes research on developing autonomous experimental systems for materials characterization and phase diagram mapping. Key points discussed include:
- Using active clustering algorithms and Gaussian process classification to analyze x-ray diffraction data and autonomously map phase diagrams without human labeling or supervision.
- Developing infrastructure for autonomous experiments involving autonomous data analysis, selection of new experimental conditions based on analysis, and control of experimental equipment to acquire new data.
- Demonstrating this approach on systems like VNbO2 and VWO2 to map phase diagrams and metal-insulator transition temperatures as a function of composition and temperature.
Predicting local atomic structures from X-ray absorption spectroscopy using t...aimsnist
The document discusses using theory, computation, and machine learning to interpret experimental X-ray absorption spectroscopy data and determine local atomic structures. It presents examples of using density functional theory calculations of X-ray absorption near edge structure (XANES) spectra to benchmark predictions against experiments and develop machine learning models for structure classification. The models are able to classify local structures like tetrahedral, square pyramidal, and octahedral coordination with over 85% accuracy across different materials systems. This approach provides a way to solve the inverse problem of determining structures from spectroscopy measurements in real time.
The Transformation of Systems Biology Into A Large Data ScienceRobert Grossman
Systems biology is becoming a data-intensive science due to the exponential growth of genomic and biological data. Large projects now produce petabytes of data that require new computational infrastructure to store, manage, and analyze. Cloud computing provides elastic resources that can scale to support the increasing data needs of systems biology. Case studies show how clouds are used for large-scale data integration and analysis, running combinatorial analysis over genomic marks, and enabling reanalysis of biological data through elastic virtual machines. The Open Cloud Consortium is working to provide open cloud resources for biological and biomedical research through testbeds and proposed bioclouds.
1. The study proposes a deep learning approach for automatically contouring organs at risk (OARs) on reduced field CT (RF-CT) images taken during radiation therapy based on an initial simulation CT and contours.
2. The approach was tested on lung, prostate, and brain cases and achieved average Dice coefficients of 0.944, 0.949 and 0.960 respectively when compared to expert contours on the RF-CTs.
3. The method shows potential as part of an online adaptive radiation therapy (ART) workflow by automating re-contouring and reducing planning time from hours to minutes.
Real-Time Analysis of Streaming Synchotron Data: SCinet SC19 Technology Chall...Globus
This document describes a real-time workflow for analyzing streaming synchrotron data using high-performance computing resources. Synchrotron experiments produce large amounts of data that need to be reconstructed in real-time. The workflow includes data acquisition from multiple simulated beamlines, distribution to compute nodes, tomographic reconstruction using TraceX, denoising using TomoGAN, and visualization of results. A demonstration of this workflow is being run on Argonne Leadership Computing Facility's Theta supercomputer to process streaming data from 16,000 cores in real-time and provide reconstructed volumes and feedback to experiments.
What can we learn from molecular dynamics simulations of carbon nanotube and ...Stephan Irle
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Error of Multileaf collimator prediction using recurrent neural network (LSTM)
1. MLC error prediction
using recurrent neural network (LSTM)
Wonjoong Cheon1), Kim Seong Jung2), Youngyih Han3),
Hyebin Lee4), Byung Jun Min4*), Heerim Nam4)
1) Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, 06351, Korea.
2) Department of Computer Engineering, Yonsei University, 03722, Seoul, Korea.
3) Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea.
4) Department of Radiation Oncology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, 03181, Korea.
1
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
2. Contents
I. Introduction
II. Material & Method
1) Convert Rtplan to Expected position of MLC
2) Prediction Actual position of MLC using artificial neural network
3) Statistical analysis
III. Results
IV.Conclusion
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
4. Ⅰ. Introduction
Multi-Leaf Collimator
160 MLC™
(Siemens)
Agility™
(Elekta)
• Intensity modulated radiation therapy (IMRT) technique uses MLCs for modifying the beam fluence in the same treatment field
in order to improve the conformity of prescribed dose distribution around the tumor region. (Mundt & Roeske, 2005)
• The volumetric modulated arc therapy (VMAT) technology is a novel delivery method that is capable of producing highly conformal
dose distributions through concomitant optimization of MLC shapes, dose rate, and gantry speed.(Schreibmann et al., 2009)
Millennium™ MLC
(Varian)
Figure resource: www.elekta.com, www.varian.com, www.healthcare.siemens.com
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
5. Actual
MLC position
Expected
MLC position
Ⅰ. Introduction
MLC log files
Dicom RT
Eclipse™ Treatment Planning System, Varian
Dynalog file
Trilogy® System Linear Accelerator, Varian
• MLC leaf position uncertainties directly affect the dose delivered to tumor targets and sensitive
structures in IMRT. (Mu et al., 2008)
• Actual multileaf collimator (MLC) position data can be tracked throughout a treatment delivery in the
form of MLC log files. (Kerns, Childress, & Kry, 2014)
Mismatch (=Errors)
Figure resource: www.varian.com
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
6. Radiation treatment plan
(RTplan)
- plans may contain
fractionation information,
and define external beams
and/or brachytherapy
Expected position
- Position of each leaf
calculated and predefined
by RTplan file
Actual position
- Position of each leaf
conducted and written to
dynalog file
- Containing machine errors
Ⅰ. Introduction
RT workflow
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
RT workflow
7. Ⅰ. Introduction
MLC positional error
• Most of the studies on MLC positional error — both random and systematic errors were analyzed —
have reported that the random errors were insignificant, while systematic errors have shown significant effects on dose distributions,
even with only 1 mm of positional errors applied for MLC positions. (Nithiyanantham et al., 2015)
• MLC position and velocity errors are affected by Friction and Gravity. (Wijesooriya, K., et al., 2005, Lee, Jeong-Woo, et al.,)
• Varian said that “The leaves have to fight gravity”
Figure reference : http://medicalphysicsweb.org/cws/article/research/44068
1
2
3
4
5
6
56
57
58
59
60
2
3
4
Friction Gravity
2
3
4
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
8. Ⅰ. Introduction
Aim of this study
• Prediction multi-leaf collimator position and velocity errors before radiation treatment
using radiation treatment plan (RTplan) file and artificial neural network (RNN:LSTM).
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
Treatment
Planning
Computer
RT plan
Batch
Folder
LinacView
Computer
Logfile
Batch
Folder
Linac Console
RT Plans
Log files
RT Plans
Convert
RTplan to
Expected position
of MLC
MLC position
expected
Prediction
Actual position
of MLC
Artificial Neural NetworkStatistical
analysis
Probability
error function
1
2
3
Is this error
acceptable?
No
Warning
MLC error prediction
RT workflow
If I operate the this
linear accelerator
based on my
RTplan file, will it
work safely?
10. Ⅱ. Material & Method
StepⅠ
Convert Radiation treatment plan to Expected position of MLC
• Computer Language: Matlab
• Parameters: Monitor unit per second, Dose rate, Gantry angle
Step Ⅱ
Prediction Actual position of MLC using Artificial neural network
• Computer Language: Python
• Type of neural net: RNN: LSTM
• Deep learning framework: Tensorflow (Google)
• Parameters: MLC position (expected, actual), Gantry angle, collimator angle, Beam on/off sign
Step Ⅲ
Statistical analysis :Generate Probability error function
• Preprocess: Central limit theorem
• Model: Single Gaussian distribution (mean, std)
• Computer Language: Matlab
11. Radiation treatment plan file Dynalog file (expected position)
Ⅱ. Material & Method
Convert RTplan to Expected position using Matlab
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
12. Ⅱ. Material & Method
StepⅠ
Convert Radiation treatment plan to Expected position of MLC
• Computer Language: Matlab
• Parameters: Monitor unit, Dose rate
Step Ⅱ
Prediction Actual position of MLC using Artificial neural network
• Computer Language: Python
• Type of neural net: RNN: LSTM
• Deep learning framework: Tensorflow
• Parameters: MLC position (expected, actual), Gantry angle, collimator angle, Beam on/off sign
Step Ⅲ
Statistical analysis :Generate Probability error function
• Preprocess: Central limit theorem
• Model: Single Gaussian (mean, std)
• Computer Language: Matlab
13. • RNN’s ability to anticipate also makes them capable of surprising creativity.
• You can ask them to predict which are the most likely next notes in a melody,
then randomly pick one of these notes and play it. Then ask the net for the next
most likely notes.
How it is work ?
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
Ⅱ. Material & Method
Prediction Actual position using Artificial neural network
14. Learning for prediction of actual position of MLC
Header leaf 1 leaf 2 leaf 3 leaf 4 leaf 60
1480Dynalog
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
Ⅱ. Material & Method
Prediction Actual position using Artificial neural network
Window size: 7 time stamp
Gantry angle: Ga(t)
Collimator angle: Ca(t)
Beam on/off signal: B(t)
Leaf expected position: leafPosi(t)
Leaf actual position:
Actual position of 2nd leaf
Time: t=8
Ga(t)Ca(t) B(t) leafPosi(t)
15. Learning for prediction of actual position of MLC
Header leaf 1 leaf 2 leaf 3 leaf 4 leaf 60
1480Dynalog
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
Ⅱ. Material & Method
Prediction Actual position using Artificial neural network
Window size: 7 time stamp
Gantry angle: Ga(t)
Collimator angle: Ca(t)
Beam on/off signal: B(t)
Leaf expected position: leafPosi(t)
Leaf actual position:
Actual position of 2nd leaf
Time: t=9
Ga(t)Ca(t) B(t) leafPosi(t)
16. Learning for prediction of actual position of MLC
Header leaf 1 leaf 2 leaf 3 leaf 4 leaf 60
1480Dynalog
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
Ⅱ. Material & Method
Prediction Actual position using Artificial neural network
Window size: 7 time stamp
Gantry angle: Ga(t)
Collimator angle: Ca(t)
Beam on/off signal: B(t)
Leaf expected position: leafPosi(t)
Leaf actual position:
Actual position of 2nd leaf
Time: t=10
Ga(t)Ca(t) B(t) leafPosi(t)
17. Learning for prediction of actual position of MLC
Header leaf 1 leaf 2 leaf 3 leaf 4 leaf 60
1480Dynalog
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
Ⅱ. Material & Method
Prediction Actual position using Artificial neural network
Window size: 7 time stamp
Gantry angle: Ga(t)
Collimator angle: Ca(t)
Beam on/off signal: B(t)
Leaf expected position: leafPosi(t)
Leaf actual position:
Actual position of 2nd leaf
Time: t=11
Ga(t)Ca(t) B(t) leafPosi(t)
18. Learning for prediction of actual position of MLC
Header leaf 1 leaf 2 leaf 3 leaf 4 leaf 60
1480Dynalog
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
Ⅱ. Material & Method
Prediction Actual position using Artificial neural network
Window size: 7 time stamp
Gantry angle: Ga(t)
Collimator angle: Ca(t)
Beam on/off signal: B(t)
Leaf expected position: leafPosi(t)
Leaf actual position:
Actual position of 2nd leaf
Time: t=12
Ga(t)Ca(t) B(t) leafPosi(t)
19. Learning for prediction of actual position of MLC
Header leaf 1 leaf 2 leaf 3 leaf 4 leaf 60
1480Dynalog
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
Ⅱ. Material & Method
Prediction Actual position using Artificial neural network
Window size: 7 time stamp
Gantry angle: Ga(t)
Collimator angle: Ca(t)
Beam on/off signal: B(t)
Leaf expected position: leafPosi(t)
Leaf actual position:
Actual position of 2nd leaf
Time: t=13
Ga(t)Ca(t) B(t) leafPosi(t)
20. FC
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
𝑖𝑛𝑝𝑢𝑡𝐺𝑎𝑡𝑒(𝑡) = 𝜎(𝑊𝑥𝑖 ∙ 𝑥(𝑡) + 𝑊ℎ𝑖𝑇 ∙ ℎ(𝑡 − 1) + 𝑏𝑖)
𝑓𝑜𝑟𝑔𝑜𝑡𝐺𝑎𝑡𝑒(𝑡) = 𝜎(𝑊𝑥𝑓 ∙ 𝑥(𝑡) + 𝑊ℎ𝑓𝑇 ∙ ℎ(𝑡 − 1) + 𝑏𝑓)
𝑜𝑢𝑡𝑝𝑢𝑡𝐺𝑎𝑡𝑒(𝑡) = 𝜎(𝑊𝑥𝑜 ∙ 𝑥(𝑡) + 𝑊ℎ𝑜𝑇 ∙ ℎ(𝑡 − 1) + 𝑏𝑜)
• LSTM structure
• MLCLogNet structure
𝐿𝑜𝑛𝑔 𝑆ℎ𝑜𝑟𝑡 𝑡𝑒𝑟𝑚 𝑚𝑒𝑚𝑜𝑟𝑦 3 𝑐𝑒𝑙𝑙
𝐹𝑢𝑙𝑙𝑦 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑 𝑙𝑎𝑦𝑒𝑟
ℎ𝑖𝑑𝑑𝑒𝑛 dim = 20
o𝑢𝑡𝑝𝑢𝑡 dim = 1
Ⅱ. Material & Method
Prediction Actual position using Artificial neural network
Learning for prediction of actual position of MLC
Structure of learning frame
21. Validation of MLCLog Net
Log 1
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
Log 2 Log 3 Log 4
Log 175 Log 176 Log 178
Log 1 Log 2 Log 3
Training log set Test log set
Ⅱ. Material & Method
Prediction Actual position using Artificial neural network
Log 30
22. Ⅱ. Material & Method
StepⅠ
Convert Radiation treatment plan to Expected position of MLC
• Computer Language: Matlab
• Parameters: Monitor unit, Dose rate
Step Ⅱ
Prediction Actual position of MLC using deep learning
• Computer Language: Python
• Type of neural net: RNN: LSTM
• Deep learning framework: Tensorflow
• Parameters: MLC position (expected, actual), Gantry angle, collimator angle, Beam on/off sign
Step Ⅲ
Statistical analysis :Generate Probability error function
• Preprocess: Central limit theorem
• Model: Single Gaussian (mean, std)
• Computer Language: Matlab
25. StepⅠ
Convert Radiation treatment plan to Expected position of MLC
Step Ⅱ
Prediction Actual position of MLC using Artificial neural network
Step Ⅲ
Statistical analysis :Generate Probability error function
Ⅲ. Results
26. Footnote: Validation of calculated Expected position from RTplan Footnote: Comparison calculated expected position and expected
position extracted from dynalog file (30 th leaf of MLC)
Ⅲ. Results
Convert RTplan to Expected position using Matlab
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
27. Plot the cost value of l2 loss function Prediction actual position of MLC using
recurrent neural network (LSTM)
Ⅲ. Results
Prediction Actual position of MLC using Artificial neural network
28. Plot the cost value of l2 loss function Prediction actual position of MLC using
recurrent neural network (LSTM)
Ⅲ. Results
Prediction Actual position of MLC using Artificial neural network
30. Ⅰ. Introduction
Aim of this study
• Prediction multi-leaf collimator position and velocity errors before radiation treatment
using radiation treatment plan (RTplan) file and artificial neural network (RNN:LSTM).
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
Treatment
Planning
Computer
RT plan
Batch
Folder
RT PlansRT Plans
Convert
RTplan to
Expected position
of MLC
MLC position
expected
Prediction
Actual position
of MLC
Artificial Neural NetworkStatistical
analysis
Probability
error function
1
2
3
Is this error
acceptable?
No
Warning
MLC error prediction
[mm] [mm]
32. Ⅳ. Conclusion
• Multi-leaf collimators have had a major impact on the development of radiation therapy such
as IMRT, VMAT and IMPT.
• Small defects of the multi-leaf collimator can have a large effect on the results of radiation
therapy.
• We build workflow for predicting mechanical errors of MLC before radiation treatment.
• Consequently, our team developed a method of prediction multi-leaf collimator position
and velocity errors before radiation treatment
using radiation treatment plan (RTplan) file and artificial neural network (RNN:LSTM).
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
33. V. Further study
• Actual dose prediction using recurrent neural network (LSTM)
• Expected dose distribution • Actual dose distribution
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
34. Thank you for your attention.
2017/11/10
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐
Acknowledgement:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government
(MSIP) (No. NRF-2017R1C1B2011257)
다엽콜리메이터, Multileaf collimator는 beam fluence에 변화를 시간에 따라 주면서dose conformity를 향상시켰습니다.
Dose rate가 한 치료 내에서 변할 뿐만아니라 모든 각도를 사용하는 VMAT 방법의 경우에는
높은 dose conformit를 위해서 MLC 또한 정교한 움직임은 필수적입니다.
치료의 정확성 향상을 위해서 치료복잡도가 증가함에 따라 MLC leaf의 position uncertainty 또한 증가하게 되었습니다.
치료계획의 MLC 위치와 실제 장비에서 운영된 MLC의 위치를 확인하기 위해 가속기에서 움직인 파라미터들은 로그 형태로 기록됩니다.
가속기를 판매하고 있는 Varian 사의 경우에는 Dynalog라고 부르고 있고, 2000년대 초반에 Dynalog를 분석하는 연구가 많이 이뤄져왔습니다.
발표를 진행함에 있어서 자주 언급되는 단어들을 간단하게 정리해 보았습니다.
Radiation treatment plan file, Rtplan 파일은 fraction , external beams 등 치료에 관련 된 정보를 가지고 있습니다.
이중 하나가 MLC의 segment 집합입니다.
생성된 Rtplan은 Linac consol을 통해서 장비로 들어가고, 장비는 치료를 수행하게 됩니다.
시간에 다른 장비 운영에 대한 기록을 담고 있는 Dynalog 파일이 장비에 의해서 생성됩니다.
Dynalog 파일 내의 Expected positio은
Rtplan파일로 부터 치료수행 시간동안에 동작되길 바랬던 각 파라미터의 정보들이 들어있습니다.
반대로 Actual positio의 경우에는
치료수행 동안에 실제적으로 동작된 각 파라미터의 정보들이 들어있습니다.
즉 Machine error가 포함된 값을 가지고 있습니다.
MLC에 발생할 수 있는 에러의 경우, Random error, systemetic error 그리고,
오른쪽에 2번 leaf을 기준으로 보았을 때, 근접된 MLC의 운동방향이 반대반향일 때 Friction에 의해서 영향을 받을 수 있고,
Gantry angle이 90, 270도일 경우에 Gravity에 의해서 실제 위치는 영향을 받을 수 있습니다.
그래서 저희 연구팀은 MLC의 위치에러를 예측할 수 있는 방법에 대한 연구를 진행하였습니다.
기존의 Dynalog분석은 pre-treatment QA과정에서 장비운영 후 수행하는 사후 분석이지만,
둘간의 관계성이 명확한 Rtplan을 MLC expected position으로 변환하고,
Expected positoin을 machine error가 포함된 actual position으로 변환 할 수 있는 deep learning network을 사용하여
에러가 포함 된 정보를 만들고 분석을 통해 사전에 예측하는 방법입니다.
Rtplan과 expected position 사이의 관계는 명확합니다.
움직일 것으로 예상하는 계산값이기 때문입니다.
명확한 둘 사이의 관계를 Matlab으로 구현하여 변환을 시도하였습니다.
계산 된 expected position 을 인풋으로 machine error 를 포함한 actual positio을 예측하는 neural network 을 학습시켰습니다.
먼저 사용된 neural network에 대해서 설명드리겠습니다.
Recurrent neural network은 예측을 하는 문제에 좋은 성능을 내고 있는 기본 네트워크 골격입니다.
작동방식은 4개의 음표가 주어졌을때, 초록색 음표를 예측하고,
예측 된 음표를 포함하여 다음 음표를 예측하는 방식으로 동작합니다.
해당 컨셉을 MLC에 적용해 보도록 하겠습니다.
Dynalog를 그래픽컬 하게 시각화 하였습니다.
Dynalog의 Header에서 gantry angle, collimator angle, beam on and off signal 과
7개의 time stamp 동안 Leaf들의 expected positoin을 이용하여
Actual position을 예측하게 됩니다.
그래서 저희 연구팀은 MLC의 위치에러를 예측할 수 있는 방법에 대한 연구를 진행하였습니다.
기존의 Dynalog분석은 pre-treatment QA과정을 통한 사후 분석이지만,
둘간의 관계성이 명확한 Rtplan을 MLC expected position으로 변환하고,
Expected positoin을 machine error가 포함된 actual position으로 변환 할 수 있는 deep learning network을 사용하여
에러가 포함 된 정보를 만들고 분석을 통해 예측하는 방법입니다.