AAPM Grand Challenge: SPAREamos3.aapm.org/abstracts/pdf/137-41908-452581-138082-560681006.pdf ·...
Transcript of AAPM Grand Challenge: SPAREamos3.aapm.org/abstracts/pdf/137-41908-452581-138082-560681006.pdf ·...
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AAPM Grand Challenge: SPARE
Chun-Chien Shieh
Xun Jia, Bin Li, Yesenia Gonzalez, Simon Rit, Paul Keall
Sparse-view Reconstruction Challenge for 4D Cone-beam CT
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By the end of this talk
– Aim of the SPARE challenge
– How the challenge datasets were generated
– How image quality was quantified
– The top 4 performing teams
– Access to the full datasets
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4D cone-beam CT
– 2-4 minutes
– Undersampling artifacts
4D-CBCT3D-CBCT
– 1 minute scan
– Motion blur
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4D-CBCT better predicts intrafraction motion range than 4DCT
4D-CT 4D-CBCT
Steiner et al., WE-HI-KDBRB1-10, 1:45-3:45 pm
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4DCBCT-based model for intrafraction motion monitoring
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High quality 4D-CBCT from a standard one-minute scan?
– Shorter scan time
– Lower dose
– High quality 4D-CBCT on every system
Standard 3D reconstruction Conventional 4D reconstruction
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Algorithms for reconstructing undersampled 4D-CBCT data
0
5
10
15
20
25
30
Number of publications on 4D-CBCT reconstruction
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Algorithms for reconstructing undersampled 4D-CBCT data
– Iterative
– Total-variation
– PICCS
– Motion compensation
– Projection space
– Image space
– Prior deformed
– Hybrid
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SPARE Challenge
Sparse-view Reconstruction Challenge for 4D Cone-beam CT
Spare scan time
Spare dose
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Aims
– To systematically investigate the efficacy of various algorithms for 4D-
CBCT reconstruction from a one minute scan.
– Provide a common dataset for future 4D-CBCT reconstruction studies.
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The challenges of hosting a 4D-CBCT challenge
– Ground truth
– Realistic images
– Patient images
– Poisson noise
– Scatter
– Monte Carlo simulation
of real patient CTs
NCAT phantom XCAT phantom
Patient – no scatter Patient – scatter
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Data – source volumes for simulation
4D-Lung dataset
• 20 locally-advanced NSCLC patients
• Patients had multiple 4D-CTs
• Respiratory signal
• 12 patients had at least two 4D-CTs
with acceptable quality
• 32 scans in total
Prof Geoff Hugo [email protected]
https://wiki.cancerimagingarchive.net/display/Public/4D-Lung
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Data – Monte Carlo simulation
Respiratory signal
Monte Carlo simulation
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Data – Monte Carlo Datasets
4D-CT
Patient XX
Scan 1
Scan 2
……
– Half-fan scan
– 680 projections over 360 degrees
– No scatter
– 40 mA; 20 ms
– Poisson noise
– With scatter
– 40 mA; 20 ms
– Poisson noise + scatter
– Low dose & with scatter
– 20 mA; 20 ms
– Poisson noise + scatter
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Data - Clinical scans
Clinical Varian Dataset
• CBCT scans from the 4D-Lung dataset
• 4 minutes, 2400 half-fan projections
• Down-sample to 680 projections
• Respiratory signal: RPM
• 5 patients. 30 scans.
Fully-sampled Down-sampled
Clinical Elekta Dataset
• Regular 4D-CBCT on an Elekta Versa HD
• 3 minutes, 1000 full-fan projections
• Down-sample to 340 projections
• Respiratory signal: Amsterdam Shroud
• 5 patients. 20 scans.
Fully-sampled Down-sampled
Prof Geoff Hugo Dr Simon Rit
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Data - overview
Datasets
Monte Carlo
Clinical Varian
Clinical Elekta
Provided to
participants
4D-CT
CBCT projections
Respiratory signal
9 patients, 29 scans
3 training scans
5 patients, 25 scans
5 training scans
5 patients, 15 scans
5 training scans
PTV contour
For each patient
For each CBCT scan
Blinded from
participants
Ground truth/
Reference volumes
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How the challenge was conducted
1. Registration (Dec 2017-15 Jan 2018)
2. Datasets and instructions sent to participants (31 Jan 2018)
3. The fun began!
4. Deadline: 30 April 2018
5. Analysis completed and summarized to the participants in May
6. Results sent to AAPM
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Participant demographics
19 participating teams
US
Europe
Asia
Russia
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Evaluation metrics
Image similarity
▪ Structural similarity (SSIM)
Target localization accuracy
▪ Alignment of PTV
Body
Lungs
PTV
Bony anatomy
Ground truth
Reconstruction
Alignment
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Results
– 19 participating teams
– 4 teams completed the entire challenge
– with really impressive results
Let’s remind ourselves this is what can be
achieved with conventional FDK reconstruction…
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Top 4 performing methods – Monte Carlo case#1
Ground truth Method #1 Method #2 Method #3 Method #4
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Top 4 performing methods – Monte Carlo case#2
Ground truth Method #1 Method #2 Method #3 Method #4
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Top 4 performing methods – Monte Carlo case#3
Ground truth Method #1 Method #2 Method #3 Method #4
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Monte Carlo case#3 – Large CT-CBCT difference
CT CBCT
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Results – Structural similarityBody Lungs PTV Bony anatomy
SSIM
Better
Method
Structurally, Method#2 is
closest to ground truths
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Results – Target localization accuracy
3D
err
or
(mm
)
Method
2>3>1>4
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Results – target localization accuracy
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Results – scatter noise and imaging dose
1 2 3 4 1 2 3 4 1 2 3 4
0 .6 0
0 .6 5
0 .7 0
0 .7 5
0 .8 0
0 .8 5
M e th o d
SS
IM
N o s c a tte r
W ith s c a tte r
L o w d o se
Scatter noise and Poisson
noise affect all algorithms
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Results – Clinical Varian Datasets
Reference Method #1 Method #2 Method #3 Method #4
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Results – Clinical Elekta Datasets
Reference Method #1 Method #2 Method #3 Method #4
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Results – top performing teams
Yawei Zhang
Zhuoran Jiang
Xiaoning Liu
Lei Ren
(Duke University)
Prior deformed
Matthew Riblett
(VCU)
Geoffrey Hugo
(Washington University)
Data-driven
motion-compensation
Simon Rit
(CREATIS)
4DCT-based
motion-compensation
Cyril Mory
(CREATIS)
Motion-aware temporal
regularization
(MA-ROOSTER)
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Results – top performing teams
Prior deformedData-driven
motion-compensation
4DCT-based
motion-compensation
Motion-aware temporal
regularization
(MA-ROOSTER)
▪ Best overall quality
and accuracy
▪ Occasional minor
artifacts
▪ Good quality and
accuracy
▪ Residual blur
▪ Clinically used
▪ Data driven
▪ Good accuracy
▪ Motion can be
“visually” unnatural
▪ Best visual quality
and details
▪ “CT-like”
▪ Sensitive to CT-
CBCT difference
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MC-PICCS
PICCS
MC-PICCS
Motion compensation
Prior
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Ground truth MA-ROOSTER MC-PICCS
MA-ROOSTER vs MC-PICCS
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MA-ROOSTER vs MC-PICCS
Ground truth MA-ROOSTER MC-PICCS
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MA-ROOSTER vs MC-PICCS
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Room for improvements
– Noise and artifacts in the ground
truth volumes
– Lack of beam information for
projection calibration and scatter
correction
– CT-CBCT alignment was not
provided
– Better ways to under-sample the
clinical datasets
Ahmad 2012 Med Phys
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Access to the full dataset
– All the data provided to the
participants
– All the ground truth & reference
reconstructions
– MATLAB scripts to automatically
compute evaluation metrics
– A common dataset for future 4D-
CBCT reconstruction studies
– Available from Aug-Sep 2018
▪ AAPM
▪ ACRF Image X Website
http://sydney.edu.au/medicine/image-x/
▪ Contact Dr Andy Shieh
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Summary
– Accurate and high quality 4D-CBCT from
a one-minute scan is challenging, but
possible
– The use of motion model is critical
– Each method has its own advantages
– Overall, the combination of motion
compensation and iterative regularization
gives the best results
– The SPARE Challenge datasets will be
publicly available for future studies