Preclinical Spectral Computed Tomography of Gold Nano-Particles Ewald Roessla∗, David Cormodeb†, Bernhard Brendela‡, Klaus J¨ urgen Engelc§, Gerhard Martensa¶, Axel Thranak, Zahi Fayadb∗∗ , Roland Proksaa†† a
Philips Research Europe - Hamburg, Digital Imaging, R¨ ontgenstrasse 24-26, D-22335 Hamburg, Germany b
Translational and Molecular Imaging Institute,
Mount Sinai School of Medicine, One Gustave L. Levy Place, New York, NY 10029, USA. c
Philips Research Europe - Aachen, X-ray imaging systems, Weisshausstr. 2, D-52066 Aachen, Germany
September 27, 2010
1
Abstract
2
Today’s state-of the art clinical computed tomography (CT) scan-
3
ners exclusively use energy-integrating, scintillation detector technol∗ Corresponding author: Tel. +49 40 5078 2965; Fax. +49 40 5078 2510, Email:
[email protected] †
[email protected] ‡
[email protected] § klaus.j.engel@@philips.com ¶
[email protected] k
[email protected] ∗∗
[email protected] ††
[email protected]
1
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ogy, despite the fact that a part of the information carried by the
5
transmitted x-ray photons is lost during the detection process. Room-
6
temperature semiconductors, like CdTe or CZT, operated in energy-
7
sensitive photon-counting mode provide information about the energy
8
of every single x-ray detection event. This capability allows novel,
9
promising approaches to selectively image abnormal tissue types like
10
cancerous tissue or atherosclerotic plaque with the CT modality.
11
In this article we report on recent dual K-edge imaging results ob-
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tained in the domain of pre-clinical, energy-sensitive photon counting
13
CT. In this approach, the tuning of threshold levels in the detector
14
electronics to the K-edge energy in the attenuation of contrast agents
15
(CA) offers highly specific, quantitative imaging of the distribution of
16
the CA on top of the conventional, morphological image information.
17
The combination of the high specificity of the K-edge imaging tech-
18
nique together with the powerful tool of targeting specific diseases in
19
the human body by dedicated contrast materials might enrich the CT
20
modality with capabilities of functional imaging known from the nu-
21
clear medicine imaging modalities, e.g., positron-emission-tomography
22
but with the additional advantage of high spatial and temporal res-
23
olution. We also discuss briefly the technological difficulties to be
24
overcome when translating the technique to human CT imaging and
25
present the results of simulations indicating the feasibility of the K-
26
edge imaging of vulnerable plaque using targeted gold nano-particles
27
as contrast materials. Our experiments in the pre-clinical domain
2
28
show that dual-K edge imaging of iodine and gold based CAs is fea-
29
sible while our simulations for the imaging of gold CAs in the clinical
30
case support the future possibility of translating the technique to hu-
31
man imaging.
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Keywords: photon-counting x-ray detectors; K-edge imaging; gold nano-
33
particles; x-ray computed tomography
34
1
35
Recent years have seen increased research efforts in energy-sensitive, photon
36
counting detectors [5,6,8,17] and their application to computed tomography
37
imaging [11, 13–16].
Introduction
38
As today’s commercially available computed tomography scanners exclu-
39
sively employ scintillation detectors operated in current-integrating mode, a
40
part of the information about the attenuation by the object carried by the
41
transmitted flux of x-rays is lost during the detection process. Hence, it is
42
reasonable to ask to what extent the additional information acquired with
43
the help of more advanced detection technology can be used to the benefit
44
of a better clinical diagnosis, the reduction of the dose administered to the
45
patient or to the development of novel CT imaging applications.
46
Intuitively it is clear that the optimal data acquisition in CT would con-
47
sist of a precise measurement of the energies of all individual x-ray photons 3
48
in the transmitted x-ray spectrum, thus operating each detector pixel in
49
spectroscopic mode. In practice, however, it turns out that near-optimal
50
imaging performance can be obtained with much less sophisticated detection
51
techniques [2], because the relative contributions to the total x-ray attenua-
52
tion caused by the photo-electric effect and the Compton effect do not vary
53
strongly for different types of human tissue. For the case of basis material
54
decompositions it has been shown recently that all the information contained
55
in the transmitted spectrum regarding a given N-dimensional basis material
56
decomposition of the object can be retrieved by performing a number of N
57
appropriately weighted spectral measurements. However, the experimental
58
realization of these remains challenging [22]. It has been further shown that
59
the number of energy thresholds required to acquire most of the available,
60
additional information by energy-discriminating photon-counting detectors
61
is rather small [10]: about 4-6 if the energy thresholds are optimized for the
62
imaging task.
63
In this paper we focus on a new application of the energy-selective de-
64
tection of x-rays by photon counting detectors, the so-called spectral K-edge
65
imaging technique. In this technique, a contrast medium is administered
66
having, as is usual for CT, an element with high atomic number as its active
67
component. The energy of the attenuation K-edge is characteristic for the
68
element. By adjusting one of the energy thresholds in the detector to the
69
K-edge of the contrast element, the technique allows to distinguish between
70
the attenuation caused by the contrast medium and the surrounding non4
71
contrasted tissue. Thus, a physical mechanism is provided that allows the
72
selective imaging of the material being administered. The decomposition of
73
the attenuation can be achieved, e.g., with the help of the maximum likeli-
74
hood method, the details of which can be found in [11] and an experimental
75
realization thereof in [13].
76
In this paper we are also considering the situation in which two different
77
contrast media (based on two different elements and, thus, different K-edge
78
energies) are present at the same time. Then, a first of two available energy
79
thresholds is adjusted to the K-edge energy of the first element and a second
80
available energy threshold to the K-edge energy of the second element. Hence,
81
it becomes possible to separate between the attenuation coming from the
82
first contrast agent, the second contrast agent and the surrounding tissue.
83
Note that in the latter case of two contrast agents, at least four energy
84
thresholds are required as the correct modeling of human tissue requires two
85
basis materials [3].
86
We present results obtained in pre-clinical imaging in connection with
87
dedicated, targeted contrast agents based on gold nano-particles. The struc-
88
ture of the paper is the following: in section 2 we briefly describe the scanner
89
hardware on which the reported experiments were performed, as well as the
90
imaging protocol used. In section 3 we summarize our imaging results. In
91
section 4 we present the results of a simulation of a photon-counting hu-
92
man CT system with the detection of vulnerable plaque targeted by gold
93
nano-particles. We will discuss the technological hurdles to be overcome, 5
94
in particular the count-rate problem and possible ideas for mitigations in
95
section 5 and we conclude in section 6.
96
2
97
2.1
98
The experiments we report here were performed with a spectral CT scanner
99
prototype installed at Philips Research in Hamburg. The specification of the
100
scanner have been described elsewhere [13], only the most important infor-
101
mation is summarized here. A KEVEX micro-focus x-ray tube and a planar,
102
single-row, 1024 pixels, 3 mm thick CdTe detector from Gamma Medica -
103
Ideas, Fornebu, Norway were used on a rotating gantry. Spatial resolution is
104
determined by the flexible mounting distances of tube and detector from the
105
iso-center. The effective CdTe pixel height was 1.2 mm and the average pixel
106
pitch was 400 µm, achieving an in-plane spatial resolution of about 100 µm
107
(in the high-resolution arrangement). The electronics performs pulse ampli-
108
fication, shaping and binning of the pulses by the use of six independently
109
adjustable comparators for each of the 1024 pixel.
110
2.2
111
In the pre-clinical dual-K-edge experiment the apo E-KO mouse model of
112
atherosclerosis was used, see [4]. The mice had been on a high-colesterole diet
Materials and Methods Scanner Prototype
Imaging Protocols
6
113
for about 10 months prior to being injected with gold high-density lipoprotein
114
nano-particles (Au-HDL) at a dose of 500 mg Au per kilogram of body weight.
115
For the case shown here, the mice were further injected after 24 hours with
116
150 µl of Fenestra VC (ART Inc., CA), an iodinated bloodpool contrast
117
material for pre-clinical use. The details of the study can be found in [4].
118
The spectral CT imaging parameters were optimized in order to obtain a
119
maximum in specificity with respect to iodine and gold at the same time.
120
This is reflected in the choice of the energy thresholds as follows: 25, 34, 51,
121
80, 91, and 110 keV. Two of the thresholds were set close to the K-edges of
122
iodine at 33.2 keV and gold at 81.0 keV, respectively.
123
The raw data composed of six sinograms, one for each energy bin, were
124
decomposed into a photo-electric effect-, Compton-effect-, iodine- and gold-
125
material basis. The reconstruction of the basis material data sets was per-
126
formed using conventional 2D filtered-back-projection on a reconstruction
127
grid of size 100 × 100 × 200 µm3 . Due to the known noise amplification in
128
the material separation step, the K-edge material images had to be smoothed
129
after reconstruction.
7
130
3
Results
131
3.1
132
In this section we present results of the simultaneous imaging of a non-
133
targeted blood-pool contrast material based on iodine (Fenestra VC) and a
134
macrophage-targeted, high-density lipoprotein nano-particle contrast agent
135
(Au-HDL) [4] as an example of the dual-K-edge imaging technique. Fig. 1
136
shows volume rendered overlays of the photo-effect image showing the bones
137
(grey) the gold material image (yellow) and the iodine material image (red)
138
with an accumulation of gold nano-particles close to the bifurcation of the
139
aorta. In another volume rendered view shown in Fig. 2, we see the uptake
140
of Au-HDL particles in the lymph-nodes of the mouse.
Dual K-edge imaging of Iodine and Gold
141
[Figure 1 about here.]
142
[Figure 2 about here.]
143
In Fig. 3 we show overlays of the gold and iodine data sets above a conven-
144
tional CT image obtained by combining the counts of the six energy windows
145
into one data set and by reconstructing the resulting data.
146
[Figure 3 about here.]
8
147
4
Translation to human imaging
148
In this section we present the results of simulations we performed to inves-
149
tigate the possible translation of the results from the preclinical domain to
150
the domain of human imaging. To be as realistic as possible, we based our
151
simulations on a reconstructed CT volume data-set of a normal-sized human
152
thorax, the central axial slice of which is shown in Fig. 4. [Figure 4 about here.]
153
154
The data set was first low-pass filtered in order to remove the influence
155
of noise in the original reconstructed image and then segmented by a sim-
156
ple thresholding procedure. In this way we obtained a soft-tissue equivalent
157
image and a bone-equivalent image. As the original CT scan was performed
158
using iodinated contrast material, the hyper-dense regions containing iodine
159
were mainly included in this bone-equivalent image. Thus, in these sim-
160
ulations the attenuation coming from the iodine was not modeled as such
161
but was included in the bone-equivalent image. The detection of iodine by
162
its K-edge is not feasible in human CT due to the low energy of the iodine
163
K-edge.
164
In order to simulate a small volume of vulnerable plaque, we added a
165
third basis image set containing only a small cylinder of radius 1 mm and
166
length 10 mm adjacent to one of the blood vessels close to the patients
167
heart. This cylinder contained a 0.1 molar solution of gold on top of a
168
background of water. This concentration was determined by the experience 9
169
gained from the animal experiments an corresponds to about the highest
170
local concentration observed there. At the same time, the image values in
171
the two segmented data sets were put to zero at this very location of the
172
simulated plaque. Then, the three basis data sets were independently forward
173
projected. This was done by determining in a first step the mass density for
174
each voxel in both, the soft-tissue equivalent and the bone-equivalent basis
175
images using the attenuation coefficients denoted Average Soft Tissue male
176
and Skeleton Vertebral Column D6L3 in the ICRU report 46 [1] at an average
177
energy of the simulated spectrum of 71 keV. For this conversion we used:
178
µsof ttissue = 0.0195 mm−1 and µbone = 0.0293 mm−1 . In a second step the
179
basis material densities were then forward-projected to yield complete sets of
180
basis material line-integrals Aj . Finally, the data was spectrally combined to
181
simulate an energy-binned, photon counting detector with 6 energy windows
182
according to the following equation:
λi (Aj ) = Ω IA T
Z∞
−
Φ(E) Si (E) e
3 P
fj (E)Aj
j=1
dE , i = 1, .., 6 .
(1)
0
183
where Ω is the solid angle of the detector pixel, IA the anode tube current
184
and T the time of one integration period. The sensitivity function of the six
185
bins Si (E) were computed using: h
Si (E) =
ZEi
R(E, E ′ )dE ′ .
Eil
10
(2)
186
The detector response function R(E, E ′ ) quantifies the probability of an inci-
187
dent photon of energy E giving rise to an observed energy E ′ . The response
188
functions were obtained on a 1 keV grid by a Monte-Carlo simulation for
189
3 mm thick cadmium-zinc-telluride (CZT) detector material [9]. Fig.
190
shows selected response curves for primary energies of 40, 60, 80, 100 and
191
120 keV. Note that in this simulation we did not normalize the response to
192
unity in order to take into account the case where no interaction takes place.
193
In order to include the effects of electronic noise, the response curves shown
194
in Fig. 5 were smoothed by a Gaussian filter kernel of standard deviation
195
3 keV.
196
5
[Figure 5 about here.]
197
As the source spectrum Φ(E) we followed the model described in [20]
198
with a tube peak voltage of 130 kVp and an additional 2.7 mm of aluminum
199
filtration. The spectrum is depicted in Fig. 6 below.
200
[Figure 6 about here.]
201
Poisson noise was added to the projection data according to an anode tube
202
current of 400 mA. A tube voltage of 130 kVp and a total of 1200 views per
203
turn of the CT gantry and a gantry rotation time of 0.27 s were simulated.
204
The acquisition geometry was assumed to be circular with a detector of
205
64 spectral slices. Physical detector sizes were assumed to be 1.4x1.1mm2 .
206
After a basis-material decomposition using the likelihood method [11], the 11
207
decomposed data sets of the photo-electric effect, the Compton effect and the
208
Au basis-material were reconstructed using a 3D cone-beam reconstruction
209
algorithm described in [7], [21]. The basis material images are shown for one
210
axial slice containing the gold-contrast agent insert in Fig. 7.
211
[Figure 7 about here.]
212
In Fig. 8 the gold basis material image is shown as a colored overlay (yellow)
213
on a quasi-monochromatic image at 60 keV. The latter has been obtained by
214
summing up the contributions of the three material images shown in Fig. 7
215
to the total attenuation of the phantom at the energy of 60 keV.
216
[Figure 8 about here.]
217
A volume rendering of the reconstructed quasi-monoenergetic image at 60 keV
218
and the Au-basis image as a yellow color overlay is shown in Fig. 9. The win-
219
dowing in the rendering display for the 60 keV CT image was chosen such
220
that only the contrasted regions with large attenuation were visible.
221
[Figure 9 about here.]
222
It can be seen from the image that even for the rather moderate tube loading
223
of about 110 mAs, the selective detection of gold can be achieved by means
224
of K-edge imaging even in the case for a human Thorax, assuming that an
225
accumulation of the contrast medium to a local concentrations of 0.1 molar
226
can be reached.
12
227
Despite the fact that we tried to simulate a scenario as realistic as possi-
228
ble, we made certain assumptions that are hardly met in practice. The most
229
important shortcoming of our simulation is that it did not include effects of
230
pulse-pileup at the very high rates employed nowadays in computed tomog-
231
raphy scanners. A comparison of typical x-ray fluxes for three common x-ray
232
imaging modalities is given in Tab. 1 for mammography, radiography and
233
computed tomography [9]. As can be seen, the x-ray flux in computed to-
234
mography is highest. The count-rate performance of state-of-the art photon-
235
counting detectors is summarized in Tab. 2 below [17] together with typical
236
ASIC dimensions and the derived maximally achievable count-rates per de-
237
tector surface area.
238
[Table 1 about here.]
239
[Table 2 about here.]
240
5
Discussion
241
As can be seen from the above tables, there remains a gap between the
242
rates that currently appear handleable and the actual rates in CT scanners
243
of about one order of magnitude. Note, however, that the rates given in
244
Fig. 1, refer to the rates in the unattenuated primary beam. In order to
245
homogenize the x-ray flux onto the detector in the presence of a patient,
246
so-called bow-tie filters are used that attenuate the beam stronger at larger 13
247
fan-angles than at smaller fan-angles (close to the central beam). This has
248
the effect of lowering the maximal rates of photons hitting the detector. In
249
application to photon-counting CT the design of the bow-tie filters will have
250
to be reconsidered under the aspects of the reduced dynamic range of photon
251
counting detectors.
252
Another way to mitigate the count rate problem for human photon-
253
counting CT is the use of detector pixel sizes that are well below the sizes
254
currently used in CT. The aim here is not to gain in spatial resolution but
255
primarily to distribute counts on as many different readout channels as pos-
256
sible [19], [12]. This idea is already reflected in the comparison shown in
257
Tab. 2. Of course this approach is limited by the degrading spectral detec-
258
tor performance due to cross-talk between neighboring pixel, an effect that
259
increases with decreasing pixel size.
260
We should also mention here continuous efforts to reduce the dose ad-
261
ministered to the patient in CT scans for example by means of iterative
262
image reconstruction techniques. This development would benefit the feasi-
263
bility of photon-counting CT as the reduced dose translates to scanning at
264
reduced anode tube current, hence to a reduction of the photon flux hitting
265
the detector. Finally, we note that the effect of pulse pile-up is particularly
266
severe at high-rates. Not only does it complicate the task of registering all
267
of the incoming photons but it also will degrade spectral performance. The
268
superposition of pulses leads to missing events and to systematic errors in
269
the determination of the photon energies. Here, sophisticated pile-up models 14
270
tailored to the particular realization of the detector electronics will help to
271
correctly compute the expected number of counts in a certain energy bin
272
in the presence of first, and higher order pulse pileup. See [18] for recent
273
progress on the subject.
274
6
275
We have demonstrated the feasibility of simultaneously imaging the anatomy,
276
as well as two different contrast materials based on iodine and gold by dual K-
277
edge imaging in the case of pre-clinical imaging. The differentiation between
278
iodine and gold was achieved by tuning two of the available energy thresh-
279
olds to the K-edge energies of iodine at 33.2 keV and of gold at 81.0 keV,
280
respectively. We further investigated the possibility of translating the K-edge
281
technique to human imaging. Our results indicate that at least for the imag-
282
ing of gold as a contrast material, we obtain sufficient sensitivity to separate
283
the Au-targeted plaque structure from the hyper-dense surrounding regions,
284
under the assumption of a gold concentration of 0.1 molar in the vulnera-
285
ble plaque structure. In future work we plan to improve our simulations by
286
taking into account the combined effects of a physical detector response and
287
pulse-pileup.
Conclusions
15
288
Acknowledgment
289
We thank the following people for their support and very helpful discus-
290
sions: Roger Steadman, Christoph Herrmann, Oliver Muehlens (Philips Re-
291
search Europe, Aachen, Germany), Thomas Koehler (Philips Research Eu-
292
rope, Hamburg, Germany) and Naor Wainer and Ami Altman (CT Global
293
Research and Advanced Development, Philips Healthcare, Haifa, Israel).
294
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393 394 395 396 397 398 399 400 401 402
8
Volume rendering of photo-effect basis material image (grey), the iodine basis material image (red) and the gold basis material image (yellow) showing the accumulation of Au-HDL particles close to the bifurcation of the aorta. . . . . . . . . Volume rendering of photo-effect basis material image (grey), the iodine basis material image (red) and the gold basis material image (yellow) showing the uptake of Au-HDL in four lymph nodes. . . . . . . . . . . . . . . . . . . . . . . . . . . Color overlay of iodine basis material image (red) and gold basis material image (yellow) on an image obtained by the addition of the six binned photon counting measurements into a single CT image simulating a conventional, non-spectral CT image. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conventional CT image of a human Thorax serving as the starting point for the simulations. Central slice. L/W settings: 100HU/1500HU. . . . . . . . . . . . . . . . . . . . . . . . . Detector response functions obtained by a Monte-Carlo simulation shown for primary energies of 40, 60, 80, 100 and 120 keV. The response was computed for 3 mm of cadmium-zinctelluride (CZT) material. . . . . . . . . . . . . . . . . . . . X-ray tube source spectrum used for the simulation following [20] with an additional filtration of 2.7 mm of aluminum. . Basis material images of the Photo-electric effect (a), the Compton effect (b) and gold (c) obtained by reconstructing the decomposed data sets obtained after a pixel-wise numerical maximization of the likelihood function for the measured binned photon counting data. Only one slice containing the vulnerable plaque insert is shown. The images are displayed on a linear grey scale with black/white corresponding to the minimal/maximal values appearing in the images. . . . . . Colored overlay of the gold basis material image shown in yellow over the mono-energetic image at 60 keV. The plaque structure is indicated by the white arrow. . . . . . . . . . .
21
. 23
. 24
. 25
. 26
. 27 . 28
. 29
. 30
403 404 405
9
Volume rendering of the Au data set together with the rendering of the conventional CT image. The vulnerable plaque is shown in a shade of gold and indicated by the white arrow.
22
31
Figure 1: Volume rendering of photo-effect basis material image (grey), the iodine basis material image (red) and the gold basis material image (yellow) showing the accumulation of Au-HDL particles close to the bifurcation of the aorta.
23
Figure 2: Volume rendering of photo-effect basis material image (grey), the iodine basis material image (red) and the gold basis material image (yellow) showing the uptake of Au-HDL in four lymph nodes.
24
Figure 3: Color overlay of iodine basis material image (red) and gold basis material image (yellow) on an image obtained by the addition of the six binned photon counting measurements into a single CT image simulating a conventional, non-spectral CT image.
25
Figure 4: Conventional CT image of a human Thorax serving as the starting point for the simulations. Central slice. L/W settings: 100HU/1500HU.
26
Response of 3 mm CZT 0.10
40keV
0.08
60keV 80keV
0.06 100keV 0.04
120keV
0.02
20
40
60
80
100
120
Emeasured @keVD
Figure 5: Detector response functions obtained by a Monte-Carlo simulation shown for primary energies of 40, 60, 80, 100 and 120 keV. The response was computed for 3 mm of cadmium-zinc-telluride (CZT) material.
27
photon fluence [photons/(mAs sr keV)]
1.4e+11 1.2e+11 1e+11 8e+10 6e+10 4e+10 2e+10 0 0
20
40
60
80
100
120
140
E[keV]
Figure 6: X-ray tube source spectrum used for the simulation following [20] with an additional filtration of 2.7 mm of aluminum.
28
(a)
(b)
(c) Figure 7: Basis material images of the Photo-electric effect (a), the Compton effect (b) and gold (c) obtained by reconstructing the decomposed data sets obtained after a pixel-wise numerical maximization of the likelihood function for the measured binned photon counting data. Only one slice containing the vulnerable plaque insert is shown. The images are displayed on a linear grey scale with black/white corresponding to the minimal/maximal values appearing in the images. 29
Figure 8: Colored overlay of the gold basis material image shown in yellow over the mono-energetic image at 60 keV. The plaque structure is indicated by the white arrow.
30
Figure 9: Volume rendering of the Au data set together with the rendering of the conventional CT image. The vulnerable plaque is shown in a shade of gold and indicated by the white arrow.
31
406
407
List of Tables 1
408 409 410
2
X-ray flux rates for three different x-ray imaging modalities in comparison taken from [9]. . . . . . . . . . . . . . . . . . . . 33 Count-rate performance of state-of-the-art photon-counting xray detectors in comparison, see [17] for details. . . . . . . . . 34
32
Table 1: X-ray flux rates for three different x-ray imaging modalities in comparison taken from [9].
33
Table 2: Count-rate performance of state-of-the-art photon-counting x-ray detectors in comparison, see [17] for details.
34