17th International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine
16 - 21 July 2023, Stony Brook, NY, USA
Dirk Schäfer1, Fredrik Ståhl2,3, Artur Omar4,5, and Gavin Poludniowski4,6
1Medical Image Acquisition, Philips Research, Hamburg, Germany
2 Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
3Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
4Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
5Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
6Department of Clinical Science, Intervention and Technology, Karolinska Institute, Huddinge, Sweden
Abstract
Dual-energy CBCT C-arm imaging is a new imaging modality, which
may provide a variety of different spectral results in the interventional
suite. We analyze model observers, i.e. the detectability index, of
different infarct models, to get insight into which spectral reconstruction
may be best suited for identifying infarcts prior to an intervention treating
acute stroke patients. MTF and NPS measurements have been performed
on a clinical dual-layer (DL) CBCT prototype system and used for model
observer analysis. Infarct models with reconstruction dependent contrast
amplitude are investigated using a variety of standard and spectral
reconstructions. Clinical examples of subjects with small and large
infarcts imaged on the same DL-CBCT system are compared visually and
be means of CNR analysis of the infarcts to the results of the model
observers. The model observer representing most closely a human reader
prefers VMI images at higher keV values, and the same trend is obtained
with simple CNR analysis. This result is partially substantiated by the
CNR analysis of the clinical patient data.
The prototype DL-CBCT system scans were conducted
with 120 kV tube voltage, 2.5 mm aluminum-equivalent
tube current
.
Both, standard and spectral images were generated from the
DL-CBCT scans. The reconstruction of the front-layer
(front) corresponds to the standard reconstruction of single
layer C-arm system. The addition of front- and back-layer
data yields the combined reconstruction (comb). The
following spectral results were derived from materialdecomposed Compton scatter and photoelectric base
functions [3]: Virtual monoenergetic image (VMI) and
virtual non-contrast VNC images. Noise suppression in
spectral images was realized by exploiting the anticorrelated noise behavior after material decomposition [4].
The denoising strength was selected by a clinical reader
pilot study to yield best visualization of brain tissues for
assessment of ischemic changes in stroke patients [5].
The modulation transfer function (MTF) was calculated
from six scans of the head module of the multi-energy CT
phantom model 662 (CIRS, Norfolk, VA, USA) using an
established methodology [6], utilizing different inserts
equivalent to various tissues, blood and iodine
concentrations. Examples of the radially averaged MTFs for
iodine blood inserts are shown in Fig. 1. Since they do not
deviate appreciably, the low-contrast spatial resolution was
assumed constant for a given reconstruction and the MTF
curves for the 2 mg/ml I in blood was used subsequently.
1 Introduction
Spectral dual-layer C-arm CBCT is a new imaging modality
and may be used during stroke treatment workflow to
improve visualization of infarct regions in the beginning of
a procedure and support blood-iodine separation for
hemorrhage identification at the end of the procedure.
Presenting the most relevant information to the
interventionalist is a challenging task. We aim to get insight
into which spectral reconstruction is best suited to support
a neuroradiologist in the task of identifying infarcts in
CBCT reconstructions in acute stroke treatment. Reader
studies based on dual-layer, dual-energy CT have shown
optimal visualization of infarcts for Virtual Monoenergetic
Image (VMI) reconstructions around 70 keV [1].
Availability of clinical data for dual-energy CBCT of acute
stroke patients is limited, therefore we investigate the
possibility to use model observers or measures such as CNR
to support the choice of most suited spectral reconstructions
for the infarct identification task.
2 Materials and Methods
Measurements in this study were made with a noncommercial dual-layer CBCT prototype system [2]. The
spectral DL-CBCT system is a
a
commercial interventional C-arm X-ray system with CBCT
imaging capability (Allura Xper FD20/15, Philips
Healthcare, The Netherlands), equipped with a dual-layer
20 inch (379.4 mm x 293.2 mm) detector prototype. The
detector prototype consists of two detector layers stacked
on top of each other.
Figure 1. Radially averaged task-related modulation transfer
functions (MTF) for Iodine blood inserts for VMI 75 keV.
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17th International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine
The 3D noise power spectrum (NPS) was calculated from
six consecutive scans made of the homogenous water
phantom for all reconstructions investigated in this study,
using well-established techniques [7]. The 2D NPS was
then calculated. A 2D NPS of the 75keV VMI
reconstructions is shown in Fig. 2.
16 - 21 July 2023, Stony Brook, NY, USA
perceived noise power spectrum. The quantity
is
and assumed to be real functions.
The signal can be represented as [9]:
,
where
is the 2D Fourier transform of the contrast
profile C
of the feature of interest, the MTF is the
spatial resolution for the low-contrast task, and VTF is an
assumed the visual transfer function (VTF) for a human
Different modelled contrast profiles C
have been
investigated representing infarct regions of different size (5,
10 and 15 mm). The contrast amplitude was estimated
based on different assumed percentages of water content
mixed with gray matter brain tissue. Calculations of
contrast were based on observed average CT numbers for
water and gray matter substitutes for the different
reconstructions. The resulting contrast and CNR values for
standard and spectral reconstructions are shown for an
infarct of 5 mm diameter with 5% water content in Fig 4.
Figure 2. A 2D noise power spectrum corresponding to the
trans-axial plane for VMI 75 keV.
The model observers are evaluated on 2D trans-axial slices,
corresponding to the clinical practice of reviewing
reconstructions in acute stroke treatment. The trans-axial
NPS is radially averaged, as shown in Fig. 3 for selected
reconstructions, and used as
on a resampled
cartesian grid in the following.
Figure 4. Modeled contrast, noise derived from NPS and
resulting CNR used for model observers.
In this study, the VTF due to Eckstein [10] is used with a
peak sensitivity at approximately 4 cycles/degree:
with
,
Figure 3. Radially averaged trans-axial 2D noise power
spectrum for selected reconstructions
where is the reconstruction field-of-view, R is the viewing
distance (assumed to be 50 cm) and D is the display size of
the FOV at a viewing station (assumed to be 25 cm).
The (observed) noise power spectrum can be represented as,
of the squared signal-to-noise ratio of a matched-filter
model observer is given by [8]:
.
In this study, the matched filter will either be prewhitening
or non-prewhitening [11]:
where
is the expected two-dimensional (2D) signal
as perceived by the observer and
is the
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17th International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine
or
16 - 21 July 2023, Stony Brook, NY, USA
higher VMI. The results for a 5 mm infarct with water
content of 10% and 15% simply scale up by a factor of 2
and 3, respectively (not shown). The results for 5% water
content with lesion diameter of 10 mm and 15 mm are
generally higher by a factor of approximately 2.5 and 4,
respectively (not shown), but similar in trend with more
pronounced maxima at VMI70 for pw and npw observer
and similar flat npwe results slighlty increasing to higher
energy levels of the VMI.
,
respectively. Note, that in the first case, the observer is able
to decorrelate the perceived noise. This leads to
cancelling and the numerator being equal to the square of
the denominator.
Three model observers will be explored:
1. PW: prewhitening matched filter. This
corresponds to an ideal linear observer.
2. NPW: non-prewhitening filter. This corresponds to
an observer unable to undo noise correlations.
3. NPWE: non-prewhitening filter with an eye filter.
This corresponds to the NPW observer with the
sensitivity of the human eye incorporated.
Moreover, visibility of infarct regions in clinical examples
of spectral DL-CBCT is assessed visually and by means of
CNR analysis. The patient data has been acquired in a
prospective single center clinical trial (NCT04571099),
which enrolled consecutive patients, 50 years or older, with
ischemic or hemorrhagic stroke on initial CT [5].
3 Results
The detectability index
for the modelled infarct with
5 mm diameter and 5% water content is shown for the
standard and spectral reconstructions in Fig 5. The prewhitening (pw) model observer
values compared to the non-pw (npw) observer, but the
relative trend is very similar with a maximum around
VMI70. The results of the npw observer with eye filter
(npwe) are sgnificantly reduced and the trend for different
VMI is more flat with the maximum of shifted towards
Figure 5. Results of different model observers for lesion size of
5 mm and water content of 5%.
Clinical examples of a small and a large infarct are shown
in Fig.6. The ROIs used to quantify the CNR of the infarct
region with respect to gray matter brain tissue are indicated
on the front layer reconstruction. The CNR values for both
infarcts are plotted in Fig. 7.
Fig. 6. Example of subject with a hyperacute, small infarct region (top row) and an acute large infarct region (bottom row). ROIs for
measuring CNR are indicated in the front-layer reconstructions (left) and values reported in Fig. 7. All reconstructions are displayed
with level / window of 25 / 70 HU and slice thickness of 5 mm .
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17th International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine
16 - 21 July 2023, Stony Brook, NY, USA
approximated as a signal known exactly/background known
exactly scenario.
5 Conclusion
This study presents the use of model observers to
investigate different spectral reconstructions for infarct
visualization in spectral DL-CBCT. The model observer
representing most closely a human reader prefers VMI
images at higher keV values, and the same trend is obtained
with simple CNR analysis. This result is substantiated by
the CNR analysis of a large infarct, while the clinical
example of a small infarct showed less dependence on the
energy level of the VMI reconstruction. These results have
to be verified by clinical reader studies, but this framework
may help to investigate and optimize system performance
for different tasks with limited availability of clinical data.
Fig. 7. CNR of the infarct/GM ROIs shown in Fig. 6
4 Discussion
There is a general observation that VMI and VNC
reconstructions provide better CNR and detectability index
for the task of infarct visualization compared to front-layer
and combined reconstructions (see Figs. 4, 5). The CNR
values of the clinical example (Fig. 7) show a similar
behavior for energy levels > 50 keV. This is partly caused
by the additional denoising involved in the spectral
reconstructions and proper separation of these effects is not
trivial and beyond the scope of this paper.
Interestingly, the optimal energy level of the VMI
reconstruction for this task depends on the model observer.
The non-prewhitening filter with an eye filter (npwe) is
expected to match best to the performance of a human
observer. The Npwe observer (Fig.5) and CNR (Fig.4)
indicate higher mono-energy levels as optimal.
Optimal CNR values for the clinical example with large
infarct are also obtained for higher energy levels of the
VMI, whereas the small infarct shows relatively flat
dependence. The different CNR trends for the different
clinical cases indicate that the composition of infarcts
merits further attention as well as any other potential
confounding factors.
These investigations aim to identify the optimal spectral
reconstruction to be presented for the task of infarct
detection, which shall be cross checked in future work with
reader studies on clinical data. The detectability index can
be related to the area-under-the-curve (AUC) from receiver
operating characteristics (ROC) studies or the percentage
correct (PC) responses in multiple alternative forced choice
(AFC) studies:
, where
is the standard cumulative normal distribution. The
inclusion of so-called internal noise of the observer into the
model may be necessary to quantitatively predict human
performance [12].
Potential limitations of the approach include the assumption
of quasi-linearity (permitting image analysis in the Fourier
domain) and the assumption that the clinical task can be
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