ieee nss mic 2016 poster N30-21 Pixel Discrimination using Artificial Neural Network for Gamma Camera Module

1. Pixel Discrimination Using Artificial Neural
Network for Gamma Camera Detector Module
Daeun Kim, Yong Choi, Kyu Bom Kim, Sangwon Lee, and
Donghyun Jang
Molecular Imaging Research & Education (MiRe) Laboratory, Department of Electronic Engineering,
Sogang University, Seoul, Korea

2. Ⅰ. Introduction
 Various pixel discrimination algorithms are employed to identify the radiation interaction
position in gamma camera detector module.
 The nonlinearities and noise properties deteriorate the discrimination accuracy as the
size of detector increases especially at the edges of scintillation crystal .
 As the candidate, the artificial neural network (ANN) algorithm was implemented to
identify the radiation interaction position.
 However, the ANN algorithm was computationally bulky and overloaded by the
requirement of thousands of parameters.
 Therefore, the simplification and optimization were required to make use of the ANN
algorithm for practical use and real-time application.

3.  Artificial neural network (ANN) was implemented as classifier to discriminate pixels and
localize the radiation interaction position on the sensor readout by resistive charge
multiplexing circuit.
 The proposal for simplifying ANN was attained by acquiring datasets along a line parallel
to the x-axis and y-axis respectively.
 Consequently, ANN structure was simplified and the number of parameters was
drastically decreased.
 Energy resolution and uniformity were measured for performance evaluation.
Ⅱ. Purpose

4. A. Scintillation crystal and SiPM
 LYSO crystal (Sinocera, China)
• 3 mm x 3 mm x 20 mm
• 4 x 4 array
 Silicon photomultiplier (SiPM)
• SPMArray4 (SensL, Ireland)
• Pixel chip area : 3.16 mm x 3.16 mm
• Number of microcells : 3640 per pixel
• Photon detection efficiency : 10 ~ 20%
 Resistive charge multiplexing circuit
• Array of 100 Ω Resistors
• 𝑋 =
𝐵+𝐷
𝐴+𝐵+𝐶+𝐷
• 𝑌 =
𝐶+𝐷
𝐴+𝐵+𝐶+𝐷
Ⅲ. Materials and Methods
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
A B
C D
+
Fig. 1. 4 x 4 matrix
of LYSO and SiPM
Fig. 2. Resistive multiplexing circuit for 12 x 12 pixels

5. B. Gamma camera detector configuration
 Readout module was designed on an electronics board with A/D converter.
 Wave forms from four readout channels were fed into FPGA to estimate the peak values.
 Four peak values were transferred to PC through USB 3.0 communication.
 Artificial neural network algorithm was implemented on the PC.
Ⅲ. Materials and Methods
FPGA
PeakDetection
USB3.0
PC
Real-time
Control
Artificial
Neural
Network
12 pixels 12pixels
……… A
B
C
D
Electronics Board
Readout
SiPMArray
(12x12)
…
…
…
…
…
Amplifier
+
A/Dconverter
Na-22 or Cs-137
Fig. 3. Block diagram of gamma camera detector module

6. C. Experiment setup
 Compact Gamma Camera Detection Module
• Module Size : 5 cm (width) x 8 cm (length) x 5 cm (height)
• Anlog Power 7 V, Digital Power ±5 V
• DAQ program : C# on Windows OS
• USB 3.0 Speed : Maximum 400 MB/s
Front Rear ADC + FPGA
Scintillator + SiPM
Resistive Multiplexing Curcit
ADC x 4 + FPGA
USB 3.0
Real-time
GUI Control
Artificial
Neural
Network
Processing
Acqisiton PC
Fig. 4. Photographs of analog, digital signal processing boards and acquisition PC.

7. D. Artificial Neural Network
 Artificial Neural Network Topology
• Input Node : 2 (x, y) per class
• Hidden Node : 4 per class
• Output Node : 1 per class
• Activation function : 𝐹 =
1
1+𝑒−𝑎 , (𝑠𝑖𝑔𝑚𝑜𝑖𝑑)
• 𝐻𝑖𝑑𝑑𝑒𝑛_𝑛𝑜𝑑𝑒𝑖 = 𝑊1𝑖 𝑋 + 𝑊2𝑖 𝑌 + 𝑏𝑖
• 𝑂𝑢𝑡𝑝𝑢𝑡_𝑛𝑜𝑑𝑒𝑗 = 𝑖=1
4
𝑊𝑖𝑗 𝐹𝑖 + 𝑏𝑗
 Training Dataset
• Pair of x and y (for 12 Column set)
• Pair of x and y (for 12 Row set)
 Test Dataset
• Pair of x and y (for 12 Column set)
• Pair of x and y (for 12 Row set)
Ⅲ. Materials and Methods
O10
O11
O12
… ...
… ...
Σ Σ Σ Σ
F F F F
Σ
F
W22
W13
W23
W13
W23
w11 w21 w31 w41
Σ Σ Σ Σ
F F F F
Σ
F
X Y
W11
W21
W12
W22
W13
W23
W13
W23
w11 w21 w31 w41
O1
x 12
Independent
network unit for a
class
Fig. 5. Artificial neural network topology.

8. E. Training Process
1. Training Datasets were acquired by the collimated source along a line parallel to x-axis and y-
axis respectively
12 pixels
12pixels
Na-22 or Cs-137
Column Dataset
12 pixels
12pixels
RowDataset
Fig. 6. Training datasets for 12 columns and 12 rows.

9. E. Training Process
2. During the ANN training process, each column (or row) dataset produces the probability map
in accordance with it’s distribution on the floodmap.
3. After training, all probability map were overlapped to make crystal position map.
Fig. 7. First column dataset and trained result. Fig. 8. First row dataset and trained result.
Fig. 9. Third column dataset and trained result. Fig. 10. Third row dataset and trained result.

10. Ⅳ. Results
A. Crystal Position Map
 Conventional Method : Watershed algorithm or Gaussian Mixture Model
 Proposed Method : Artificial neural network algorithm
Conventional Method Proposed Method
Fig. 11. (a) Crystal position map processed by conventional method, (b) Crystal position map by proposed method.
(a) (b)

11. Ⅳ. Results
A. Crystal Position Map
 Conventional Method such as watershed or Gaussian mixture model failed at discriminating adjacent pixels
at the edges of scintillation crystal.
Fig. 12. (a) Low counts at the edge
of crystal, (b) 3D flood histogram of
12 x 12 pixels, (c) Profile of selected
white line on (b).
(a) (b)
(c)

12. Conventional Method Proposed Method
Average : 22.8 % Average : 15.7 %
Ⅳ. Results
B. Energy Resolution
 Source : Na-22, Photopeak : 511 keV
 Spectra of the edge pixels by conventional and proposed method respectively.
Fig. 13. (a) Energy spectrum at the edge by conventional method, (b) Energy spectrum at the edge by proposed
method at photopeak 511 keV of Na-22.
(a) (b)

13. Ⅳ. Results
C. Counts Uniformity and Energy Resolution
Evaluation Criteria : Root mean square error : 𝑅𝑀𝑆𝐸 = 𝐸( 𝑚𝑒𝑎𝑛 − 𝑝𝑖𝑥𝑒𝑙 𝑐𝑜𝑢𝑛𝑡(𝑖, 𝑗) 2)
Proposed Method : Artificial neural network algorithm
Counts uniformity Energy resolution
Mean counts ( x104) RMSE ( x104) Mean energy resolution (%) RMSE (%)
7.07 2.16 18.0 % 3.83 %
Fig. 14. (a) Counts uniformity and (b) Energy resolution by proposed method.
(a) (b)

14. Ⅳ. Results
D. Flood map
Fig. 15. (a) Original flood map and (b) Flood map after remapping by ANN
(a) (b)

15. Ⅴ. Summary and Conclusion
 Gaussian mixture model or Watershed algorithm is challenged by non-uniformity and non-linearity at the
edges of large detector modules.
 The use of proposed ANN overcomes these challenges and improves the discrimination accuracy.
 Computationally, real-time application is also obtained by the benefit of simplification on ANN.
 Besides, this algorithm is potentially scalable for furtherly extended size and the additional input features
are applicable for better positioning accuracy.

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[2] F.Mateo,R.J.Aliaga,et al., “High-precision position estimation in PET using artificial neural networks”, Nucl. Instr. and Meth.
A604, pp. 366-369, 2009.
[3] P. Bruyndonckx, S. Léonard, S. Tavernier, C. Lemaître, O. Devroede, Y. Wu, and M. Krieguer, “Neural network-based position
estimators for PET detectors using monolithic LSO blocks,” IEEE Trans. Nucl. Sci., vol. 51, no. 5, pp. 2520-2525, Oct. 2004.
[4] K.A. Stronger et al., “Optimal calibration of PET Crystal position maps using Gaussian mixture models,” IEEE Trans. Nucl. Sci.
51-1, 2004.
Ⅵ. References