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 Chapter 5

State-Of-The-Art X-Ray Digital Tomosynthesis Imaging

Tsutomu Gomi

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.81667

Abstract
Digital tomosynthesis (DT) is a notable modality in medical imaging because it shows the
spread of the target area with lower radiation dose relative to computed tomography. In
this section, we describe the technique in two parts: (1) image quality (contrast) and (2) DT
image reconstruction algorithms, including state-of-the-art total variation minimization
reconstruction algorithms with single-energy X-ray conventional polychromatic imaging
and novel dual-energy (DE) virtual monochromatic imaging. The novel DE virtual mono-
chromatic image-processing algorithm provides adequate overall performance (especially,
reduction of beam-hardening, reduction of noise). The DE virtual monochromatic image-
processing algorithm appears to be a promising new option for imaging in DT because it
provides three-dimensional visualizations of high-contrast images that are far superior to
those of images processed by using conventional single-energy polychromatic image-
processing algorithms.

Keywords: tomosynthesis, reconstruction, virtual monochromatic imaging,

polychromatic imaging, dual-energy imaging

1. Introduction

Approximately 30 years have passed since Dr. Hounsfield developed a practical computed
tomography (CT) system. The arrival of CT devices in the field of medical diagnosis has led to
a revolution equivalent to the discovery of X-rays by Dr. Roentgen. Since then, most
researchers who aim to improve medical diagnosis quality have worked toward the functional
improvement of CT instruments, thus leading to the development of fan-beam CT, helical scan
CT, multislice CT, and cone-beam CT instruments. These new instruments reduce the time
needed for image reconstruction and significantly improve image quality. Given the increasing
demand for better technology, there has been continued research and development of high-
performance CT instruments.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
68 Medical Imaging and Image-Guided Interventions

Interest in digital tomosynthesis (DT) and its clinical applications has been revived by recent
advances in digital X-ray detector technology. Conventional tomography technology provides
planar information about an object from its projection images. In tomography, an X-ray tube
and an X-ray film receptor lie on either side of the object. The relative motion of the tube and
film is predetermined on the basis of the location of the in-focus plane [1]. A single image plane
is generated by a scan, and multiple scans are required to provide a sufficient number of
planes to cover the selected structure in the object. DT acquires only one set of discrete X-ray
projections that can be used to reconstruct any plane of the object retrospectively [2]. The
technique has been investigated in angiography and in the imaging of the chest, hand joints,
lungs, teeth, and breasts [3–8]. Dobbins et al. [9] reviewed DT and showed that it outperformed
planar imaging to a statistically significant extent. Various types of DT reconstruction methods
have been explored.
Current DT mainly involves image acquisition/reconstruction using polychromatic imaging.
Material decomposition or virtual monochromatic image processing using dual energy (DE)
has been studied in CT, and many basic and clinically useful applications have been reported
[10–12]. Similar to CT, it can be expected that DT also benefits from image quality improvements.
In this chapter, the fundamental image quality characteristics of various reconstruction algo-
rithms (including a state-of-the-art reconstruction algorithm) using polychromatic imaging and
virtual monochromatic DT imaging that were verified in phantom experiments are discussed.

2. DT reconstruction

Existing tomosynthesis algorithms can be divided into three categories: (1) backprojection
(BP), (2) filtered backprojection (FBP), and (3) iterative reconstruction (IR) algorithms.

2.1. BP

The BP algorithm is referred to as “shift-and-add” (SAA) [9], whereby projection images taken
at different angles are electronically shifted and added to generate an image plane focused at a
certain depth below the surface. The projection shift is adjusted such that the visibility of the
features in the selected plane is enhanced, whereas those in other planes are blurred. By using
a digital detector, the image planes at all depths can be retrospectively reconstructed from one
set of projections. The SAA algorithm is valid only if the motion of the X-ray focal spot is
parallel to the detector.

2.2. FBP

FBP algorithms are widely used in CT when many projections acquired at >360 are used
to reconstruct cross-sectional images. The number of projections typically ranges from a few
hundred to approximately 1000. The Fourier central slice theorem is fundamental to FBP
theory. In two-dimensional (2D) CT imaging, a projection of an object corresponds to sampling
that object along the direction perpendicular to the X-ray beam in the Fourier space [13]. For
 State-Of-The-Art X-Ray Digital Tomosynthesis Imaging 69
http://dx.doi.org/10.5772/intechopen.81667

many projections, information about the object is well sampled, and the object can be restored
by combining the information from all projections.
In three-dimensional (3D) cone-beam imaging, information about the object in Fourier space is
related to the Radon transform of the object. The relationship between the Radon transform
and cone-beam projections has been well studied, and solutions to the cone-beam reconstruc-
tion have been provided [14, 15]. The Feldkamp algorithm generally provides a high degree of
precision for 3D reconstruction images when an exact type of algorithm is employed [16].
Therefore, this method has been adopted for the image reconstruction of 3D tomography and
multidetector cone-beam CT. A number of improved 3D reconstruction methods have been
derived from the Feldkamp algorithm.

We denoted the intensity of the incident X-rays as I 0 ¼ ðδ; c; dÞ and that of the X-rays that
passed through the structure at the location ðδ; c; dÞ as I ¼ ðδ; c; dÞ. The image data dF ¼ ðδ; c; dÞ
were calculated as follows:

I 0 ðδ; c; dÞ
dF ðδ; c; dÞ ¼ ln (1)
I ðδ; c; dÞ

The original projection data are used to generate an FBP image:

R2 ~
ðθ
FDK
f ¼ 2
dFðδ; c; dÞDδ (2)
θU

U ðX; Y; δÞ ¼ R þ X cos δ þ Y sin δ

X sin δ þ Y cos δ
cðX; Y; δÞ ¼ R
R þ X cos δ þ Y sin δ

R
dðX; Y; Z; δÞ ¼ Z
R þ X cos δ þ Ysinδ
!
~dFðδ; c; dÞ ¼ R F
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi d ðδ; c; dÞ ∗gP ðγÞ
R2 þ c2 þ d2

where the projection angle δ, fan-angle γ, source trajectory R, acquisition angle θ, and gp ðγÞ
represent a convolution with the Ramachandran–Lakshminarayanan filter. The Ramachandran–
Lakshminarayanan (ramp) filter is shown below:
 2
p γ
g ðγ Þ ¼ (3)
sin α

2.3. IR

An IR algorithm performs the reconstruction in a recursive fashion [17, 18], unlike the one-step
operation in backprojection and FBP algorithms. During IR, a 3D object model is repeatedly
70 Medical Imaging and Image-Guided Interventions

updated until it converges to the solution that optimizes an objective function. The objective
function defines the criteria of the reconstruction solution.
Algebraic reconstruction methods assume that the image is an array of unknowns, and the
reconstruction problem can be established as a system of linear equations. The unknowns of
this system are approximated with respect to the ray sums iteratively [17, 18]. In each iteration,
current reconstruction is reprojected and updated according to how much it satisfies the
projection measurements.

Gx ¼ f (4)

In the DT reconstruction, x represents the pixel values for x ∈ ℜN , whereas f represents the
observed data for f ∈ ℜM. The weighting value G ∈ ℜMN was created by using a forward
projection, and the matrix G combines the submatrices of each projection. Gmn is defined as
the length of intersection of the mth ray with the nth cell. DT reconstruction starts with an initial
guess x0 and computes x1 by projecting x0 onto the first hyperplane in Eq. 4. The algebraic
reconstruction technique (ART) update procedure (or error correction) is shown below:
! !ðk 1Þ
!ðkÞ !ðk 1Þ bi aix !
x ¼x þβ 2
ai (5)
!
ai

!
where ai ¼ ðai1 ; ai2 ; …; ain Þ is the ith row of the projection matrix, and β is the relaxation param-
! 2
eter (1.0). ai is the normalization factor. The update is performed for each projection
measurement bi separately, that is, the kth iteration consists of a sweep through the m projec-
tion measurements. The algorithm iterates through the equations periodically; therefore,
i = (i-1) mod (m) + 1.

The ART algorithm updates the image vector per ray such that the update satisfies only a
single equation representing the corresponding ray integral. By contrast, the simultaneous IR
technique (SIRT) algorithm updates the image vector after all equations are considered. The
update procedure of SIRT is given in Eq. 6 according to [19].

m ! !ðk 1 Þ
!ðkÞ !ðk 1Þ 1 bi a x
Pn i
X
x ¼x þ β Pm aij (6)
i aij i j¼1 aij

The simultaneous ART (SART) algorithm [20] is proposed as a combination of the ART and
SIRT algorithms. SART updates the superior implementation of ART and is based on a simul-
taneous update of the current reconstruction similar to SIRT. In the SART algorithm, the
update procedure is applied for all rays in a given scan direction (projection) instead of each
ray separately (similar to conventional ART) or instead of all rays simultaneously (similar to
SIRT). The SART update is expressed as follows:
 State-Of-The-Art X-Ray Digital Tomosynthesis Imaging 71
http://dx.doi.org/10.5772/intechopen.81667

X ! !ðk 1 Þ
!ðkÞ !ðk 1Þ 1 bi a x
x ¼x þβ aij Pn i (7)
Σi ∈ Ωt aij i ∈ Ω j¼1 aij
t

where Ωt is the set of indices of the rays sent from the tth projection direction.
The objective function in the maximum likelihood expectation–maximization (MLEM) algo-
rithm is the likelihood function, which is the probability of obtaining the measured projections
in a given object model. The solution of the MLEM algorithm is an object model that maxi-
mizes the probability of obtaining the measured projections.
The transition matrix αði; jÞ represents the probability for an event generated in the area of the
source covered by pixel i to be detected. The count registered by the detector is represented by
the vector y(j) = [y(1), y(2), y(3),..., y(J)]:

xðkþ1Þ ðiÞ ¼ xðkÞ ðiÞCðkÞ ðiÞ (8)

1 XJ
yðjÞ
CðkÞ ðiÞ ¼ Pn Pn ðkÞ ðiÞ
αði; jÞ (9)
j¼1 α ð i; j Þ j¼1 i¼1 α ð i; j Þx

1 X yðjÞαði; jÞ
J
x ðkþ1Þ
ðiÞ ¼ Pn xðkÞ ðiÞ Pn ðkÞ (10)
j¼1 αði; jÞ j¼1 i¼1 x αði; jÞ

i = 1, 2, …, n.

2.4. Total variation minimization reconstruction algorithm

An iterative algorithm using total variation (TV)-based compressive sensing was recently devel-
oped for volume image reconstruction from a tomographic scan [21]. The image TV has been
used as a penalty term in iterative image reconstruction algorithms [21]. The TV of an image is
defined as the sum of the first-order derivative magnitudes for all pixels in the image. TV
minimization is an image domain optimization method associated with compressed sensing
theory [21]. As TV-minimization IR for image reconstruction, the TV-minimization IR technique
is the SART [20] with algebraic IR for constraining the TV-minimization problem, which is called
SART-TV [21]. In TV-minimization IR, adding a penalty to the data–fidelity–objective function
tends to smooth out noise in the image while preserving edges within the image [21–25].
SART-TV minimizes the Rudin, Osher, and Fatemi (ROF) model [26], that is, SART-TV also
takes into account the image information when minimizing the TV of the image. If only the TV-
minimization step was performed in the rest of the algorithms, the result would be a flat
image; alternatively, the ROF model ensures that the image does not change considerably.
The SART-TV optimal parameters include the iteration number for TV minimization [100
(np)] and the length of each gradient-descent step [50 (q)]. These optimal parameters have been
72 Medical Imaging and Image-Guided Interventions

shown to be very relevant to image quality [21, 24]. In our study, we minimized the image
quality by using these optimal parameters for the SART-TV algorithms.
The SART-TV algorithm is expressed in the form of a pseudo code as follows:

β ¼ 1:0; np; 100 q; 50

!
x¼ 0

repeat main loop

! !
x0 ¼ x

(SART)
! !
! ! 1 X bi Ai
x
for i ¼ 1 : N d , x ¼x þβ P Aij Pn
i ∈ Ωt Aij i ∈ Ωt j¼1 Aij

for i ¼ 1 : N i , if xi < 0 then xi ¼ 0

(enforce positivity)
! !
xres ¼ x

for i = 1:np do TV-minimization loop

! !
Δx ¼ x x0

! !
d x ¼ ∇! x
x TV

! . !
^ ¼ dx dx
dx

! ! ^
x¼x q∗ Δx∗ dx

end
!
return x res

3. Phantom specification

For the evaluation of low-contrast resolution, a contrast-detail (CD) phantom was used with
epoxy slabs. The CD phantoms of different diameters (signal region, CaCO3) and thicknesses
were arranged within the epoxy slabs. For X-ray imaging, we placed polymethyl methacrylate
(PMMA) slabs (200  200 mm) with 50 mm thickness on the top of the CD phantom (Figure 1).
 State-Of-The-Art X-Ray Digital Tomosynthesis Imaging 73
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Figure 1. Illustration of the CD phantom used in this study.

4. Virtual monochromatic DT imaging using DE

4.1. Theory of material decomposition processing

Given that the photoelectric effect and Compton scattering of X-ray photons in the diagnostic
range (E < 140 keV) are the predominant mechanisms responsible for X-ray attenuation
(monochromatic X-ray), the mass attenuation coefficient of any material can be expressed with
sufficient accuracy as a linear combination of the photoelectric and Compton attenuation
coefficients. Consequently, the mass attenuation coefficient can also be expressed as a linear
combination of the attenuation coefficients of two basis materials [10]:
   
μ μ
μðr; EÞ ¼ ðEÞ
r1 ðrÞ þ ðEÞ
r2 ðrÞ (11)
r 1 r 2

where the basis materials exhibit different photoelectric and Compton characteristics, (μ/r)i(E)
and i = 1, 2 denote the mass attenuation coefficients of the two basis materials, and ri(r) and
i = 1, 2 denotes the local densities (g/cm3) of the two basis materials at location r.
In principle, DE can only accurately decompose a mixture into two materials. Therefore, for DE
measurement–based mixture decomposition into three constitutive materials, a third constitu-
ent must be provided to solve for three unknowns by using only two spectral measurements.
In one solution, the sum of the volumes of the three constituent materials is assumed to be
74 Medical Imaging and Image-Guided Interventions

equivalent to the volume of the mixture (volume or mass conservation) [11]. In this work, we
used a simple projection space (prereconstruction) decomposition method to estimate the
material fractions ( fn) of the CaCO3 ( fcaco3, local density; 2.711 g/cm3), PMMA ( fPMMA, local
density; 1.17 g/cm3), and epoxy resin ( fepoxy, local density; 1.11 g/cm3) in the phantom.
Three basis materials can also be expressed as a linear combination of the attenuation coeffi-
cients:
     
μ μ μ
μðr; EÞ ¼ ðE Þ
r 1 ðrÞ þ ðEÞ
r2 ðrÞ þ ðE Þ
r 3 ðrÞ (12)
r 1 r 2 r 3

In DE acquisition, the detected image intensity can be expressed as follows:

ð        
μ μ μ
I L ¼ PL ðEÞ exp ðEÞ
L1 ðEÞ
L2 ðEÞ
L3 dE (13)
r 1 r 2 r 3
ð        
μ μ μ
I H ¼ PH ðEÞ exp ðEÞ
L1 ðEÞ
L2 ðEÞ
L3 dE (14)
r 1 r 2 r 3

L1 ∗ L2 ∗ L3 ¼ 1:0 (15)
ð
L1 ¼ r1 ðrÞdl
ð
L2 ¼ r2 ðrÞdl
ð
L3 ¼ r3 ðrÞdl

where PL(E) represents the low-energy primary intensities, PH(E) represents the high-energy
primary intensities, IL represents the low-energy attenuated intensities, and IH denotes the
high-energy attenuated intensities. The equivalent densities (g/cm2; L1, L2, and L3) of the three
basis materials must be determined for each ray path. Eqs. (13, 14, 15) can be solved for the
equivalent area density, where L1, L2, and L3 are the unknown materials. Therefore, the basis
material decomposition can be accomplished by solving simultaneous equations to calculate
the values of L1, L2, and L3 from the measured projection pixel values [12]. By using the density
corresponding to the area with the three basis materials, the linear attenuation coefficient
μ(r, E) can be calculated for any photon.

We used the local and area densities for each material to calculate the theoretical linear attenu-
ation coefficient curves shown in Figure 2; these values were generated by inputting the chem-
ical compositions of the CaCO3, epoxy resin, and PMMA into the XCOM program developed by
Berger and Hubbell [27]. The curves show that the linear attenuation coefficient of CaCO3
decreases more rapidly than those of the foam epoxy and PMMA in the energy band <100 keV.
Finally, we used the projection space decomposition approach to generate material decomposi-
tion images for the CaCO3, epoxy resin image, and PMMA by using the following process.
 State-Of-The-Art X-Ray Digital Tomosynthesis Imaging 75
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Figure 2. The linear attenuation coefficients of CaCO3, epoxy, and PMMA with respect to the photons.

Eqs. (13) and (14) were used to calculate the values for IL_caco3, IL_PMMAr, IL_epoxy, IH_caco3,
IH_PMMA, and IH_epoxy as simulated attenuation intensities of these materials at the two energy
levels. These values were then used to construct a sensitivity matrix, and the material fractions
were obtained from the inverse of this matrix (Eq. 16):
3 12
f CaCO3
2 3 2 3
I L_CaCO3 I L_PMMA I L_epoxy DT EL
6 7 6 7 6 7
6 f PMMA 7 ¼ 6 I H_CaCO3 I H_PMMA I H_epoxy 7 (16)
5 4 DT EH 5
6 7
4 5 4
f epoxy 1:0 1:0 1:0 1:0

f CaCO3 I L_CaCO3 þ f PMMA I L_PMMA þ f epoxy I L_epoxy ¼ DT EL

f CaCO3 I H_CaCO3 þ f PMMA I H_PMMA þ f epoxy I H_epoxy ¼ DT EH

f CaCO3 þ f PMMA þ f epoxy ¼ 1:0

4.2. Virtual monochromatic image processing

After decomposition by matrix inversion, the “inv” function available in MATLAB was used
(Mathworks; Natick, MA, USA); this function constrains the possible fraction to [0,1] while
imposing a sum of one. Accordingly, the processing pipeline yields three material fraction
outputs corresponding to CaCO3, epoxy, and PMMA.
Virtual monochromatic processing is performed according to Eq. 15:
76 Medical Imaging and Image-Guided Interventions

     
∗ μ ∗ μ ∗ μ
Mono_p_img ¼ f CaCO3 ðEÞ þ f PMMA ðEÞ þ f epoxy ðEÞ (17)
r CaCO3 r PMMA r epoxy

where Mono_p_img is the virtual monochromatic projection image, and [μ/r]caco3(E), [μ/
r]PMMA(E), and [μ/r]epoxy(E) are the mass attenuation coefficients of each material. The gener-
ated virtual monochromatic X-ray projection image was reconstructed by using each algo-
rithm for energies of 60, 80, 100, 120, and 140 keV. The real projection data acquired on a DT
system were used for reconstruction. All image reconstruction calculations, including DE
material decomposition processing and reconstruction, as well as FBP, SART, SART-TV, virtual
monochromatic processing, and MLEM, were implemented in MATLAB.

5. DT system

5.1. DT overview

The DT system (SonialVision Safire II; Shimadzu Co., Kyoto, Japan) comprised an X-ray tube
[anode: tungsten with rhenium and molybdenum; real filter: inherent; aluminum (1.1 mm),
additional; aluminum (0.9 mm), and copper (0.1 mm)] with a 0.4 mm focal spot and 362.88 
362.88 mm amorphous selenium digital flat-panel detector (detector element, 150  150 μm2).
The source-to-isocenter and isocenter-to-detector distances were 924 and 1100 mm, respec-
tively (antiscatter grid, focused type; grid ratio, 12:1).

5.2. DE-DT acquisition

Collimator motion was synchronized by measuring the misalignment of the low-voltage

(60 kV) and high-voltage (120 kV) images at a constant tube motion. A large energy gap
between low and high tube potential kVp imaging yields better material decomposition [28–33].
We selected the abovementioned kV values because this study aimed to improve contrast and
artifact reduction during DT acquisition while maintaining an imaging performance similar to
that of conventional DT.

Pulsed X-ray exposures and rapid switching between low and high tube potential kVp values
were used for DE-DT imaging. Linear system movement and a swing angle of 40 were used
when performing tomography, and 37 low- and high-voltage projection images were sampled
during a single tomographic pass. We used a low voltage, and each projection image was
acquired at 416 mA. A 9.4 ms exposure time was used for low-voltage (60 kV) X-rays at
416 mA, and a 2.5 ms exposure time was used for high-voltage (120 kV) X-rays. To generate
reconstructed tomograms of the desired height, we used a 768  7684 matrix with 32 bits
(single-precision floating number) per image (pixel size, 0.252 mm/pixel; reconstruction inter-
val, 1 mm).
 State-Of-The-Art X-Ray Digital Tomosynthesis Imaging 77
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6. Optimization parameter

The experiments were performed according to the scheme shown in Figure 3. A range of
optional parameters have been identified for IR algorithms. Among these parameters, some
are important for determining algorithmic behavior. In this study, we compared the root-mean-
square error (RMSE) and mean structural similarity (MSSIM; reconstructed volume image from
the previous iterations between the current iteration) to optimize the iteration numbers (i).
The RMSE was defined in this study as follows:
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
uP ∧ 2
u n
t i¼1 y k yk
RMSE ¼ (18)
n

∧
where yk is the observed image [current reconstructed image (in-focus plane)], y k is the
referenced image [previous reconstructed image (in-focus plane)], and n is the number of
compounds in the analyzed set.

The MSSIM of local patterns of luminance- and contrast-normalized pixel intensity were
compared to determine the structural similarity (SSIM) index of contrast preservation. This
image quality metric is based on the assumed suitability of the human visual system for
extracting structure-based information [34].

Figure 3. For DT acquisition, the phantom was arranged parallel to the x–y detector plane.
78 Medical Imaging and Image-Guided Interventions

Figure 4. The RMSE characteristics caused by the differences in the number of iterations of each IR algorithm. (60 and
120kVp; polychromatic, 60, 80, 100, 120, and 140 keV; virtual monochromatic).

The SSIM index between pixel values x and y was calculated as follows:

SSIMðx; yÞ ¼ ½lðx; yފα
½cðx; yފβ
½sðx; yފγ (19)

where l is the luminance, c is the contrast, and s is the structure. Subsequently,

α ¼ β ¼ γ ¼ 1:0

The MSSIM was then used to evaluate the overall image quality:

M
1X
MSSIMðX; YÞ ¼ SSIM xi ; yj (20)
M j¼1

where X and Y are the reference [previous reconstructed image (in-focus plane)] and objective
[current reconstructed image (in-focus plane)] images, respectively; xi and yj are the image
contents at the jth pixel; and M is the number of pixels in the image.
 State-Of-The-Art X-Ray Digital Tomosynthesis Imaging 79
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Figure 5. The MSSIM characteristics caused by differences in the number of iterations of each IR algorithm. (60 and
120kVp; polychromatic, 60, 80, 100, 120, and 140 keV; virtual monochromatic).

It was feasible to maintain a steady convergence of polychromatic IR images for inconsistency

after the fourth iteration and a convergence of monochromatic IR images for inconsistency
after the fifth iteration (Figures 4 and 5).

7. Evaluation

The contrast derived from the contrast-to-noise ratio (CNR) in the in-focus plane (15 mm φ;
CaCO3, 175 mg/ml, and 100 mg/ml) was also evaluated as a quantitative measure of the
reconstructed image quality. In DT, the CNR is frequently used to estimate low-contrast
detectability and was defined in this study as follows:
μFeature μBG
CNR ¼ (21)
σBG

where μFeature is the mean object pixel value, μBG is the mean background area pixel value, and
σBG is the standard deviation of the background pixel values. The latter parameter includes the
80 Medical Imaging and Image-Guided Interventions

photon statistics, the electronic noise from the results, and the structural noise that could
obscure the object. The sizes of all regions of interest (ROIs) used to measure the CNR were
adjusted to an internal signal (ROI diameter; eight pixels).

8. Results

Figure 6 shows each density projection image generated by the material decomposition pro-
cess. A novel DE virtual monochromatic image processing is performed from a density projec-
tion image, and Figures 7 and 8 show the image generated by each reconstruction algorithm.

Figure 6. DE material decomposition straight projection images. The DE material decomposition projections for CaCO3,
epoxy, and PMMA were window level = 0.59 and window width = 0.26, window level = 0.69 and window width = 0.33,
and window level = 0.21 and window width = 0.24, respectively.

Figure 7. Comparisons among the polychromatic (P) and virtual monochromatic (VM) images acquired by using the FBP
reconstruction algorithm (window level = 0.05, window width = 0.11).
 State-Of-The-Art X-Ray Digital Tomosynthesis Imaging 81
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Figure 8. Comparisons among the polychromatic (P) and virtual monochromatic (VM) images with each IR algorithm (P
MLEM: Window level = 0.32, window width = 0.17; VM MLEM: Window level = 0.25, window width = 0.12; P&VM SART:
Window level = 0.22, window width = 0.37; P&VM SART-TV: Window level = 0.24, window width = 0.34).

Figure 9. Comparisons of the CNR of the in-focus plane images obtained by using each reconstruction algorithm with
polychromatic and virtual monochromatic image processing (CaCO3; 15 mm ϕ, 175 mg/ml).
82 Medical Imaging and Image-Guided Interventions

Figure 10. Comparisons of the CNR of in-focus plane images obtained by using each reconstruction algorithm with
polychromatic and virtual monochromatic image processing (CaCO3; 15 mm ϕ, 100 mg/ml).

In the novel DE virtual monochromatic image processing performed according to Eq. 17, an
image with decreased noise can be obtained as the photon energy was increased. In the
conventional polychromatic image, noise is reduced in the high-voltage image. With regard
to the contrast extraction capability of high-density objects, the novel DE virtual monochro-
matic images showed high CNRs. In particular, the SART-TV algorithm provided high-
contrast extraction capability (Figure 9). Along with the decrease in the concentration of the
object, the contrast extraction ability was almost the same for both monochromatic and virtual
monochromatic imaging (Figure 10).

9. Conclusions

This chapter showed that the novel DE virtual monochromatic image-processing algorithm
yielded adequate overall performance. The novel DE virtual monochromatic images produced
by using this algorithm yielded good results independent of the type of contrast present in the
CD phantom. Furthermore, this processing algorithm successfully removed noise from the
images, particularly at high contrast with high-density objects.
In summary, this DE virtual monochromatic image-processing algorithm appears to be a prom-
ising new option for DT imaging, as evidenced by the 3D visualizations of high-contrast images
that were far superior to those of images processed by conventional single-energy polychromatic
image-processing algorithms. The flexibility in the choice of imaging parameters, which is based
 State-Of-The-Art X-Ray Digital Tomosynthesis Imaging 83
http://dx.doi.org/10.5772/intechopen.81667

on the desired final images and DT imaging conditions, of this novel DE virtual monochromatic
image-processing algorithm may be beneficial to users.

Acknowledgements

We wish to thank Mr. Kazuaki Suwa and Yuuki Watanabe at Department of Radiology
Dokkyo Medical University Koshigaya Hospital for support on experiment.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by
any of the authors.

Informed consent

This articles does not contain patient data.

Author details

Tsutomu Gomi
Address all correspondence to: gomi@kitasato-u.ac.jp
Kitasato University, Sagamihara, Japan

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