Evaluation of the Adaptive Statistical Iterative Reconstruction

Algorithm in Chest CT (Computed Tomography)

A Preliminary Study toward Its Employment in Low Dose Applications,

Also in Conjunction with CAD (Computer Aided Detection)

Patrizio Barca

1,2,3

, Federica Palmas

1,3

, Maria Evelina Fantacci

1,3

and Davide Caramella

Department of Physics, University of Pisa, Pisa, Italy

Unit of Medical Physics, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy

INFN, Pisa Section, Pisa, Italy

Department of Radiology, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy

Keywords: Chest Computed Tomography, Image Quality, Modulation Transfer Function, Noise Power Spectrum,

Contrast.

Abstract: Lung cancer is one of the leading cause of cancer death worldwide. Computed Tomography (CT) is the best

imaging modality for the detection of small pulmonary nodules and for this reason its employment as a

screening tool has been widely studied. However, radiation dose delivered in a chest CT examination must

be considered, especially when potentially healthy people are examined in screening programs. In this

context, iterative reconstruction (IR) algorithms have shown the potential to reduce image noise and

radiation dose and computer aided detection (CAD) systems can be employed for supporting radiologists.

Thus, the combined use of IR algorithms and CAD systems can be of practical interest. In this preliminary

work we studied the potential improvements in the quality of phantom and clinical chest images

reconstructed trough the Adaptive Statistical Iterative Reconstruction (ASIR, GE Healthcare, Waukesha,

WI, USA) algorithm, in order to evaluate a possible employment of this algorithm in low dose chest CT

imaging with CAD analysis. We analysed both clinical and phantom CT images. Noise, noise power

spectrum (NPS) and modulation transfer function (MTF) were estimated for different inserts in the phantom

images. Image contrast and contrast-to-noise ratio (CNR) of different nodules contained in clinical chest

images were evaluated. Noise decreases non-linearly when increasing the ASIR blending level of

reconstruction. ASIR modified the NPS. The MTF for ASIR-reconstructed images depended on tube load,

contrast and blending level. Both image contrast and CNR increased with the ASIR blending level.

1 INTRODUCTION

Lung cancer is one of the most frequently diagnosed

cancers and the leading causes of cancer death in

men worldwide (Malvezzi et al., 2015; Siegel et al.,

2015). In fact, most of lung cancer cases are

diagnosed in the late stages when the survival rate is

very low. An early detection considerably improves

the survival rate and for this reason the

implementation of screening programs is of relevant

interest. The best diagnostic tool for the detection of

pulmonary nodules is Computed Tomography (CT)

and its employment in screening trials has shown

great results in terms of reduced mortality (The

National Lung Screening Trial Research Team,

2011). An additional tool that can be helpful in lung

cancer screening is represented by computer aided

detection (CAD) systems. In fact, many studies have

shown satisfactory results in terms of sensitivity and

specificity (Lopez Torres et al., 2015; Fantacci et al.,

2017), so as CAD systems can be employed to

reduce radiologist workload and improving the

quality of chest CT scan interpretation in screening

examinations. However, in order to minimise the

risk of radiation induced cancer in patients

(potentially healthy in screening examinations) low-

dose CT is required. Recent advances in CT

technology include different methods of dose

optimisation and reduction (Goo, 2012). In

particular, iterative reconstruction algorithms (IR)

show the potential of improving image quality in

low-dose image acquisitions as compared to

Barca P., Palmas F., Fantacci M. and Caramella D.

Evaluation of the Adaptive Statistical Iterative Reconstruction Algorithm in Chest CT (Computed Tomography) - A Preliminary Study toward Its Employment in Low Dose Applications, Also in

Conjunction with CAD (Computer Aided Detection).

DOI: 10.5220/0006750706880694

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (HEALTHINF 2018), pages 688-694

ISBN: 978-989-758-281-3

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

, 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

=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,

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.

delrin and teflon).

Additionally, images of patients that underwent

chest CT clinical examinations were anonymised

and retrospectively reconstructed

using different

ASIR-FBP blending levels (from 20% to 100%). In

these images, nodules of different sizes (4 mm to 7

mm) were identified, then, the CNR and the

percentage contrast were computed. The CNR was

estimated as follows:

nodule background

HU HU

CNR =

σ + σ



(1)

where HU

nodule

/σ

nodule

and HU

background

/σ

background

are

the mean/standard deviation of HU values in a

circular ROI in the considered nodule and

background region. The percentage contrast was

defined by the following formula:

%100%

nodule background

background

HU HU

C( ) =



(2)

Uncertainties associated to data values were

computed as standard deviations of repeated

measurements. For some measurements this

approach was not possible (e.g. for contrast analysis

in clinical images). However, we tested the

reproducibility of our measurements adopting the

clinical chest scan protocol and computing the

coefficient of variation (COV) from a set of ten

acquisitions. The COV resulted <0.03 for each

ASIR-FBP blending level employed in the

reconstruction process.

Image data analysis was performed using ImageJ

(Wayne Rasband, National Institute of Health, USA)

and Matlab (The MathWorks, Inc., USA) software

packages.

Table 1: Standard chest scan protocol routinely adopted in

the Discovery 750-HD CT machine for chest

examinations.

Scan protocol standard ches

Modality helical

Tube load 126 mAs

Tube voltage 100 -120

kVp

Pitch 0.984:1

Slice thickness 2.5 mm

S-FOV M Body

D-FOV 220 mm

Collimation width 40 mm

The kVp value could be set to 100 kVp or 120 kVp depending on

the patient size.

3 RESULTS

Figure 1 shows the noise obtained varying tube load

and tube voltage for conventional FBP algorithm

and ASIR with different blending levels of

reconstruction (20%, 40%, 60%, 80%, 100%). Noise

decreased non-linearly with the increase of ASIR

blending level of reconstruction as well as with

increasing tube load and tube voltage.

Figure 1: Noise (standard deviation) for conventional FBP algorithm and different ASIR blending levels of reconstruction

(20%, 40%, 60%, 80%, 100%) with varying tube load (panel A) and tube voltage (panel B). Other parameters of acquisition

were set as in Table 1.

These images were acquired with the standard chest

protocol (Table 1) and first reconstructed with FBP.

Figure 3: MTF for ASIR-reconstructed CT images (blending level of 100%) and medium-low (LDPE) contrast level, with

varying tube load (panel A). MTF for low (32 mAs) tube load and low (polystyrene) contrast CT images, with varying

reconstruction methods.

Figure 2: Radial NPS for conventional FBP algorithm and

ASIR algorithm with different blending levels of

reconstruction. Images were acquired adopting the chest

scan protocol of Table 1.

Results about NPS are reported in Figure 2.

ASIR algorithm acts as a low pass filter whose effect

increases with the increase of blending level of

reconstruction.

Results about spatial resolution are reported in

Figures 3 and 4. For ASIR-reconstructed CT images

and low contrast level, the MTF decreased with

decreasing tube load (Fig. 3 A). The MTF of ASIR-

reconstructed CT images varied even with the

contrast level (Fig. 4), especially at low tube load. In

particular, the MTF decreased with decreasing

contrast level. While for high contrast objects or

high tube load values ASIR preserves the spatial

resolution obtained by FBP reconstruction, for

medium (e.g. 126 mAs, which is the value adopted

in the standard chest protocol on the Discovery 750-

HD CT) and low (e.g. 32 mAs, the lowest value that

was employed in our study) tube load and contrast

level, the MTF of ASIR-reconstructed CT images

was lower than the MTF of conventional FBP-

reconstructed images, and the first decreased with

increasing blending level of reconstruction (Fig. 3

B).

Figure 5 shows one example of clinical image

containing a nodule of 5 mm. Examples of the CNR

and percentage contrast results are reported in

Figures 6 and 7 for nodules of 4, 5 and 6 mm of

diameter. CNR and percentage contrast of the

nodules increased non-linearly with increasing the

ASIR level of reconstruction (up to 43% and 9%

respectively).

4 DISCUSSIONS

In this study we evaluated CT image quality through

the assessment of different indexes related to noise

(noise and NPS), contrast (CNR and percentage

contrast) and spatial resolution (MTF) properties of

phantom and clinical images, comparing the

performance of two different reconstruction

technologies.

As expected, our findings confirm the noise

reduction potential of the ASIR algorithm.

Specifically, when compared to FBP, ASIR can

reduce image noise up to 55 % (Figure 1), showing

the potential of image quality improvement at low

radiation exposures. Furthermore, it seems that

ASIR acts as a non-linear low-pass filter, which can

modify noise texture and affect spatial resolution

especially at low contrast and low radiation exposure

when medium/high blending levels of reconstruction

are employed (Figures 2 and 3).

Figure 4: MTF for ASIR-reconstructed CT images (blending level of reconstruction of 0% (panel A), 60% (panel B) and

100% (panels C and D) and medium (126 mAs, panel A, B and C) /low (32 mAs, panel D) tube load.

Figure 5: Example of a pulmonary nodule (5 mm of

diameter) employed for CNR and percentage contrast

evaluation (The ROI in green represented the “background

ROI”, while the ROI in blue represented the “nodule

ROI”).

Figure 6: Examples of CNRs for nodules of 4 mm (nodule

1), 5 mm (nodule 2) and 6 mm (nodule 3) of diameter.

Clinical images were acquired adopting the chest scan

protocol of Table 1.

In addition, chest clinical images reconstructed

with ASIR exhibit interesting properties in terms of

increased CNR and percentage contrast of small

nodules (Figures 6 and 7). These results are in

agreement with previous qualitative studies in which

the detection of lesions seems to be improved when

the ASIR algorithm is employed in the

reconstruction process instead of the conventional.

FBP (Willemink et al., 2013). Another study

highlighted that a 50% ASIR-FBP blending level

allowed to maintain acceptable CNR and image

noise levels in low-dose images of different chest

phantoms (Mathieu et al, 2014). The authors showed

that, as compared to conventional FBP, radiation

dose could be reduced by 40% by using 50% ASIR-

FBP blending level of reconstruction without

affecting overall image quality.

Figure 7: Examples of percentage contrasts for nodules of

4 mm (nodule 1), 5 mm (nodule 2) and 6 mm (nodule 3) of

diameter. Clinical images were acquired adopting the

chest scan protocol of Table 1.

As shown in our results, the quality of ASIR-

reconstructed images is strictly related to the

blending level of reconstruction. However, even

though the choice of the ASIR-FBP blending level is

extremely important to avoid losses in details

detection, ASIR images exhibit considerably better

noise and contrast properties as compared to FBP

images. Notice that while the CNR increment may

be due to the noise reduction performed by ASIR,

the percentage contrast is not directly related to

image noise (Eq. 2). This means that ASIR could

also improve tissue differences in terms of HU

values and thus have positive influences on CAD

performances. Therefore, the ASIR algorithm may

be employed in low-dose chest CT acquisitions

(which are required in screening examinations) and

in combination with CAD systems as suggested by

previous studies (Hyun et al, 2015). It should be

reminded that spatial resolution with ASIR

reconstruction depends also on radiation dose and on

the objects contrast in the images (Figures 3 and 4).

These dependences should be carefully considered

in order to evaluate and optimise the combined

employment of ASIR and the CAD system available

for us (Torres et al., 2015; Fantacci et al., 2017) for

pulmonary nodules detection in ultra-low dose

conditions.

5 CONCLUSIONS

In this work we assessed the noise, contrast and

spatial resolution properties of phantom and human

chest CT images reconstructed through different

ASIR-FBP blending levels.

An important noise reduction and CNR

increment is achieved in images reconstructed

through the ASIR algorithm. Percentage contrast

also increases with the blending level of

reconstruction. For these reasons ASIR may be

employed in low-dose chest CT examinations and it

could positively influence CAD performances.

However, since spatial resolution decreases with the

increasing of the blending level of reconstruction in

low dose acquisitions, this parameter should be

carefully optimised.

Even though further studies are needed, our

findings provide additional insights into the

characterisation of the ASIR algorithm performance

and can be of practical interest toward an its

adequate employment.

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