standard filtered back-projection (FBP)
reconstruction (Willemink et al., 2013). Therefore,
IR algorithms could be employed in low-dose CT
examinations preserving the diagnostic quality of
images (which conversely is degraded when FBP
reconstruction is adopted). For this reason, recent
works were focused on combining IR methods with
CAD systems (Huber et al., 2016; Yoon et al., 2015;
Wielpütz et al., 2015; Harder et al., 2016). These
studies showed that in many clinical situations low-
dose chest CT with IR algorithms does not
significantly worsen the CAD sensitivity obtained
with standard chest CT and conventional FBP
reconstruction. However, some studies have shown
that image quality obtained through iterative
reconstruction depends on image contrast and
radiation dose (Richard et al., 2012; Samei et al.,
2015). Thus, more insights on the performance of IR
algorithms for chest examinations can be of practical
interest.
In a previous phantom study, we assessed the
image quality performance of a CT scanner (Optima
CT660, GE Healthcare, Waukesha, WI, USA) which
implements the Adaptive Statistical Iterative
Reconstruction (ASIR, GE Healthcare, Waukesha,
WI, USA) algorithm (Barca et al., 2017). We
performed a systematic analysis of noise, contrast-
to-noise ratio (CNR) and spatial resolution by
varying the main exposure parameters in a wide
range of values and testing the ASIR’s performance
on different image contrasts. We demonstrated that a
relevant noise reduction and CNR increment in CT
images can be achieved with the ASIR algorithm
with respect to the conventional FBP reconstruction.
Additionally, spatial resolution decreases with
increasing the ASIR blending level of reconstruction
for low dose acquisitions and low contrast objects.
However, only a quality control protocol was
adopted in image acquisition without any clinical
and only phantom images were analysed.
In this study, we investigate potential strengths
of the ASIR algorithm in terms of image quality that
could be of practical interest in conjunction with
lung CAD system at low and very low radiation
exposure levels. We study the dependence of
different image quality parameters on the ASIR-FBP
blending level of reconstruction, both in phantom
and clinical chest images. The analysis performed in
the previous work was repeated to characterise the
quality of images obtained through ASIR in a 128-
slice CT scanner (Discovery 750 HD, GE
Healthcare, Waukesha, WI, USA). However, while
in the previous study images were acquired through
scan protocols often used in quality controls, in this
analysis we employed a clinical chest scan protocol
to acquire the phantom images. Then, we focused
our attention on clinical chest acquisitions of
patients with pulmonary nodules, whose images
were retrospectively reconstructed using different
ASIR-FBP blending levels; we studied noise and
contrast properties of these images in order to
evaluate the employment of ASIR and its effect on
nodule detectability.
2 MATERIALS AND METHODS
Images of the Catphan-504 phantom (The Phantom
Laboratory, NY, USA) were acquired on the
Discovery 750-HD CT using a scan protocol
routinely adopted in chest CT examinations (Table
1) and varying the main exposure parameters in a
wide range of values (Specifically, four values of
tube voltage and eight values of tube load were
employed: 80, 100, 120 and 140 kVp, 32
1
, 63, 84,
105, 126, 147, 168, 189 mAs). The Catphan-504
phantom is composed of 4 modules with cylindrical
shape (internal diameter of 15 cm). We employed
the CTP486 module (a homogeneous water-
equivalent module) and the CTP404 module
(composed of many inserts of different materials in a
water-equivalent background).
Image quality was evaluated through the
assessment of noise, noise power spectrum (NPS)
and modulation transfer function (MTF).
Noise and NPS were computed from images of
the CTP486 phantom section while for MTF
assessment we employed images of the CTP404
phantom section.
Noise was measured by computing the standard
deviation (σ) of Hounsfield units (HU) within a
region of interest (ROI), while for the NPS
assessment we adopted the Siewerdsen et al.
approach: we computed the 3D NPS and then we
obtain a radial representation of the NPS by
selecting the f
z
=0 plane of the 3D NPS and
performing an average of several radial profiles.
The MTFs were derived following the circular
edge method through edge spread function (ESF)
measurements (Richard et al., 2012; Samei et al.,
2015). ESFs were referred to six different inserts of
the CTP404 section (air, PMP, LDPE, polystyrene,
1
In order to evaluate the spatial resolution performance of
the ASIR algorithm at low radiation exposure, a set of
images of the CTP404 section were acquired at 32 mAs
(lowest value used in our analysis). This value was only
employed for MTF evaluation.