Noise reduction and image quality improvement of low dose and ultra low dose brain perfusion CT by HYPR-LR processing.

Noise Reduction and Image Quality Improvement of Low
Dose and Ultra Low Dose Brain Perfusion CT by HYPR-LR
Processing
Radko Krissak1*, Charles A. Mistretta2, Thomas Henzler1, Anastasios Chatzikonstantinou3, Johann
Scharf4, Stefan O. Schoenberg1, Christian Fink1
1 Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany,
2Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, United States of America, 3Department of Neurology, University Medical Center
Mannheim, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany, 4Department of Neuroradiology, University Medical Center Mannheim, Medical
Faculty Mannheim, Heidelberg University, Heidelberg, Germany
Abstract
Purpose: To evaluate image quality and signal characteristics of brain perfusion CT (BPCT) obtained by low-dose (LD) and
ultra-low-dose (ULD) protocols with and without post-processing by highly constrained back-projection (HYPR)–local
reconstruction (LR) technique.
Methods and Materials: Simultaneous BPCTs were acquired in 8 patients on a dual-source-CT by applying LD
(80 kV,200 mAs,1461.2 mm) on tube A and ULD (80 kV,30 mAs,1461.2 mm) on tube B. Image data from both tubes was
reconstructed with identical parameters and post-processed using the HYPR-LR. Correlation coefficients between mean and
maximum (MAX) attenuation values within corresponding ROIs, area under attenuation curve (AUC), and signal to noise
ratio (SNR) of brain parenchyma were assessed. Subjective image quality was assessed on a 5-point scale by two blinded
observers (1:excellent, 5:non-diagnostic).
Results: Radiation dose of ULD was more than six times lower compared to LD. SNR was improved by HYPR: ULD vs.
ULD+HYPR: 1.960.3 vs. 8.461.7, LD vs. LD+HYPR: 5.060.7 vs. 13.462.4 (both p,0.0001). There was a good correlation
between the original datasets and the HYPR-LR post-processed datasets: r = 0.848 for ULD and ULD+HYPR and r = 0.933 for
LD and LD+HYPR (p,0.0001 for both). The mean values of the HYPR-LR post-processed ULD dataset correlated better with
the standard LD dataset (r = 0.672) than unprocessed ULD (r = 0.542), but both correlations were significant (p,0.0001).
There was no significant difference in AUC or MAX. Image quality was rated excellent (1.3) in LD+HYPR and non-diagnostic
(5.0) in ULD. LD and ULD+HYPR images had moderate image quality (3.3 and 2.7).
Conclusion: SNR and image quality of ULD-BPCT can be improved to a level similar to LD-BPCT when using HYPR-LR
without distorting attenuation measurements. This can be used to substantially reduce radiation dose. Alternatively, LD
images can be improved by HYPR-LR to higher diagnostic quality.
Citation: Krissak R, Mistretta CA, Henzler T, Chatzikonstantinou A, Scharf J, et al. (2011) Noise Reduction and Image Quality Improvement of Low Dose and Ultra
Low Dose Brain Perfusion CT by HYPR-LR Processing. PLoS ONE 6(2): e17098. doi:10.1371/journal.pone.0017098
Editor: Juri Gelovani, University of Texas, M.D. Anderson Cancer Center, United States of America
Received November 18, 2010; Accepted January 19, 2011; Published February 11, 2011
Copyright: � 2011 Krissak et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported in part (R.K., S.O.S., C.F.) by a research grant from the Federal Office for Radiation Protection (Bundesamt fu¨r Strahlenschutz;
StSch 4543). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external
funding was received for this study.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: radko.krissak@umm.de
Introduction
Cranial CT is still the most used imaging test for the diagnostic
workup of stroke since CT is available on a 24 h routine base in
most medical centers. Because unenhanced CT may underesti-
mate both the severity of ischemia and the spatial extent of
hypoperfusion, brain perfusion CT (BPCT) has been proposed for
improving the detection of ischemic stroke and evaluating the
extent and severity of hypoperfusion as this has major impact on
the indication of thrombolytic therapy [1,2,3].
Recently, the very small but finite cancer risk associated with
radiation by CT has attracted greater public attention [4,5].
Consensus has been reached that the ‘as low as reasonably
achievable’ (ALARA) principle should be applied more consis-
tently. Comprehensive stroke imaging on a 64 slice multi-detector
CT (MDCT) including a noncontrast scan, perfusion CT and CT
angiography may result in a total dose of up to 9.5 mSv with
possible organ doses of the brain of up to 490 mGy [6]. Although
critical doses for organ damage are not reached, physicians need to
be aware of possible radiation induced sequelae particularly in
repetitive examinations [6]. For this reason, BPCT is usually
performed as a low dose protocol using low milliampere and low
kilovolt settings with a radiation dose lower than that of a standard
unenhanced CT of the brain [7]. The low dose protocol is
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unfortunately resulting in a higher amount of image noise, which is
compensated by using thick-slices and/or reduced matrix
reconstructions and/or spatial smoothing, all at cost of lowering
spatial resolution [2,7].
The CT formulation of HighlY constrained back PRojection
(HYPR) imaging was introduced recently and recommended for
use in time-resolved CT angiography [8]. The HYPR algorithm
was originally developed for application to contrast-enhanced
magnetic resonance angiography to shorten the data acquisition
time and improve temporal resolution [9]. When this new post-
processing technique is applied to CT imaging, the improved noise
properties provide the potential to considerably reduce the
radiation dose in each time frame without significantly compro-
mising the image quality. Two different approaches can be used to
lower the delivered radiation dose using HYPR methods: the first
method is to reduce the tube current, and the second method is to
acquire fewer projection views [8].
The purpose of this feasibility study was to prospectively
evaluate the image quality and signal characteristics of an ultra low
dose (ULD, more than 6-fold dose reduction) BPCT protocol post-
processed with the HYPR local reconstruction (LR) algorithm
compared to a simultaneously acquired standard low dose (LD)
BPCT. Furthermore, the image quality and signal characteristics
of HYPR-LR-post-processed standard LD BPCT datasets was
evaluated. To our knowledge, this is the first patient study
evaluating HYPR-LR for perfusion CT.
Materials and Methods
Patients
In this feasibility study, simultaneous LD und ULD BPCT
acquisitions were performed in 8 patients (2 women, 6 men; mean
age 61615 years; age range 35–88 years) who were already
hospitalized and had neurological symptoms suspicious of
subacute ischemic stroke. Emergency patients eligible for throm-
bolysis were not included in this feasibility study. None of the
investigated patients had contraindication against contrast-en-
hanced CT such as renal insufficiency or allergy to a contrast
material. The study protocol was approved by the institutional
review board of the University Medical Center Mannheim.
Explanations about the nature of the imaging procedure were
provided to each patient and written informed consent was
obtained.
CT technique
All exams were performed on a 64-channel dual-source CT
scanner (Somatom Definition, Siemens Medical Solutions, For-
chheim, Germany). The system is equipped with two X-ray tubes
and two corresponding detectors which are orientated in the
gantry with an angular offset of 90u. One detector array
(corresponding to tube A) provides a field of view (FOV) of
50 cm, while the other detector array (corresponding to tube B) is
restricted to a FOV of 26 cm [10,11]. Both X-ray tubes can be
operated at different kV and mA settings, allowing the
simultaneous acquisition of a ultra low dose scan and a standard
reference scan in one examination without compromising the
image quality or patient comfort [10]. Thus it creates an
opportunity for a direct comparison of different CT protocols.
The patients were well centered in the scanner gantry to ensure
that the entire head is covered by the smaller field-of-view of the
second tube detector array. A diagnostic native spiral CT of the
brain (120 kV, 420 mAs, 4060.6 mm collimation, 0.55 pitch,
5 mm slice thickness reconstruction) was performed before the
BPCT examinations. BPCT was performed with 80 kV tube
voltage for tube A and B with a standard of reference LD protocol
[12,13] on tube A and ULD protocol on tube B. The LD and the
ULD BPCT datasets were acquired simultaneously in one scan
using tube currents of 200 mA on tube A and 30 mA on tube B.
Automatic tube current modulation (CARE Dose 4D) was turned
off during the acquisition. The detector collimation, rotation time
and table feed were 1461.2 mm, 330 ms and 0 mm. The imaging
range was positioned above the orbits. Continuous cine scanning
was performed during a total scanning time of 60 seconds, with a
scan rate of 1 image per second. 50 ml of Iomeprol 400 (Imeron
400, Bracco Imaging S.p.A., Milan, Italy) were injected using a
power injector with an injection rate of 5 ml/s and followed by a
saline chaser of 20 ml using the same flow rate. The scan was
started with a delay of 4 s. A separate dataset for each tube with a
slice thickness of 5 mm and a reconstruction increment of 4 mm
using a soft tissue kernel (B30f) was calculated. The volume CT
dose index (CTDIvol) and dose length product (DLP) were
recorded for each acquisition to estimate the radiation dose.
HYPR-LR post-processing
After image reconstruction of the two data sets from both tubes
(80 kV/200 mAs and 80 kV/30 mAs), the DICOM-images were
further processed by the HYPR-LR noise-reduction algorithm
using Matlab (Mathworks, Natick, Massachusetts, USA). An
example of images before and after HYPR-LR-post-processing is
presented in Figure 1. The functionality of HYPR was initially
described by Mistretta et al. [9], further development to HYPR-
LR was described by Johnson et al. [14]. Supanich et al.
introduced the CT formulation for HYPR [8]. The functionality
Figure 1. Example images of ultra low dose (ULD), HYPR-LR-
post-processed ultra low dose (ULD+HYPR), low dose (LD) and
HYPR-LR-post-processed low dose (HYPR+LD) brain perfusion
CT of a 58-years old male patient with no pathology. The images
are all reconstructed with the same slice thickness (5 mm) and
presented with identical window settings.
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of the algorithm is described in detail in the mentioned original
publications. In brief, the HYPR-LR algorithm consists of two
fundamental steps used to combine spatial and temporal
information. The first step is to reconstruct a low-noise-variance
composite image C(x, y, z) using information all time frames. In
the composite image, there is no dynamic information about the
image object, but the signal-to-noise level is superior to that of the
individual time frame. In the second step, the composite image is
used to constrain the reconstruction of an individual low-radiation-
dose time frame. The final image at a time frame denoted by I(x, y,
z; t) is given by the product of the composite image and a
weighting image, w(x, y, z; t), as follows:
I (x, y, z; t)~C x, y, zð Þ:w(x, y, z; t)
The image noise variance in the final image only weakly
depends on the noise properties of the individual time frame. This
property is the key to implementing radiation dose reduction in
time-resolved imaging.
Data analysis
First a diagnosis was assessed in consensus by two radiologists, a
board certified neuroradiologist with 15 years of experience in CT
imaging and a third year resident, using all existing data including
laboratory tests, optionally performed MRI or biopsy findings.
Then, subjective image quality of the LD, ULD and the
corresponding HYPR-LR-post-processed datasets was assessed
on a 5 point scale (1: excellent, 5: non-diagnostic) in consensus by
two radiologists blinded to the dataset acquisition/post-processing
technique, a board certified radiologist with 10 years of experience
in CT imaging and a fourth year resident.
Normalized Cerebral Blood Flow (CBF), Cerebral Blood
Volume (CBV), Mean Transit Time (MTT) and Time to Peak
(TTP) color-coded maps were automatically created using IB
Neuro (Imaging Biometrics, Elm Grove, Wisconsin, USA) after
defining a reference arterial input function in three arterial
timepoints in different arterial segments spatially separated by at
least 2 cm and normal appearing white matter within a region of
interest drawn by the user (board certified radiologist with 10 years
of experience in CT). The utilized software does not use reduced
matrix reconstructions or spatial smoothing, the images are left
noisy. The calculated color-coded images were not used for
diagnosis. Only evaluation of subjective diagnostic quality was
performed. For each slice location, a rectangular region of interest
(ROI) of 11611 pixels was drawn into the brain parenchyma in
the temporal lobe in the first image of every LD time series and
was copied to all subsequent images of the same time series as well
as to all corresponding images of the original ULD and the
respective HYPR-LR post-processed time series. The temporal
lobe was chosen in all patients for the purpose of standardization,
the size and shape of the ROI were chosen arbitrarily to cover an
area of at least 100 pixels.
Statistical analysis
Pearson correlation coefficients were calculated to assess the
correlations within all ROIs between the mean attenuation values
in the original and the post-processed datasets (LD vs. LD
+HYPR, ULD vs. ULD+HYPR), between those in the LD and the
ULD+HYPR datasets as well as between those in the LD and the
ULD datasets. Bland-Altman analysis was also applied to evaluate
the agreement between the mean attenuation values of the above
listed datasets [15]. Area under the attenuation curve (AUC),
maximum attenuation, minimum attenuation in the brain
parenchyma were assessed in each time series. The signal to noise
ratio (SNR) of the brain parenchyma in every time series was
Figure 2. Ultra Low Dose (ULD), HYPR-LR-post-processed ULD (ULD+HYPR), Low Dose (LD) and HYPR-LR-post-processed LD
(LD+HYPR) brain perfusion CT of a 68-year old male patient with a chronic infarction in the posterior territory of the right MCA
(detail view). All images acquired at the same time point (last image of the 60 s time series). The ULD image was rated non-diagnostic (5), the
infarction is not clearly visualized, details like small vessels (arrow in the other images) can not be clearly identified due to high image noise. Ring
artifacts, which were present but not recognizable in the noisy ULD image are recognizable after HYPR post-processing (arrowhead in ULD + HYPR).
Despite the artifacts, the subjective image quality was rated equal in ULD+HYPR and LD and images (both classified as 3). Excellent subjective image
quality (classified as 1) in the LD+HYPR image with good differentiation between gray and white matter.
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calculated as mean SNR of all images in the time series. Paired
Student’s t-test was calculated to compare the AUC, maximum
attenuation, minimum attenuation and SNR values between the
datasets.
Results
In the consensus reading, three out of 8 patients had perfusion
abnormalities within the examined volume. These included a
chronic right frontal infarction, a chronic right posterior boundary
zone infarction, and an enhancing lung cancer metastasis adjacent
to the right thalamus as well as a chronic left frontal infarction.
The diagnosis of normal pressure hydrocephalus without focal
perfusion abnormalities was made in one patient. No acute stroke
was diagnosed.
The radiation dose of ULD BPCT (mean DLP 47.16
0.4 mGy6cm, range 47–48 mGy6cm; mean CDTIVOL
28.0960.05 mGy, range 28.06–28.12 mGy) was more than six times
lower than that of LD BPCT (mean DLP 362.160.4 mGy6cm,
range 362–363 mGy6cm; mean CDTIVOL 215.3560.38 mGy,
range 215.14–216.10 mGy). The subjective image quality was rated
excellent (1.360.5, range 1–2) in the LD+HYPR and non-diagnostic
(5.060.2, range 4–5) in ULD images. LD and ULD+HYPR images
both were rated as having moderate image quality (3.360.5, range 3–
4 and 2.760.7, range 2–4). Details like small vessels, which were
present but not recognizable in the noisy ULD BPCT images were
clearly recognizable after HYPR-LR post-processing, but also ring
artifacts resulting from low X-ray detector input were visualized
(Figure 2). No sufficient visual analysis of the normalized CBF, CBV,
MTT and TTP perfusion maps was possible in the unprocessed ULD
BPCT datasets (Figure 3).
One patient has slightly moved his head several times starting 8
seconds after the initiation of the data acquisition. As the HYPR-
algorithm is using information of all time frames in the composite
image for the calculation of the individual images, this resulted in
an artifact visible in all HYPR-LR post-processed images of this
patient (Figure 4). Because all individual frames in a time series are
used to calculate the CBF, CBV, MTT and TTP maps, this
artifact was also equally present in the non-HYPR-processed
color-coded perfusion maps.
There was a good correlation between the mean attenuation
values of the original datasets and the HYPR-LR-post-processed
datasets for ULD vs. ULD+HYPR and LD vs. LD+HYPR,
respectively (r = 0.848 and r = 0.933, p,0.0001). The mean
Figure 3. Low dose (LD, a), HYPR-LR-post-processed low dose (LD+HYPR, b), ultra low dose (ULD, c) and HYPR-LR-post-processed
ultra low dose (ULD+HYPR, d) brain perfusion CT of a 75-year old male patient with a lung cancer metastasis adjacent to the right
thalamus and chronic left frontal infarction. Last image of the 60 s time series (1) and the normalized cerebral blood flow (CBF, 2), mean transit
time (MTT, 3), time to peak (TTP, 4), cerebral blood volume (CBV, 5) maps. The utilized software does not use reduced matrix reconstructions or spatial
smoothing, the images are left noisy. The pathology is recognizable in a, b and d with excellent subjective image quality and low noise in b. No
diagnosis possible in c.
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attenuation values of the HYPR-LR-post-processed ULD dataset
showed a higher correlation with the standard LD dataset than
with the unprocessed ULD dataset (r = 0.672 vs. 0.542), but both
correlations were significant (p,0.0001). The correlation coeffi-
cients with 95% confidence intervals are summarized in Table 1.
The agreement between the mean attenuation values of the
datasets is also visualized in Bland-Altman plots in Figure 5.
There was no significant difference in the calculated AUC and in
the maximum attenuation values. There was a significant
difference between the minimum attenuation values between
the ULD and the LD datasets (3665 HU vs. 4063 HU,
p = 0.001). However, there was no significant difference in
minimum attenuation values between the ULD+HYPR dataset
and the LD dataset (3864 vs. 4063 HU, p= 0.13). Example
attenuation curves of the corresponding time series are provided
in Figure 6. SNR could be significantly improved by HYPR-LR
compared to the original datasets: ULD vs. ULD+HYPR:
1.960.3 vs. 8.461.7 (p,0.0001), LD vs. LD+HYPR: 5.060.7
vs. 13.462.4 (p,0.0001). The SNR of ULD+HYPR was also
significantly higher than that of LD (p,0.001). The values are
summarized in Table 2.
Discussion
Although BPCT is already routinely performed in low dose
technique [12,13], further dose reduction is desirable for every CT
examination. Depending on the clinical situation serial BPCT
examinations may be performed which result in a local radiation
dose accumulation with radiation-induced tissue damage such as
temporary hair loss as previously reported by a group performing
serial examinations with a 4 row MDCT and DSA [16]. New
technologic advances in CT (e.g. wider detector coverage, periodic
spiral scans) make it possible to perform perfusion CT of the entire
brain[17,18]. Using the current generation of scanners covering
up to 16 cm, even a higher cumulative radiation dose may occur
in such patients. Radiologists should be aware that a cumulative or
multiplier effect of radiation exposure from multiple diagnostic
techniques may be present and should always look for new
methods for lowering the radiation dose. In this feasibility patient
study we evaluated the HYPR-LR post-processing algorithm,
which can significantly increase the SNR in a time resolved series
and thus could possibly help to further reduce radiation dose in
BPCT exams [8]. After more than 6-fold radiation dose reduction
by simply lowering the tube current, we observed a good
correlation between the mean measurement values in LD and
the ULD (more than six fold dose reduction), but a high level of
noise in the ULD images which did not allow sufficient calculation
of brain perfusion maps. After post-processing the ULD dataset by
the HYPR-LR-algorithm, the SNR has improved significantly by
an average factor of more than 4. The correlation factor between
the standard LD and ULD+HYPR was higher than between the
standard LD und ULD. The Bland-Altman plots also demonstrate
a better agreement between the mean attenuation of the brain
parenchyma in the standard LD and the HYPR-LR post-
processed ULD images compared to LD and ULD. The fact that
there was no significant difference in maximum attenuation but
significant difference in minimum attenuation in a time series
between LD and ULD can be explained by higher SNR after
Figure 4. Ultra low dose (ULD), HYPR-LR-post-processed ultra
low dose (ULD+HYPR), low dose (LD) and HYPR-LR-post-
processed low dose (HYPR+LD) brain perfusion CT of a 35-
years old patient with no pathology. This patient has slightly
moved his head several times starting after 8 seconds of the data
acquisition. As the HYPR-algorithm is using information of all time
frames in the composite image for the calculation of the individual
images, this resulted in an artifact visible in all HYPR-LR-post-processed
images of this patient with a double contour of the skull and the brain
on the right side and a frontal right hypodensity. The frontal right
hypodensity was also visible in some non-post-processed images. The
subjective image quality of the LD+HYPR image (rated 3) was still
preferred to LD and ULD+HYPR (both rated 4). The ULD image was
subjectively non-diagnostic (5). In the case of motion artifacts image
registration might further improve image quality if used before the
HYPR-LR algorithm is applied.
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Table 1. Correlation of mean attenuation between original and post-processed data sets.
Correlation coefficient 95% confidence interval p-value
ULD and ULD+HYPR 0.848 0.760–0.905 ,0.0001
LD and LD+HYPR 0.933 0.892–0.959 ,0.0001
LD and ULD+HYPR 0.672 0.520–0.765 ,0.0001
LD and ULD 0.542 0.358–0.699 ,0.0001
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contrast enhancement. Minimum values are measured in the first
native time frames with low SNR, maximum values are measured
after iodine enhancement, which is responsible for higher SNR in
both datasets. The increase of SNR is proportionally higher in the
ULD dataset, leading to a better match between the maximum
attenuation values in a time series. Higher SNR after HYPR-LR-
post-processing also reduces the difference between ULD and LD
for the minimum attenuation values.
A sufficient brain perfusion analysis is possible after a standard
LD acquisition, but mostly at cost of lowering spatial resolution by
using thick-slices and/or reduced matrix reconstructions and/or
spatial smoothing [2,12]. In our study, the SNR in the LD datasets
could be improved by an average factor of 2.7 after HYPR-LR-
post-processing, resulting in excellent subjective image quality. In
cases where image quality or spatial resolution is preferred over
radiation dose reduction, a LD acquisition with HYPR-LR-post-
processing could be alternatively performed. One of major
concerns in CT image post-processing is to ensure that the
measurement values are not significantly modified. The high
correlation coefficients and the good agreement in the Bland-
Altman plots between the original and the post-processed datasets
demonstrate reliable attenuation measurement values in the brain
parenchyma after HYPR-LR post-processing.
The potential for reduction of radiation dose using HYPR-LR
in time-resolved CT exams was previously investigated by
Supanich et al.[8]. Both, mathematical simulations and in vivo
canine studies, demonstrated the potential of a 6-8 -fold dose
reduction. No patients studies were available for the evaluation of
HYPR-LR in CT. In our feasibility study, we could validate a
more than 6-fold dose reduction in patients. The average SNR of
ULD+HYPR was about 1.7 times higher than that of standard
LD, the subjective quality of ULD+HYPR was also slightly better
compared to LD (2.7 vs. 3.3). This indicates that there is still a
potential of further dose reduction. In a study performed on renal
perfusion CT in pigs, a 10-fold dose reduction was evaluated
without loss of accuracy. The image quality of the one-tenth dose
images could be improved to be near that of the routine dose
images in pigs by using the HYPR-LR noise-reduction algorithm
[19]. In contrast to BPCT, which is accepted in the clinical
routine, renal perfusion CT is mostly performed for research
purposes. For this reason we decided to evaluate HYPR-LR for
BPCT in humans. Ring artifacts caused by the combination of 3rd
generation CT geometry and low X-ray detector input[20] were
observed in the ULD and ULD+HYPR images in our study. Such
artifacts were not described in the animal studies mentioned
above. This might be explained by the non-central position of the
Figure 5. Bland-Altman plots describing the relationship between the mean attenuation values (1440 measurements) of the brain
parenchyma in ultra low dose (ULD), HYPR-LR-post-processed ultra low dose (ULD+HYPR), low dose (LD) and HYPR-LR-post-
processed low dose (HYPR+LD) brain perfusion CT.
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examined small animal organ inside the CT gantry. These artifacts
could limit a further dose reduction with the current CT
generation.
The head movement of one patient in our study has created
motion artifacts observed in all HYPR-LR-post-processed images of
this patient. The HYPR-algorithm itself does not have a mechanism
to account for the motion induced blurring artifacts. Especially in
neuro-applications, accidental motion is typically isolated to a few
time frames, and an image registration step can be used to correct
the mis-registration before the HYPR technique is applied.
A limitation of our feasibility study is that patients with acute
infarction were not examined. Subjectively high image quality of
Figure 6. Mean attenuation values in a corresponding 60 s time series of the ultra low dose (ULD), the HYPR-LR-post-processed
ultra low dose (ULD+HYPR), the low dose (LD) and the HYPR-LR-post-processed low dose (HYPR+LD) images.
doi:10.1371/journal.pone.0017098.g006
Table 2. Subjective image quality scores and and quantitative attenuation measurements of LD and ULD and HYPR-LR post-
processed BPCT data sets.
Subjective image
quality score
Maximum attenuation
in time series (HU)
Minimum attenuation
in time series (HU)
Area under attenuation
curve in time series SNR
ULD, mean 6 SD
(range)
5.060.2 (4–5) 5766 (49–71) 3665 (30–46) 27246263 (2241–3210) 1.9360.26 (1.40–2.32)
ULD + HYPR,
mean 6 SD (range)
2.760.7 (2–4) 5667 (44–68) 3864 (31–47) 27246263 (2241–3210) 8.3961.67 (5.08–11.32)
LD, mean 6 SD (range) 3.360.5 (3–4) 5566 (44–67) 4063 (35–47) 27676210 (2341–3157) 4.9660.74 (3.77–6.69)
LD + HYPR,
mean 6 SD (range)
1.360.5 (1–2) 5466 (44–65) 4163 (36–47) 27586211 (2341–3157) 13.3662.36 (8.92–17.07)
p-value ULD vs.
ULD + HYPR
0.33 0.07 0.99 ,0.0001
p-value LD vs.
LD + HYPR
0.63 0.35 0.89 ,0.0001
p-value LD vs.
ULD + HYPR
0.67 0.13 0.53 ,0.0001
p-value ULD vs. LD 0.15 0.001 0.53 ,0.0001
Note: Subjective quality score are reported as mean values.
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HYPR-LR-post-processed images with good differentiation be-
tween gray and white matter, good correlation between the
measurement values, and visualization of chronic infarctions and
metastasis in HYPR-LR-post- processed images in our feasibility
study indicate similar results in acute stroke. A small patient
number in our feasibility study does not allow a definitive
evaluation of the potential of the HYPR-LR algorithm in BPCT,
but the results justify for a further evaluation of the algorithm in a
larger patient group.
In conclusion, SNR and image quality of ultra low dose BPCT
can be improved to a level similar to low dose BPCT when using
the HYPR-LR algorithm without distorting the attenuation
measurements. This can be used to reduce the radiation dose by
a factor of six compared to a standard low dose protocol.
Alternatively, low dose BPCT images can be improved by HYPR-
LR to a higher diagnostic quality.
Author Contributions
Conceived and designed the experiments: RK CAM. Performed the
experiments: RK CF TH. Analyzed the data: RK CF TH JS. Contributed
reagents/materials/analysis tools: RK CAM CF AC. Wrote the manu-
script: RK AC CF JS SOS.
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Brain Perfusion CT Using HYPR-LR
PLoS ONE | www.plosone.org 8 February 2011 | Volume 6 | Issue 2 | e17098