Four Dimensional Computed Tomography in Radiation Therapy - basic principles and implementation in a radiation oncology setting

1. 4D CT in Radiation
Therapy
Parminder S. Basran, PhD
Dept. Medical Physics
BCCA-Vancouver Island Centre

2. Outline
• The PTV problem
– ITV, MTV
• Various solutions
• Our solution
• Protocol

3. ICRU standard definitions for
margins in radiation therapy
GTV
CTV
ITV
PTV

4. PTV margin assessment
• Requires compartmentalizing
“independent” sources of
displacement errors
– Combine errors in quadrature
– ‘Recipe’ based (ex: VanHerk formulas)

5. PTV margin components
- Inter-fraction motion: i.e., motion between
fractions
- Primarily a function of the immobilization and
localization methodology
- Other considerations: patient preparation (ex:
bowel status), other confounding factors during
treatment (ex: degrading pulmonary status)
- Intra-fraction motion
- Primarily a function of the extent of the target
motion during the course of treatment

6. PTV: Intra-fraction motion
- Intra-fraction motion
- motion during the course of treatment
- Major factors determine the extent of
target motion during treatment
- Voluntary motion
- Involuntary motion (done without intention)
- Breathing motion
- Gastro-intestinal motion
- Cardiac motion and pulsatile motion
- Coughing, sneezing, swallowing, twitches, etc.,

7. PTV: Intra-fraction motion
due to Breathing
Large magnitude of displacement errors for
lung, liver, upper GI disease : MTV
Somewhat periodic/regular
Confounds the treatment two key ways
- localization of the target
- Irradiation of the surrounding normal tissue
(i.e., lung)

8. Breathing Motion:
Target Localization (current approach)
GTV
CTV
PTV = CTV + standard margin

9. Breathing Motion:
normal tissue toxicity
• Growing evidence that significant portions
of lung can be spared if the margin
associated with respiratory motion not
included in the PTV definition
GTV
CTV
PTV = CTV + standard margin

10. Breathing Motion:
Target Localization
• Ample evidence that the PTV margin
allotted for respiratory motion may be
severely under- or over- estimated if one
simply allots a standard ‘1 cm’ margin for
the PTV
GTV
CTV
PTV = CTV + standard margin

11. Breathing Motion:
Target Localization
• Methods for assessing the PTV
– Fluoroscopy and CT
– CT scans at exhale, inhale and normal
respiration
• CT1 RPM Gating System
• DIBH LSB
– 4D CT
• New CT scanner

12. Breathing Motion:
What is 4-D CT?
• CT simulation provides a volumetric dataset
for dose calculation
• 4D CT provides volumetric datasets for
assessing target motion due to breathing

13. Assume a simple moving target
What is 4-D CT?
GTV
25%
50%
75%
100%
Snap-shots of motion at
0, 25, 50, 75 and 100%

14. What is 4-D CT?

15. What is 4-D CT?
• A longer than conventional CT scan (about 5X
longer, ~ 40 seconds)
• Slightly more dose , ~ extra 6 cGy (versus ~2 cGy
for the first scan)
• Requires a signal to sort out the phases of the
breathing cycle (use the RPM gating block)
• Provides up to 10 different phases, or sets of CT
images (divided in roughly equal parts of the
breathing cycle)

16. What is 4-D CT?
• Assumptions
– Block motion correlates with lung motion
• Highly dependent on the patients pulmonary status
– The (reconstructed) images provide an accurate
measure of the lung motion
– The respiratory motion exhibited during the scan
is representative of that during treatment
• Not quantified, but seems reasonable for those
patients whose pulmonary status has not changed
significantly

17. Result
• 4D CT will result in a number of CT
image sets that can be used for PTV
definition
Question:
Do you want to contour on all 10
datasets to define you PTVs?

18. 4D CT at VIC
• 4D CT will result in a number of CT
image sets that can be used for MTV
definition
• One can then fuse all 10 data sets with
the original planning CT for MTV
definition

19. Question:
• Do you want to contour on all 10
datasets to define you PTVs?

20. Answer
• No because I can contour on a MIP
instead of all of the different phase-
related images

21. Question
• What is a MIP?
• Maximum Intensity Projection

22. MIPs
• Use the fact that in lung, higher
density tumors will appear to move
against a lower density lung
0% 25% 50% 75% = phases of motion
GTV
25%
50%
75%
100%

23. 5 different phases
MIP =

24. MIPs
• Will provide a ‘blurred’ image of the
target
• Ignores surrounding tissues, and will
therefore
– Make lung volumes smaller than reality
(due to chest wall and heart motion)
– Make external contours larger than
reality
– ‘blur’ vessels in the lung

25. Some examples: Case 1
Conventional MIP

26. Case 1
Conventional MIP

27. Case 1
Conventional MIP

28. Case 2
Conventional MIP

29. Case 2
Conventional MIP

30. Case 3
Conventional MIP

31. Case 4
Conventional MIP

32. Case 5
Conventional MIP

33. MIPs
Pros Cons
- 1 dataset to contour the GTV
(ITV), not 10
-Difficult to define ITV for tumors
near chestwall, mediastinum
structures and diaphragm
- Captures 3D range of motion, as
opposed to just exhale/inhale
- Artificially increases all CT values
(hence standard window/level will
result in brighter images)
- Great for lung tumors against
background lung, and liver nodes
against normal liver
- Provides data for only 1 of many
structures that need contouring

34. Proposal
When should I use 4D CT?
• Non-palliative cases
• When lung toxicity is a concern
• When the target is close to the
diaphragm
• When the target is not in the apex ?

35. Suggested Workflow
• CT Simulation
– Set-up for RPM scan
– Perform a standard CT
– Perform a retrospective 4DCT w/ RPM
– Mark-up patient
– Start 4DCT analysis …

36. Suggested Workflow
• Advantage Workstation
– Reconstruct CT images using 10 phases
– Create the MIP AND Average CT
– For SBRT cases, also export 0 and 50%
phases
– Export 2-4 datasets into Eclipse
– Start contouring …

37. Suggested Workflow
• Eclipse
– There will be at least 2 CT datasets
• MIP
– Contour GTV_MIP
• Planning CT
– Contour GTV
– All other normal tissues
– May be used for planning planning purposes
• Average CT
– Contour GTV
– All other normal tissue
– May be used for planning purposes

38. Once we are
comfortable with
the AVERAGE
CT datasets for
normal tissue
contouring, drop
the conventional
CT and just
perform a 4DCT
scan

39. Conclusions
• MIPs are a simple means of collapsing the 4D data
into a single data set
• There are some shortcomings
• MIPs along with the 0% (rest exhale) and 50%
(inhale) phase images will be available for planning
purposes
• Audio coaching… some data to suggest better
quality images…