Feasibility Study of a Time-of-Flight Brain Positron Emission Tomography Employing Individual Channel Readout Electronics
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sensors
Article
Feasibility Study of a Time-of-Flight Brain Positron Emission
Tomography Employing Individual Channel Readout Electronics
Kuntai Park , Jiwoong Jung * , Yong Choi * , Hyuntae Leem and Yeonkyeong Kim
Molecular Imaging Research & Education (MiRe) Laboratory, Department of Electronic Engineering,
Sogang University, Seoul 04107, Korea; hj11113@gmail.com (K.P.); lht0898@gmail.com (H.L.);
dusrud026@gmail.com (Y.K.)
* Correspondence: aegis0223@gmail.com (J.J.); ychoi@sogang.ac.kr (Y.C.)
Abstract: The purpose of this study was to investigate the feasibility of a time-of-flight (TOF) brain
positron emission tomography (PET) providing high-quality images. It consisted of 30 detector
blocks arranged in a ring with a diameter of 257 mm and an axial field of view of 52.2 mm. Each
detector block was composed of two detector modules and two application-specific integrated circuit
(ASIC) chips. The detector module was composed of an 8 × 8 array of 3 × 3 mm2 multi-pixel
photon counters and an 8 × 8 array of 3.11 × 3.11 × 15 mm3 lutetium yttrium oxyorthosilicate
scintillators. The 64-channel individual readout ASIC was used to acquire the position, energy, and
time information of a detected gamma ray. A coincidence timing resolution of 187 ps full width at
half maximum (FWHM) was achieved using a pair of channels of two detector modules. The energy
resolution and spatial resolution were 6.6 ± 0.6% FWHM (without energy nonlinearity correction)
and 2.5 mm FWHM, respectively. The results of this study demonstrate that the developed TOF brain
PET could provide excellent performance, allowing for a reduction in radiation dose or scanning
Citation: Park, K.; Jung, J.; Choi, Y.; time for brain imaging due to improved sensitivity and signal-to-noise ratio.
Leem, H.; Kim, Y. Feasibility Study of
a Time-of-Flight Brain Positron Keywords: positron emission tomography (PET); brain PET; time-of-flight (TOF); application-specific
Emission Tomography Employing integrated circuit (ASIC); TOFPET2; coincidence timing resolution (CTR)
Individual Channel Readout
Electronics. Sensors 2021, 21, 5566.
https://doi.org/10.3390/s21165566
1. Introduction
Academic Editor: Arunas
Ramanavicius As population aging progresses globally, the importance of early diagnosis and pe-
riodic monitoring of brain diseases such as Alzheimer’s disease and Parkinson’s disease
Received: 24 July 2021 is increasing [1–3]. Positron emission tomography (PET) is a widely used clinical and
Accepted: 15 August 2021 research medical imaging device, which is very useful for diagnosing brain diseases as
Published: 18 August 2021 well as various cancers and heart diseases [3,4]. However, conventional whole-body PET is
not optimized for brain imaging, resulting in increased radiation dose or scanning time
Publisher’s Note: MDPI stays neutral and compromised image quality [3,5–8]. Therefore, in order to accurately and efficiently
with regard to jurisdictional claims in diagnose brain diseases, it is necessary to develop a dedicated brain PET with excellent
published maps and institutional affil- system performance that provides high-quality images.
iations. In order to increase the sensitivity, which depends on the geometric efficiency, the ring
diameter of the dedicated brain PET needs to be reduced as much as possible. However,
the reduced ring diameter of the dedicated brain PET induces the increased scatter and
random coincidence counting rates (CCRs), and the decreased noise equivalent counting
Copyright: © 2021 by the authors. rate (NECR) [3,6]. The increases in the scatter and random CCRs add a relatively uniform
Licensee MDPI, Basel, Switzerland. background to the image, reducing contrast-to-noise ratio, signal-to-noise ratio (SNR), and
This article is an open access article quantitative accuracy [9–11]. In particular, since the random CCR is proportional to the
distributed under the terms and square of the single counting rate, it increases more than the true CCR and scatter CCR.
conditions of the Creative Commons Furthermore, unlike the scatter CCR, it is proportional to the width of the coincidence
Attribution (CC BY) license (https:// timing window related to the coincidence timing resolution (CTR) [9]. Therefore, a PET
creativecommons.org/licenses/by/ detector with excellent CTR is required to decrease the random CCR.
4.0/).
Sensors 2021, 21, 5566. https://doi.org/10.3390/s21165566 https://www.mdpi.com/journal/sensors
Sensors 2021, 21, 5566 2 of 15
Time-of-flight (TOF) PET is theoretically possible to determine the location where
an annihilation event occurred along the line of response (LOR) using the difference in
arrival times of two 511-keV gamma rays detected by a pair of detectors. The location of
the occurred annihilation event with respect to the midpoint between the two detectors
is given by ∆x = c × ∆t/2, where c is the velocity of light (3 × 1010 cm/s) and ∆t is the
time difference. In terms of the clinical performance of a PET, this decreases radiation dose
and scanning time and enables more accurate quantification. In terms of the image quality,
this improves SNR by reducing the background added to the image. The SNR is roughly
proportional to the square root of the NECR assuming that an object is a cylinder with
a uniform activity distribution located in the center of the field of view (FOV) and that
attenuation, scatter, and random corrections are perfectly applied. If TOF information is
incorporated into√ the analytical image reconstruction algorithm, the estimated TOF SNR
gain is equal to D/∆x, where D is the size of the object. That is, as the CTR proportional
to the error of ∆t improves, the SNR of the image improves due to an increase in the NECR
and a decrease in spatial uncertainty (error of ∆x) [9,11–14].
The TOF technology with improved CTR should be applied to the dedicated brain
PET with the reduced ring diameter to improve SNR and quantitative accuracy and to
achieve meaningful TOF gain (SNR and NECR). Previously, we determined an optimal
geometry by evaluating the performance of a TOF brain PET such as sensitivity, NECR, and
spatial resolution while changing the ring diameter and scintillator thickness using Monte
Carlo simulation. The determined ring diameter and scintillator thickness were 257 and
15 mm, respectively [15]. In addition, a TOF PET detector with optimized optical properties
was developed by evaluating the performance of the detector (photopeak position, energy
resolution, and CTR) while changing the surface treatment and reflective materials of the
scintillator. The scintillator of the developed detector was mechanically polished except
for a roughening entrance surface. The entrance surface of the scintillator was optically
isolated by Teflon tape. In addition, all lateral surfaces were optically isolated by both
enhanced specular reflector (ESR) film and Teflon tape [16].
The CTR is mainly determined by the physical properties of the PET detector related to
light generation, light transport, and light conversion. However, due to readout electronics,
the CTR deteriorates as the output signal of a silicon photomultiplier (SiPM) corresponding
to the detected gamma ray is distorted or the amplitude of noise increases. The improved
CTR can be achieved using individual channel readout electronics that minimize distor-
tion of the output signal in comparison to a multiplexing circuit based on the method
such as charge division or signal delay [11,17–24]. Therefore, the fast TOF PET could be
achieved using low-noise individual channel readout electronics that allow for fast TOF
measurements as well as a PET detector with excellent CTR.
The purpose of this study was to investigate the feasibility of a TOF brain PET pro-
viding high-quality images. This paper describes the overall system configuration of the
developed TOF brain PET including the individual channel readout electronics as well as
the geometry and detector. It also presents the performance evaluation of the conceptual
brain PET to verify the feasibility of the proposed TOF PET using a point source and
a phantom.
2. Materials and Methods
2.1. Geometry of the TOF Brain PET
The developed TOF brain PET consisted of 30 detector blocks with 3840 channels
placed in a circle on a custom-made acetal gantry. The 30 detector blocks were arranged at
equal angular intervals of 12 degrees in a ring with an axial FOV of 52.2 mm, as shown
in Figure 1a,b. The ring diameter was chosen to be 257 mm to improve sensitivity [15]. A
transaxial FOV of the developed TOF brain PET was approximately 181 mm, covering up
to the maximum size of the human brain [25].
Sensors 2021, 21, x FOR PEER REVIEW 3 of 15
Sensors 2021, 21, 5566 3 of 15
transaxial FOV of the developed TOF brain PET was approximately 181 mm, covering up
to the maximum size of the human brain [25].
(a) (b)
(c)
FigureFigure
1. (a) Geometry of theofproposed
1. (a) Geometry TOFTOF
the proposed brain PET.
brain (b)(b)
PET. Photograph
Photographofofthe
thedeveloped
developed TOF brain PET
TOF brain PETconsisting
consistingofof 30
detector blocks connected to flexible flat cables. (c) Configuration of the TOF PET detector block.
30 detector blocks connected to flexible flat cables. (c) Configuration of the TOF PET detector block.
Figure
Figure 1c shows
1c shows thethe configuration
configuration of of
thethe TOF
TOF PET
PET detectorblock.
detector block.Each
Each detectorblock
detector
block was composed of two detector modules and a front-end module (FEM) consisting of
was composed of two detector modules and a front‐end module (FEM) consisting of two
two boards equipped with an application-specific integrated circuit (ASIC) chip, a SiPM
boards equipped with an application‐specific integrated circuit (ASIC) chip, a SiPM inter‐
interface board, and an ASIC interface board. The two detector modules, each of which
facehad
board, andofan25.8
an area ASIC interface
× 25.8 board.
mm2 , were The connected
axially two detector modules,
to the each ofboard
SiPM interface which had an
with
areaanofaxial
25.8gap
× 25.8 mm 2, were axially connected to the SiPM interface board with an axial
of 0.6 mm. The two ASIC boards were assembled almost perpendicularly to
gapboth
of 0.6
themm.
SiPM The two ASIC
interface boardboards
and thewere
ASICassembled almost perpendicularly to both the
interface board.
SiPM interface board and the ASIC interface board.
2.2. TOF PET Detector
2.2. TOFThe
PETTOF PET detector module was composed of an 8 × 8 array of 3 × 3 mm2 multi-
Detector
pixel photon counters (MPPCs) (S13361-3050AE-08, Hamamatsu Photonics K.K., Hama-
The TOF
matsu, Japan)PETanddetector module
an 8 × 8 array was
of 3.11 × composed
3.11 × 15 mm of3 an 8 × 8 yttrium
lutetium array ofoxyorthosilicate
3 × 3 mm2 multi‐
pixel photon
(LYSO) counters(Crystal
scintillators (MPPCs) (S13361‐3050AE‐08,
Photonics, Hamamatsu
Inc., Sanford, FL, USA), as shown Photonics
in FigureK.K., Hama‐
2a. The
matsu, Japan)
thickness ofand an 8 ×was
the LYSO 8 array
chosenofas3.11 × 3.11
15 mm by ×considering
15 mm lutetium
3 yttrium
the trade-off amongoxyorthosilicate
sensitivity,
(LYSO) scintillatorsand
TOF capabilities, (Crystal Photonics,
parallax error [15]. Inc., Sanford, FL, USA), as shown in Figure 2a.
The thickness of the LYSO was chosen as 15 mm by considering the trade‐off among sen‐
sitivity, TOF capabilities, and parallax error [15].
Figure 2b shows the design of the LYSO array. To achieve excellent CTR, each LYSO
was mechanically polished except for a roughening entrance surface. All lateral surfaces
and the entrance surface of the LYSO were optically isolated by ESR film and Teflon tape,
Sensors 2021, 21, x FOR PEER REVIEW 4 of 15
Sensors 2021, 21, 5566 4 of 15
respectively. In addition, all lateral surfaces of the LYSO array were optically isolated by
Teflon tape [16].
(a) (b)
Figure2.2.(a)
Figure (a)88×× 88 array
array of
of 33 ×
× 33 mm
mm2 MPPCs
2
MPPCs (left)
(left) and
and 88 ××88array
arrayofof3.11
3.11××3.11
3.11× ×
15 15
mm mmLYSO
3 3
LYSOscintillators (right).
scintillators (b)
(right).
Design of the LYSO array considering optimal optical properties (surface treatment and reflective materials).
(b) Design of the LYSO array considering optimal optical properties (surface treatment and reflective materials).
The MPPC
Figure and LYSO
2b shows arrays
the design ofwere coupled
the LYSO using
array. a silicone
To achieve adhesive
excellent (3145
CTR, RTV,
each The
LYSO
Dow
was Chemical Company,
mechanically polished Midland,
except forMI, USA) with entrance
a roughening a refractive index All
surface. of 1.49 at 430
lateral nm to
surfaces
match
and thethe refractive
entrance index
surface of between
the LYSOoptical boundaries
were optically (MPPC:
isolated by 1.55
ESR at 450and
film nm,Teflon
LYSO: 1.82
tape,
at 420 nm) taking Snell’s law into account. In addition, this optical coupling
respectively. In addition, all lateral surfaces of the LYSO array were optically isolated by material was
used to
Teflon improve
tape [16]. the collection efficiency of scintillation light and to maintain its adhesion
and The
optical
MPPCproperties
and LYSOfor aarrays
long time
were[26,27].
coupled using a silicone adhesive (3145 RTV, The
Dow Chemical Company, Midland, MI, USA) with a refractive index of 1.49 at 430 nm to
2.3. Individual
match Channel
the refractive Readout
index betweenElectronics
optical boundaries (MPPC: 1.55 at 450 nm, LYSO: 1.82
at 420The
nm)individual
taking Snell’s law into account.
channel readout electronicsIn addition,
were this
basedoptical
on a coupling
64‐channel material was
commercial
used
ASICto(TOFPET2,
improve the collection
PETsys efficiency
Electronics of scintillation
S.A., light and
Oeiras, Portugal) to maintain
to enable its adhesion
fast TOF measure‐
and
mentoptical properties
[28]. Figure for a long
3 shows time
a block [26,27].of the individual channel readout electronics
diagram
for the developed TOF brain PET. They were composed of the FEM, a front‐end board
2.3. Individual Channel Readout Electronics
(FEB) for data acquisition (DAQ) called FEB/D, a DAQ board, and a Clock&Trigger board.
The individual channel readout electronics were based on a 64-channel commercial
ASIC (TOFPET2, PETsys Electronics S.A., Oeiras, Portugal) to enable fast TOF measure-
ment [28]. Figure 3 shows a block diagram of the individual channel readout electronics for
the developed TOF brain PET. They were composed of the FEM, a front-end board (FEB)
for data acquisition (DAQ) called FEB/D, a DAQ board, and a Clock&Trigger board.
2.3.1. Front-End Module (FEM)
As described in Section 2.1, the main components of the FEM were the ASIC board,
called FEB/A, the SiPM interface board, called FEB/S, and the ASIC interface board called
FEB/I [29]. FEB/A was equipped with the TOFPET2 ASIC chip, a temperature sensor, and two
connectors to plug in FEB/S and FEB/I. The ASIC were low-power and low-noise electronics
providing readout and digitization of signals from 64 individual channels to acquire the
position, energy, and time information of an interacted gamma ray in each scintillator.
As shown in Figure 4, it consisted of a preamplifier, two transimpedance amplifiers,
three fast discriminators, a quad-buffered charge-to-digital converter (QDC), and a quad-
buffered dual ramp time-to-digital converter (TDC) for each channel [30]. The output
current signal of the detector module passed through the preamplifier, a current conveyor
with low input impedance, and was then replicated to three branches: T, E, and Q. The cur-
rent signals of the T and E branches were converted into voltage signals by transimpedance
amplifiers, and that of the Q branch was digitized by the QDC comprising four integrators
and a 10-bit Wilkinson analog-to-digital converter (ADC) for energy measurement.
Figure 3. Block diagram of the individual channel readout electronics for the developed TOF brain
PET.
2.3. Individual Channel Readout Electronics
The individual channel readout electronics were based on a 64‐channel commercial
ASIC (TOFPET2, PETsys Electronics S.A., Oeiras, Portugal) to enable fast TOF measure‐
ment [28]. Figure 3 shows a block diagram of the individual channel readout electronics
Sensors 2021, 21, 5566 5 of 15
for the developed TOF brain PET. They were composed of the FEM, a front‐end board
(FEB) for data acquisition (DAQ) called FEB/D, a DAQ board, and a Clock&Trigger board.
Sensors 2021, 21, x FOR PEER REVIEW 5 of 15
2.3.1. Front‐End Module (FEM)
As described in Section 2.1, the main components of the FEM were the ASIC board,
called FEB/A, the SiPM interface board, called FEB/S, and the ASIC interface board called
FEB/I [29]. FEB/A was equipped with the TOFPET2 ASIC chip, a temperature sensor, and
two connectors to plug in FEB/S and FEB/I. The ASIC were low‐power and low‐noise elec‐
tronics providing readout and digitization of signals from 64 individual channels to ac‐
quire the position, energy, and time information of an interacted gamma ray in each scin‐
tillator.
As shown in Figure 4, it consisted of a preamplifier, two transimpedance amplifiers,
three fast discriminators, a quad‐buffered charge‐to‐digital converter (QDC), and a quad‐
buffered dual ramp time‐to‐digital converter (TDC) for each channel [30]. The output cur‐
rent signal of the detector module passed through the preamplifier, a current conveyor
with low input impedance, and was then replicated to three branches: T, E, and Q. The
current signals of the T and E branches were converted into voltage signals by transim‐
pedance amplifiers, and that of the Q branch was digitized by the QDC comprising four
integrators and a 10‐bit Wilkinson analog‐to‐digital converter (ADC) for energy measure‐
Figure
3. 3. Block
Block diagram
diagram of the individual channel readout electronics for the developed TOF brain
Figure
PET.
ment.of the individual channel readout electronics for the developed TOF brain PET.
T branch Trigger
VTH_T1 signal
Current Transimpedance Discriminator Delay
Preamplifier
signal amplifier
Trigger
signal
Discriminator
VTH_T2 Logic
Controller
E branch Trigger
VTH_E signal
Transimpedance Discriminator
amplifier
Q branch
Integrator
Integrator
Integrator
ADC Energy measurement
Integrator
Quad-buffered QDC
TAC
TAC
TAC ADC Timing measurement
TAC
Quad-buffered TDC
VTH_T1 = (63 – T1) × 6.5 mV, VTH_T2 = (63 – T2) × 6.5 mV, VTH_E = (63 – E) × 6.5 mV x64
Figure
Figure 4.
4. Block
Block diagram
diagram of
of TOFPET2
TOFPET2 ASIC
ASIC in
in QDC
QDC mode.
mode.
The
The voltage
voltage signal of the T T branch
branch was
wastransferred
transferredto totwo
twodiscriminators
discriminatorseach
eachhaving
havinga
athreshold
thresholdvoltage
voltagevalue, called
value, VTH_T1
called , for
VTH_T1 time
, for measurement
time measurementand aandthreshold voltage
a threshold value,
voltage
called V
value, called
TH_T2 ,
V for rejecting
TH_T2, for the
rejectingdark
thecount
dark and
countstarting
and the charge
starting the integration
charge window.
integration The
win‐
TDC, which comprised four time-to-amplitude converters (TACs)
dow. The TDC, which comprised four time‐to‐amplitude converters (TACs) and a 10‐bitand a 10-bit Wilkinson
ADC, was ADC,
Wilkinson used forwastime
usedmeasurement. The voltageThe
for time measurement. signal of thesignal
voltage E branch was
of the transferred
E branch was
to a discriminator
transferred with a threshold
to a discriminator with voltage
a thresholdvalue, calledvalue,
voltage VTH_E ,called
for rejecting lowrejecting
VTH_E, for voltage
low voltage signal. All threshold voltage values were set by DAQ software as digital val‐
ues, called T1, T2, and E, as follows:
VTH_T1 = (63 − T1) × 6.5 mV (1)
Sensors 2021, 21, 5566 6 of 15
signal. All threshold voltage values were set by DAQ software as digital values, called T1,
T2, and E, as follows:
VTH_T1 = (63 − T1) × 6.5 mV (1)
VTH_T2 = (63 − T2) × 6.5 mV (2)
VTH_E = (63 − E) × 6.5 mV (3)
Trigger signals output from these discriminators were transferred to a logic controller.
The logic controller activated the QDC and TDC under certain conditions and determined
that the detected gamma ray was a valid event when all trigger signals were on the rising
edge. The raw data, including information about valid events, were transmitted to the
ASIC interface board through four LVDS output links with a maximum data rate per
link of 800 Mbit/s and a maximum output event rate per channel was 0.6 kevent/s. The
specifications of the ASIC are summarized in Table 1.
Table 1. Brief specifications of the ASIC.
Description TOFPET2 (Version 2c)
Technology 110 nm CMOS
Supply voltage 1.2 V, 2.5 V
Power consumption 8.2 mW/channel
Main clock frequency 400 MHz
TDC clock frequency 200 MHz
TDC time bin 31 ps
TDC resolution 20 ps (r.m.s.)
QDC dynamic range 0–1500 pC
Maximum data rate 3.2 Gbit/s
Maximum output event rate 40 Mevent/s
FEB/S was equipped with connectors for transmission of 128 output signals of two
MPPC arrays to two FEB/As, and two sensors for monitoring of the temperature of two
MPPC arrays. FEB/I managed the communication between the two FEB/As and FEB/D
through a 50 cm-long flexible flat cable connected to an on-board FEM port.
2.3.2. Front-End Board for Data Acquisition (FEB/D)
Figure 5 shows FEB/D comprising a motherboard, a BIAS-16P mezzanine board, and
a COMM-DAQ mezzanine board [29,31]. The motherboard was equipped with a field
programmable gate array (FPGA) chip, and eight FEM ports for operating up to eight
FEMs through 50 cm-long flexible flat cables. The role of the motherboard was to distribute
power, clock, and configuration signals to each FEM, to receive temperature values and
raw data on the ASIC from each FEM, and to collect raw data into data frames, as shown
in Figure 3. The motherboard only required a single external supply voltage of 12 V with a
maximum current of 4 A and used a set of switching regulators to supply power to eight
FEMs as well as the entire FEB/D.
The BIAS-16P mezzanine board supplied voltage to 16 MPPC arrays through two
on-board DC/DC converters, each with a maximum current of 20 mA. Each DC/DC
converter provided eight positive voltage lines capable of independent voltage settings
within the range 0–72 V by DAQ software, with an average current per line of 2.5 mA.
The COMM-DAQ mezzanine board was equipped with an enhanced small form-
factor pluggable (SFP+) optical transceiver module for transmission of data frames to the
DAQ board and receipt of configuration signals from the DAQ board. The communication
between the COMM-DAQ mezzanine board and the DAQ board was through the SFP+
high-speed serial optical link. The maximum output event rate per link was 100 Mevent/s
at up to 6.6 Gbit/s. In addition, the COMM-DAQ mezzanine board was equipped with a
small multiple connector type Q (SMC-Q) for receipt of clock, synchronization, and trigger
signals from the Clock&Trigger board through a 2 m-long flat cable assembly.
programmable gate array (FPGA) chip, and eight FEM ports for operating up to eight
FEMs through 50 cm‐long flexible flat cables. The role of the motherboard was to distrib‐
ute power, clock, and configuration signals to each FEM, to receive temperature values
Sensors 2021, 21, x FOR PEER REVIEW and raw data on the ASIC from each FEM, and to collect raw data into data frames, 7 ofas15
Sensors 2021, 21, 5566 shown in Figure 3. The motherboard only required a single external supply voltage of7 of 1215
V with a maximum current of 4 A and used a set of switching regulators to supply power
to eight FEMs as well as the entire FEB/D.
The BIAS‐16P mezzanine board supplied voltage to 16 MPPC arrays through two on‐
board DC/DC converters, each with a maximum current of 20 mA. Each DC/DC converter
provided eight positive voltage lines capable of independent voltage settings within the
range 0–72 V by DAQ software, with an average current per line of 2.5 mA.
The COMM‐DAQ mezzanine board was equipped with an enhanced small form‐fac‐
tor pluggable (SFP+) optical transceiver module for transmission of data frames to the
DAQ board and receipt of configuration signals from the DAQ board. The communication
between the COMM‐DAQ mezzanine board and the DAQ board was through the SFP+
high‐speed serial optical link. The maximum output event rate per link was 100 Mevent/s
at up to 6.6 Gbit/s. In addition, the COMM‐DAQ mezzanine board was equipped with a
(a)small multiple connector type Q (SMC‐Q) for receipt of clock, (b) synchronization, and trigger
Figure signals
Figure5.5.Photographs
Photographs fromcapable
ofofFEB/D
FEB/D the Clock&Trigger
ofof
capable connecting board through
upuptotoeight
connecting a front
eightFEMs:
FEMs:2 front
m‐long flat
(a)(a)and
and cable
back (b)
back assembly.
views.
(b) views.
2.3.3.
2.3.3. DAQ
DAQ Board
Board and
and Clock&Trigger
Clock&Trigger Board
As
As shown
shown in in Figure 6a, the DAQ board was equipped with an FPGA chip and a
CoaXPress
CoaXPress (CXP) optical transceiver module
module capable
capable of connecting
connecting up
up to 12 SFP+ modules
through a 5 m-long
through m‐long 12xCXP/SFP+
12xCXP/SFP+ linklink[32].
[32].The
TheDAQDAQboard
board was
was mounted
mounted on on a PCIe 2.0
x4 slot on a computer to be operated and controlled. The The role of the DAQ board was to
generate signals for ASIC
generate ASIC configuration,
configuration, to run the
the calibration
calibration process
process for transimpedance
transimpedance
amplifiers,
amplifiers, discriminators, QDCs, and TDCs,
TDCs, to receive data frames from FEB/Ds, to store
to receive data frames from FEB/Ds,
them on the computer,
them computer, andandtotoconvert
convertthem
theminto
intobinary
binarylist-mode
list‐modedata,
data,asasshown
shownininFigure 3.
Figure
The
3. DAQ
The DAQboard alsoalso
board monitored the values
monitored of theoftemperature
the values sensorssensors
the temperature mounted on FEB/S
mounted on
and FEB/A.
FEB/S All of All
and FEB/A. theseoffunctions were controlled
these functions by DAQbysoftware
were controlled run on 64-bit
DAQ software run onCentOS
64‐bit
7 Linux 7[33].
CentOS The[33].
Linux maximum output event
The maximum rate
output fromrate
event the from
DAQtheboard
DAQ to board
the computer was
to the com‐
250 Mevent/s.
puter was 250 Mevent/s.
(a) (b)
Figure 6. Photographs
Figure 6. Photographs of
of the
the DAQ
DAQ board
board (a)
(a) and
and the
the Clock&Trigger board (b).
Clock&Trigger board (b). For
For the
the developed
developed TOF
TOF brain
brain PET,
PET, aa
10xCXP/SFP+ link was used to communicate with the DAQ board.
10xCXP/SFP+ link was used to communicate with the DAQ board.
Figure
Figure 6b shows
shows the
the Clock&Trigger board comprising the same motherboard and
COMM‐DAQ mezzanineboard
COMM-DAQ mezzanine boardasasFEB/D
FEB/Dand and1616SMC-Qs
SMC‐Qs[32].[32].The
Therole
roleofofthe
the Clock&Trig‐
Clock&Trigger
ger board was to generate the system reference clock of 200 MHz, synchronization,
board was to generate the system reference clock of 200 MHz, synchronization, and trigger and
trigger signals and to send them to up to 16 FEB/Ds, as shown in Figure
signals and to send them to up to 16 FEB/Ds, as shown in Figure 3. All boards equipped 3. All boards
equipped
with FPGA with FPGA
chips werechips were programmed
programmed with
with bit files bit files implemented
implemented to performtothe perform the
functions
functions of each
of each board boardthe
through through the standard
standard JTAG interface.
JTAG interface. All of theAll of the
FPGA FPGA
chips werechips were
Kintex-7
Kintex‐7 FPGAs (XC7K160T,
FPGAs (XC7K160T, Xilinx,
Xilinx, Inc., Inc.,CA,
San Jose, SanUSA).
Jose, CA, USA). Ineach
In addition, addition,
of these each of these
boards was
equipped
boards waswith a cooling
equipped fana and
with heatsink
cooling forheatsink
fan and dissipation
for of the heat from
dissipation of the the FPGA
heat fromchip.
the
FPGA chip.
Before evaluating the feasibility of the TOF brain PET employing indiv
readout electronics, the ASIC calibration process was performed at room
with a breakdown voltage of the MPPC set to 53 V. All experiments were a
Sensors 2021, 21, 5566 8 of 15
at room temperature and the TOF PET detector blocks were cooled using a
The binary list‐mode data acquired using the developed TOF brain PET we
prompt coincidence
2.4. Performance Evaluation data considering the energy window of 10%, the coinc
window of 5 ns (without
Before evaluating time
the feasibility offset
of the correction),
TOF brain and individual
PET employing all possible
channel LORs ac
The acquired
readout energy
electronics, spectra
the ASIC wereprocess
calibration not corrected for the
was performed energy
at room nonlinearity
temperature
with a breakdown voltage of the MPPC set to 53 V. All experiments were also carried out
the SiPM saturation and the nonlinear QDC response. All tomographic ima
at room temperature and the TOF PET detector blocks were cooled using air circulation.
TOF images
The binary reconstructed
list-mode data acquired without attenuation,
using the developed normalization,
TOF brain PET were sortedscatter,
into and
rections.
prompt coincidence data considering the energy window of 10%, the coincidence timing
window of 5 ns (without time offset correction), and all possible LORs across the FOV. The
acquired energy spectra were not corrected for the energy nonlinearity resulting from the
2.4.1. Energyand
SiPM saturation Resolution, Coincidence
the nonlinear QDC response.Counting Rate,
All tomographic and were
images Coincidence
nonTOF Tim
Resolution
images reconstructed without attenuation, normalization, scatter, and random corrections.
As shown
2.4.1. Energy in Figure
Resolution, 7, a Counting
Coincidence 1.8 MBqRate,
of and
NaCoincidence
point source
22
Timinghaving an
Resolution active
mmAs was placed
shown at the
in Figure 7, acenter
1.8 MBqbetween a pair
of 22 Na point of having
source channels, anddiameter
an active the dataof were a
1the
mm wasof
pair placed at the center
channels of two between a pair
detector of channels,
modules and each
facing the data wereon
other acquired
the TOF bra
using the pair of channels of two detector modules facing each other on the TOF brain PET
s.
for 240 s.
Figure 7. Experimental setup to evaluate the performance (energy resolution, CCR, and CTR) of the
Figure 7. Experimental setup to evaluate the performance (energy resolution, CCR,
detector module. The 22 Na point source was placed at the center between a pair of channels.
detector module. The 22Na point source was placed at the center between a pair of c
The energy resolution was measured by calculating the full width at half maximum
(FWHM) using a Gaussian fit to the photopeak position corresponding to 511 keV in
The energy resolution was measured by calculating the full width at h
the energy spectrum acquired with energy values of the list-mode data. The CCR was
(FWHM)
measured byusing a Gaussian
calculating fit toofthe
the total number photopeak
prompt position
coincidence corresponding
data detected during the to 5
data acquisition
energy time. The
spectrum CTR waswith
acquired measured by calculating
energy values the
of FWHM using a Gaussian
the list‐mode data. The C
fit to the coincidence peak position in the time spectrum acquired with the difference in
ured by calculating the total number of prompt coincidence data detected d
time values of the prompt coincidence data.
acquisition
To optimizetime. The CTRofwas
the performance measured
the detector bythe
module, calculating the FWHM
energy resolution, CCR, and using
CTR were measured (repeated three times) while changing
to the coincidence peak position in the time spectrum acquired the overvoltage (V ) of thethe diff
OVERwith
MPPC and T1 of the ASIC. The VOVER and T1 values were set at 1 V increments from 1 to
values of the prompt coincidence data.
7 V and five increments from 20 to 60, respectively. The optimal VOVER and T1 values were
To optimize
determined consideringthe performance
average ofenergy
values of the the detector
resolution,module,
CCR, and the
CTR.energy resolut
CTR were measured (repeated three times) while changing the overvoltage
MPPC and T1 of the ASIC. The VOVER and T1 values were set at 1 V increm
7 V and five increments from 20 to 60, respectively. The optimal VOVER and T
determined considering average values of the energy resolution, CCR, and
Sensors 2021, 21, 5566 9 of 15
2.4.2. Spatial Resolution
In order to evaluate the spatial resolution of the TOF brain PET, the data were acquired
using the 1.7 MBq of 22 Na point source placed at the center of the FOV for 240 s. In addition,
the data acquisition was repeated while moving the same point source to the following
transaxial distances from the center: 20, 40, 60, and 80 mm. The tomographic images of
the point source were reconstructed by a 2-dimensional filtered backprojection algorithm
with a Hann filter (cut-off frequency = 0.42 × Nyquist frequency) and 514 × 514 pixels
of 0.5 × 0.5 mm2 . The spatial resolution was measured by calculating the FWHM using
a Gaussian fit to each pixel peak position in response functions (1-dimensional profiles)
acquired with the reconstructed images of the point source.
2.4.3. Phantom Imaging
The data were acquired using a custom-made hot-rod phantom filled with 10.4 MBq
of 18 F placed at the center of the FOV to evaluate the imaging capability of the TOF brain
PET for 120 s. The diameter and height of the hot-rod phantom were 75 and 40 mm,
respectively, and it consisted of 40 rods with different diameters (2.5, 3.5, 4.5, 5.5, and
6.5 mm). The tomographic images of the hot-rod phantom were reconstructed by a
3-dimensional maximum-likelihood expectation-maximization algorithm with 31 itera-
tions and 514 × 514 × 514 voxels of 0.5 × 0.5 × 0.5 mm3 .
3. Results
3.1. Energy Resolution, Coincidence Counting Rate, and Coincidence Timing Resolution
Figure 8 shows average values of the photopeak position, energy resolution, CCR, and
CTR measured using a pair of channels at different VOVER and T1 values. The average pho-
topeak position was almost linearly shifted to higher QDC values with increasing VOVER
value, regardless of the T1 value (Figure 8a). The average energy resolution gradually
improved as the VOVER value increased, but rapidly deteriorated after the VOVER value of
5–6 V (Figure 8b). The average CCR increased, but the rate of change gradually decreased
as the VOVER value increased (Figure 8c). In addition, like the average energy resolution,
the effect of the T1 value on the average CCR was relatively small. The average CTR
improved as the VOVER value increased, but rapidly deteriorated after the VOVER value
of 5–6 V (Figure 8d). At the VOVER values of 5 and 6 V (T1 = 30), the average CTRs were
195.5 ± 9.2 and 194.7 ± 12.1 ps FWHM, respectively.
The optimal VOVER and T1 values were determined to be 5 V and 30, respectively,
considering the standard deviation of the CTR. The average values of the photopeak
position, energy resolution, and CCR were 335 ± 3, 5.6 ± 1.4% FWHM (without energy
nonlinearity correction), and 6.1 ± 0.2 cps, respectively at the optimal values. The best CTR
of 187 ps FWHM was achieved, as shown in Figure 9.
Figure 10 shows the representative energy spectra of 64 channels (one of 60 detector
modules) among the energy spectra of 3840 channels acquired with the TOF brain PET
using the 22 Na point source placed at the center of the FOV to evaluate the spatial resolution.
The average energy resolution and CCR of the 3840 channels were 6.6 ± 0.6% FWHM (not
corrected for the energy nonlinearity) and 13.3 kcps at 1.7 MBq, respectively.
of 5–6 V (Figure 8b). The average CCR increased, but the rate of change gradually de‐
creased as the VOVER value increased (Figure 8c). In addition, like the average energy res‐
olution, the effect of the T1 value on the average CCR was relatively small. The average
ors 2021, 21, x FOR PEER REVIEW CTR improved as the VOVER value increased, but rapidly deteriorated after the VOVER value
Sensors 2021, 21, 5566 of 5–6 V (Figure 8d). At the VOVER values of 5 and 6 V (T1 = 30), the average CTRs10were
of 15
195.5 ± 9.2 and 194.7 ± 12.1 ps FWHM, respectively.
Average energy resolution (% FWHM)
500 9
Average photopeak position (a.u.)
T1 = 60
7 280 T1 = 55
8 T1 = 50
400 T1 = 45
Average CTR (ps FWHM)
T1 = 40
260 T1 = 35
Average CCR (cps)
6 7
T1 = 30
300 T1 = 25
6 240 T1 = 20
5 200
5
220
10021, x FOR PEER REVIEW
Sensors 2021, 4 10 of 15
4 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8
VOVER (V)
200 VOVER (V)
(a) (b)
3 180
0 17 2 3 4 5 6 7 8 280
0 1 2 3 4 5 6 = 60
T1 7 8
T1 = 55
VOVER (V) VOVER (V)
Average CTR (ps FWHM)
260 T1 = 50
Average CCR (cps)
6 T1 = 45
T1 = 40
(c) (d) T1 = 35
240
T1 = 30
5
Figure 8. Average photopeak position (a), energy resolution (b), CCR (c), and CTR (d) measured using a pair of ch T1 = 25
220 T1 = 20
at different VOVER and T1 values.
4
200
The optimal VOVER and T1 values were determined to be 5 V and 30, re
3 considering the standard180 deviation of the CTR. The average values of the pho
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8
sition,
VOVER (V)energy resolution, and CCR wereVOVER 335 (V)
± 3, 5.6 ± 1.4% FWHM (without e
linearity
(c) correction), and 6.1 ± 0.2 cps, respectively (d) at the optimal values. The b
187 ps FWHM was achieved, as shown in Figure 9.
Figure 8. Average photopeak position (a), energy resolution (b), CCR (c), and CTR (d) measured using a pair of channels
Figure 8. Average photopeak position (a), energy resolution (b), CCR (c), and CTR (d) measured using a pair of channels at
atdifferent andT1
differentVVOVER and T1values.
values.
OVER
The optimal VOVER and T1 values were determined to be 5 V and 30, respectively,
considering the standard deviation of the CTR. The average values of the photopeak po‐
sition, energy resolution, and CCR were 335 ± 3, 5.6 ± 1.4% FWHM (without energy non‐
linearity correction), and 6.1 ± 0.2 cps, respectively at the optimal values. The best CTR of
187 ps FWHM was achieved, as shown in Figure 9.
Figure 9. Time spectrum acquired at the VOVER of 5 V and T1 of 30. The solid line was the result of
Figure 9. Time spectrum acquired at the VOVER of 5 V and T1 of 30. The solid line was
applying a Gaussian fit to the coincidence peak position.
applying a Gaussian fit to the coincidence peak position.
Figure 9. Time spectrum acquired at the VOVER of 5 V and T1 of 30. The solid line was the result of
applying a Gaussian fit to the coincidence peak position.
Figure 10 shows the representative energy spectra of 64 channels (one of
modules) among
Figure 10 showsthe
the energy spectra
representative ofspectra
energy 3840 ofchannels acquired
64 channels (one of 60 with the TOF
detector
using the among
modules) 22Na point source
the energy placed
spectra at channels
of 3840 the center of the
acquired FOV
with to evaluate
the TOF brain PET the spaSensors 2021, 21, 5566 11 of 15
Sensors 2021, 21, x FOR PEER REVIEW 11 of 15
Sensors 2021, 21, x FOR PEER REVIEW 11 of 15
Figure 10. Representative energy spectra acquired from 64 channels of the TOF brain PET. The thick solid lines were the
result of applying
Figure a Gaussian energy
10. Representative fit to thespectra
photopeak position
acquired fromcorresponding
64 channels oftothe
511 keV.
TOF brain PET. The thick solid lines were the
Figure 10. Representative energy spectra acquired from 64 channels of the TOF brain PET. The thick solid lines were the
result of applying a Gaussian fit to the photopeak position corresponding to 511 keV.
result of applying a Gaussian fit Spatial
3.2. to the photopeak
Resolution position corresponding to 511 keV.
3.2. Spatial
Figure 11 Resolution
shows the spatial resolution of the TOF brain PET as a function of the trans‐
3.2. Spatial Resolution
axial distance from the center of the FOV. A transaxial spatial resolution of 2.5 mm FWHM
Figure 11 shows the spatial resolution of the TOF brain PET as a function of the
Figure
wastransaxial
achieved11distance
atshows the
fromspatial
the center ofthe resolution
thecenter
axial and of the
A TOF
transaxial
of the FOV. FOV.brainspatial
transaxial PET asresolution
a function
of of
2.5the
mm trans‐
axialFWHM
distance
wasfrom the center
achieved of the of
at the center FOV. A transaxial
the axial spatialFOV.
and transaxial resolution of 2.5 mm FWHM
was achieved at the center of the axial and transaxial FOV.
4.5
(mm FWHM)
4.0
4.5
(mm FWHM)
3.5
4.0
Spatial resolution
3.0
3.5
Spatial resolution
2.5
3.0
2.0
–20 0 20 40 60 80 100
2.5
Transaxial distance from the center of the FOV (mm)
Figure 2.0Spatial
11.
Figure resolution
11. Spatial of the
resolution TOFTOF
of the brain PETPET
brain as aasfunction of the
a function transaxial
of the distance
transaxial from
distance the the
from
center 20the
–of
of the
center FOV. 0
FOV. 20 40 60 80 100
Transaxial distance from the center of the FOV (mm)
3.3.3.3. Phantom
Phantom Imaging
Imaging
FigureFigure
11. Spatial
Figure 12 resolution
shows
12 shows of the TOF
a tomographic
a tomographic brainofPET
image
image of as
the
the a function
hot-rod
hot‐rod of thewith
phantom
phantom transaxial
withaabit distance
bitdepth
depth from the
of8‐
of 8-bit
center of the
acquired FOV.
bit acquired using the TOF brain PET. The rods were clearly resolved down to a diameter of
using the TOF brain PET. The rods were clearly resolved down to a diameter
2.5mm
of 2.5 mmininthe
thehot‐rod
hot-rodphantom
phantomimage.
image.
3.3. Phantom Imaging
Figure 12 shows a tomographic image of the hot‐rod phantom with a bit depth of 8‐
bit acquired using the TOF brain PET. The rods were clearly resolved down to a diameter
of 2.5 mm in the hot‐rod phantom image.s 2021, 21, x FOR
Sensors PEER
2021, REVIEW
21, 5566 12 of 15 12
(a) (b)
Figure
Figure 12. (a) 12. (a) Photograph
Photograph of theofhot‐rod
the hot-rod phantom comprising
phantom comprising 40 rods with different
40 rods diameters (2.5,
with different 3.5, 4.5, 5.5,
diameters and3.5,
(2.5, 6.5 mm).
4.5, 5.5, and 6.5
(b) Transverse slice of the hot-rod phantom image acquired using the TOF
mm). (b) Transverse slice of the hot‐rod phantom image acquired using the TOF brain PET.brain PET.
4. Discussion
4. Discussion
In this study, we have developed a TOF brain PET by employing an optimal geometry
Into this
improve sensitivity,
study, we have a fast TOF PET detector
developed a TOFtobrain
achieve excellent
PET CTR, and individual
by employing an optimal ge
channel readout electronics to enable TOF measurement. The feasibility of the TOF brain
try to improve sensitivity, a fast TOF PET detector to achieve excellent CTR, and ind
PET has been evaluated by measuring the energy resolution, CCR, CTR, and spatial
ual channel
resolution.readout electronics
Tomographic images ofto theenable TOF hot-rod
custom-made measurement.
phantom have Thebeen
feasibility
acquired of the
brain toPET has been
evaluate evaluated
the imaging by measuring
capability the energy
of the TOF brain PET. Theresolution,
image qualityCCR, CTR, and sp
phantoms
resolution. Tomographic images of the custom‐made hot‐rod phantom not
specified in the national electrical manufacturers association (NEMA) standards were have bee
used because it is not fully functional but a proof-of-principle system demonstrating the
quiredfeasibility
to evaluate the imaging capability of the TOF brain PET. The image quality p
of the TOF brain PET. The developed TOF brain PET has the potential to improve
toms sensitivity,
specifiedspatialin the national
resolution, andelectrical
image quality manufacturers association
compared to a conventional (NEMA) stand
whole-body
were notPET in used
brainbecause
imaging itdue istonot
the fully
reduced functional butthe
ring diameter, a proof‐of‐principle
detector size, and the TOF system de
capabilities. It is expected that the TOF brain PET will provide better system performance
strating the feasibility of the TOF brain PET. The developed TOF brain PET has the p
than a conventional PET employing a multiplexing circuit that reduces the number of
tial toreadout
improve sensitivity,
channels but degradesspatial resolution,
the overall and ofimage
performance quality compared to a con
the detector.
tional whole‐body
The arrival timePETofinthebrain
detected imaging
gamma ray duewastomeasured
the reduced ring diameter,
by a leading-edge discrimi-the det
nation
size, and method
the TOFusing the discriminator
capabilities. in the individual
It is expected that channel
the TOF readout electronics,
brain PET will which
provide b
could cause timing walk, an energy-dependent timing variation, leading to degradation of
system performance than a conventional PET employing a multiplexing circuit th
the CTR [9,34]. There are two general ways to reduce it. One is to increase the amplitude
ducesofthe thenumber of readout
output signal of the SiPM channels
input tobut degrades theand
the discriminator, overall performance
the other is to decreaseof the d
tor. the threshold voltage value of the discriminator. In general, for the former, it is necessary
to increase
The arrivalthe operating
time of thevoltage
detected of the SiPM orray
gamma thewas
gain measured
of the amplifier,
by awhich causes
leading‐edge dis
an increase in the dark count or the amplitude of noise. In the latter case, as the value
ination method
continues using the
to decrease, discriminator
the arrival time of the in darkthe individual
count rather than channel
the detectedreadout
gamma electro
whichray could
may be cause timing
measured. walk, an
Therefore, energy‐dependent
in order to reduce the extenttiming variation, due
of CTR degradation leading
to to d
dationtiming
of the walk
CTRand[9,34].
dark count,
Therethe optimal
are twoVOVER and T1
general were to
ways determined
reduce from the results
it. One is to increas
shown in Figure 8. The data showed that the best CTR of 187 ps FWHM was achieved at the
amplitude of the output signal of the SiPM input to the discriminator, and the other
VOVER of 5 V and T1 of 30. Although the materials and methods used for the measurement
decreasewerethe threshold
not the same, thisvoltage
result wasvalue
superiorof to
the
thatdiscriminator. In general,
measured with commercial for the former
or prototype
necessary to increase
PET systems thetooperating
reported date (210–544voltage of the
ps), given 15 SiPM or the(not
mm LYSO:Ce gainco-doped
of the amplifier,
with w
causesCa),ananalog SiPM,inand
increase themeasurement
dark count at room
or thetemperature
amplitude [11,17,35,36].
of noise.This Inisthe
duelatter
to the case, a
value continues to decrease, the arrival time of the dark count rather than the det
gamma ray may be measured. Therefore, in order to reduce the extent of CTR degrad
due to timing walk and dark count, the optimal VOVER and T1 were determined fromSensors 2021, 21, 5566 13 of 15
optimization of the TOF PET detector module and the use of individual channel readout
electronics with a TDC resolution of 20 ps.
It was possible to clearly distinguish all rods in the tomographic image of the hot-rod
phantom and the diameter of the smallest among these was the same as the transaxial
spatial resolution measured at the center of the FOV. As shown in Figure 11, the spatial
resolution of the developed brain PET was degraded toward the outside of the FOV due to
an increase in parallax error caused by the reduced ring diameter [3,6]. If TOF technology
is applied, it is predicted that the degradation of the spatial resolution can be improved
due to allowing the use of smaller voxels in the image reconstruction [14]. In addition, it is
expected that the image quality will be greatly improved due to excellent CTR.
Since the scintillator thickness of 15 mm is shorter than that of most commercial
PET systems, although the sensitivity is reduced due to a decrease in detection efficiency,
the spatial resolution is improved due to a decrease in parallax error. Additionally, im-
portant benefits can be obtained due to TOF capabilities achieved by using the reduced
thickness [9,10,15,17]. If the tomographic image of an object having the same diameter as
the transaxial FOV is acquired using the TOF brain PET with the CTR of 187 ps, the TOF
SNR gain would be about 2.5 under the following assumptions: the object is a cylinder with
a uniform activity distribution located in the center of the FOV and attenuation, scatter,
and random corrections are perfectly applied. Considering the relationship between SNR
and NECR, the radiation dose or scanning time could be reduced by about 6.5-fold which
allows the radiation dose to reduce to about 1 mSv for a typical 18 F-fluorodeoxyglucose
(FDG)-PET study.
The performance of the MPPC array and ASIC chip in the TOF PET detector block
deteriorates as the operating temperature increases due to the heat generated by both
of those components as well as the heat transferred from the ASIC chip to the MPPC
array [18,30]. Moreover, if the temperature of the detector block is different during the
ASIC calibration process and the data acquisition process, the performance would be
nonuniform [33]. As shown in Figure 8d, the average CTRs acquired at different VOVER
and T1 values did not have a monotonic relationship due to this temperature difference.
An effective cooling method is required to constantly maintain the temperatures of all of
the MPPCs and ASIC chips low and similar, which enables the TOF PET detector blocks to
provide improved and uniform performance.
We are currently constructing a prototype TOF brain PET with an extended axial FOV
applying a water-cooled system. A further study will be performed to correct the time
offset required for TOF image reconstruction, to evaluate the performance of the TOF brain
PET according to the NEMA standards, and to acquire high-performance TOF PET images.
5. Conclusions
We have developed and evaluated the performance of a proof-of-principle TOF brain
PET with the geometry optimized for brain imaging by employing a PET detector with
optimized optical properties and individual channel readout electronics. The results of this
study demonstrate that the developed TOF brain PET could provide excellent performance,
allowing for the reduction in radiation dose or scanning time for brain imaging due to
improved sensitivity and SNR.
Author Contributions: Conceptualization, K.P., J.J., Y.C., H.L. and Y.K.; data curation, K.P.; formal
analysis, K.P., H.L. and Y.K.; investigation, K.P.; methodology, K.P., H.L. and Y.K.; project admin-
istration, J.J.; software, K.P.; supervision, Y.C.; validation, K.P., J.J. and Y.C.; visualization, K.P.;
writing—original draft, K.P.; writing—review and editing, K.P., J.J. and Y.C. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.Sensors 2021, 21, 5566 14 of 15
Data Availability Statement: The data presented in this study are not publicly available due to ongo-
ing results protection with respect to a prototype time-of-flight brain positron emission tomography
with an extended axial field of view applying a water-cooled system we are currently constructing.
Acknowledgments: This research was supported by the Basic Science Research Program through
the National Research Foundation of Korea (NRF) funded by the Ministry of Education
(No. 2019R1I1A1A01041347), the Technology Development Program (No. S2840619) funded by
the Ministry of SMEs and Startups (MSS, Korea), and the Korea Medical Device Development Fund
grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade,
Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety)
(Project Number: KMDF_PR_20200901_0006, 9991007015).
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Quigley, H.; Colloby, S.J.; O’Brien, J.T. PET imaging of brain amyloid in dementia: A review. Int. J. Geriatr. Psychiatry
2011, 26, 991–999. [CrossRef]
2. Brown, R.K.J.; Bohnen, N.I.; Wong, K.K.; Minoshima, S.; Frey, K.A. Brain PET in Suspected Dementia: Patterns of Altered FDG
Metabolism. RadioGraphics 2014, 34, 684–701. [CrossRef] [PubMed]
3. Gong, K.; Majewski, S.; Kinahan, P.E.; Harrison, R.L.; Elston, B.F.; Manjeshwar, R.; Dolinsky, S.; Stolin, A.V.; Brefczynski-Lewis,
J.A.; Qi, J. Designing a compact high performance brain PET scanner—Simulation study. Phys. Med. Biol. 2016, 61, 3681–3697.
[CrossRef] [PubMed]
4. Fraioli, F.; Punwani, S. Clinical and research applications of simultaneous positron emission tomography and MRI. Br. J. Radiol.
2014, 87, 20130464. [CrossRef]
5. Tashima, H.; Yamaya, T. Proposed helmet PET geometries with add-on detectors for high sensitivity brain imaging. Phys. Med.
Biol. 2016, 61, 7205–7220. [CrossRef]
6. Melroy, S.; Bauer, C.; McHugh, M.; Carden, G.; Stolin, A.; Majewski, S.; Brefczynski-Lewis, J.; Wuest, T. Development and
Design of Next-Generation Head-Mounted Ambulatory Microdose Positron-Emission Tomography (AM-PET) System. Sensors
2017, 17, 1164. [CrossRef] [PubMed]
7. Jung, J.H.; Choi, Y.; Hong, K.J.; Kang, J.; Hu, W.; Lim, H.K.; Huh, Y.; Kim, S.; Jung, J.; Kim, K.B. Development of brain PET using
GAPD arrays. Med. Phys. 2012, 39, 1227–1233. [CrossRef]
8. Jung, J.; Choi, Y.; Jung, J.H.; Kim, S.; Im, K.C. Performance evaluation of neuro-PET using silicon photomultipliers. Nucl. Instrum.
Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip. 2016, 819, 182–187. [CrossRef]
9. Cherry, S.R.; Sorenson, J.A.; Phelps, M.E. Physics in Nuclear Medicine, 4th ed.; Elsevier: Amsterdam, The Netherlands, 2012;
ISBN 9781416051985.
10. Turkington, T.G. Introduction to PET Instrumentation. J. Nucl. Med. Technol. 2001, 29, 4–11.
11. Vandenberghe, S.; Mikhaylova, E.; D’Hoe, E.; Mollet, P.; Karp, J.S. Recent developments in time-of-flight PET. EJNMMI Phys.
2016, 3, 3. [CrossRef]
12. Moses, W.W. Time of flight in PET revisited. IEEE Trans. Nucl. Sci. 2003, 50, 1325–1330. [CrossRef]
13. Kadrmas, D.J.; Casey, M.E.; Conti, M.; Jakoby, B.W.; Lois, C.; Townsend, D.W. Impact of Time-of-Flight on PET Tumor Detection.
J. Nucl. Med. 2009, 50, 1315–1323. [CrossRef]
14. Conti, M. Focus on time-of-flight PET: the benefits of improved time resolution. Eur. J. Nucl. Med. Mol. Imaging 2011, 38, 1147–1157.
[CrossRef] [PubMed]
15. Kim, Y.; Jung, J.; Choi, Y.; Leem, H.; Park, K. Design Optimization of Neuro PET Using Monte Carlo Simulation. In Proceedings
of the IEEE Nuclear Science Symposium and Medical Imaging Conference, Sydney, Australia, 10–17 November 2018. M-07-098.
16. Leem, H.; Choi, Y.; Jung, J.; Park, K.; Kim, Y. Optimized TOF-PET Detector Using Scintillation Crystal Array for Brain Imaging. In
Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference, Sydney, Australia, 10–17 November 2018.
M-14-057.
17. Lecoq, P. Pushing the Limits in Time-of-Flight PET Imaging. IEEE Trans. Radiat. Plasma Med. Sci. 2017, 1, 473–485. [CrossRef]
18. Yeom, J.Y.; Vinke, R.; Levin, C.S. Optimizing timing performance of silicon photomultiplier-based scintillation detectors. Phys.
Med. Biol. 2013, 58, 1207–1220. [CrossRef]
19. Anger, H.O. Scintillation Camera. Rev. Sci. Instrum. 1958, 29, 27–33. [CrossRef]
20. Siegel, S.; Silverman, R.W.; Shao, Y.; Cherry, S.R. Simple charge division readouts for imaging scintillator arrays using a
multi-channel PMT. IEEE Trans. Nucl. Sci. 1996, 43, 1634–1641. [CrossRef]
21. Downie, E.; Yang, X.; Peng, H. Investigation of analog charge multiplexing schemes for SiPM based PET block detectors. Phys.
Med. Biol. 2013, 58, 3943–3964. [CrossRef]
22. Choe, H.-J.; Choi, Y.; Hu, W.; Yan, J.; Jung, J.H. Development of capacitive multiplexing circuit for SiPM-based time-of-flight
(TOF) PET detector. Phys. Med. Biol. 2017, 62, N120–N133. [CrossRef]
23. Lee, S.; Choi, Y.; Kang, J.; Jung, J.H. Development of a multiplexed readout with high position resolution for positron emission
tomography. Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip. 2017, 850, 42–47. [CrossRef]Sensors 2021, 21, 5566 15 of 15
24. Vinke, R.; Yeom, J.Y.; Levin, C.S. Electrical delay line multiplexing for pulsed mode radiation detectors. Phys. Med. Biol.
2015, 60, 2785–2802. [CrossRef] [PubMed]
25. Sivaswamy, J.; Thottupattu, A.J.; Mehta, R.; Sheelakumari, R.; Kesavadas, C. Construction of Indian human brain atlas. Neurol.
India 2019, 67, 229–234. [CrossRef] [PubMed]
26. Montecchi, M.; Ingram, Q. Study of some optical glues for the Compact Muon Solenoid at the large hadron collider of CERN.
Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip. 2001, 465, 329–345. [CrossRef]
27. Van Elburg, D.J.; Noble, S.D.; Hagey, S.; Goertzen, A.L. Comparison of acrylic polymer adhesive tapes and silicone optical grease
in light sharing detectors for positron emission tomography. Phys. Med. Biol. 2018, 63, 05NT02. [CrossRef]
28. Bugalho, R.; Di Francesco, A.; Ferramacho, L.; Leong, C.; Niknejad, T.; Oliveira, L.; Pacher, L.; Rolo, M.; Rivetti, A.; Silveira, M.;
et al. Experimental results with TOFPET2 ASIC for time-of-flight applications. Nucl. Instrum. Methods Phys. Res. Sect. A Accel.
Spectrometers Detect. Assoc. Equip. 2018, 912, 195–198. [CrossRef]
29. TOFPET2 ASIC SiPM Readout System—Hardware User Guide (v1.3); PETsys Electronics S.A.: Oeiras, Portugal, 2018.
30. TOFPET2 2C ASIC—Datasheet (Rev 4); PETsys Electronics S.A.: Oeiras, Portugal, 2018.
31. Time-of-Flight Front End Board D (TOF FEB/D_v2)—Hardware User Guide (v1.3); PETsys Electronics S.A.: Oeiras, Portugal, 2018.
32. Time-of-Flight Data Acquisition Board (TOF DAQ_v2)—Hardware User Guide (v1.3); PETsys Electronics S.A.: Oeiras, Portugal, 2018.
33. TOFPET2 ASIC SiPM Readout System—Software User Guide (v2018.07); PETsys Electronics S.A.: Oeiras, Portugal, 2018.
34. Simpson, M.L.; Britton, C.L.; Wintenberg, A.L.; Young, G.R. An integrated, CMOS, constant-fraction timing discriminator for
multichannel detector systems. IEEE Trans. Nucl. Sci. 1995, 42, 762–766. [CrossRef]
35. van Sluis, J.; de Jong, J.; Schaar, J.; Noordzij, W.; van Snick, P.; Dierckx, R.; Borra, R.; Willemsen, A.; Boellaard, R. Performance
Characteristics of the Digital Biograph Vision PET/CT System. J. Nucl. Med. 2019, 60, 1031–1036. [CrossRef]
36. Nemallapudi, M.V.; Gundacker, S.; Lecoq, P.; Auffray, E.; Ferri, A.; Gola, A.; Piemonte, C. Sub-100 ps coincidence time resolution
for positron emission tomography with LSO:Ce codoped with Ca. Phys. Med. Biol. 2015, 60, 4635–4649. [CrossRef]You can also read
