Eur J Nucl Med Mol Imaging (2011) 38:552–561
DOI 10.1007/s00259-010-1637-4
ORIGINAL ARTICLE
Targeted multi-pinhole SPECT
Woutjan Branderhorst & Brendan Vastenhouw &
Frans van der Have & Erwin L. A. Blezer &
Wim K. Bleeker & Freek J. Beekman
Received: 24 June 2010 / Accepted: 1 October 2010 / Published online: 10 November 2010
# The Author(s) 2010. This article is published with open access at Springerlink.com
Abstract
Purpose Small-animal single photon emission computed
tomography (SPECT) with focused multi-pinhole collimation geometries allows scanning modes in which large
amounts of photons can be collected from specific volumes
of interest. Here we present new tools that improve targeted
imaging of specific organs and tumours, and validate the
effects of improved targeting of the pinhole focus.
Methods A SPECT system with 75 pinholes and stationary
detectors was used (U-SPECT-II). An XYZ stage automatically translates the animal bed with a specific sequence in
order to scan a selected volume of interest. Prior to stepping
the animal through the collimator, integrated webcams
acquire images of the animal. Using sliders, the user
designates the desired volume to be scanned (e.g.
W. Branderhorst (*) : B. Vastenhouw : F. van der Have :
E. L. A. Blezer : F. J. Beekman
Image Sciences Institute and Rudolf Magnus Institute of
Neuroscience, University Medical Centre Utrecht,
Universiteitsweg 100,
3584 CG Utrecht, The Netherlands
e-mail: w.branderhorst@umcutrecht.nl
B. Vastenhouw : F. van der Have : F. J. Beekman
Molecular Imaging Laboratories BV,
Heidelberglaan 100,
3584 CX Utrecht, The Netherlands
a xenograft or specific organ) on these optical images.
Optionally projections of an atlas are overlaid semiautomatically to locate specific organs. In order to assess the
effects of more targeted imaging, scans of a resolution
phantom and a mouse myocardial phantom, as well as in
vivo mouse cardiac and tumour scans, were acquired with
increased levels of targeting. Differences were evaluated in
terms of count yield, hot rod visibility and contrast-to-noise
ratio.
Results By restricting focused SPECT scans to a 1.13-ml
resolution phantom, count yield was increased by a factor
3.6, and visibility of small structures was significantly
enhanced. At equal noise levels, the small-lesion contrast
measured in the myocardial phantom was increased by
42%. Noise in in vivo images of a tumour and the mouse
heart was significantly reduced.
Conclusion Targeted pinhole SPECT improves images and
can be used to shorten scan times. Scan planning with
optical cameras provides an effective tool to exploit this
principle without the necessity for additional X-ray CT
imaging.
Keywords SPECT . Multi-pinhole . Small-animal imaging .
Cardiac . Tumour . Focusing pinholes
Introduction
B. Vastenhouw : F. van der Have : F. J. Beekman
Section of Radiation Detection and Medical Imaging,
Applied Sciences, Delft University of Technology,
Mekelweg 15,
2629 JB Delft, The Netherlands
W. K. Bleeker
Genmab BV,
Yalelaan 60,
3584 CM Utrecht, The Netherlands
Molecular imaging has proven to be extremely valuable in
studying animal models of human disease and in the
development of new pharmaceuticals and tracers. Many
molecular mechanisms can be assessed quantitatively in
vivo using radionuclide techniques such as single photon
emission computed tomography (SPECT) and positron
emission tomography (PET). In the past, SPECT lacked
Eur J Nucl Med Mol Imaging (2011) 38:552–561
the resolution necessary to accurately image organs of small
animals such as mice and rats. Several newly developed
dedicated small-animal SPECT systems have overcome this
limitation [1–12].
Recently, sub-half-millimetre image resolution has been
achieved in SPECT, using multi-pinhole collimators combined with high pinhole magnification factors [1, 3, 13–15].
As these systems are equipped with collimators that have
pinholes that focus on a central area in the imaging cavity,
the fraction of detected photons from specific organs or
tissue of interest is very high. This can result in improved
noise resolution trade-offs over systems with a lower level
of focusing, and the possibility of reducing the tracer dose
or the acquisition time. Using focused pinhole geometries,
detailed images of mouse and rat organs (e.g. beating heart,
kidney and the brain) and tumours have been acquired [1,
3, 13, 15–18].
Multi-pinhole collimators with focused geometries are
also able to scan larger volumes – up to the total body of
mice and rats. This is accomplished by translating the
animal through the collimator in concert with specially
adapted reconstruction methods that use projections from
all bed positions simultaneously [3, 14]. To increase count
yield from a specific organ or tumour, the field-of-view
(FOV) of a system with a highly focused geometry and an
XYZ stage can be confined to a region that mainly contains
the tissue of interest (“sensitivity painting”). This requires
making an estimate of the location of these tissues.
Disadvantages to performing this estimation based on
X-ray CT images include additional radiation dose, hardware, and scan time. Localization based on MRI, which is
currently an area of active research, also requires additional
hardware and scan time. Furthermore, using pinhole
projection images combined with a persistence scope,
accurate localization is difficult to achieve since there is a
small FOV, few gamma photons can be detected in a
limited time and the tissue being localized may have a very
low uptake. The aim of the present study was to explore the
alternative possibility of using low-cost optical cameras for
tissue localization and FOV selection, and to empirically
investigate the effects of targeting on sensitivity and the
quality of reconstructed images.
Materials and methods
SPECT system with optical cameras
U-SPECT-II [3, 19] (Fig. 1a) is a multi-pinhole SPECT
scanner for imaging rodents. It consists of three stationary
detector arrays and exchangeable collimators for different
sized animals or for specific organs such as the brain [20].
Available collimators consist of a tungsten cylinder con-
553
taining 75 micro-pinhole apertures that together provide a
FOV the shape of which is illustrated in Fig. 1c. The
pinhole geometry is chosen such that the region observed
through all pinholes simultaneously is located in the centre
of the collimator. For this part of the FOV, referred to as the
central FOV (CFOV, Fig. 1c) and a small area around it,
complete data are acquired without any translation of the
bed during scanning. For the mouse collimators in this
study, such as the general purpose mouse collimator
(MILabs, Utrecht, The Netherlands), the CFOV is approximately a cylinder of length 7 mm and diameter 12 mm. For
the rat collimators these dimensions are about twice as
large.
The FOV outside the CFOV also contributes to the
projection data, but in order to correctly reconstruct
volumes significantly larger than the CFOV, the system
must move the focus over the region of interest. This
scanning focus method [14] enables sensitivity painting
similar to dose painting in radiotherapy. The bed is
mounted on a motor-controlled XYZ stage, which allows
accurate positioning of the animal. During SPECT acquisition, the XYZ stage automatically moves the animal
stepwise through the collimator, thereby effectively moving
the CFOV over the animal in order to obtain complete data
for any part of the animal that is selected by the user. The
bed is transparent and has a half-cylindrical shape. It
contains a transparent heater pad to control the temperature
of the animal (Fig. 1a).
Description of the FOV selection tool
Optical image-based positioning
Prior to acquiring SPECT data, three optical cameras,
which are integrated with the U-SPECT-II system, take
photographs of the animal from the left, top and right
(Fig. 1a). The cameras (DFK 21F04; The Imaging Source,
Germany) are equipped with a quarter-inch CCD detector
with a resolution of 640×480 pixels. The photographs are
displayed on a graphical user interface, on which the user
can define a box to be scanned (Fig. 1a, b). Next, the
software calculates a sequence of bed positions in such a
way that the volume within the box will be sampled by the
CFOV in at least one of the bed positions. The user can
optionally check and fine-tune the position of the FOV
using real-time gamma photon projection images of the
centre of the selected FOV, obtained through pinholes that
provide views of the animal at approximately the same
angles as the optical cameras. Depending on the size of the
selection, the number of positions can range from one or
two positions (for organs such as the heart, the brain or a
tumour) up to tens of positions for total-body scanning.
Since changing the bed position takes only 0.7 s on
554
Eur J Nucl Med Mol Imaging (2011) 38:552–561
Fig. 1 a U-SPECT-II system
with close-ups of a mouse on
the animal bed with a transparent heater pad in front of optical
cameras and the graphical user
interface with optical images
and three real-time projection
images. b Example of various
FOV selection boundaries in
three dimensions. c Schematic
cross-sections of FOV and
CFOV in U-SPECT-II generalpurpose collimators
average, even for total-body studies fast dynamic acquisitions are possible using the scanning focus method [15,
21].
organ positions, which can be very useful as a guideline
during scan planning since the (rough) location of organs
can easily be mistaken.
Atlas overlay
Aligning optical images to SPECT images
Our optical image-based FOV selection allows targeting
SPECT to a joint, a xenograft (Fig. 2c) or any other part of
the animal. To further aid localization of organs, projections
of an anatomical atlas that shows the different organs of an
animal can be projected onto the optical images (Fig. 2d).
The overlay is based on projections of a manually
segmented MR image. It can be manually scaled to register
it to the optical images, for example using the nose, the
eyes, the shoulders, the root of the tail and the skin contour
as reference features. To account for different animal shapes
and positioning, the atlas projections can also be deformed
in the transaxial directions by repositioning markers on the
skin contour. This method provides an approximation of the
The mapping of the optical images (and consequently also
the FOV selection) to the SPECT reconstructed voxel grid
is determined by calibration. Before calibration, the optical
cameras were corrected for rotation, scaling and barrel
distortions based on optical images of a millimetre grid. In
order to reduce perspective errors and approximate parallel
projections, the optical images were acquired as a set of
small image strips that were stitched together. The
calibration was performed by scanning a phantom containing several point sources and registering its optical images
to maximum intensity projections of the SPECT volume.
To create the point sources, ion exchange resin beads with a
diameter of approximately 0.2 mm were dipped in a
Eur J Nucl Med Mol Imaging (2011) 38:552–561
555
Fig. 2 Different volume selections applied to obtain a specific
SPECT acquisition of (a) a resolution phantom, (b) a mouse
myocardial phantom, (c) a mouse tumour, and (d) a mouse heart
using an atlas. Red (large) box nontargeted selection, orange (middle)
box selection targeted in one dimension only, yellow (small) box
selection targeted in three dimensions
mixture of 99mTc-pertechnetate and ink, to make them
visible in both modalities.
The optical-to-SPECT registration was performed by
applying a rigid transformation that minimizes the mean
distance between the optical point sources and their
corresponding point sources in the maximum intensity
projection of the SPECT volume. After applying this
transformation to our system, the maximum distance
between any of the point sources in the optical and SPECT
images was 0.25 mm.
Resolution phantom study
Validation
Improvements due to restricting the scan area, in terms of
sensitivity and resolution, were determined with a resolution phantom, whereas improvements in contrast-to-noise
ratio were measured in scans of a mouse myocardial
phantom containing a cold lesion. Finally, in vivo studies
illustrated the effects of scan area size in mouse tumour and
myocardial perfusion imaging.
All scans discussed here were performed three times, each
time employing a different FOV selection: (1) a nontargeted
scan, (2) a 1D-targeted scan (with the FOV only restricted in
the z-dimension), emulating a system that can only target in
the axial direction, and (3) a 3D-targeted scan with the FOV
restricted in the x-, y- and z-dimensions (Fig. 2).
In order to assess the visibility of small details, a Jaszczakstyle resolution phantom (ultra-high-resolution microphantom 850.100; VANDERWILT techniques, Boxtel,
The Netherlands) with six sections containing capillaries
with diameters of 0.35, 0.40, 0.45, 0.50, 0.60 and 0.75 mm
was imaged. In this phantom, the distance between the
capillaries in each section equals the capillary diameter in
that section. The phantom was filled with 145 MBq 99mTcpertechnetate. The resolution phantom and the selected
volumes for the different protocols are shown in Fig. 2a.
A 10-min acquisition was performed for each of the
protocols (ultra-high-resolution study) and a second acquisition series with the same phantom was performed with
lower activity by repeating the three scans after 20 h (highresolution study). The ultra-high-resolution and highresolution studies were performed with, respectively, the
0.35-mm and 0.6-mm diameter pinhole mouse collimators
[3]. Images were reconstructed on a 0.1875 mm isotropic
voxel grid with ten iterations pixel-based ordered subset
expectation maximization (POSEM) with 16 subsets [22].
Furthermore, the total number of detected photons was
determined for each scan in a 20% energy window around
140 keV to estimate the sensitivity gain achieved by
restricting the scan volume. The count totals were corrected
556
Eur J Nucl Med Mol Imaging (2011) 38:552–561
for background radiation by subtracting the number of
counts detected in a separate background acquisition.
Mouse myocardial phantom study
In order to quantify improvements in signal-to-noise ratio by
restricting the scan volume, a physical cardiac phantom was
used. This phantom, made of polymethyl methacrylate,
modelled perfusion of the left ventricular myocardium of a
mouse (Fig. 3a). It contained a cavity into which a polymethyl
methacrylate insert mimicking an infarct could be placed. The
cavity resembled the left ventricular myocardial wall, which
was filled with 7.2 MBq 99mTc-pertechnetate. The dimensions
of the myocardium in the phantom (Fig. 3b) represented the
average end-diastolic dimensions reported by Wiesmann et al.
[23], which were measured in MRI data from 15 adult
C57BL/6 mice at rest. The FOV selections for the applied
scan protocols are shown in Fig. 2b.
The phantom was scanned using each of the three FOV
selection protocols. The duration of the first scan was
60 min, and the next scans were slightly extended to correct
for decay. Each of the three list-mode datasets was split into
60 noise realizations each containing the same number of
list-mode events, which were spread out regularly over the
entire scan time. This emulates 3×60 acquisitions with a left
ventricular uptake of 0.12 MBq. Because the projections
obtained each contained only 1/60 of the usual amount of
background counts, projections from 60 separate 60-min
background acquisitions without a phantom were added to
the noisy phantom projections before reconstruction. The
uptake value of 0.12 MBq was the average reconstructed left
ventricular uptake measured in two mouse 99mTc-tetrofosmin scans after applying attenuation correction as described
previously [24]. For each targeting level, ten noise realizations were reconstructed on a 0.1875-mm isotropic voxel
grid. Maximum likelihood expectation maximization instead of POSEM was used for reconstruction because the
former updates the image with smaller increments, and
therefore allows for constructing plots of contrast-to-noise
ratios based on a higher number of stages of convergence.
In vivo animal studies
Figure 2c, d shows the three FOV selection protocols
applied to, respectively, a mouse tumour scan and a
myocardial perfusion scan. All procedures employed in
these studies were approved by the local ethics committee
and were performed in accordance with international
guidelines on handling laboratory animals.
For the tumour scan, a 21-g female mouse (CB17/SCID)
was used which had a 0.5–1.0 ml A431 human carcinoma
on its right flank. The scans were acquired under isoflurane
anaesthesia 3 days after injection of 49 MBq 111In-labelled
Fig. 3 a Photograph of a mouse myocardial phantom with lesion. b
Schematic drawing of a phantom showing the dimensions of the left
ventricle and lesion in millimetres. c Reconstructed short-axis slice
with profile range used for circumferential profiles. d Different
regions used for calculation of noise and contrast (solid areas “noninfarcted” regions, dashed area “infarcted” region)
Unibody (Genmab, The Netherlands) using the 0.6-mm
diameter pinhole mouse collimator tube [3]. The duration of
each acquisition was 45 min. The images were reconstructed on a 0.1875-mm isotropic voxel grid employing six
iterations POSEM with 16 subsets [22]. The reconstructed
images were postfiltered using a gaussian filter with
σ=0.1875 mm.
For the mouse cardiac perfusion study, a 29-g male
mouse (C57BL/6J) was anaesthetized with isoflurane and
injected with 134 MBq 99mTc-tetrofosmin. At 30 min after
injection, the first SPECT scan of 45 min was acquired. The
other two scans were corrected for decay of the isotope by
adjusting the duration of each acquisition. The scans were
acquired using the 0.6-mm diameter pinhole mouse
collimator tube [3] and reconstructed on a 0.1875-mm
Eur J Nucl Med Mol Imaging (2011) 38:552–561
557
isotropic voxel grid using four iterations POSEM with 16
subsets [22], employing cardiac gating with 16 intervals.
The reconstructed images were postfiltered with a gaussian
filter in time (σ=1.27 time intervals) and space (σ=
0.75 mm).
variation from the mean was visualized by including two
profiles denoting the mean plus and minus one standard
deviation.
Results
Data analysis of the mouse myocardial phantom
Resolution phantom study
To assess differences in contrast-to-noise ratio, small-lesion
contrast and noise were measured in the reconstructed
myocardial phantom images after each iteration. Contrast
was then plotted as a function of noise for each targeting level.
Eleven volumetric regions of interest of equal size were
defined (Fig. 3d), one within the lesion and ten within the
“fully perfused myocardial tissue”. The average contrast was
defined as the average contrast over all noise realizations,
with the contrast C of one noise realization defined as:
C¼
M
1 X
Am Ai
M m¼1 Am
ð1Þ
where M equals the number of fully perfused regions, Ai is
the average intensity of the lesion and Am is the average
intensity of fully perfused region m. The normalized standard
deviation (NSD) was used as a measure of the noise of a
perfused region:
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
uN
uP
u ðAðkÞ h AiÞ2
1 tk
NSD ¼
ð2Þ
N 1
h Ai
where A(k) represents the average intensity of the region in
noise realization k, h Ai is the intensity of the region averaged
over all noise realizations and N is the number of noise
realizations. Average noise over the non-infarcted myocardium was calculated by averaging these NSD values.
Reconstructed images of the mouse myocardial phantom
were postfiltered with a gaussian filter (σ=0.1875 mm) and
profiles were generated by dividing the profile range
(depicted in Fig. 3c) into 36 segments and calculating the
mean voxel value for each segment. In addition to plotting
the profiles separately, the profiles for all ten noise
realizations were also averaged into a mean profile. The
Fig. 4 Reconstructed images
of micro-hot-rod phantom
scans with high-resolution (top)
and ultra-high-resolution
(bottom) collimators for three
different levels of targeting
Figure 4 presents reconstructed image slices from the
resolution phantom scans. No postfilter was applied. The
displayed slice thickness is 0.75 mm for the high-resolution
scans and 0.375 mm for the ultra-high-resolution scans. In
both the high- and the ultra-high-resolution scans, restricting the FOV resulted in reduced noise and more sections
having visually distinguishable rods. In the images obtained
with the 0.6-mm pinholes and nontargeted scanning, the
0.6-mm rods were hardly visible. One-dimensional targeting made all 0.5-mm rods clearly visible. With 3Dtargeting, the 0.45-mm rods seemed to be much better
separated. In the ultra-high-resolution study (0.35-mm
pinholes), the 0.35-mm segment could be resolved in the
3D-targeted image whereas in the 1D-targeted image it
appeared less clear. In the nontargeted image, even some of
the 0.45-mm rods were hardly distinguishable.
Table 1 shows the number of counts measured within a
20% energy window around 140 keV for each of the
targeting levels. The number of recorded counts increased
with better volume targeting as the phantom was positioned
inside the CFOV more often. Since there was no 99mTcpertechnetate outside the phantom, the increase in scan
sensitivity can be estimated by comparing the total number
of detected photons obtained using each of the FOV
selection protocols. Compared to a nontargeted scan, 3D
targeting was able to increase the number of counts
detected from a specific volume of interest by a factor of
approximately 3.6.
Mouse myocardial phantom study
To provide an additional measure of improvement in image
quality, Fig. 5 compares contrast-to-noise ratio curves for
558
Table 1 Sensitivity estimates
for phantom scans using various FOV selection protocols and
activity levels. Increases in sensitivity are expressed as a sensitivity increase factor, defined as
the number of counts divided by
the number of counts measured
in the nontargeted scan
Eur J Nucl Med Mol Imaging (2011) 38:552–561
High-resolution study
Number of counts
Sensitivity increase factor
Ultra-high-resolution study
Number of counts
Sensitivity increase factor
each of the three targeting levels. For all targeting levels,
average contrast improved with an increasing number of
iterations, but average noise was also increased. Compared
at an equal noise level of 0.076, the contrast-to-noise level
achieved with 1D targeting was improved by 19% over the
nontargeted reconstruction, whereas 3D targeting resulted
in an improvement of 42% over the nontargeted reconstruction. Comparing images at an equal average contrast
level of 0.75, 1D and 3D targeting improved the contrastto-noise level by, respectively, 37% and 167% over the
level measured in the nontargeted reconstruction. This
noise reduction is illustrated in Fig. 6. The profiles in the
middle row show that the variance over the ten reconstructions was reduced with more targeting, which implies
improved reproducibility of intensity in individual segments. This effect is also apparent in the bottom row, which
shows three examples of circular profiles. Better targeting
reduced the differences between these reconstructions. In
addition, the bottom row shows that better targeting
resulted in less erroneous intensity variations within the
myocardial wall. This is also reflected in the images in the
Fig. 5 Average defect contrast as a function of average noise in the
mouse myocardial phantom. Curves were generated by interpolating
values found at different iterations (dash-dots nontargeted, dashes 1Dtargeted, solid 3D-targeted). Comparisons at equal contrast and at
equal noise, as described in the text, are illustrated using markers
connected by dotted lines
Nontargeted
1D targeting
3D targeting
2,807,510
1
6,584,623
2.3
9,754,798
3.5
9,994,441
1
23,382,374
2.3
35,687,641
3.6
top row, which appear much less noisy with more accurate
targeting.
In vivo animal studies
Slices through the reconstructed volumes from the myocardial perfusion and tumour scans are shown in Fig. 7.
Displayed slice thicknesses are 0.56 mm and 0.75 mm for,
respectively, the myocardial and the tumour study. The 3Dtargeted scans appeared to have less noise than the 1Dtargeted and nontargeted images. This effect was most
apparent in the myocardial perfusion study, where the
number of counts in individual frames was low because of
gating. The myocardial images from the 3D-targeted scan
best revealed the structure of the papillary muscles and the
right ventricular wall.
Discussion
A navigation and selection tool for SPECT acquisition
based on optical imaging was developed to increase count
yield from specific organs and tissues of interest. The
results reported here show that targeting in three dimensions is important, because it improves count yield and
contrast-to-noise trade-off.
When a large FOV is required, such as in total-body
scanning, the level of targeting may become low. This
results in reduced sensitivity as the total scan time is
distributed over a large number of CFOV positions.
Previously we and others have shown that in such situations
the count rate can still be high enough to obtain excellent
images [14] even for gated total-body SPECT or for studies
which employ low doses [3]. Furthermore, although the
overall sensitivity in total body scanning may then become
similar to non-focused geometries, the combination of
focusing and moving the bed in three dimensions effectively translates the axis of rotation and may therefore have
the advantage of acquiring more angle information compared to detectors rotating around one longitudinal axis. In
the recently launched U-SPECT-II/CT and VECTor/CT (a
combined SPECT/PET/CT device with sub-millimetre
resolution [25, 26]), FOV selection can alternatively be
Eur J Nucl Med Mol Imaging (2011) 38:552–561
559
Fig. 6 Images and circumferential profiles of reconstructed short-axis
slices of the mouse myocardial phantom for nontargeted acquisition
(left), 1D-targeted acquisition (centre) and 3D-targeted acquisition
(right) show stronger noise reduction with a higher degree of
targeting. a Short-axis slices from reconstruction of one noise
realization. b Profiles of mean (solid) and mean±1SD (dashed), both
calculated over ten noise realizations. c Example profiles from
reconstructions of three different noise realizations
performed based on X-ray planar images, with or without
the option of registering projections of an atlas to them. It
should, however, be noted that these devices are also
equipped with webcams for, for example, FOV selection.
Using optical cameras with the option of registering
projections of an atlas to the optical images is a cost-
effective method and has many advantages over scan
planning using an additional CT scan. First, the limited
tissue contrast in a CT scan may hamper accurate
localization for many organs, whereas our atlas images are
already presegmented. Furthermore, X-ray imaging exposes
the animal to an extra radiation dose [27–29]. Another
Fig. 7 Reconstructed enddiastolic short-axis slices from
gated myocardial perfusion
scans (top) and transaxial slices
from the mouse tumour scans
(bottom) for three different levels of targeting
560
advantage of recording optical images is that, after
correcting for differences in positioning and distortion of
the lens, they can be related directly to images obtained
using other optical modalities such as bioluminescence or
fluorescence. Additional CT scans or scans from other
anatomic modalities can be useful in particular for the
localization of activity in unknown internal structures such
as a tumour.
In this study we used a basic atlas containing brain,
heart, lungs, liver, spleen and kidneys, based on a mouse
MRI scan. In practice, the localization of the organs worked
well in most scans, especially since the posture of the
animal was chosen to be close to that of the mouse in the
MRI scan. More extensive in vivo position verification may
be an interesting subject for future studies. In addition,
atlases and anatomical images based on MRI or other
modalities are being created, in which the animals can be
placed in many different postures. Furthermore, other
atlases of mice and rats are available [30–35]. Even when
using accurate atlases, small localization errors may remain
as a result of individual differences or pathologies. An atlas
that can be automatically deformed to match the optical
image, based on thin-plate spline deformations, is currently
being investigated [36, 37].
Conclusion
Focused multi-pinhole geometries combined with an XYZ
translation stage give the opportunity to acquire a high
number of counts from the organ of interest. As a result,
much smaller pinholes can be used to obtain ultra-highresolution SPECT images or images with reduced noise.
The results of the present study show that count yield
increases dramatically when targeting is applied, which
results in new opportunities for fast dynamic imaging of
tumours or organs. This new method could also be applied
to increasing throughput or to reducing radiation doses. We
have developed a fast and user-friendly tool for estimating
organ positions based on optical images and optional
atlases. This tool allows maximal benefit to be obtained
from the unique advantages of SPECT with focused multipinhole collimators.
Acknowledgments We thank Ruud Ramakers (UMC Utrecht) and
Jeroen van den Brakel (Genmab) for their technical support and help
with measurements, and Jan van Ewijk, Erwin Bakker and Jesse
Bosma (UMC Utrecht) for their help in designing and manufacturing
the mouse myocardial phantom.
Open Access This article is distributed under the terms of the
Creative Commons Attribution Noncommercial License which
permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are
credited.
Eur J Nucl Med Mol Imaging (2011) 38:552–561
References
1. Beekman FJ, van der Have F, Vastenhouw B, van der Linden
AJA, van Rijk PP, Burbach JPH, et al. U-SPECT-I: A novel
system for submillimeter-resolution tomography with radiolabeled
molecules in mice. J Nucl Med 2005;46(7):1194–200.
2. Furenlid LR, Wilson DW, Chen YC, Kim H, Pietraski PJ,
Crawford MJ, et al. A second-generation high-resolution dynamic
SPECT imager. IEEE Trans Nucl Sci 2004;51(3):631–5.
3. Van der Have F, Vastenhouw B, Ramakers RM, Branderhorst
W, Krah JO, Ji C, et al. U-SPECT-II: an ultra-high resolution
device for molecular small-animal imaging. J Nucl Med
2009;50(4):599–605.
4. Goertzen AL, Jones DW, Seidel J, Li K, Green MV. First results
from the high-resolution mouseSPECT annular scintillation
camera. IEEE Trans Med Imaging 2005;24(7):863–7.
5. Ishizu K, Mukai T, Yonekura Y, Pagani M, Fujita T, Magata Y, et
al. Ultra-high-resolution SPECT system using four pinhole
collimators for small animal studies. J Nucl Med 1995;36
(12):2282–7.
6. Jaszczak RJ, Li J, Wang H, Zalutsky MR, Coleman RE. Pinhole
collimation for ultra-high-resolution small-field-of-view SPECT.
Phys Med Biol 1994;39(3):425–37.
7. King MA, Pretorius PH, Farncombe T, Beekman FJ. Introduction
to the physics of molecular imaging with radioactive tracers in
small animals. J Cell Biochem Suppl 2002;39:221–30.
8. McElroy DP, MacDonald LR, Beekman FJ, Wang YC, Patt BE,
Iwanczyk JS, et al. Performance evaluation of A-SPECT: a high
resolution desktop pinhole SPECT system for imaging small
animals. IEEE Trans Nucl Sci 2002;49:2139–47.
9. Meikle SR, Kench PL, Weisenberger AG, Wojcik R, Smith MF,
Majewski S, et al. A prototype coded aperture detector for small
animal SPECT. IEEE Trans Nucl Sci 2002;49(5):2167–71.
10. Palmer J, Wollmer P. Pinhole emission computed tomography:
method and experimental evaluation. Phys Med Biol 1990;35
(3):339–50.
11. Schramm NU, Ebel G, Engeland U, Schurrat T, Béhé M, Behr
TM. High-resolution SPECT using multipinhole collimation.
IEEE Trans Nucl Sci 2003;50(3):315–20.
12. Wu MC, Tang HR, Gao DW, Ido A, O’Connell JW, Hasegawa
BH, et al. ECG-gated pinhole SPECT in mice with millimeter
resolution. IEEE Trans Nucl Sci 2000;47(3):1218–27.
13. Beekman F, van der Have F. The pinhole: gateway to ultra-high
resolution three-dimensional radionuclide imaging. Eur J Nucl
Med Mol Imaging 2007;34(2):151–61.
14. Vastenhouw B, Beekman F. Submillimeter total-body murine
imaging with U-SPECT-I. J Nucl Med 2007;48(3):487–93.
15. Vastenhouw B, van der Have F, van der Linden AJA, von Oerthel
L, Booij J, Burbach JPH, et al. Movies of dopamine transporter
occupancy with ultra-high resolution focusing SPECT. Mol
Psychiatr 2007;12:984–7.
16. Wyckhuys T, Staelens S, Van Nieuwenhuyse B, Deleye S, Hallez
H, Vonck K, et al. Hippocampal deep brain stimulation induces
decreased rCBF in the hippocampal formation of the rat. Neuroimage 2010;52(1):55–61.
17. De Bruyne S, Wyffels L, Boos TL, Staelens S, Deleye S, Rice
KC, et al. In vivo evaluation of [123I]-4-(2-(bis(4-fluorophenyl)
methoxy)ethyl)-1-(4-iodobenzyl)piperidine, an iodinated SPECT
tracer for imaging the P-gp transporter. Nucl Med Biol 2010;37
(4):469–77.
18. Van Steenkiste C, Staelens S, Deleye S, De Vos F, Vandenberghe
S, Geerts A, et al. Measurement of porto-systemic shunting in
mice by novel three-dimensional micro-single photon emission
computed tomography imaging enabling longitudinal follow-up.
Liver Int 2010;30(8):1211–20.
Eur J Nucl Med Mol Imaging (2011) 38:552–561
19. Beekman FJ. Radiation detection device, scintillation device and
detection method, as well as multiple image-forming device. PCT/
NL2006/000513. April 19, 2007.
20. Beekman FJ, Vastenhouw B, van der Wilt G, Vervloet M,
Visscher R, Booij J, et al. 3-D rat brain phantom for highresolution molecular imaging. Proc IEEE 2009;97(12):1997–
2005.
21. Vastenhouw B, Ramakers R, Beekman F. High resolution
dynamic total-body animal imaging with U-SPECT-II. J Nucl
Med 2009;50(Suppl 2):526.
22. Branderhorst W, Vastenhouw B, Beekman FJ. Pixel-based subsets
for rapid multi-pinhole SPECT reconstruction. Phys Med Biol
2010;55(7):2023–34.
23. Wiesmann F, Ruff J, Engelhardt S, Hein L, Dienesch C, Leupold
A, et al. Dobutamine-stress magnetic resonance microimaging in
mice: acute changes of cardiac geometry and function in normal
and failing murine hearts. Circ Res 2001;88(6):563–9.
24. Wu C, Van der Have F, Vastenhouw B, Dierckx RAJO, Paans
AMJ, Beekman FJ. Absolute quantitative total-body small-animal
SPECT with focusing pinholes. Eur J Nucl Med Mol Imaging
2010;37(11):2127–35.
25. Goorden MC, Beekman FJ. High-resolution tomography of
positron emitters with clustered pinhole SPECT. Phys Med Biol
2010;55(5):1265–77.
26. Beekman FJ, van der Have F, Kreuger R, Goorden MC.
Simultaneous sub-millimetre PET and SPECT with a dedicated
multi-pinhole geometry. 2010 World Molecular Imaging Congress. Kyoto, Japan.
27. Carlson S, Classic K, Bender C, Russell S. Small animal absorbed
radiation dose from serial micro-computed tomography imaging.
Mol Imaging Biol 2007;9(2):78–82.
561
28. Boone JM, Velazquez O, Cherry SR. Small-animal X-ray dose
from micro-CT. Mol Imaging 2004;3(3):149–58.
29. Figueroa SD, Winkelmann CT, Miller HW, Volkert WA, Hoffman
TJ. TLD assessment of mouse dosimetry during microCT
imaging. Med Phys 2008;35(9):3866–74.
30. Segars WP, Tsui BMW, Frey EC, Johnson GA, Berr SS.
Development of a 4-D digital mouse phantom for molecular
imaging research. Mol Imaging Biol 2004;6(3):149–59.
31. Dogdas B, Stout D, Chatziioannou AF, Leahy RM. Digimouse: a
3D whole body mouse atlas from CT and cryosection data. Phys
Med Biol 2007;52(3):577–87.
32. Stabin MG, Peterson TE, Holburn GE, Emmons MA. Voxel-based
mouse and rat models for internal dose calculations. J Nucl Med
2006;47(4):655–9.
33. Bai X, Yu L, Liu Q, Zhang J, Li A, Han D, et al. A highresolution anatomical rat atlas. J Anat 2006;209(5):707–8.
34. Wu L, Zhang G, Luo Q, Liu Q. An image-based rat model for Monte
Carlo organ dose calculations. Med Phys 2008;35(8):3759–64.
35. Keenan MA, Stabin MG, Segars WP, Fernald MJ. RADAR
realistic animal model series for dose assessment. J Nucl Med
2010;51(3):471–6.
36. Baiker M, Dijkstra J, Que I, Löwik CWGM, Reiber JHC,
Lelieveldt BPF. Organ approximation in micro-CT data with low
soft tissue contrast using an articulated whole-body atlas.
Proceedings of the 5th IEEE International Symposium on
Biomedical Imaging. Paris, France. 2008;1267–70.
37. Baiker M, Vastenhouw B, Branderhorst W, Reiber JHC, Beekman
F, Lelieveldt BPF. Atlas-driven scan planning for high-resolution
μSPECT scanning from multi-view photographs: a pilot study.
Proceedings of SPIE Medical Imaging 2009. Lake Buena Vista
(Orlando), Florida. 2009; paper #72611L, 8 pages.