Error of Multileaf collimator prediction using recurrent neural network (LSTM)

MLC error prediction
using recurrent neural network (LSTM)
Wonjoong Cheon1), Kim Seong Jung2), Youngyih Han3),
Hyebin Lee4), Byung Jun Min4*), Heerim Nam4)
1) Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, 06351, Korea.
2) Department of Computer Engineering, Yonsei University, 03722, Seoul, Korea.
3) Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea.
4) Department of Radiation Oncology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, 03181, Korea.
1
대한방사선수술물리연구회 제15차 학술대회 (2017.11.10) @ 서울산업진흥원 컨텐

Contents
I. Introduction
II. Material & Method
1) Convert Rtplan to Expected position of MLC
2) Prediction Actual position of MLC using artificial neural network
3) Statistical analysis
III. Results
IV.Conclusion

Ⅰ. Introduction

Ⅰ. Introduction
Multi-Leaf Collimator
160 MLC™
(Siemens)
Agility™
(Elekta)
• Intensity modulated radiation therapy (IMRT) technique uses MLCs for modifying the beam fluence in the same treatment field
in order to improve the conformity of prescribed dose distribution around the tumor region. (Mundt & Roeske, 2005)
• The volumetric modulated arc therapy (VMAT) technology is a novel delivery method that is capable of producing highly conformal
dose distributions through concomitant optimization of MLC shapes, dose rate, and gantry speed.(Schreibmann et al., 2009)
Millennium™ MLC
(Varian)
Figure resource: www.elekta.com, www.varian.com, www.healthcare.siemens.com

Actual
MLC position
Expected
MLC position
Ⅰ. Introduction
MLC log files
Dicom RT
Eclipse™ Treatment Planning System, Varian
Dynalog file
Trilogy® System Linear Accelerator, Varian
• MLC leaf position uncertainties directly affect the dose delivered to tumor targets and sensitive
structures in IMRT. (Mu et al., 2008)
• Actual multileaf collimator (MLC) position data can be tracked throughout a treatment delivery in the
form of MLC log files. (Kerns, Childress, & Kry, 2014)
Mismatch (=Errors)
Figure resource: www.varian.com

 Radiation treatment plan
(RTplan)
- plans may contain
fractionation information,
and define external beams
and/or brachytherapy
 Expected position
- Position of each leaf
calculated and predefined
by RTplan file
 Actual position
- Position of each leaf
conducted and written to
dynalog file
- Containing machine errors
Ⅰ. Introduction
RT workflow
RT workflow

Ⅰ. Introduction
MLC positional error
• Most of the studies on MLC positional error — both random and systematic errors were analyzed —
have reported that the random errors were insignificant, while systematic errors have shown significant effects on dose distributions,
even with only 1 mm of positional errors applied for MLC positions. (Nithiyanantham et al., 2015)
• MLC position and velocity errors are affected by Friction and Gravity. (Wijesooriya, K., et al., 2005, Lee, Jeong-Woo, et al.,)
• Varian said that “The leaves have to fight gravity”
Figure reference : http://medicalphysicsweb.org/cws/article/research/44068
1
2
3
4
5
6
56
57
58
59
60
2
3
4
Friction Gravity
2
3
4

Ⅰ. Introduction
Aim of this study
• Prediction multi-leaf collimator position and velocity errors before radiation treatment
using radiation treatment plan (RTplan) file and artificial neural network (RNN:LSTM).
Treatment
Planning
Computer
RT plan
Batch
Folder
LinacView
Computer
Logfile
Batch
Folder
Linac Console
RT Plans
Log files
RT Plans
Convert
RTplan to
Expected position
of MLC
MLC position
expected
Prediction
Actual position
of MLC
Artificial Neural NetworkStatistical
analysis
Probability
error function
1
2
3
Is this error
acceptable?
No
Warning
RT workflow
If I operate the this
linear accelerator
based on my
RTplan file, will it
work safely?

Ⅱ. Material & Method

StepⅠ
 Convert Radiation treatment plan to Expected position of MLC
• Computer Language: Matlab
• Parameters: Monitor unit per second, Dose rate, Gantry angle
Step Ⅱ
 Prediction Actual position of MLC using Artificial neural network
• Computer Language: Python
• Type of neural net: RNN: LSTM
• Deep learning framework: Tensorflow (Google)
• Parameters: MLC position (expected, actual), Gantry angle, collimator angle, Beam on/off sign
Step Ⅲ
 Statistical analysis :Generate Probability error function
• Preprocess: Central limit theorem
• Model: Single Gaussian distribution (mean, std)

Radiation treatment plan file Dynalog file (expected position)
Convert RTplan to Expected position using Matlab

StepⅠ
• Parameters: Monitor unit, Dose rate
Step Ⅱ
• Deep learning framework: Tensorflow
Step Ⅲ
• Model: Single Gaussian (mean, std)

• RNN’s ability to anticipate also makes them capable of surprising creativity.
• You can ask them to predict which are the most likely next notes in a melody,
then randomly pick one of these notes and play it. Then ask the net for the next
most likely notes.
 How it is work ?
Prediction Actual position using Artificial neural network

 Learning for prediction of actual position of MLC
Header leaf 1 leaf 2 leaf 3 leaf 4 leaf 60
1480Dynalog
 Window size: 7 time stamp
 Gantry angle: Ga(t)
 Collimator angle: Ca(t)
 Beam on/off signal: B(t)
 Leaf expected position: leafPosi(t)
 Leaf actual position:
Actual position of 2nd leaf
Time: t=8
Ga(t)Ca(t) B(t) leafPosi(t)

1480Dynalog
Time: t=9

1480Dynalog
Time: t=10

1480Dynalog
Time: t=11

1480Dynalog
Time: t=12

1480Dynalog
Time: t=13

FC
𝑖𝑛𝑝𝑢𝑡𝐺𝑎𝑡𝑒(𝑡) = 𝜎(𝑊𝑥𝑖 ∙ 𝑥(𝑡) + 𝑊ℎ𝑖𝑇 ∙ ℎ(𝑡 − 1) + 𝑏𝑖)
𝑓𝑜𝑟𝑔𝑜𝑡𝐺𝑎𝑡𝑒(𝑡) = 𝜎(𝑊𝑥𝑓 ∙ 𝑥(𝑡) + 𝑊ℎ𝑓𝑇 ∙ ℎ(𝑡 − 1) + 𝑏𝑓)
𝑜𝑢𝑡𝑝𝑢𝑡𝐺𝑎𝑡𝑒(𝑡) = 𝜎(𝑊𝑥𝑜 ∙ 𝑥(𝑡) + 𝑊ℎ𝑜𝑇 ∙ ℎ(𝑡 − 1) + 𝑏𝑜)
• LSTM structure
• MLCLogNet structure
𝐿𝑜𝑛𝑔 𝑆ℎ𝑜𝑟𝑡 𝑡𝑒𝑟𝑚 𝑚𝑒𝑚𝑜𝑟𝑦 3 𝑐𝑒𝑙𝑙
𝐹𝑢𝑙𝑙𝑦 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑 𝑙𝑎𝑦𝑒𝑟
ℎ𝑖𝑑𝑑𝑒𝑛 dim = 20
o𝑢𝑡𝑝𝑢𝑡 dim = 1
Structure of learning frame

 Validation of MLCLog Net
Log 1
Log 2 Log 3 Log 4
Log 175 Log 176 Log 178
Log 1 Log 2 Log 3
Training log set Test log set
Log 30

StepⅠ
• Parameters: Monitor unit, Dose rate
Step Ⅱ
 Prediction Actual position of MLC using deep learning
• Deep learning framework: Tensorflow
Step Ⅲ
• Model: Single Gaussian (mean, std)

Statistical analysis:Generate probability error function
𝑷𝒐𝒔𝒊𝒕𝒊𝒐𝒏𝑬𝒓𝒓𝒐𝒓 𝒏, 𝒕 = 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑𝑃𝑜𝑠𝑖𝑡𝑖𝑜𝑛 𝑛, 𝑡 − 𝐴𝑐𝑡𝑢𝑎𝑙𝑃𝑜𝑠𝑖𝑡𝑖𝑜𝑛(𝑛, 𝑡)
𝑽𝒆𝒍𝒐𝒄𝒊𝒕𝒚𝑬𝒓𝒓𝒐𝒓 𝒏, 𝒕 = 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑𝑉𝑒𝑙𝑜𝑐𝑖𝑡𝑦 𝑛, 𝑡 − 𝐴𝑐𝑡𝑢𝑎𝑙𝑉𝑒𝑙𝑜𝑐𝑖𝑡𝑦(𝑛, 𝑡)
𝐴𝑐𝑡𝑢𝑐𝑎𝑙𝑉𝑒𝑙𝑜𝑐𝑖𝑡𝑦 𝑛, 𝑡
=
)−𝐴𝑃 𝑡 + 2, 𝑛 + 8 ∗ 𝐴𝑃 𝑡 + 1, 𝑛 − 8 ∗ 𝐴𝑃 𝑡 − 1, 𝑛 + 𝐴𝑃(𝑡 − 2, 𝑛
𝑑𝑒𝑙𝑡𝑎 𝑡 ∗ 12
𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑𝑉𝑒𝑙𝑜𝑐𝑖𝑡𝑦 𝑛, 𝑡
=
)−𝐸𝑃 𝑡 + 2, 𝑛 + 8 ∗ 𝐸𝑃 𝑡 + 1, 𝑛 − 8 ∗ 𝐸𝑃 𝑡 − 1, 𝑛 + 𝐸𝑃(𝑡 − 2, 𝑛
𝑑𝑒𝑙𝑡𝑎 𝑡 ∗ 12
Central limit theorem
Central limit theorem
[mm]
[mm/s]

Ⅲ. Results

StepⅠ
Step Ⅱ
Step Ⅲ
Ⅲ. Results

Footnote: Validation of calculated Expected position from RTplan Footnote: Comparison calculated expected position and expected
position extracted from dynalog file (30 th leaf of MLC)
Ⅲ. Results
Convert RTplan to Expected position using Matlab

Plot the cost value of l2 loss function Prediction actual position of MLC using
recurrent neural network (LSTM)
Ⅲ. Results
Prediction Actual position of MLC using Artificial neural network

Ⅲ. Results
Statistical analysis :Generate Probability error function
[mm] [mm/s]

Ⅰ. Introduction
Aim of this study
• Prediction multi-leaf collimator position and velocity errors before radiation treatment
Treatment
Planning
Computer
RT plan
Batch
Folder
RT PlansRT Plans
Convert
RTplan to
Expected position
of MLC
MLC position
expected
Prediction
Actual position
of MLC
Artificial Neural NetworkStatistical
analysis
Probability
error function
1
2
3
Is this error
acceptable?
No
Warning
[mm] [mm]

Ⅳ. Conclusion

Ⅳ. Conclusion
• Multi-leaf collimators have had a major impact on the development of radiation therapy such
as IMRT, VMAT and IMPT.
• Small defects of the multi-leaf collimator can have a large effect on the results of radiation
therapy.
• We build workflow for predicting mechanical errors of MLC before radiation treatment.
• Consequently, our team developed a method of prediction multi-leaf collimator position
and velocity errors before radiation treatment

V. Further study
• Actual dose prediction using recurrent neural network (LSTM)
• Expected dose distribution • Actual dose distribution

Thank you for your attention.
2017/11/10
Acknowledgement:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government
(MSIP) (No. NRF-2017R1C1B2011257)

ERROR RANGE 1
Max acc 49.49 Max acc 55.65
With BCGWithout BCG

ERROR RANGE 2
Max acc :84.28 Max acc :84.74

Max acc :96.33
ERROR RANGE 3
Max acc :97.19

Max acc :99.45
ERROR RANGE 4
Max acc :99.60

Max acc : 1.00
ERROR RANGE 5
Max acc : 1.00

Error of Multileaf collimator prediction using recurrent neural network (LSTM)

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