J. Biomedical Science and Engineering, 2011, 4, 443-453 JBiSE
doi:10.4236/jbise.2011.46056 Published Online June 2011 (http://www.SciRP.org/journal/jbise/).
Published Online June 2011 in SciRes. http://www.scirp.org/journal/JBiSE
X-ray digital linear tomosynthesis imaging
Tsutomu Gomi1, Hiroshi Hirano2, Masahiro Nakajima3, Tokuo Umeda1
1School of Allied Health Sciences, Kitasato University, Sagamihara, Japan;
2Department of Radiology, Shinshu University Hospital, Matsumoto, Japan;
3Department of Radiology, Dokkyo Medical University Hospital, Koshigaya, Japan.
Received 30 April 2011; revised 24 May 2011; accepted 1 June 2011.
ABSTRACT
Aims: The purpose of this review includes the fol-
lowing: 1) to identify indications for volumetric
X-ray digital linear tomosynthesis by using a filtered
back projection (FBP) algorithm and 2) to compare
X-ray digital linear tomosynthesis, X-ray digital ra-
diography, conventional tomography, and computed
tomography. Review: The methods include the fol-
lowing: 1) an overview of the tomosynthesis system in
comparison with conventional X-ray imaging tech-
nology; 2) an overview of the properties of diagnostic
imaging for the chest, hip joint, and temporomandibular
joint when imaging overlying structures and their
effect of various artificial images; and 3) a review of
each system. Summary: Tomosynthesis is worthy of
further evaluation because of its flexibility and ability
to suppress streak artifacts through an appropriate
choice of an FBP algorithm. Tomosynthesis may be
considered the imaging technique of choice for inves-
tigation of bone changes and detection of pulmonary
nodules. Understanding the potential of tomosynthe-
sis imaging will improve diagnostic accuracy in clin-
ical applications.
Keywords: Tomosynthesis; Temporomandibular Joint;
Arthroplasty; Pulmonary Nodules; Dual-Energy
Subtraction
1. INTRODUCTION
Interest in tomosynthesis and its clinical applications has
been revived by recent advances in digital X-ray detector
technology. Conventional tomography technology pro-
vides planar information of an object from its projection
images. In tomography, an X-ray tube and X-ray film
receptor are positioned on either side of the object. The
relative motion of the tube and film is predetermined
based on the location of the in-focus plane [1]. A single
image plane is generated by a scan, and multi-slice
computed tomography (CT) scans are required to pro-
vide a sufficient number of planes to cover the selected
structure in the object. Tomosynthesis acquires only one
set of discrete X-ray projections that can be used to re-
construct any plane of the object retrospectively [2].
This technique has been investigated in angiography and
imaging of the chest, hand joints, lungs, teeth, and
breasts [3-8]. In a review of tomosynthesis by Dobbins
et al. [9], tomosynthesis was demonstrated to outperform
planar imaging to a statistically significant extent.
2. TOMOSYNTHESIS RECONSTRUC-
TION METHODS
Existing tomosynthesis algorithms can be divided into
three categories: 1) backprojection algorithms, 2) filtered
backprojection (FBP) algorithms, and 3) iterative algo-
rithms (Figures 1-3). The backprojection algorithm is
referred to as “shift-and-add” (SAA), whereby projec-
tion 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 so that the visibility of features in the selected
plane is enhanced while that in other planes is blurred.
Using a digital detector, image planes at all depths can
be retrospectively reconstructed from one set of projec-
tions. The SAA algorithm is valid only if the motion of
the X-ray focal spot is parallel to the detector.
FBP algorithms are widely used in CT in which many
projections acquired at greater than 360 degrees are used
to reconstruct cross-sectional images. The number of
projections typically ranges between a few hundred to
about one thousand. The Fourier central slice theorem is
fundamental to the FBP theory. In two-dimensional (2D)
CT imaging, a projection of an object corresponds to
sampling the object along the direction perpendicular to
the X-ray beam in the Fourier space [10]. For many pro-
jections, information about the object is well sampled
and the object can be restored by combining the infor-
mation from all projections. In three-dimensional (3D)
cone-beam imaging, the information about the object in

T. Gomi et al. / J. Biomedical Science and Engineering 4 (2011) 443-453 444
＋
＋
＝
X-Ray tube
Object
Detector
Figure 1. The first view shown here highlights the diffi-
culty in visualizing 3D information in X-ray radiography.
In the second view it turns an acquisition direction to
avoid superimposition of an object.
＋
＋
＝
Sweep direction
X-Ray tube
Object
Detector
＋
＋
＝
Figure 2. The three resulting projection images may be
shifted and added (SAA) so as to bring either the circles or
triangles to coincide (i.e., focus), with the complementary
object smeared out.
＋
＋
＝
Sweep direction
projection filtered-back projection
filtering
(Shepp-Logan filter)
X-Ray tube
Object
Detector
Figure 3. The basis for filtered back-projection (FBP) is the
back-projection of data acquired in projections acquired over
all angles. This procedure is performed for each pixel in a
projection, and for all possible angles of the projected data,
then one has created a simple back-projected image of the
object.
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 reconstruction have been
provided [11,12]. The Feldkamp algorithm (conventional
FBP) generally provides a high degree of precision for
3D reconstruction images when an exact type algorithm
is employed [13]. Therefore, this method has been
adopted for image reconstruction of 3D tomography and
multi-detector cone-beam CT. A number of improved 3D
reconstruction methods have been derived from the
Feldkamp method.
An iterative algorithm performs the reconstruction in
a recursive fashion [14,15], unlike the one-step operation
in backprojection and FBP algorithms. During iterative
reconstruction, a 3D object model is repeatedly updated
until the model converges to the solution that optimizes
an objective function. The objective function defines the
criteria of the reconstruction solution. The objective
function in the maximum likelihood (ML) algorithm is
the likelihood function, which is the probability of get-
ting the measured projections in a given object model.
The solution of the ML algorithm is an object model that
maximizes the probability of getting the measured pro-
jections.
3. ACQUISITION PARAMETERS
The tomosynthesis system (SonialVision Safire, Shima-
dzu Co., Kyoto, Japan) consisted of an X-ray tube with a
0.4-mm focal spot and a 432 × 432 mm amorphous sele-
nium digital flat-panel detector. The X-ray collimator
was shifted during the acquisition to allow the detector
to identify the X-ray illumination area. The motion of
the collimator was synchronized with the motion of the
tube. An anti-scatter grid was used. The distance from
the source to the isocenter was 980 mm and that to the
detector was 1100 mm. For detailed estimates of the
acquisition parameters, refer to a Table.
4. CONVENTIONAL FBP VS. MODIFIED
FBP: EVALUATION OF THE COM-
PUTER SIMULATION
A previously described modified FBP algorithm pro-
vides a description of filtering that can be used in com-
bination with backprojection to yield tomosynthesis slice
images with desired properties [16]. The following de-
scription of the generalized 3D impulse function was
adapted from this previous work [17]. The 3D version of
the Fourier slice theorem states that when a 2D image of
an object is acquired at a particular orientation to that
object, the 2D Fourier transformation of that projection
image yields a plane through the 3D Fourier space of the
object. Characteristics are estimated in the modified FBP
by low-pass filtering processing. The Fourier space
low-pass filtering unit applies a low-pass filter along the

T. Gomi et al. / J. Biomedical Science and Engineering 4 (2011) 443-453 445
Table 1. The detailed estimates of the acquisition paramers.
sectional axis of the Fourier space data that have under
gone a 3D Fourier transformation. This process is effec-
tive in reducing artifacts in the 3D volume data gener-
ated by 3D back Fourier transformation of the Fourier
space data after low-pass filtering (see appendix A).
We performed a computer simulation using a 3D Shepp-
Logan phantom, which is defined by 12 solid ellipsoids.
This phantom is a simplified model of the human head.
The effective X-ray absorption coefficient at any given
point is the sum of the relative parameters of the ellip-
soids containing that point. The plane locations included
the centre and off-centre planes from the location of the
object. In the numerical simulation, the 3D Shepp-Logan
phantom was used for evaluation by comparing the
modified FBP algorithm with the conventional FBP al-
gorithm. The traditional conventional FBP algorithm
suffered from a significant decrease in intensity from the
mid-plane (Figure 4; see “off-centre”), which is a well-
known drawback of the conventional FBP algorithm.
However, our modified FBP algorithm improved the
image quality remarkably and yielded better image qual-
ity of both the centre and off-centre planes compared
with the conventional FBP algorithm.
5. ACQUISITION PARAMETERS
5.1. CT vs. Tomosynthesis Using Modified FBP
Imaging by X-ray CT has improved over the last three
decades and is now a powerful tool in medical diagnos-
tics. CT imaging has become an essential noninvasive
imaging technique since the advent of spiral CT imaging
in the 1990 s, which led to shorter scan times and im-
proved 3D spatial resolution. CT provides high resolu-
tion in the tomographic plane but limited resolution in
the axial direction. However, the quality of images gen-
erated by a CT scanner can still be reduced due to the
presence of metal objects in the field of view. Imaging of
patients with metal implants, such as marker pins, dental
fillings, or hip prostheses, is susceptible to artifacts gen-
erally in the form of bright and dark streaks, cupping and
capping, etc. This artifact susceptibility is mostly due to
quantum noise, scattered radiation, and beam hardening
[18]. Metal artifacts influence image quality by reducing
contrast and by obscuring details, thus impairing the
ability to detect structures of interest and possibly lead-
ing to misdiagnosis. In addition, CT values are impaired,
which can lead to errors when using these data e.g., for
off-center
Ideal
conventional FBP
modified FBP
Pixel numbers
Pixel Numbers
Pixel Value (Rel. Val.)
Pixel Value (Rel. Val.)
Ideal
conventional FBP
modified FBP
Sweep
direction Sweep direction
Sweep
direction Sweep direction
ideal phantom conventional FBP modified FBP
ideal phantom conventional FBP modified FBP
center
Figure 4. Reconstruction simulation images of the three-
dimensional (3D) Shepp-Logan phantom at different heights. In
addition, comparison of axial profile plots through the recon-
structed 3D image function in comparison with discretization of
the 3D Shepp-Logan phantom (centre and off-centre planes).
Digital Linear Tomosynthesis System
(hip joint, temporomandibular joint)
Device SonialVision Safire (Shima-
dzu Co., Kyoto, Japan)
Source-image-distance (SID)
Source-object-distance (SOD) 110 cm 98 cm
Detector 1024 × 1024 pixels, direct
flat-panel detector
Exposure setting 80 kVp, 160 mA,
200 ms/view
Projection and acquisition degree
67 projection, 40 degree
acquisition
(chest)
Device SonialVision Safire Ⅱ (Shi-
madzu Co., Kyoto, Japan)
Source-image-distance (SID)
Source-object-distance (SOD) 110 cm 98 cm
Detector 1280 × 1280 pixels, direct
flat-panel detector
Exposure (single-energy
acquisition) 120 kVp, 200 mA, 25 ms/view
Exposure (dual-energy
acquisition) 60 kVp and 120 kVp, 200 mA, 25 ms/view
Projection and acquisition
degree 74 projection, 40 degree acquisition
CT System
Device LightSpeed Scanner (GE Medical Systems, Milwaukee,
WI, USA)
Data acquisition system (DAS) 0.625 mm × 16 colimation
Exposure setting 120 kVp, 200 mAs/rotation
Reconstruction MPR images (2 mm slice thickness, 2 mm interval, 580
µm voxel size)
X-Ray Radiographic System
Device DigitalDiagnost (Philips Medical Systems, Hamburg,
Germany)
Detector Indirect flat-panel-detector
(CsI(Tl))
Exposure setting 70 kVp, 200 mA, 100 ms
Panoramic radiograph & Conventional tomography
(for temporomandibular joint)
Device Verview Epocs (Morita Co.
Tokyo, Japan)
Exposure setting 72 kVp, 3 mAs (panoramic
radiograph), 74 kVp, 5 mAs
(conventional tomography)

T. Gomi et al. / J. Biomedical Science and Engineering 4 (2011) 443-453 446
attenuation correction in PET/CT imaging [19]. The me-
tallic components of arthroplasty devices are high-
contrast objects that generate artifacts when imaged us-
ing CT scans. These artifacts can make it extremely dif-
ficult or impossible to interpret images obtained by de-
vices. The presence of artifacts along with PVE severely
limits the potential for objective quantification of total
joint replacement with CT.
Methods for reduction of metal artifacts aim to im-
prove the quality of images affected by these artifacts. In
recent years, modified iterative [20-23] or wavelet re-
construction techniques [24] have produced promising
results. However, these methods cannot be combined
with the fast and robust FBP algorithm, which is the
standard reconstruction technique implemented in mod-
ern CT scanners.
Digital linear tomosynthesis using the modified FBP
algorithm shows adequate overall performance, but its
effectiveness depends strongly on the region of the im-
age. Digital linear tomosynthesis using modified FBP
algorithm images gives good results independent of the
type of metal present in the patient and shows good re-
sults for the removal of noise artifacts, especially at
greater distances from metal objects. Application of dig-
ital linear tomosynthesis to the imaging of hip prostheses
appears promising. In addition, flexibility in the choice
of digital linear tomosynthesis imaging parameters based
on the desired final images and generation of high qual-
ity images may be beneficial (Figure 5).
5.2. Metal Artifact Reduction for Prosthesis
Imaging
Metal artifacts influence image quality by reducing con-
trast and obscuring detail, thus impairing the ability to
detect structures of interest and making diagnosis im-
possible. The objective of this report is to evaluate the
clinical application of digital linear tomosynthesis in
imaging a phantom and hip prosthesis using a relatively
new tomosynthesis instrument and applying a selection
of reconstruction algorithms. Tomosynthesis images
were compared with the results from artifact reduction
processing and a conventional FBP algorithm.
Artifacts caused by high-attenuation features in hip
prostheses were observed in digital linear tomosynthesis
reconstruction as a result of the small number of projec-
tions and narrow angular range typically employed in
tomosynthesis imaging. Gomi et al. [25] developed arti-
fact reduction methods based on a modified Shepp- Lo-
gan reconstruction filter kernel by taking into account
the additional weight of direct current components in the
frequency domain space. Processing leads to an increase
in the ratio of low frequency components in an image
(Figure 6, see appendix B). Artifact reduction process-
CT
( MPR images )
( bone information )
MAR-CT
( MPR images )
( bone information )
CT
( MPR images )
( soft tissue information )
MAR-CT
( MPR images )
( soft tissue information )
Modified FBP tomosynthesis images
( soft tissue information ) ( bone information )X-Ray Radiography
Figure 5. Patient (52-year-old woman; total hip arthroplasty;
THA). Anteroposterior (AP) radiographs of the hip and knee
joint prostheses are demonstrated. AP radiograph of these
joints demonstrate the excellent visualization of the prosthe-
ses in this view. AP radiograph is difficult to visualize 3D
information in an AP radiograph as shown. Coronal slice im-
ages of the hip prosthesis at center heights on Metal Artifact
Reduction (MAR) CT (MAR-CT) and non MAR-CT scans at
approximately the same level. Remarkable metal artifacts can
be seen occurring in the neighborhood of the hip prosthesis.
However, MAR-CT processing reduced the metal artifacts.
Tomosynthesis images of the prostheses at center heights at
the same level. The new diagnostic information that could
not be acquired from CT images is provided. Reduction in
metal artifacts was obtained in the images as shown here. The
use of tomosynthesis allowed better visualization of the
prosthesis caused by the blurring of anatomic structures
above and below the visualized planes.
* W* (1 –W)
Best artifact reduction image
Shift-Add imageModified FBP image
W=0.04 W=0.06 W=0.08
large effect of metal artifact small
small effect of blurring large
Image acquistion(projection generation)
＋
Image reconstruction using FBPand Shift-and-add technique
Selection of best artifact reduction image
Figure 6. Concept of the artifact reduction tomosynthesis
method. Artifact reduction tomosynthesis realized by tak-
ing into account additional weighting coefficients (W).

T. Gomi et al. / J. Biomedical Science and Engineering 4 (2011) 443-453 447
ing was performed with a basic and modified FBP algo-
rithm. Artifact reduction processing provides a method
of filtering that can be used in combination with the
backprojection algorithm to yield sliced images with
desired properties by means of tomosynthesis (Figure
7).
The quality of CT images is governed by the strength
of artifacts, which depends on numerous factors such as
size, shape, density, atomic number and position of met-
al objects, patient size, and patient’s cross-section shape.
For small implants manufactured from relatively light
metals (e.g., titanium), the effects of beam-hardening
and scattering are low. Therefore, the corrupted CT val-
ues as well as noise-induced streaking artifacts that pose
a major problem to image quality can be neglected. In
such cases, the digital linear tomosynthesis approach to
artifact reduction processing appears to be promising for
the reduction of artifacts stemming from metals with a
relatively high atomic number.
5.3. Temporomandibular Joint Imaging
Since the discovery of X-rays in 1895 and their applica-
tion to dentistry, radiographic imaging of oral anatomy
has consisted primarily of viewing 3D structures pro-
jected onto a 2D plane. This form of imaging, known as
radiography, is characterized by a point source of radia-
tion producing a beam that passes through the patient
and strikes a relatively flat image receptor, which is
usually a film. This essentially produces an attenuation
map of the structures through which the beam has been
transmitted. While the dental profession has relied on
this method for obtaining information about the hard
tissues of the oral cavity, it inevitably superimposes
anatomy and metallic restorations, which confound the
problems of identifying and/or localizing diseases or
objects in 3D.
The temporomandibular joint is a difficult area to in-
vestigate radiographically. A number of imaging tech-
niques have been developed. However, there is no tech-
nique that provides accurate imaging of all components
of the complex anatomy of the joint. Modern imaging
Shift-add tomosynthesis Modified FBP tomosynthesis ML tomosynthesis Artifact reduction
tomosynthesis
(W = 0.06)
Figure 7. Comparison of images obtained from artifact reduc-
tion tomosynthesis (W = 0.06), conventional FBP tomosynthe-
sis, shift-add tomosynthesis, and maximum likelihood tomo-
synthesis (ML, four subsets & 15 iterations) of the center plane.
Artifact reduction tomosynthesis provided better visualization
of the hip prosthesis by eliminating blurring and reducing arti-
facts above and below visualized planes.
modalities, such as magnetic resonance imaging and CT,
including cone-beam CT, are now being used more fre-
quently for radiographic examination by panoramic ra-
diography, conventional tomography, etc., of the tem-
poromandibular joint.
The digital linear tomosynthesis system used here
showed adequate overall performance, but it is obvious
that its effectiveness is strongly dependent on scan pa-
rameters such as tomographic angle, number of views,
and section thickness [26,27]. Digital linear tomosynthe-
sis images give good results independent of the type of
temporomandibular joint in the patient [28] (Figure 8).
The potential for application of digital linear tomosyn-
thesis to imaging of the temporomandibular joint ap-
pears promising. In addition, flexibility in the choice of
digital linear tomosynthesis imaging parameters based
on the desired final images and realistic imaging condi-
tions may be beneficial.
The digital linear tomosynthesis images in this study
were acquired using linear motion of the X-ray tube and
detector. The type of motion used during data acquisition
dictates the type of blurring of off-focal-plane objects in
the image. Linear motion blurs objects in one dimension
only, which leads to linear streak artifacts caused by
high-contrast off-focal-plane objects.
6. CHEST IMAGING
6.1. Single-Energy Acquisition
Lung cancer is currently the leading cause of cancer
death and continues to be an increasing cause of death
worldwide. Due to its high sensitivity, normal-dose hel-
ical CT is currently considered the gold standard for
Panoramic radiography Conventional tomography
Tomosynthesis
(R)
R
(R)
Figure 8. Patient (56-year-old woman; inflammatory disorder
of the right temporomandibular joint). Panoramic radiograph
(closed and open mouth) showing the mandibular condyle.
Conventional tomography showing the right and left mandibu-
lar condyle. X-ray digital linear tomosynthesis images (differ-
ent level positions) showing the mandibular condyle.

T. Gomi et al. / J. Biomedical Science and Engineering 4 (2011) 443-453 448
lung cancer detection. Early reports indicate that low
dose helical CT has the potential to detect early lung
cancer, and thus decrease morbidity [29]. CT solves the
problem of reduced detection caused by overlapping
anatomy. However, there are disadvantages to using CT
compared with chest radiography, such as high radiation
dose and cost. Advantages of chest radiography include
short examination time, low cost, and easy access.
However, there are important disadvantages, such as low
sensitivity and specificity. In chest radiography, the 3D
chest is projected onto a 2D image. Consequently, the
ability to detect pathologic findings is limited by the
overlapping anatomy rather than by quantum noise.
These methods offer much lower sensitivity and speci-
ficity than chest radiography as it is practiced currently.
Chest radiography has been shown to have relatively low
sensitivity for detection of pulmonary nodules. The poor
sensitivity for chest radiography precludes its use as a
screening modality, despite its advantages of low cost,
low dose, and wide distribution of devices. With respect
to images of nodules having similar size, the contrast
was greater when tomosynthesis imaging was used for
processing, as compared with that of radiography [30-
32].
Digital linear tomosynthesis is a method that provides
some of the tomographic benefits of CT but at a reduced
dose and cost and with an approach that is easily imple-
mented in conjunction with chest radiography. Tomo-
synthesis originates from the older technique of geomet-
ric tomography, which has largely fallen out of favor in
chest imaging owing to positioning difficulty, high ra-
diation dose, and residual blur from out-of-plane struc-
tures. Tomosynthesis overcomes the difficulties of geo-
metric tomography by permitting reconstruction of nu-
merous slices of the image from a single low dose ac-
quisition of image data. Although improving detection of
pulmonary masses may be an early area of emphasis for
the application of tomosynthesis, it also has potential for
use in the thorax.
In addition, there are some limitations with chest to-
mosynthesis. For example, patients undergoing tomo-
synthesis have to be able to stand still and hold their
breath firmly. Also, chest tomosynthesis has a limited
depth resolution, which may explain why pathology in
the subpleural region is more difficult to interpret and
artefacts from medical devices may occur [33].
6.2. Dual-Energy Acquisition
In the differentiation of benign and malignant pulmonary
masses, two radiographic findings give indications of a
benign lesion: the presence of calcifications in the mass
and stability of the mass [34-38]. A benign pattern of the
calcifications has been considered necessary to exclude
malignancy [35,39-41]. In the evaluation of diffusely
disseminated pulmonary nodules, identification of dif-
fusely disseminated pulmonary nodules and calcifica-
tions in the nodules has been helpful in limiting the dif-
ferential diagnosis [42]. Conventional radiography and
conventional tomography have been used to detect calci-
fications, but they have been largely replaced by CT
[36-37].
Dual energy subtraction (DES) imaging has been
proposed and investigated by many researchers as a
means of reducing the impact of anatomic “noise” on
disease detection by chest radiography. DES involves
making two radiographic projections of the patient using
different energy X-ray beams. By exploiting the differ-
ence in the energy dependence of attenuation between
bone and soft tissue, the bone contrast can be reduced to
produce a soft tissue image and the contrast of the soft
tissue can be reduced to produce a bone image [43]. Re-
cent computed radiography (CR) systems have been
hampered by poor subtraction effectiveness, workflow
inconveniences, and limitations in detective quantum
efficiency of the CR technology. DES digital radiogra-
phy has been found useful in detecting calcifications
[35,38,44-47]. Projection images acquired using DES
techniques, however, are susceptible to overlap of ana-
tomic features (e.g., calcifications superimposed over the
ribs or spine).
DES digital linear tomosynthesis [48] is a new tech-
nique (Figure 9), and therefore there is no guidance for
its integration into the clinical practice of chest radiog-
raphy. The most reliable signs for discriminating be-
tween benign and malignant masses are the growth rate
of the mass and presence or absence of calcifications
within the mass. Since calcifications are commonly ob-
served in benign masses and no other radiographic char-
acteristic is specific in characterizing a mass, it is im-
portant to detect and characterize calcification within
lesions. Using DES digital linear tomosynthesis, the
presence, distribution, and characteristics of calcifica-
tions in lung nodules can be assessed to an extent that is
not possible with currently available CT imaging and
projection-type DES techniques. In addition, this tech-
nique is not susceptible to the problems of image overlap,
PVE, or shifting of the image plane (Figure 10).
7. POTENTIAL ARTIFACTS
7.1. Blurring
Ideally, structures in a given plane of interest should be
clearly displayed in the corresponding tomosynthesis
reconstruction plane, whereas structures located outside
of that plane should not be visible. Essentially, the lim-
ited angular range of the tomosynthesis image acquisi-
tion geometry dictates that the spatial resolution is lim-

T. Gomi et al. / J. Biomedical Science and Engineering 4 (2011) 443-453 449
High kVpLow kVp
Projection data (Dual-Energy acquisition)
Bone reconstruction image Soft tissue reconstruction image
Bone projection image Soft tissue projection image
DES image decomposition
Volume reconstruction
(
)
( )
caleHL
caleLH
SXCXimageboneDES
SXXCimagetissuesoftDES
×⋅−=
×−⋅=
C: coefficient
(compensation of different absorption)
X
H
: high-voltage image
X
L
: Low-voltage image
S
cale
: scale factor
DES processing
projection image projection image
Figure 9. Illustration of the imaging sequence and processing
of dual-energy acquisition.
X-ray radiography image Tomosynthesis image
(Shift-and-add) Tomosynthesis image (FBP) Tomosynthesis image
(DES)
Figure 10. Patient (74-year-old man; calcified tuberculous
foci). Comparison of chest images obtained using X-ray radi-
ography, shift-add tomosynthesis, conventional digital tomo-
synthesis, and dual-energy subtraction digital tomosynthesis.
For detection of calcifications in pulmonary nodules, dual-
energy subtraction tomosynthesis proved better than any other
modality imaging methods.
ited in the dimension perpendicular to the detector plane.
As a result, out-of-plane structures cannot be completely
removed from the reconstruction plane. Out-of-plane
structures are present in every reconstruction plane, but
most are not visible because the various low-amplitude
structures from projections overlap each other in the
reconstruction plane, and therefore are blurred. Out-of-
plane structures from high-attenuation features cannot be
blurred. They appear as multiple replicates of the par-
ticular feature in every reconstruction plane except for
the one in which the actual high-attenuation feature is
located. At one projection angle, these ghosting features
are distributed along the line made by the X-ray source
and actual feature (Figure 11).
7.2. Ripple
Quantum noise plays an important role in the degrada-
tion of contrast resolution of radiographs. It increases
inversely with the X-ray exposure and constitutes the
dominant noise source at low radiation exposure levels.
Because of quantum noise, the technical factors used to
reduce radiation dose in our system are limited to those
levels usually employed in conventional tomography.
However, synthesized tomograms can be obtained with
the same technical factors used for radiography when the
presence of quantum noise can be tolerated. Any calcifi-
cations are visible in the presence of the overwhelmingly
rippled artifact on DES digital linear tomosynthesis im-
ages (Figure 12). This artifact is a consequence of the
inherent misalignment between the low and high kVp
images because the X-ray tube moves continuously.
These features may be amenable to filtration, which may
eliminate desired clinical features.
8. FUTURE DIRECTIONS
The digital linear tomosynthesis images in this review
were acquired using linear motion of the X-ray tube and
detector. The type of motion used during data acquisition
dictates the type of blurring of off-focal-plane objects in
the image. Linear motion blurs objects in one dimension
Phantom (titanium alloy)
Clinical case (THA)
Sweep
direction
Sweep
direction
in-focus plane out-focus plane
Figure 11. Blurring occurs along the sweep direction and re-
sults from imaging studies show that a high contrast structure
exists out of the slice plane that is continuously perpendicular
to the sweep direction.
A
B
C
D
A B C D
Height
Slice A
Slice B
Slice C
Slice D
Chest phantom
Figure 12. Dual-energy subtraction digital tomosynthesis im-
ages of a chest phantom obtained different heights above the
dorsal ribs. Blurring changes into ripple as the perpendicular
distance from the dorsal ribs to the plane in focus increases
above a certain threshold.

T. Gomi et al. / J. Biomedical Science and Engineering 4 (2011) 443-453 450
only, which leads to linear streak artifacts caused by
high-contrast off-focal-plane objects. On the other hand,
3D reconstruction schemes, such as tomosynthesis and CT,
require complete knowledge of the X-ray source projec-
tion geometry prior to exposure. This limitation precludes
much of the potential task-dependent flexibility. This li-
mitation also precludes accurate reconstruction from pro-
jections acquired from a patient who moves unpredictably
between exposures, as this is geometrically equivalent to
not knowing the projection geometry. An alternative ap-
proach to tomosynthesis imaging is to determine the
number of views that can be acquired given imaging con-
straints (e.g., time restrictions from patient motion, dose
restrictions of the detector). The tomographic angle can be
selected to yield images with an acceptable level of arti-
facts. The tomographic angle then determines the achiev-
able section thickness. This is certainly the case with our
modified FBP algorithm (indicated by results presented
here) and is suspected to be the case with simple backpro-
jection (or the SAA algorithm). It is possible, however,
that this restriction could be reduced by the use of an al-
ternative reconstruction scheme.
Artifact reduction processing showed an adequate
overall performance, but its effectiveness strongly de-
pended on the image region. Digital linear tomosynthesis
images gave good results independent of the type of
metal present in the patient and showed good results for
the removal of noise artifacts, particularly at greater dis-
tances from metal objects. The potential for application
of digital linear tomosynthesis to the imaging of pros-
theses appears promising. Flexibility in the choice of
imaging parameters in artifact reduction processing
based on the desired final images and realistic imaging
conditions may be beneficial.
Initial data from our study suggest that DES digital lin-
ear tomosynthesis will substantially enhance sensitivity
and specificity of pulmonary nodule detection. Despite its
potential, DES digital linear tomosynthesis is a new tech-
nique. Therefore, there is no guidance for its integration
into the clinical practice of chest radiography. The most
reliable signs for discriminating between benign and ma-
lignant masses are the growth rate of the mass and pres-
ence or absence of calcifications within the mass. Since
calcifications are commonly observed in benign masses
and no other radiographic characteristics is specific in
characterizing masses, it is important to detect and charac-
terize calcification within lesions. In addition, this tech-
nique is not susceptible to the problems of image overlap,
PVE, or shifting of the image plane.
9. ACKNOWLEDGEMENTS
We wish to thank for Shimadzu Corporation for her helpful research
assistance in this work.
REFERENCES
[1] Ziedses des Plante, B.G. (1932) Eine neue methode zur
differenzierung in der roentgenographie (planigraphie).
Acta Radiologica, 13, 182-192.
doi:10.3109/00016923209135135
[2] Grant, D.G. (1972) Tomosynthesis: a three-dimensional
radiographic imaging technique. IEEE Transactions on
Bio-Medical Engineering, 19, 20-28.
doi:10.1109/TBME.1972.324154
[3] Stiel, G., Stiel, L.G., Klotz, E., et al. (1993) Digital
flashing tomosynthesis: a promising technique for an-
giographic screening. IEEE Transactions on Medical
Imaging, 12, 314-321.
doi:10.1109/42.232261
[4] Warp, R.J., Godfrey, D.G. and Dobbins, J.T. (2000)
Applications of matrix inverse tomosynthesis. Procee-
dings of SPIE, 3977, 376-383.
[5] Duryea, J., Dobbins, J.T. and Lynch, J.A. (2003) Digital
tomosynthesis of hand joints for arthritis assessment.
Medical Physics, 30, 325-333.
doi:10.1118/1.1543573
[6] Sone, S., Kasuga, T., Sakai, F., et al. (1995) Image
processing in the digital tomosynthesis for pulmonary
imaging. European Radiology, 5, 96-101.
doi:10.1007/BF00178089
[7] Badea, C., Kolitsi, Z. and Pallikarakis, N. (2001) A 3D
imaging system for dental imaging based on digital to-
mosynthesis and cone beam CT. Proceedings Interna-
tional Federation for Medical and Biological Engi-
neering, 2, 739-741.
[8] Niklason, L.T., Christian, B.T., Niklason, L.E., et al.
(1997) Digital tomosynthesis in breast imaging. Radi-
ology , 205, 399-406.
[9] Dobbins, J.T. III and Godfrey, D.J. (2003) Digital x-ray
tomosynthesis: curent state of the art and clinical poten-
tial. Physics in Medicine and Biology , 48, R65-106.
doi:10.1088/0031-9155/48/19/R01
[10] Kak, A. and Slaney, M. (1988) Principles of computer-
ized tomographic imaging. IEEE.
[11] Smith, D.B. (1985) Image reconstruction from cone-
beam projections: necessary and sufficient conditions
and reconstruction methods. IEEE Transactions on
Medical Imaging, 4, 14-25.
doi:10.1109/TMI.1985.4307689
[12] Grangeat, P. (1991) Mathematical framework of cone-
beam 3D reconstruction via the first derivative of the
Radon transform. Math Methods Tomogr, 1497, 66-97.
doi:10.1007/BFb0084509
[13] Feldkamp, L.A., Davis, L.C. and Kress, J.W. (1984)
Practical cone-beam algorithm. Journal of the Optical
Society of America, 1, 612-619.
doi:10.1364/JOSAA.1.000612
[14] Ruttimann, U., Groenhuis, R. and Webber, R. (1984)
Restoration of digital multilane tomosynthesis by a con-
strained iteration method. IEEE Transactions on Medi-
cal Imaging, MI-3, 141-148.
doi:10.1109/TMI.1984.4307670

T. Gomi et al. / J. Biomedical Science and Engineering 4 (2011) 443-453 451
[15] Bleuet, P., Guillemaud, R., Magin, I., et al. (2002) An
adapted fan volume sampling scheme for 3D algebraic
reconstruction in linear tomosynthesis. IEEE Transac-
tions on Nuclear Science, 3, 2366-2372.
doi:10.1109/TNS.2002.803683
[16] Gomi, T. and Hirano, H. (2008) Clinical potential of
digital linear tomosynthesis imaging of total joint artro-
plasty. Journal of Digital Imaging , 21, 312-322.
doi:10.1007/s10278-007-9040-9
[17] Lauritsch, G. and Harer, W.H. (1998) A theoretical
framework for filtered backprojection in tomosynthesis.
Proceedings of SPIE, 3338, 1127-1137.
doi:10.1117/12.310839
[18] Hsieh, J. (1995) Image artifacts; causes; and correction.
In: Goleman, L.W. and Fowlkes, J.B., Eds., Medical CT
and ultrasound; current technology and applications,
Advanced Medical Publishing, Madison, 487-518.
[19] Kamel, E.M., Burger, C., Buck, A., et al. (2003) Impact
of metallic dental implants on CT-based attenuation
correction in a combined PET/CT scanner. European
Radiology, 13, 724-728.
doi:10.1007/s00330-002-1564-2
[20] Wang, G., Snyder, D.L., O’Sullivan, J.A., et al. (1996)
Iterative debluring for metal artifacts reduction. IEEE
Transactions on Medical Imaging, 15, 657-664.
doi:10.1109/42.538943
[21] Wang, G., Vannier, M.W. and Cheng, P.C. (1999)
X-ray coneeam tomography for metal artifacts reduction
and local region reconstruction. Microscopy and Mi-
croanalysis, 5, 58-65.
doi:10.1017/S1431927699000057
[22] Wang, G., Frei, T. and Vannier, M.W. (2000) A fast
iterative algorithm for metal artifact reduction in x-ray
CT. Academic Radiology, 7, 607-614.
doi:10.1016/S1076-6332(00)80576-0
[23] Man, B. De, Nuyts, J., Dupont, P., et al. (2000)
Reduction of metal streak artifacts in x-ray computed
tomography using a transmission maximum a posteriori
algorithm. IEEE Transactions on Nuclear Science, 47,
997-981. doi:10.1109/23.856534
[24] Robertson, D.D., Yuan, J., Wang, G., et al. (1997) Total
hip prosthesis metal-artifact suppression using iterative
deblurring reconstruction. Journal of Computer Assisted
Tomography, 21, 293-298.
doi:10.1097/00004728-199703000-00024
[25] Gomi, T., Hirano, H. and Umeda, T. (2009) Evaluation
of the X-ray digital linear tomosynthesis reconstruction
processing method for metal artifact reduction. Com-
puterized Medical Imaging and Graphics, 33, 257-274.
doi:10.1016/j.compmedimag.2009.01.004
[26] Stevens, G.M., Fahrig, R. and Pelc, N.J. (2001) Filtered
backprojection for modifying the impulse response of
circular tomosynthesis. Medical Physics, 28, 372-379.
doi:10.1118/1.1350588
[27] Stevens, G.M., Birdwell, R.L., Beaulieu, C.F., et al.
(2003) Circular tomosynthesis: potential in imaging of
breast and upper cervical spine-preliminary phantom
and in vitro study. Radiology , 228, 569-575.
doi:10.1148/radiol.2282020295
[28] Gomi, T., Yokoi, N. and Hirano, H. (2007) Evaluation of
digital linear tomosynthesis imaging of the temopro-
mandibular joint: initial clinical experience and
evaluation. Dentomaxillofac Radiology , 36, 514-521.
doi:10.1259/dmfr/26026102
[29] Yankelevitz, D.F., Reeves, A.P., Kostis, W.J., Zhao, B.
and Henschke, C.I. (2000) Small pulmonary nodules:
volumetrically determined growth rates based on CT
evaluation. Radiology, 217, 251-256.
[30] Vikgren, J., Zachrisson, S., Svalkvist, A., et al. (2008)
Comparison of chest tomosynthesis and chest radiogra-
phy for detection of pulmonary nodules: human ob-
server study of clinical cases. Radiology, 249, 1034-
1041. doi:10.1148/radiol.2492080304
[31] Zachrisson, S., Vikgren, J., Svalkvist, A., et al. (2009)
Effect of clinical experience of chest tomosynthesis on
detection of pulmonary nodules. Acta Radiologica, 50,
884-891.
doi:10.1080/02841850903085584
[32] Dobbins, J.T. III, Mcadams, H.P., Song, J.W., et al.
(2008) Digital tomosynthesis of the chest for lung nod-
ule detection: interim sensitivity results from an ongo-
ing NIH-sponsored trial. Medical Physics , 35, 2554-
2557. doi:10.1118/1.2937277
[33] Johnsson, A.A., Vikgren, J., Salkvist, A., et al. (2010)
Overview of two years of clinical experience of chest
tomosynthesis at sahlgrenska university hospital. Ra-
diation Protection Dosimetry, 139, 124-129.
doi:10.1093/rpd/ncq059
[34] Godwin, J.D. (1983) The solitary pulmonary nodule.
Radiologic Clinics of North America, 21, 709-721.
[35] Littleton, J.T. (1983) Pluridirectional tomography in
diagnosis and management of early bronchogenic car-
cinoma. In: Little, J.T. and Durizch, M.L., Sectional
imaging methods. A comparison, University Park Press,
Baltimore, 155.
[36] Siegelman, S.S., Khouri, N.F., Leo, F.P., et al. (1986)
Solitary pulmonary nodules. CT assessment. Radiology,
160, 307-312.
[37] Siegelman, S.S., Zerhouni, E,A., Loe, F.P., et al. (1980)
CT of the solitary pulmonary nodule. American Journal
of Roentgenology , 135, 1-13.
[38] Zerhouni, E.A., Caskey, C. and Khouri, N.F. (1988)
The pulmonary nodules. Seminars in Ultrasound, CT,
and MR, 9, 67-78.
[39] Fraser, R.G., Hickey, N.M., Niklason, L.T., et al.
(1986) Calcification in pulmonary nodules. Detection
with dual-energy digital radiography. Radiology, 160,
595-601.
[40] McLendon, R.E., Roggli, V.L., Foster, W.L. Jr., et al.
(1985) Carcinoma of the lung with osseous stromal
metaplasia. Archives of Pathology & Laboratory Medi-
cine, 109 , 1051-1053.
[41] O’Keefe, M.E. Jr., Good, C.A. and McNonald, J.R.
(1957) Calcification in solitary nodules of the lung.
American Journal of Roentgenology, 77, 1023-1033.
[42] Burgener, F.A. and Kormano, M. (1991) Differential di-
agnosis in conventional radiology. Thieme Verlag, Berlin.
[43] Brody, W.R., Butt, G., Hall. A. and Macovski, A.
(1981) A method for selective tissue and bone visuali-
zation using dual-energy scanned projection radiogra-
phy. Medical Physics, 8, 659-667.
doi:10.1118/1.594957
[44] Hickey, N.M., Niklason, L.T., Sabbagh, E., et al. (1987)

T. Gomi et al. / J. Biomedical Science and Engineering 4 (2011) 443-453 452
Dual-energy digital radiographic quantification of cal-
cium in simulated pulmonary nodules. American Jour-
nal of Roentgenology, 148, 19-24.
[45] Ishigaki, T., Sakuma, S., Horikawa, Y., et al. (1986)
One-shot dual-energy subtraction imaging. Radiology ,
161, 271-273.
[46] Ishigaki, T., Sakuma, S. and Ikeda, M. (1988) One-shot
dual-energy subtraction chest imaging with computed
radiography. Radiology, 168, 67-72.
[47] Nishitani, H., Umezu, Y., Ogawa, K., et al. (1986) Dual-
energy projection radiography using condenser X-ray
generator and digital radiography apparatus. Radiology,
161, 533-535.
[48] Gomi, T., Nakajima, M., Fujiwara, H., et al. (2011)
Comparison of chest dual-energy subtraction digital
tomosynthesis imaging and dual-energy subtraction ra-
diography to detect simulated pulmonary nodules with
and without calcifications: a phantom study. Academic
Radiology, 18, 191-196.
doi:10.1016/j.acra.2010.09.021
Appendix A
Modified FBP Algorithm
The 3D Fourier transform of the 3D volume data gener-
ated by the backprojection is based on the following
Eq.1:
(
)
{ }
,,
(,,)exp()
xyz
xyz
F
fxyzjxyzdxdydz
ωωω
ωωω
=⋅−⋅+⋅+⋅⋅⋅
∫∫∫ (1)
where
(
)
,,
fxyz
is the simple backprojection interme-
diate image, and x, y, and z are real numbers. The mean-
ing of the filtering process performed in 3D Fourier
space is described below, and it is mathematically ex-
pressed by the following Eq.2:
(
)
(
)
(
)
,,,,,,
xyzxyzxyz
FMFM
ωωωωωωωωω
=⋅ (2)
where
(
)
,,
xyz
FM
ωωω
is the filtered 3D Fourier distri-
bution image, and
(
)
,,
xyz
M
ωωω
is a function repre-
senting filter characteristics. The filtering process carried
out in 3D Fourier space is to weight the 3D Fourier dis-
tribution image of complex data with the real-valued
filter function M dependent on the respective frequency
values. The weighting function M is compressed in the
z
ω
direction.
(
)
,,
xyz
M
ωωω
is expressed by the fol-
lowing Eq.3 as a product of three functions representing
the filter characteristic:
(
)
(
)
(
)
(
)
,,
xyzprofzspecrinverse
MHHHR
ωωωωωω=⋅⋅
(3)

T. Gomi et al. / J. Biomedical Science and Engineering 4 (2011) 443-453 453
(
)
profz
H
ω
has a low-pass filter characteristic, i.e., a
Gaussian characteristic, which is expressed by the fol-
lowing Eq.4:
( )
2
exp0.693 z
profz
HCFD
ω
ω


=−





(4)
where CFD is the frequency with the Gaussian attenua-
tion halved.
(
)
specr
H
ω
has a filter characteristic which is ex-
pressed by the following Eq.5:
( )
( )( )
( )
12
1sin/
222
02
specrr
r
specrr
specrr
CFRWFR
Hcase
CFRWFR
CFRWFRCFRWFR
Hcase
CFRWFR
Hcase
ωω
ω
ωω
ωω

−

=<





−−⋅π −+

=<<





+


=<



(5)
However,
222
rxyz
ωωωω
=++. The function has a
sine wave form with high-frequency components
smoothly attenuated. CFR is the cut-off frequency, and
WFR is the total transition frequency width of the filter
strength.
2
CFRWFR
+ is the Nyquist frequency and
2
CFRWFR
− is the no-processing region frequency.
This
(
)
specr
H
ω
removes high-frequency components
from the origin of the 3D Fourier space.
(
)
inverse
HR
ω
has a filter characteristic which is expressed by the fol-
lowing Eq.6:
( )
22
,
ω ωωω
ω
inversexy
HRRR==+ (6)
The 3D back Fourier transforms the Fourier space da-
ta back to 3D volume data, having undergone Fourier
space low-pass filtering. The 3D back Fourier transform
is expressed by the following Eq.7:
( ) () ()
{ }
3
1
,,,,exp
8
xyzxyzxyz
fmxyzFMjxyzddd
ωωωωωωωωω
=π⋅⋅+⋅+⋅
∫∫∫ (7)
where Eq.7 is the transformation from the frequency
domain to the space domain. It is the inverse of the rela-
tion described by Eq.1.
APPENDIX B
Metal artifact processing
)( RHinverse ω has a filter characteristic expressed by the
following Eq.8:
22
_
Rtempxy
W
ωωω
=++
_
__
Rtemp
Rnorm
Rmax
ω
ωω =
(
)
_
inverseRnorm
HRωω= (8)
where W is the addition of a direct current component.
_max
R
ωis the maximum value of a _
Rtemp
ω value. The
characteristics in the negative direction along the hori-
zontal axis are omitted because these are in linear sym-
metry about the vertical axis with the characteristics in
the positive direction.
(
)
(
)
specrinverse
HHR
ωω⋅ is ex-
pressed by the following Eq.9:
( )() ( )
( )() ( )
_
2π
sin
π2
0
H
specrinverseRnorm H
specrinverse
HHRH
HHRH
ρ
ρ
ρ
ωωωρρ
ωωρρ

⋅=+≤




⋅=>


(9)
H
ρ
is the Nyquist frequency 2
CFRWFR
+



and
ρ
is
the no-processing region frequency 2
CFRWFR
−



.
The 3D back Fourier transforms the Fourier space da-
ta back to 3D volume data, having undergone Fourier
space low-pass filtering.