An Introduction to Intensity Modulated

RadiotherapyPHY778 Lecture

April 1, 2009

Outline Introduction Process Physics Technological Implementation Sites

Introduction IMRT

Ability to deliver many beamlets of varying radiation intensity within one treatment field

Beamlet Smallest element to be

modified

Beamlet

Field Width

Fie

ld L

engt

h

Introduction Inverse treatment planning process

Clinical objectives (goals) specified first Planning system optimizes the plan’s parameters

to satisfy the defined clinical objectives Field’s fluence is optimized for IMRT

Introduction Optimization yields the set of beamlet

weights (fluence map) Fluence map is converted to deliverable

sequences which depend on technique

Implementation

ProcessPatient Selection

↓Simulation

↓Target and Tissue Delineation

↓Treatment Planning/Optimization

↓Plan Evaluation

↓Quality Assurance

↓Treatment Delivery

↓Followup

Patient Selection Nasopharynx T2N2

Improved salivary outcome No difference in reported xerostomia score

Patient Selection Prostate Cancer

Dose escalation improves outcome Rectal toxicity is dose dependent

Rx to 76 Gy

95%

50%

Patient Selection Early stage breast cancer

Less heterogeneity Less moist desquamation Improved late cosmesis

Patient Selection Lung

Proximity to large critical structures Pulmonary toxicity life-threatenning Dose-volume relationship poorly understood Target motion may be a problem

Simulation Tumour localization is critical

Conformal dose distribution Minimize expansion of PTV into OAR

Simulation Patient immobilization depends on site

Masks Chest board Characterize device

Simulation Inter-fraction motion

Implanted gold seed fiducials Surgical clips Bony anatomy Calcifications

Simulation Intra-fraction motion

Bladder/bowel preparation Compression Gated therapy

Simulation Fuse images from various modalities

Contouring Evidence-based Gross disease Potential routes of local and regional spread of

disease Patterns of failure Principles of ICRU 50/62 Normal tissue tolerances/avoidance criteria Radiobiology of dose-fraction-time

Target Delineation ICRU 50, 62

(A) GTV+CTV(B) (A) + IM (organ

motion)(C) (B) + SM (setup

uncertainty)(D) SM + IMPTV = non-linear

combination of SM + IM

PTV versus OAR

Conflicts can arise

Contouring Nodal atlas (normal anatomy)

Contouring example

Planning Number and placement of beams Objectives Specify dose Concurrent boost

Number and Placement of beams Standard beam arrangements usually used Usually 5-9 fields sufficient Some planning systems capable of Beam

Angle Optimization prior to Fluence map optimization

Planning Number and placement of beams

1 9

8

7

65

4

3

2 1

2

34 5

6

7

Beam number and placement Considerations:

Complexity of the target shape Proximity to critical organs Previous RT? Central or lateralized target volume Equally spaced Gantry angles that best cover the target and miss

critical structures Ideally no opposing beams (some exceptions)

Parallel Opposed Fields

During optimization, rays (beamlets) 2 & 2’ compete with each other

Not possible to optimize dose to one point without changing dose to the other points

Rays do not compete with each other

All rays are useable Possible to optimize dose to

all points A, B, and C individually

Beam number and placement Use of non-coplanar fields may help to

significantly decrease the dose to critical structures

Collimator Rotation Minimize leakage Maximize coverage

Objectives Allows definition of clinical goals Each objective has a priority assigned Objectives may be base on:

1. dose

2. clinical knowledge

3. equivalent uniform dose (EUD)

4. TCP or NTCP (estimate local control or toxicity)

Objectives Minimum dose objective

Penalty if any point in a TARGET structure receives less than a specified dose

Maximum dose objective Penalty if any point in an OAR structure receives

more than a specified dose

Objectives- Max and Min dose

Maximum = 50 Gy

Minimum = 70 Gy

Objectives – Uniform dose

Maximum Dose

Maximum = 70 Gy

Minimum = 70 Gy

Objectives – Dose volume objectives

Maximum Dose

> 70 Gy to 90% Target

< 60 Gy to 10% OAR

Objectives – Equivalent Uniform Dose

aN

i

aidN

/1

1

1EUD

Region EUD a

Target 71 -8.0

Parotid 30 5.0

Brainstem 49 4.6

Cord 43 7.4

Physics Objective Functions:

Physics Minimize objective function using gradient

based methods:

I is a vector of beamlet weights If Mold = 1, steepest descent algorithm If Mold = Hold, Newton’s method where Hold is

the inverse of the Hessian matrix

Physics – optimization algorithms Simulated annealing

Attempts to find global minimum The probability for accepting a trial configuration is

controlled by the temperature and is given by:

where ΔF is the increase of the objective function and T is the system temperature.

The temperature is gradually lowered according to an empirically chosen cooling schedule.

Physics – optimization algorithms Iterative algorithms most common Simulated annealing and genetic algorithms

too computationally intensive Filtered back-projection and direct Fourier

transformation methods have difficulty with handling negative fluence and non-invariant kernels.

Physics – Optimization in Eclipse All contours are sampled by point clouds

Physics - optimization in Eclipse Dose contribution from

beamlet to each cloud point is established

In the process of optimization, “weight” of each beamlet is changed

Physics - optimization in Eclipse Multi-Resolution Dose Calculation (MRDC)

Algorithm used for a fast dose estimation during an optimization phase

Objective (penalty) function combines all objectives into a single function

Physics - optimization in Eclipse Goal is to minimize a

value of objective function Total penalty (black

curve) always goes down

Objectives may be changed on the fly Creates a discontinuity

in the objective function

Physics - optimization in Eclipse Dose volume optimizer (DVO) handles

optimization of fluence Optimized fluence passed on to the leaf motion

calculator (LMC) Delivery method chosen:

Multiple static fields Sliding window

AAA algorithm used for final dose calculation LMC determines MUs

Physics - optimization in Pinnacle First generation

Physics - optimization in Pinnacle Direct Machine Parameter Optimization (DMPO)

IMRT - optimization in practice Overlapping structures

with competing dose objectives Assign overlap region to

new structure Assign distinct dose

objective to each structure

IMRT - optimization in practice PTV ring

Represents margin around CTV

95% < PTV ring dose < 100%

Outer ring Represents normal tissue

surrounding PTV Outer ring dose < 95% Improves conformity of

distribution

IMRT - optimization in practice Treat aggressively/Spare lightly

If optimization problem is constructed so that it is physically possible to meet objectives, then a solution will be found.

PTV coverage will be achieved and solution will not be sensitive to objective weights.

If it is not physically possible to meet objectives, solution will be very sensitive to choice of objective weights and typically clinically unacceptable

IMRT plan not optimized but designed IMRT planning often a trial-and-error process.

IMRT - optimization in practice Must set minimum segment area

Dosimetry of small irregularly shaped segments is uncertain, particularly in the presence of heterogeneities.

Must limit minimum segment MUs Some LINACS have limited dose linearity down

to small MUs (not Varian due to gridded gun). Must limit maximum number of segments

Plan must deliver efficiently.

IMRT – radiation protection Increased MUs needed for IMRT

Typical values for 200 cGy fraction, : 700 MU for step-and-shoot 1200 MU for sliding window

Higher beam energy lowers peripheral dose but >10 MV neutron generation important

Secondary cancer risk is potentially greater

IMRT – MLC technical details Carriage (bank)

Part of MLC which carries leaves

Leaf Part of MLC used as

final beam limiting device

IMRT – MLC technical details MLC leaf design

IMRT – MLC technical details Tongue and groove effect

IMRT – MLC technical details Inter-digitation

IMRT – MLC transmission Amount of radiation

transmitted through the leaves fully blocking the beam

Consists of : Inter-leaf

transmission Intra-leaf

transmission

IMRT – MLC dosimetric leaf gap Accounts for extra transmission through the

rounded leaf edge Modeled as an apparent gap between two closed

straight edge leaves

IMRT – MLC minimum dose dynamic leaf gap

Minimal tip to tip distance which needs to be maintained for any moving leaf pair in the dMLC mode

IMRT – MLC leaf speed Speed of the leaf at the level of isocenter.

Maximum limit for Varian MLC is 3cm/ s. Model 2.5 cm/s in LMC. Allows for adjustment of any leaf during

treatment.

IMRT – Dose/Arc dynamic leaf tolerance

Maximum allowed difference between the planned and actual leaf positions

IMRT – Jaw over travel The maximum distance the collimator jaw

can extend over the central axis.

IMRT – Leaf over travel The maximum distance an MLC leaf can

extend over the central axis

IMRT – Leaf Positions Fully retracted leaf is

in the basic position within the carriage. The leaf is fully inside the carriage, at the “end” position.

Extended leaf is extended from the carriage

IMRT – Leaf Span Maximum distance

from a tip of the most retracted leaf to the tip of the most extended leaf.

Limits a maximum field size, which can be delivered without repositioning of a carriage.

IMRT – Multiple carriage delivery Used for large

modulated fields Carriages cannot move

while the beam is on Treatment field split into

subfields with a width smaller than the leaf span

Electronic compensation Replacement of physical compensator by

means of dynamic MLC delivery. Forward planned IMRT. Electronic Compensator (planar) Irregular Surface Compensator (curved surface) Field in Field Compensator (segmented fields)

Complete Irradiated Area Outline (CIAO) For portal verification

of intensity modulated field

Based on threshold fluence

IMRT QA – Ion chamber array

Reliable 2%/2mm digital evaluation

Real-time analysis 4 patients in 30

minutes

IMRT QA

Ion-chamber array

IMRT QA – Day 0

Pinnacle Fluence Map EPID image