This document provides guidance on fine-tuning a treatment planning system's (TPS) commissioning through adjustments to the dosimetric leaf gap (DLG) and multileaf collimator (MLC) transmission parameters. It recommends using the MPPG 5A report as a reference for validation tests and adjusting parameters iteratively based on measurements of intensity-modulated radiation therapy and volumetric modulated arc therapy plans. The process involves creating a separate "commissioning" machine in the TPS to test adjustments without affecting clinical data. Point dose and planar film measurements of representative clinical plans are used to evaluate adjustments to DLG and MLC transmission and refine the beam model.
This is a made easy summary of ICRU 89 guidelines for gynecological brachytherapy. Extra practical questions for MD/DNB Radiotherapy exams are also attached.
This is a made easy summary of ICRU 89 guidelines for gynecological brachytherapy. Extra practical questions for MD/DNB Radiotherapy exams are also attached.
Mind the Gap: Dealing with Interruptions in Radiotherapy TreatmentVictor Ekpo
A review of guidance on compensatory steps to take due to unscheduled interruptions in patient radiotherapy treatment, due to patient illness, staff illness or machine breakdown.
Interruptions are quite often. Different centres in different literature have quoted from 6 up to 63% of patients experience interruption. To reduce the risk of cancer recurrence, the Medical Physicist needs to calculate and determine compensatory action in dose, number of fraction or other action required.
This seminar is presented as a part of weekly journal club and seminar presented in Apollo Hospital,Kolkata Department of Radiation Oncology.This seminar is moderated by Dr Tanweer Shahid.
Conventional radiotherapy treatments are delivered with radiation beams that are of uniform intensity across the field (within the flatness specification limits). Wedges or compensators are used to modify the intensity profile to offset contour in irregularities and produce more uniform composite dose distributions such as in techniques using wedges. This process of changing beam intensity profile to meet the goals of a composite plan is called intensity modulation
IMRT refers to a radiation therapy technique in which nonuniform fluence is delivered to the patient from any given position of the treatment beam to optimize the composite dose distribution. The optimal fluence profiles for a given set of beam directions are determined through inverse planning. The fluence files thus generated are electronically transmitted to the linear accelerator, which is computer controlled, to deliver intensity modulated beams (IMBs) as calculated.
Mind the Gap: Dealing with Interruptions in Radiotherapy TreatmentVictor Ekpo
A review of guidance on compensatory steps to take due to unscheduled interruptions in patient radiotherapy treatment, due to patient illness, staff illness or machine breakdown.
Interruptions are quite often. Different centres in different literature have quoted from 6 up to 63% of patients experience interruption. To reduce the risk of cancer recurrence, the Medical Physicist needs to calculate and determine compensatory action in dose, number of fraction or other action required.
This seminar is presented as a part of weekly journal club and seminar presented in Apollo Hospital,Kolkata Department of Radiation Oncology.This seminar is moderated by Dr Tanweer Shahid.
Conventional radiotherapy treatments are delivered with radiation beams that are of uniform intensity across the field (within the flatness specification limits). Wedges or compensators are used to modify the intensity profile to offset contour in irregularities and produce more uniform composite dose distributions such as in techniques using wedges. This process of changing beam intensity profile to meet the goals of a composite plan is called intensity modulation
IMRT refers to a radiation therapy technique in which nonuniform fluence is delivered to the patient from any given position of the treatment beam to optimize the composite dose distribution. The optimal fluence profiles for a given set of beam directions are determined through inverse planning. The fluence files thus generated are electronically transmitted to the linear accelerator, which is computer controlled, to deliver intensity modulated beams (IMBs) as calculated.
The use of Adaptive designs is becoming quite popular and well-perceived by the regulatory agencies such as the FDA in the US. “Adaptation” can occur in different fashion and potentially make studies more efficient (e.g. shorter duration, fewer patients) more likely to demonstrate an effect of the drug if one exists, or more informative (see “Adaptive Design Clinical Trials for Drugs and Biologics” FDA guidance).
The aim of this presentation is to illustrate a case where an adaptive design was used in a Phase III oncology pivotal study having Overall Survival as a primary end-point. The particular adaptation implemented was an un-blinded SSR that applied a promising zone approach.
The main focus will be how the adaptive design impacted the SDTM modelling, the design of some ADaM datasets (e.g. those containing the time-to-event endpoints and therefore using ADTTE ADaM model) and later on how some mapping and analysis decisions were described in both the study and analysis reviewer guide.
Measurement Procedures for Design and Enforcement of Harm Claim ThresholdsPierre de Vries
Presentation at DySPAN 2017, March 2017
Paper forthcoming on IEEE Xplore
Paper authors:
Janne Riihijärvi, Petri Mähönen (RWTH Aachen University, Germany)
J. Pierre de Vries (Silicon Flatirons Centre, University of Colorado, USA)
LVTS - Image Resolution Monitor for Litho-MetrologyVladislav Kaplan
Significant challenges for various Critical Dimension (CD) measurement matching procedures are reaching a comparable complexity as result of negative effects of roughness on the features. Due to the constant trend of integrated circuit in features reduction, impact of roughness start to be more destructive for various sets of measurement algorithms. Commonly used attempts to increase magnification for pattern recognition in measurement mode could in turn detect higher deviation from predefined patterns and thus initiate shift in placement of measurement gate. The purpose of this paper is to discuss how to reduce measurement gate (MG) placement variation impact and filter acquired data using edge correlation approach. The essence of listed above approach is to create set of width correlation function represents particular feature under test and compare it to “golden” one as a mean of detection of uncorrelated scans, which in turn should be excluded from overall computation of matching results. We describe general approach for algorithm stepping and various techniques for judgment of measurement comparison validity. Presented approach also has particular interest in determination of specified tool performance for predefined pattern recognition feature as well as for pattern recognition algorithm robustness study - direct interest for manufacturer. Precise matching estimation as part of Round Robin (RR) routines creating possibility to work with restricted amount of data and perform quick reliable qualification procedures. This paper concentrated on practical approach and used both simulation and actual data measurements data before and after proposed optimization taken by various generation tools by Hitachi (S-8840, S-9300, S-9380) in production environment
ARIMA Model for analysis of time series data.pptREFOTDEBuea
Applying the classical linear regression approach to time series data seriously violates one of the key assumptions, known as uncorrelated error terms. Therefore, there is a need for appropriate statistical tools to model these types of data. ARIMA.
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Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
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TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
IMRT_VMAT_Session 5_How to fine-tune the commissioning of a TPS.pptx
1. 5 How to fine-tune the
commissioning of a TPS
Stephen Gardner
Medical Physicist
June 18
Henry Ford Health System (Detroit, MI, USA)
1
2. Outline for today’s session
1. Spot checking your commissioning data
1. Using MPPG 5A report as a reference/outline for
photon beam model validation
2. Summary of validation tests
2. Tweaking TPS: DLG and MLC transmission
• Create a new “commissioning” machine in your TPS
• Compare to measured data and perform iterations of
DLG and MLC transmission adjustments
2
3. Learning Objectives: After this session, you will…
o Be able to perform a comprehensive but quick spot-check of
commissioning data
o Understand the detailed, step-by-step process of how to adjust
DLG and MLC transmission in the TPS, referencing their clinic’s
data
o Be able to execute the logistics and team communications
needed to safely fine-tune commissioning (e.g., creating a
separate “machine”)
5. ZOOM POLL
The dosimetric leaf gap (DLG) represents:
A. The difference in the physical leaf end and the dosimetric field edge
B. The minimum gap that is possible for an MLC system
C. The width of the sweeping gap field used during measurements
D. The width of the penumbra of an MLC-defined field
5
6. ZOOM POLL
The dosimetric leaf gap (DLG) represents:
A. The difference in the physical leaf end and the dosimetric field edge
B. The minimum gap that is possible for an MLC system
C. The width of the sweeping gap field used during measurements
D. The width of the penumbra of an MLC-defined field
6
7. References/Guides for Photon Beam
Commissioning
o MPPG 5A has proven to be an extremely
helpful guide for commissioning photon
algorithms – published in 2015
• https://doi.org/10.1120/jacmp.v16i5.5768
o The strength of this document is that it
covers many aspects of algorithm
performance in a comprehensive, clear,
and concise way
o Scope of the document – gives
recommendations for:
• Data Acquisition and Processing
• Algorithm Validation (Photon and Electron)
– Basic model validation
– Heterogeneity correction validation (not
included in this lecture)
– IMRT/VMAT dose validation
7
8. MPPG 5A Breakdown
o Table 3
• Basic validation of beam model – comparison in beam configuration
workspace, dose normalization is accurate
o Table 4
• More in-depth validation of 3D-CRT delivery – measurements of dose
agreement in high-dose region, penumbra, and out-of-field region
o Table 5
• Summary of dose agreement tolerances
o Table 6 (N/A for this lecture) – heterogeneity correction
validation
o Table 7
• IMRT/VMAT Validation – including TG-119/Clinical Case validation
9. Table 3: Basic Model Validation
o Use Table 3 from the MPPG document to perform basic validation of the model:
• (5.1) Within the physics/beam config module, compare PDD and profile for large field (calculated
vs. measured)
• (5.2) Within a test plan in the TPS - calculate a plan using absolute dose calibration conditions
and ensure that you are calculating 1 cGy/MU at the calibration point (sanity check!)
• (5.3) Quick check of PDD and output factor (OF) – within a test plan in the TPS, compare
calculated PDD and OF to measured data
o Note – these tests don’t require any new measurements! Just some re
9
10. Table 3: Examples –
Test 5.1
o Test 5.1 – Within
the physics module
(beam
configuration
workspace),
compare the PDD
and profile –
measured vs.
calculated dose
10
11. Table 3: Examples – Test 5.2
o Test 5.2 – Calculate plan using
calibration geometry and
ensure dose at calibration
depth is 1 cGy/MU.
** For our clinic, the calibration
geometry is 10x10 cm2 field
size, 100 cm SSD, with
calibration depth at dmax.
11
12. Table 3: Examples – Test 5.3
o Test 5.3 – Calculate a plan in
the TPS that simulates
scanning data and compare
calculated vs. measured
• This requires the user to take
a line profile at the
appropriate depth
** For our clinic, this involved
calculating a plan at 100 SSD for
the relevant field size
13. Table 4: Basic Photon Beam Validation Summary
o To perform this set of validation tests, measure the absolute dose at several
points for each of these fields:
• Different depths (slightly beyond dmax, mid-range/10-15 cm depth, and deep/25-30 cm
depth)
• Different off-axis positions – high dose, penumbra, and low dose
13
14. Table 4: Examples - Test
o Test 5.4 – small MLC-defined
field (4x4 cm2 MLC field and
5x5 cm2 jaw)
o Test 5.5 – large MLC-defined
field with extensive blocking
o Test 5.6-5.7 – use same
aperture with different SSD
** For each of these, perform
measurements at high dose,
penumbra, low dose regions at
dmax depth, 10 cm depth, and
25 cm depth.
Test 5.4
Test 5.5
Tests 5.6-5.7
15. Table 5: Evaluation Methods and Tolerances
o Three different regions are specified: high dose, penumbra, and low
dose tail
• For high dose and low dose tail regions – tolerance is based on dose
difference (Percent)
• For penumbra – tolerance is based on distance to agreement (DTA)
16. Table 7: VMAT/IMRT Summary
o The IMRT/VMAT tests are in
addition to the tests in Table
3/4 from prior slides
o In my opinion - the most
important tests from this
group are the TG-119 tests
(7.3) and Clinical tests (7.4)
• These plans will be used to
validate and adjust the TPS
model as needed to ensure
optimal IMRT/VMAT delivery
17. Summary – Validation Testing
(Scanning/Open Field)
o Scanning Data –
• Verify profiles using flatness and symmetry for small, mid-size, and large field sizes
at dmax, 10 cm, and 30 cm depth
– For example: 4x4, 10x10, and 30x30 cm2 field sizes (you can verify additional field
sizes if warranted)
– Typically, we want to see symmetry < 1% and then flatness consistent with past
results (within 1% of baseline if available)
– Compare measurement to calculation in TPS to verify model behavior
• Verify PDD for small, mid-size, and large field sizes by comparing measured and
calculated doses
– While you are scanning these - include MLC-defined small field PDD to satisfy
MPPG 5A test 7.1 (if you have access to a small field detector such as diode)
o Output Factors – verify agreement between calculated and measured
output factors for a variety of field sizes, ranging from 3x3 to 40x40 cm2
• While you are measuring these - include MLC-defined small field OF to satisfy
MPPG 5A test 7.2 (again if you have access to a small field detector such as diode,
micro ion chamber, etc.)
18. TG-119 and Clinical Case PSQA
o TG-119 Planning Guide, Reporting Form, and
Structure Sets can be found on the AAPM website:
https://www.aapm.org/pubs/tg119/default.asp
o TG-119 data set includes DICOM CT, RT Structure
files as well as planning goals and instruction
document:
• C-Shape
• Mock HN plan
• Mock Prostate
• Multi-Target
o Additional plans for testing – representative plans
from previous patients
• Replan as needed with new machine/beam model
• Should include typical disease sites encountered at your
center – HN, Prostate, Lung, Brain, etc.
20. Overview for this Section
o MLC DLG and Transmission – effect on the dose distribution
for various delivery modalities (static fields, IMRT, and VMAT)
o Practical Overview/Tips on MLC Parameter Adjustment
o Creating a new ‘commissioning’ machine for your testing
o How to adjust DLG and MLC Transmission - Detailed, step-by-
step process of how to adjust these within the TPS, referencing
their clinic’s data
o Practical dry-run of adjusting model parameters with realistic
clinical data
21. MLC DLG and Transmission –
Effect on the Dose Distribution for
Various Delivery Modalities
30. How to – create a new ‘commissioning’
machine in your TPS and adjust DLG/MLC
Transmission
o General idea is to…
• Create a copy of the machine
• Create a copy of the beam model
• Adjust DLG/MLC Transmission to improve agreement
31. Creating a ‘Commissioning’ Machine
o For ARIA Users, the
process is:
• RT Admin Workspace
– Step 1: Select
machine to copy ->
Insert -> Export
Machine…
– Step 2: Re-import
the machine you
just exported and
name it
appropriately
– Step 3: Rename
imported machine
something like
‘Test_Physics’
– Step 4: Ensure MLC
add-on information
matches the real
machine and enter
starting DLG/MLC
transmission values
Step 1: Export
Step 2: Re-import
Step 4: MLC Add-on
Material and DLG/MLC trans
Step 3: Update
Machine Name
32. Creating a copy of the
beam model for testing
o For ARIA Users, the process is:
• Beam Config Workspace
– Step 1: select the machine
(Test_Physics) / energy (6x) /
algorithm (AAA_11030) and right-click
-> New Beam Data…
– Step 2: setup copy of beam model
▪ Enter appropriate therapy unit name
▪ Select ‘Copy existing data to the
calculation model’
• DO NOT select ‘assign’
• Ensure that you have selected the correct
beam model to copy and click OK
– Step 3: Match and Assign Add-Ons –
select ‘In Use’ for open field and EDW
and select Automatic Match for All.
– Step 4: Spot check values for Gamma
Error Histogram and Output Factors
to verify consistency against clinical
machine and approve test model
(right-click on the model -> Approve)
Step 3: Match and assign
Add-ons
Step 2: Setup copy of
beam model
Step 1: select
model
Step 4:
Approve Model
34. How do I adjust the DLG/MLC Transmission
Values?
o For ARIA v13 and later:
• RT Admin Workspace –
– **Note – this will define these values for all beam models for this machine**
– Go to ‘Radiation and Imaging Devices’
– Select the test physics machine and go to ‘MLC’ tab
– Enter values for MLC Transmission factor and DLG within the ‘Dosimetric
Properties’ section
o For ARIA v11 and earlier:
• Beam Configuration Workspace –
– Select the test physics beam model
– Go to Beam Data -> Dosimetric Data and enter MLC Transmission factor and
DLG
o For other TPS vendors – consult with the manual for instructions on
this process
35. Initial
Overview –
Iterative
Tweaking
Process
• Resource for TPS beam model validation – Medical Physics Practice Guideline 5.A
• MLC Parameter/Beam Model Optimization (like any optimization process) is
iterative
• The overall process goes something like:
1. Acquire initial measurements for MLC parameters -> input to TPS
2. Calculate Beam Model
3. Generate/calculate IMRT/VMAT plans for verification
• For conventional planning – use TG-119 dataset and some previous
clinical plans if available
• For SRS/SBRT planning – critical to use representative stereotactic
plans to validate the beam model!!!
• These treatment plans should meet the relevant clinical
goals/constraints to best simulate a typical IMRT/VMAT delivery
4. Acquire point dose measurements for verification IMRT/VMAT plans
• Proper detector selection is critical – ideal chamber is a small volume
ion chamber such as CC01 or PinPoint chamber
• High dose readings to simulate the target volume!
• Low dose readings to simulate critical organs at risk!
• The point dose measurements will the primary means for MLC
parameter selection
5. Acquire planar dose measurements using Gafchromic film or array device
• Compare results once MLC parameters are finalized from point dose
measurements
36. MLC
Parameter
Testing –
Practical Key
Points
• Key Points for Emphasis
• Use real IMRT/VMAT plans to validate the beam model/MLC parameter values
• TG-119 data sets
• Previous clinical cases
• Make sure the intended use of the linac is included in the test cases!
• Measurements should include both point and planar dose analysis
• My clinic preference – use point dose measurements for initial tweaking of
MLC parameters
• When is the model good enough?
• TG-119 utilized confidence limits for QA results
• High Dose Point Measurement → CL = ±4.5%
• Low Dose Point Measurement → CL = ±4.7%
• Planar Dose Measurement → Gamma(3%,3mm) > 87.6%
• TG-218 proposed tolerance limits and action limits for pre-treatment QA:
• Tolerance Limit:
• Ion Chamber Measurement <2%
• Gamma(3%,2mm) > 95%
• Action Limit:
• Ion Chamber Measurement <2%
• Gamma(3%,2mm) > 95%
• Investigate outliers for additional measurements
• Aim to get average percent difference close to 0% (Mean Perc. Diff.)
• Minimize spread in QA results (Standard Deviation Of Perc. Diff.)
• Compare to other institutions/literature with similar linac and TPS
• If possible – obtain independent audit of IMRT/VMAT delivery from another
physicist/institution
• How much can you tweak the TPS values?
• My preference – tweak as little as possible to get agreement that fulfills
clinical goals of the machine
37. MLC Parameters –
Interpreting Results
• Which way do I need to tweak the
value?
• Increasing DLG value
→ Increase in calculated dose
• Increasing MLC Transmission value
→ Increase in calculated dose
• Example:
• If plan dose is higher than measured
dose → the next step is to increase DLG
and/or MLC Transmission and re-
calculate
Example: Plan dose is higher
than film dose → consider
decreasing DLG and/or MLC
Transmission
38. DLG Adjust
Details – Test
Plan Process
1. Treatment Planning – Develop a good quality plan using the test plan
structure set. Have primary treatment planning staff generate the
plan if possible!
2. QA Plan – Map the plan from step 1 onto the appropriate phantom
• Need to perform both chamber measurement and a planar
dose/fluence measurement
• Planar dose measurement can be using film, detector array,
or even the EPID
• Phantom choices include:
• Solid Water slab phantom with place for chamber/film
• Acrylic phantom with place for chamber/film
• Detector array (MapCheck, ArcCheck, Delta4, Matrixx, etc.)
• EPID measurement (Portal Dosimetry)
3. Measure QA plan and compare to predicted dose from TPS
calculation
4. Compile all test plan results (IMRT and VMAT) before making any
adjustments
• Note – IMRT and VMAT trends can differ!!
39. Practical dry-run of adjusting model
parameters with realistic clinical data
o The background information for this example:
• TG-119 plans and Clinical Cases (IMRT and VMAT) for 10x
energy modewere measured with ion chamber in high dose
and lose dose region
• Starting point was the initial measured DLG value of 0.115
cm and MLC Transmission = 1.65%
• Total Case Breakdown
– TG-119: 3 IMRT and 3 VMAT
– Clinical Cases: 2 IMRT and 2 VMAT
40. MLC Parameter
Selection
Summary
Points – 10 MV
beam modeling
process
• Decision on MLC parameters such as DLG and
Leaf Transmission depend on:
• The clinical goals for the beam model:
• Which disease sites will be treated
routinely?
• Which modality will be used more
often – IMRT or VMAT?
• The trend in the data:
• Which parameter values will minimize
the percent difference between
planned and measured doses?
• Which parameter values will minimize
the spread in the results comparing
planned and measured doses?
• Which parameter values will minimize
outliers in the data?
41. Plan Type
Measured Point
Dose (cGy)
Calculated Dose
(DLG = 0.115 cm)
[MLC_trans = 0.0185]
Perc. Diff.
C Shape 2.154 2.052 4.73%
H&N 2.100 2.095 0.24%
H&NSIB 2.176 2.135 1.86%
Prostate 1.988 1.957 1.54%
Prostate LN 1.968 1.946 1.11%
C Shape 2.481 2.470 0.45%
H&N 2.195 2.191 0.19%
H&NSIB 2.122 2.127 -0.22%
Prostate 1.997 1.977 1.02%
Prostate LN 1.893 1.937 -2.32%
VMAT Average 1.90%
VMAT St. Dev. 1.70%
IMRT Average -0.18%
IMRT St. Dev. 1.28%
Overall Average 0.86%
Overall St. Dev. 1.79%
Plan Type
Measured Point
Dose (cGy)
Calculated Dose
(DLG = 0.115 cm)
[MLC_trans = 0.0185]
Perc. Diff.
C Shape 0.320 0.344 -7.51%
H&N 1.314 1.297 1.28%
H&NSIB 1.124 1.126 -0.18%
Prostate 1.322 1.279 3.22%
Prostate LN 0.890 0.874 1.80%
C Shape 0.489 0.520 -6.41%
H&N 1.316 1.331 -1.16%
H&NSIB 1.201 1.236 -2.94%
Prostate 1.650 1.630 1.18%
Prostate LN 1.136 1.163 -2.35%
VMAT Average -0.28%
VMAT St. Dev. 4.22%
IMRT Average -2.33%
IMRT St. Dev. 2.77%
Overall Average -1.31%
Overall St. Dev. 3.54%
IMRT
Results (TG-119 Table VII)- High Dose
VMAT
IMRT
Results (TG-119 and Clinical Cases)- Low Dose
VMAT
Iteration 1 – DLG = 0.115 cm /
MLC_Trans = 1.65%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
0 1 2 3 4 5 6
Percent
Difference
Iteration #
VMAT - High Dose
IMRT - High Dose
VMAT - Low Dose
IMRT - Low Dose
42. Iteration 2 – DLG = 0.140 cm /
MLC_Trans = 1.65%
Plan Type
Measured Point
Dose (cGy)
Calculated Dose
(DLG = 0.140 cm)
[MLC_trans = 0.0165]
Perc. Diff.
C Shape 2.154 2.048 4.91%
H&N 2.100 2.087 0.62%
H&NSIB 2.176 2.128 2.19%
Prostate 1.988 1.954 1.69%
Prostate LN 1.968 1.942 1.31%
C Shape 2.481 2.448 1.34%
H&N 2.195 2.173 1.01%
H&NSIB 2.122 2.113 0.44%
Prostate 1.997 1.974 1.17%
Prostate LN 1.893 1.921 -1.47%
VMAT Average 2.15%
VMAT St. Dev. 1.65%
IMRT Average 0.49%
IMRT St. Dev. 1.15%
Overall Average 1.32%
Overall St. Dev. 1.60%
Plan Type
Measured Point
Dose (cGy)
Calculated Dose
(DLG = 0.140 cm)
[MLC_trans = 0.0165]
Perc. Diff.
C Shape 0.320 0.338 -5.64%
H&N 1.314 1.287 2.04%
H&NSIB 1.124 1.117 0.62%
Prostate 1.322 1.275 3.52%
Prostate LN 0.890 0.867 2.59%
C Shape 0.489 0.496 -1.50%
H&N 1.316 1.311 0.36%
H&NSIB 1.201 1.220 -1.61%
Prostate 1.650 1.626 1.43%
Prostate LN 1.136 1.149 -1.11%
VMAT Average 0.63%
VMAT St. Dev. 3.66%
IMRT Average -0.49%
IMRT St. Dev. 1.33%
Overall Average 0.07%
Overall St. Dev. 2.66%
IMRT
Results (TG-119 Table VII)- High Dose
VMAT
IMRT
Results (TG-119 and Clinical Cases)- Low Dose
VMAT
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
0 1 2 3 4 5 6
Percent
Difference
Iteration #
VMAT - High Dose
IMRT - High Dose
VMAT - Low Dose
IMRT - Low Dose
43. Plan Type
Measured Point
Dose (cGy)
Calculated Dose
(DLG = 0.115 cm)
[MLC_trans = 0.0165]
Perc. Diff.
C Shape 2.154 2.040 5.29%
H&N 2.100 2.074 1.24%
H&NSIB 2.176 2.116 2.74%
Prostate 1.988 1.945 2.14%
Prostate LN 1.968 1.932 1.82%
C Shape 2.481 2.385 3.88%
H&N 2.195 2.151 2.01%
H&NSIB 2.122 2.096 1.24%
Prostate 1.997 1.966 1.57%
Prostate LN 1.893 1.892 0.06%
VMAT Average 2.65%
VMAT St. Dev. 1.57%
IMRT Average 1.75%
IMRT St. Dev. 1.39%
Overall Average 2.20%
Overall St. Dev. 1.48%
Plan Type
Measured Point
Dose (cGy)
Calculated Dose
(DLG = 0.115 cm)
[MLC_trans = 0.0165]
Perc. Diff.
C Shape 0.320 0.337 -5.33%
H&N 1.314 1.276 2.88%
H&NSIB 1.124 1.108 1.42%
Prostate 1.322 1.262 4.50%
Prostate LN 0.890 0.862 3.15%
C Shape 0.489 0.482 1.36%
H&N 1.316 1.290 1.96%
H&NSIB 1.201 1.203 -0.19%
Prostate 1.650 1.615 2.09%
Prostate LN 1.136 1.131 0.47%
VMAT Average 1.33%
VMAT St. Dev. 3.88%
IMRT Average 1.14%
IMRT St. Dev. 0.98%
Overall Average 1.23%
Overall St. Dev. 2.67%
IMRT
VMAT
IMRT
Results (TG-119 and Clinical Cases)- High Dose
Results (TG-119 and Clinical Cases)- Low Dose
VMAT
Iteration 3 – DLG = 0.140 cm /
MLC_Trans = 1.85%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
0 1 2 3 4 5 6
Percent
Difference
Iteration #
VMAT - High Dose
IMRT - High Dose
VMAT - Low Dose
IMRT - Low Dose
44. Plan Type
Measured Point
Dose (cGy)
Calculated Dose
(DLG = 0.16 cm)
[MLC_trans = 0.0165]
Perc. Diff.
C Shape 2.154 2.056 4.54%
H&N 2.100 2.097 0.15%
H&NSIB 2.176 2.138 1.73%
Prostate 1.988 1.960 1.39%
Prostate LN 1.968 1.949 0.96%
C Shape 2.481 2.498 -0.68%
H&N 2.195 2.192 0.14%
H&NSIB 2.122 2.127 -0.22%
Prostate 1.997 1.981 0.82%
Prostate LN 1.893 1.943 -2.64%
VMAT Average 1.75%
VMAT St. Dev. 1.67%
IMRT Average -0.52%
IMRT St. Dev. 1.31%
Overall Average 0.62%
Overall St. Dev. 1.85%
Plan Type
Measured Point
Dose (cGy)
Calculated Dose
(DLG = 0.16 cm)
[MLC_trans = 0.0165]
Perc. Diff.
C Shape 0.320 0.340 -6.26%
H&N 1.314 1.296 1.36%
H&NSIB 1.124 1.123 0.09%
Prostate 1.322 1.285 2.76%
Prostate LN 0.890 0.872 2.03%
C Shape 0.489 0.508 -3.96%
H&N 1.316 1.328 -0.93%
H&NSIB 1.201 1.234 -2.77%
Prostate 1.650 1.633 1.00%
Prostate LN 1.136 1.155 -1.64%
VMAT Average 0.00%
VMAT St. Dev. 3.64%
IMRT Average -1.66%
IMRT St. Dev. 1.88%
Overall Average -0.83%
Overall St. Dev. 2.86%
IMRT
Results (TG-119 Table VII)- High Dose
VMAT
IMRT
Results (TG-119 and Clinical Cases)- Low Dose
VMAT
Iteration 4 – DLG = 0.160 cm /
MLC_Trans = 1.65%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
0 1 2 3 4 5 6
Percent
Difference
Iteration #
VMAT - High Dose
IMRT - High Dose
VMAT - Low Dose
IMRT - Low Dose
45. Plan Type
Measured Point
Dose (cGy)
Calculated Dose
(DLG = 0.16 cm)
[MLC_trans = 0.0185]
Perc. Diff.
C Shape 2.154 2.059 4.40%
H&N 2.100 2.105 -0.23%
H&NSIB 2.176 2.145 1.40%
Prostate 1.988 1.963 1.24%
Prostate LN 1.968 1.954 0.70%
C Shape 2.481 2.520 -1.57%
H&N 2.195 2.209 -0.63%
H&NSIB 2.122 2.141 -0.88%
Prostate 1.997 1.984 0.67%
Prostate LN 1.893 1.960 -3.53%
VMAT Average 1.50%
VMAT St. Dev. 1.74%
IMRT Average -1.19%
IMRT St. Dev. 1.54%
Overall Average 0.16%
Overall St. Dev. 2.10%
Plan Type
Measured Point
Dose (cGy)
Calculated Dose
(DLG = 0.16 cm)
[MLC_trans = 0.0185]
Perc. Diff.
C Shape 0.320 0.345 -7.83%
H&N 1.314 1.306 0.60%
H&NSIB 1.124 1.132 -0.71%
Prostate 1.322 1.289 2.46%
Prostate LN 0.890 0.879 1.24%
C Shape 0.489 0.531 -8.66%
H&N 1.316 1.348 -2.45%
H&NSIB 1.201 1.250 -4.11%
Prostate 1.650 1.637 0.76%
Prostate LN 1.136 1.173 -3.23%
VMAT Average -0.85%
VMAT St. Dev. 4.07%
IMRT Average -3.54%
IMRT St. Dev. 3.40%
Overall Average -2.19%
Overall St. Dev. 3.81%
IMRT
Results (TG-119 Table VII)- High Dose
VMAT
IMRT
Results (TG-119 and Clinical Cases)- Low Dose
VMAT
Iteration 5 – DLG = 0.160 cm /
MLC_Trans = 1.85%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
0 1 2 3 4 5 6
Percent
Difference
Iteration #
VMAT - High Dose
IMRT - High Dose
VMAT - Low Dose
IMRT - Low Dose
47. Summary – DLG Testing Plan Process
1. Treatment Planning – Develop a good quality plan using the test plan structure set. Have primary
treatment planning staff generate the plan if possible!
2. QA Plan – Map the plan from step 1 onto the appropriate phantom
• Need to perform both chamber measurement and a planar dose/fluence measurement
• Planar dose measurement can be using film, detector array, or even the EPID
• Phantom choices include:
• Solid Water slab phantom with place for chamber/film
• Acrylic phantom with place for chamber/film
• Detector array (MapCheck, ArcCheck, Delta4, Matrixx, etc.)
• EPID measurement (Portal Dosimetry)
3. Measure QA plan and compare to predicted dose from TPS calculation
4. Compile all test plan results (IMRT and VMAT) before making any adjustments
• Note – IMRT and VMAT trends can differ!!
48. Practical Tips – Adjusting Parameters on
Approved Beam Models
• If you have a beam model that is already approved and need to make adjustments for IMRT/VMAT
commissioning:
1. Use caution! Think about what could go wrong before making any adjustments. Discuss
with other physicists to make sure you have thought of everything that could come up.
2. Communicate! Once you have a plan, talk about it with relevant staff
3. Calculate! Plan out a time when you can perform the dose calculations with the
preliminary MLC DLG/Transmission values.
1. This may need to be done after-hours or on a weekend.
4. Reset! Depending on the workflow, make sure to reset the MLC parameters back to the
clinically approved values.
1. If you have multiple DLG/Transmission values you would like to test (multiple
iterations), this is the time to perform all iterations
5. Verify! Once you have reset the MLC parameters back to the original values, re-calculate a
set of test plans to verify constancy.
6. Compare! Once you have the calculations done, you can compare to the measured values
and determine optimal parameters
49. SRS/SBRT – You may need a
separate algorithm!
• My experience – the level of
modulation for conventional
IMRT/VMAT planning is quite
different than for SRS/SBRT
planning
• Typically, the optimal
DLG value for TG-119
planning is different
than for representative
SRS/SBRT cases
• Example at left: 6FFF
beam used for SRS/SBRT
delivery at one of our
• More data for this
example shown on
next slide
50. CONCLUSION
o A ‘commissioning’ machine can be created to test out the
parameters for the TPS model
o The TPS model will be adjusted based on comparison of
calculated and measured results for IMRT/VMAT plans
o A representative process for adjusting MLC parameters has
been shared for learning purposes
o The next lecture is transitions from commissioning and
adjusting the beam model to routine QA of treatment plans. The
topic is: “Patient-Specific and High-yield Machine QA for IMRT”
50
51. REFERENCES
o AAPM Task Group 119:
https://www.aapm.org/pubs/tg119/default.asp
o Medical Physics Practice Guidelines (MPPG) 5A:
https://doi.org/10.1120/jacmp.v16i5.5768
51
Thank you for your attention!