1. A PATIENT POSITIONING SYSTEM FOR MASKLESS HEAD AND NECK
RADIOTHERAPY Electrical Engineering Department, UT Dallas, TX
4/23/2015 1 Presented by Olalekan Ogunmolu. April 23, 2015 2.
Olalekan Ogunmolu: Bio Snippet o Semester of PhD Enrolment: Fall
2014 GPA: 4.00/4.00 o Advisor: Nicholas Gans, Assistant Professor o
MSc (in Engineering) in Control Systems 2011 - 2012 The University
of Sheffield, England, UK MS Thesis: Autonomous Navigation of a
Rotorcraft Unmanned Aerial Vehicle Using Machine Vision, The
University of Sheffield, South Yorkshire, England, Sept. 2012.
Advisor: Tony J. Dodd, Professor of Autonomous Systems Engineering,
ACSE Dept., UoS. o Research Thrusts o Vision-based Control:
Features tracking, Active Appearance Models, Neural Networks o
Control Systems and Automation: Classical Control, Non-linear
Systems, Switched Systems o Publication o Olalekan Ogunmolu et. al,
A Real-Time Soft Robotic Patient Positioning System for Maskless
Head and Neck Cancer Radiotherapy, IEEE Conference on Automation
and Systems Engineering [Submitted], Gothenburg, Sweden, August
2015Electrical Engineering Department, UT Dallas, TX 4/23/2015 2 3.
Overview of Presentation A first-stage systematic examination of a
novel method at automating patient positioning systems during
cancer head and neck cancer radiotherapy (RT) Visual-servoing
control using a radio-transparent soft robot on the
flexion/extension cranial motion of a patient during mask-less
radiotherapy Proof-of-concept validation of one degree of freedom
patients motion control with designed soft robot system Electrical
Engineering Department, UT Dallas, TX 4/23/2015 3 4. Background and
Motivation Head and Neck (H&N) Cancers contribute nearly 3% to
all cancer developments in the United States [Jemal, A.] Very
dangerous to benign tissues/organs nearby malign cancerous
formations in H&N region Treatment often involves intensity
modulated radiotherapy (IMRT) Clinical studies have shown that
small perturbations cause high sensitivity to IMRT treatment dose
[Xing., L] State-of-the-art robotic positioning treatments assume
immobilized patient motion on a 6D couch which is unrealistic
Electrical Engineering Department, UT Dallas, TX 4/23/2015 4 5.
Recent Research Results Frameless and Maskless Cranial SRS, Cervino
et al Anthropomorphic head phantoms employed in checking the
accuracy of a 3D surface imaging system (AlignRT System) Compared
results from an infra-red optical tracking system with the AlignRT
software system For different couch angles, the difference between
phantom positions recorded by the two systems were within 1mm
displacement and 1 rotation Electrical Engineering Department, UT
Dallas, TX 4/23/2015 5 6. Recent Research Approaches Recent
Research Thrusts 6D Motion Frameless SRS Approach, Wiersma et al
Electrical Engineering Department, UT Dallas, TX 4/23/2015 6 7.
Typical Frame-based Radiotherapy Electrical Engineering Department,
UT Dallas, TX 4/23/2015 7 8. Research Overview Proof-of-concept
study and experiment demonstrating a 1-DOF flexion/extension
control of patient cranial motion during H&N Cancer RT Testbed
is a Mannequin head lying in a supine position on an inflatable air
bladder (IAB) Soft-robot consists of the IAM, two two-port SMC
Pnematics Co. proportional valves, and silicone tubes for conveying
air from a pressurized air canister A Kinect RGB-D camera is
employed for head motion sensing and feedback to a classical
control network implemented on an NI myRIO hardware Work in
partnership with the Drs. Xuejun Gu and Steve Jiang of the
Radiation Oncology Department of UT Southwestern, Dallas, TX, USA
Electrical Engineering Department, UT Dallas, TX 4/23/2015 8 9.
Recent Research Results Average max. volunteer head motion in the
head mold during the 20 minutes interval in any direction was 0.7mm
(range 0.4 1.1mm) Patient motion due to couch motion was less than
0.2mm Drawbacks Minimal immobilization provided by head mold
results in poor positioning Relies heavily on patient cooperation
to achieve immobilization Inter-fractional motions often ignored
during pre-treatments Electrical Engineering Department, UT Dallas,
TX 4/23/2015 9 10. Research Aims and Objectives Aims Accurate and
automatic patient positioning system (pre- treatment) In-treatment
automatic and accurate patient positioning with patient drift
compensation Objectives Surface-image control of the cranial
flexion/extension motion of a patient during simulated H&N RT
(pre-treatment) Use of radio-transparent soft robot system for
positioning/manipulation tasks Electrical Engineering Department,
UT Dallas, TX 4/23/2015 10 11. Current System Set-up Electrical
Engineering Department, UT Dallas, TX 4/23/2015 11 Kinect RGBD
Camera Sensor Hair packed with medic neck pillow to reduce effect
of infrared wavelengths scattering and minimize dropped pixels
Inflatable Air Bladder (IAB) Mannequin Head NI myRIO
microcontroller Current Source (Air Flow) Regulator Circuit
Inlet/Outlet Silicone Tubes Torso Ball Joint Simulator Inlet
Proportional Flow Control Valve Data Processing System 24V DC Power
Supply 12. Vision Sensing System Kinect RGB-D Camera employed for
position-based visual servoing: Better depth image and alignment;
Skeleton tracking Real-time Human Pose Recognition in Parts from
Single Depth Images. Jamie Shotton, et. al, CVPR 2011, Best Paper
Award Real-time 3D face-tracking based on active appearance model
constrained by depth data. Nikolai Smolyanski et. al, Image and
Vision Computing, 2014, MS SDK v1.5.2 Online Resources: OpenNI,
ofxKinect OpenNI Process Generates 640 480 image at 30 fps with
depth resolution of 40 centimeters Electrical Engineering
Department, UT Dallas, TX 4/23/2015 12 13. Kinect Depth Imaging
Works with low light levels; Color and texture invariant
Subtraction of background simplified Easy synthesis of realistic
depth images of objects Cheap computational cost of building a
large training dataset 3 trees, 20 deep, 300k training images per
tree, 2000 training example pixels per image, 2000 candidate
features , and 50 candidate thresholds Electrical Engineering
Department, UT Dallas, TX 4/23/2015 13 14. Depth Constrained 3D
Face Tracking MS Kinect SDK v1.5.2 Uses depth data from the Kinect
RGB-D Commodity camera to enable 3D tracking using an active
appearance method image difference patterns corresponding to
changes in each model parameter are learned and used to modify a
model estimate Resolves the monocular tracking problem by fitting
an energy term into the 2D+3D AAM fitting that minimizes a distance
between 3D face model vertices and depth data coming from a RGBD
camera. Electrical Engineering Department, UT Dallas, TX 4/23/2015
14 15. Depth Constrained 3D Face Tracking Process Flow in Face
Tracking System Find a face rectangle in a video frame using a face
detector Use a neural network to find five points inside the face
area eye centers, mouth corners, and tip of the nose Precompute
scale of tracked face from the five points un- projected to 3D
camera space and scale 3D camera space appropriately. Initialize
next frames 2D face shape based on the correspondences found by a
robust local feature matching between that frame and the previous
frame. Results With the depth constrained 2D+3D AAM fitting, we
found good position-estimation results on a human subject when
object is at a distance of 1 to 2.5m from the Kinect System
Electrical Engineering Department, UT Dallas, TX 4/23/2015 15 16.
Face Tracking Results Generalization errors and hence incorrect
position estimation errors with respect to the mannequin head due
to inconsistency in depth data An ongoing investigation So we
mostly relied on the OpenNI depth map centroids which we computed
for our position estimation Electrical Engineering Department, UT
Dallas, TX 4/23/2015 16 Tracking of a Human Face Tracking of a
Mannequin Head 17. Modeling of Soft Robot System Modeling
procedure: Overview Collect Data Set, , of input signal, , and
output measurement, (), respectively where = { 1 , 1 , , , }, 1 (1)
Obtain a continuous-time parametric model structure similar to a
one- step ahead predictor = 1 1 0 + + 1 1 + + 1 + 0 (2) Introduce
vectors = 1, , 0 1, , 0 and = 1 (3) Since the model output depends
on past data (2), we call the estimated value . Therefore, = .
Identification Goal: identify the best model, , in the set guided
by the frequency distribution analysis Choice of Excitation Input
Signal: Sawtooth Waveform Integral and differential of a sawtooth
waveform preserves the sawtooth waveform with only phase and
amplitude shifts Electrical Engineering Department, UT Dallas, TX
4/23/2015 17 18. Modeling of Soft Robot System Spectrum contains
both even and odd harmonics of the fundamental frequency i.e. it
contains all integer harmonics = 2 =1 8800 sin 2 (4) where A is the
amplitude, A = 180mA and f is the signal frequency, = 10 Fig 1.
Excitation Signal and Corresponding Head Position (Kinect
Measurement) Electrical Engineering Department, UT Dallas, TX
4/23/2015 18 19. Data Pre-Processing Data Pre-processing We removed
means and linear trends in collected data to minimize disturbances
above interest to desired system dynamics, mitigate measurement
outliers and non-continuous records in collected data Means removal
() = () ; = (); (5) where () and () are the respective averages of
the input and output signals; () = 1 =1 () = 1 =1 ; n is the
discrete data length and N is the total data length Data
Pre-processing Data detrending = , = , (6) are solutions to the
least square fit equations = , = Electrical Engineering Department,
UT Dallas, TX 4/23/2015 19 20. Data Pre-Processing and = 1 1 1 1 1
1 1 1 (7) Electrical Engineering Department, UT Dallas, TX
4/23/2015 20 Fig 2. Data Preprocessing: Means Removal 21.
Cross-Correlation Analysis The cross-correlation function provides
an estimate of the system impulse response and is defined as: = =+1
() =1 2 =1 () 2, = 0, 1, , 1 (8) The cross-correlation function
(CCF) is given by the convolution of the system impulse response
and the process auto-correlation function (Wiener-Hopf equation)
()= [ ( + )d = ( )d The cross correlation function between the
output and test input is proportional to the system impulse
response when the input is white noise = 1 (1) where is a zero mean
white input sequence and 1 is an autoregressive model filter thus
defined: 1 = 1 + 1 1 + Electrical Engineering Department, UT
Dallas, TX 4/23/2015 21 22. Cross-Correlation Analysis We estimate
the parameters = 1, 2, , by fitting an AR model to () using a least
squares algorithm. The estimates of the filter, , are used to
filter the input and output signals as follows = () = () (9) By
estimating the filter = 20, the estimate of the cross- correlation
function was generated. The auto-correlation function tells of the
quality of the filter and is defined as 11 = =+1 [ ] =1 [ ]2 , = 0,
1, , 1 (10) Electrical Engineering Department, UT Dallas, TX
4/23/2015 22 23. Correlation Analysis Electrical Engineering
Department, UT Dallas, TX 4/23/2015 23 Fig 3. ACF of detrended
excitation signal 24. Correlation Analysis Electrical Engineering
Department, UT Dallas, TX 4/23/2015 24 Fig. 4. CCF between
pre-whitened input and output 25. Correlation of Residuals To
determine the model structure of the system, we used the original
detrended data We chose a linear, second-order grey-box model set
whose quality is measurable by a mean-square error (MSE) Our
choice, garnered from erstwhile analysis, ensures cost of model is
not too high in solving for ; a high order complex model is more
difficult to use for simulation and control design. If it is not
marginally better than a simpler model, it may not be worth the
higher price [Llung, 16.8] Electrical Engineering Department, UT
Dallas, TX 4/23/2015 25 26. Sub-model Selection & Model
Validation 4/23/2015Electrical Engineering Department, UT Dallas,
TX 26 A control system will perform well with an optimal linear
sub- model, tolerate disturbances and nonlinearities. We pick the
linear frequency range: 0.00232 rad/sec to 6.85 rad/sec to
represented the model of the soft robot system We carried out the
canonical correlation to gain insight into the desired model
characteristics The correlation is measured between the measured
head position, (), and estimated position by the auto-regressive
model we chose, i.e. the residuals , = The prediction errors (model
error model) in the obtained model are computed as a frequency
response from the input to the residuals 27. Model Validation
4/23/2015Electrical Engineering Department, UT Dallas, TX 27 Fig.
6. Correlation analysis of residuals between output, (), and its
estimate, () Fig.5.Bodeplotofinputandoutput
Fig.6.Correlationfunctionofresiduals Fig. 7. Estimates of standard
variation of model from validation data set 28. Model Validation
4/23/2015Electrical Engineering Department, UT Dallas, TX 28 The
confidence interval compares the estimate with the estimated
standard deviation from the validation dataset A 99% confidence
region (yellow bands) encloses the model response informing us we
have a reliable model [Llung (1999), 16.6] Comparing Model
Structure Evaluation of different model structures and comparing
quality of offered models Best fit: a second-order process model
with delay and a RHP zero = 0.006( 1.7137) (+0.01)(+0.1028) 2 (11)
The model has an 87.35% fit to original data with a mean square
error of 0.054982 and a final prediction error of 1.672. The
open-loop step response of the identified transfer function is
shown in Fig. 8. 29. Soft Robot Step Response Electrical
Engineering Department, UT Dallas, TX 4/23/2015 29 Fig. 8. Open
Loop Step Response of Identified System 30. Control Analysis We see
that the system is non-minimum phase with very slow transient
response. We require a controller that will increase the response
time, guarantee closed-loop stability whilst balancing robustness
and controller aggressiveness. Approximating the delay with the
second-order Pade function, = 2 3 + 3 2 + 3 + 3 we introduce a PI
controller, = 3.79 + 0.0344 s nested within a PID controller, =
3.4993 + 0.054765 + 55.8988, as in Fig. 9. Electrical Engineering
Department, UT Dallas, TX 4/23/2015 30 31. Close Loop Diagram and
Step Responses 4/23/2015Electrical Engineering Department, UT
Dallas, TX 31 Fig 10. Closed Loop Step Response of Simulated System
Fig. 9. Block Diagram of Control Network Fig. 11. Experimental
Results: Constant Set-point 32. Experimental Results: Video Video
1. Experimental Results: Set-points Tracking Electrical Engineering
Department, UT Dallas, TX 4/23/2015 32 33. Conclusions and Future
Work Deviations from desired positions during H&N Cancer RT
cause dose variations and degenerate treatment efficacy We have
presented and demonstrated accurate control of cranial
flexion/extension motion of a patient during maskless H&N RT
Our soft robot system can accurately track desired trajectory
within 14 seconds after start-up with the aid of a PID/PI
feedforward network Future efforts include Extending results to
deformable motions of the upper torso, and H&N. Improved
bladder control: Adaptive Control, Gain Scheduling e.t.c.
Incorporation of multi-bladders to accommodate multi-axis
positioning Benefits Comprehensive and accurate control of the
patients position Elimination of anatomical deformations as a
result of positioning errors Electrical Engineering Department, UT
Dallas, TX 4/23/2015 33 34. References Cervino, L. I., et al.
Frame-less and mask-less cranial stereotactic radiosurgery: a
feasibility study. 2010, Physics In Medicine And Biology 55(7):
1863-1873. Jemal A, Siegel R, Xu J, Ward E. Cancer statistics,
2010. CA: A Cancer Journal for Clinicians2010; 60(5):277300. L.
Llung, System Identification Theory for the User, 2nd Edition,
Upper Saddle River, NJ, USA. Prentice Hall, 1999. Xing, L.
Dosimetric effects of patient displacement and collimator and
gantry angle misalignment on intensity modulated radiation therapy.
Radiotherapy & Oncology, 2000. 56(1): p. 97 - 108 Electrical
Engineering Department, UT Dallas, TX 4/23/2015 34
