Automated Image Analysis Software for Quality Assurance of a
Radiotherapy CT Simulator
Andrew J Reilly
Imaging PhysicistOncology PhysicsEdinburgh Cancer CentreWestern General HospitalEDINBURGH EH4 2XU
Phone: 0131 537 1161Fax: 0131 537 1092E-Mail: [email protected]: http://www.oncphys.ed.ac.uk
Overview
• Radiotherapy imaging
• RT Imaging QA: problems and solution
• Describe features of auto analysis software
• Demonstrate application to CT-Sim and Sim-CT
• Outline experience to date
Imaging Modalities for RT
• Common• Simulator (fluoroscopy)
• CT-simulator
• Digitally Reconstructed Radiographs (DRRs)
• Simulator-CT (single slice and cone-beam)
• Electronic Portal Imaging Devices (EPIDs)
• ‘Emerging’• Ultrasound
• MRI
• PET
• On treatment cone-beam CT and kV radiography
Integrated System
RT Imaging QA: Essential Tests
• Geometric Accuracy in 3D• In and out of image plane (pixel size, couch travel)• Mechanical alignments• Laser alignment
• Image quality• Sufficient for purpose?• Consistent over time
• Accurate physical information• CT number / HU calibration -> electron density
• Testing of overall system• Geometrical co-registration• Transfer of image data
The Problems…
• Different tests are specified for different modalities
• Range of ‘equivalent’ test objects
• Most tests are only semi-quantitative
• Operator dependency
• Frequent (daily/fortnightly) comprehensive testing is required BUT most tests are time-consuming
• Some imaging equipment performs too well!
• Difficult to test integrated system.
The Solution…
• Develop single, uniform approach for all RT imaging modalities• + display devices, film processors, etc.
• Robust, fully objective and quantitative
• Analysis performed by computer
• Results automatically stored in database for trend analysis, etc.
The Approach
1. Develop Appropriate Phantom
Signal s1
s2
SNRin = s1 / s2
2. Acquire Image of Phantom
Signal s1s2
SNRout = s1 / s2
fDQESNR
SNR
in
out
2
Determining the DQE
NPS
MTFDKfDQE
2
Modulation Transfer Function (Phantom)
Noise Power Spectrum (Phantom)
Dose and acquisitionsetting dependent.
Varian Ximatron EX Sim-CT
AdditionalCollimators
Varian Performance Phantom
WATER
MTF
LUNGINNERBONE
CORTBONE
AIR
A
P
R L
A
P
R L
1 2
3
MTF
INNERBONE WATER
A
P
L R
P
R L
A
1 2
3
Varian Uniformity Phantoms
44 cm 34 cm
Polyurethane CastingHU -580
Geometry: Phantom Alignment
• Detect phantom edge• Threshold at –580
• Trace edges and choose largest contour
• Calculate COM
• Compare against CT zero position
Geometry: Pixel Size
• Measure distance between holes
• Use centre of phantom and expected pixel size to identify ‘seek area’
• Local minimum is centre of hole
A
P
R L
1 2
3
A
P
R L
1 2
3
Hounsfield Unit Calibration
-1500
-1000
-500
0
500
1000
1500
2000
2500
0.0 0.5 1.0 1.5 2.0 2.5
Electron Density Rel to Water
CT
Nu
mb
er
ICRU 42
Ax, 80kV, 150mA
Ax, 80kV, 300mA
Ax, 120kV, 150mA
Ax, 120kV, 300mA
Ax, 140kV, 150mA
Ax, 140kV, 250mA
Baseline Values Measured During Commissioning
Hounsfield Unit Calibration
W ATER
MTF
LUNGSOFTBONE
HARDBONE
AIR
A
P
R L
• Calculate from impulse object
Modulation Transfer Function
xPSFFTfMTF
Finite size(DSF) xDSFxPSFxOSF
xDSFFTxPSFFTxOSFFT
xDSFFT
xOSFFTfMTF
Calculation from Impulse Object
Object Spread Function(From ALL pixels in ROI)
Uniformity Phantom Analysis
• Define Useful FOV (UFOV) as 90% FOV
• Calculate:
mean
dev std Variation, oft Coefficien CoV
mean
meanpU
max y, UniformitIntegral
mean
meanpU
min y, UniformitIntegral
mean
pU d
max y, UniformitalDifferenti
1000 Index, Uniformity
peripherycentreUCT
Uniformity Phantom Analysis
Uniformity ProfilesCT Sim: 50 cm FOV
Sim-CT
Urethane Norm
Air Norm
Noise Power Spectrum
• Region of Interest from Uniformity Phantom
• Remove DC component (subtract mean value)
• Perform 2D FFT
• Separation of stochastic noise
area
vuvuvuNPS
22 ,Im,Re,
n
ROINPS
n
NPSNPS nn
s
NPS Example• 100 images of Uniformity Phantom, 50 cm FOV
Production of DRRs• Ray trace from virtual source of x-rays through stack of
CT slices and model attenuation of beam.
SAD100 cm
isocentre
Imaging Plane
X-ray source
Reference: Milickovic et al,Physics in Medicine and Biology (2000) 45:10;2787-2800
Projected backto isocentre
DRR Production Example
CT Slices3D array of voxels
DRR
Edinburgh DRR Phantom
Software Demo
Experience & Conclusions
• New approach appears complicated, but…• Significantly faster than previous methods• More robust, fully objective and quantitative• Greater confidence in results• New ability to follow trends
• Need to finalise DRR phantom• Expand to include other RT imaging modalities