Download Optimization of image acquisition techniques for dual

Optimization of image acquisition techniques for dual-energy imaging
of the chest
N. A. Shkumat
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada M5G 2M9
J. H. Siewerdsena兲
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada M5G 2M9
and Ontario Cancer Institute, Princess Margaret Hospital, Toronto, Ontario, Canada M5G 2M9
A. C. Dhanantwari and D. B. Williams
Ontario Cancer Institute, Princess Margaret Hospital, Toronto, Ontario, Canada M5G 2M9
S. Richard
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada M5G 2M9
N. S. Paul
Department of Medical Imaging, Princess Margaret Hospital, Toronto, Ontario, Canada M5G 2M9
J. Yorkston and R. Van Metter
Carestream Health, Inc., Rochester, New York 14650
共Received 20 April 2007; revised 6 July 2007; accepted for publication 6 August 2007;
published 20 September 2007兲
Experimental and theoretical studies were conducted to determine optimal acquisition techniques
for a prototype dual-energy 共DE兲 chest imaging system. Technique factors investigated included the
selection of added x-ray filtration, kVp pair, and the allocation of dose between low- and highenergy projections, with total dose equal to or less than that of a conventional chest radiograph.
Optima were computed to maximize lung nodule detectability as characterized by the signaldifference-to-noise ratio 共SDNR兲 in DE chest images. Optimal beam filtration was determined by
cascaded systems analysis of DE image SDNR for filter selections across the periodic table
共Zfilter = 1 – 92兲, demonstrating the importance of differential filtration between low- and high-kVp
projections and suggesting optimal high-kVp filters in the range Zfilter = 25– 50. For example, added
filtration of ⬃2.1 mm Cu, ⬃1.2 mm Zr, ⬃0.7 mm Mo, and ⬃0.6 mm Ag to the high-kVp beam
provided optimal 共and nearly equivalent兲 soft-tissue SDNR. Optimal kVp pair and dose allocation
were investigated using a chest phantom presenting simulated lung nodules and ribs for thin,
average, and thick body habitus. Low- and high-energy techniques ranged from 60– 90 kVp and
120– 150 kVp, respectively, with peak soft-tissue SDNR achieved at 关60/ 120兴 kVp for all patient
thicknesses and all levels of imaging dose. A strong dependence on the kVp of the low-energy
projection was observed. Optimal allocation of dose between low- and high-energy projections was
such that ⬃30% of the total dose was delivered by the low-kVp projection, exhibiting a fairly weak
dependence on kVp pair and dose. The results have guided the implementation of a prototype DE
imaging system for imaging trials in early-stage lung nodule detection and diagnosis. © 2007
American Association of Physicists in Medicine. 关DOI: 10.1118/1.2777278兴
Key words: dual-energy imaging, flat-panel detector, imaging performance, image quality, imaging
dose, optimization, image acquisition technique, cardiac gating, thoracic imaging, lung cancer
I. INTRODUCTION
As the leading cause of cancer death for both men and
women, lung cancer presents an enormous burden to
society.1–4 Because survival is very low for advanced stage
disease 共e.g., 5-year survival of 38% – 61%, 24% – 34%,
5 % – 13% and 1% at Stages I, II, III, and IV, respectively5兲,
the key to survival is early detection. Conventional chest
radiography has proven inadequate in the detection of earlystage disease, missing 50% of nodules measuring 10 mm or
less.6 The lack of sensitivity is attributed in large part to the
superposition of anatomical structures in the projection
image7—i.e., the obscuration of subtle soft-tissue nodules by
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Med. Phys. 34 „10…, October 2007
overlying “anatomical noise,” such as the ribs and clavicles.
Low-dose CT 共LDCT兲 offers a dramatic improvement in diagnostic sensitivity;8 however, diagnostic specificity 共as well
as increased cost and radiation dose兲 presents a remaining
challenge, limited in part by the lack of fine material
characterization.9–11
Dual-energy 共DE兲 imaging has been shown to offer a potentially promising alternative or adjuvant to accurate, earlystage detection of lung disease12–18—reducing the influence
of “anatomical noise” by decomposition of the image into
distinct material bases 共e.g., soft-tissue and bone兲 and offering the potential for fine material characterization 共e.g.,
analysis of nodule calcification兲. Conventionally, DE imag-
0094-2405/2007/34„10…/3904/12/$23.00
© 2007 Am. Assoc. Phys. Med.
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Shkumat et al.: Dual-energy optimization for chest imaging
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FIG. 1. Research prototype for DE
chest imaging. The system is based on
a radiographic chest stand 共Kodak
RVG 5100, Carestream Health, Inc.,
Rochester, NY兲, modified to perform
cardiac-gated DE imaging.
ing has been limited by suboptimal clinical implementation,
a relatively high radiation dose, and the lack of a highperformance detector. The availability of flat-panel detectors
共FPDs兲 offering real-time digital readout and performance
consistent with the demands of chest radiography, however,
promises to remove conventional limitations, permitting
high-performance DE imaging at total dose equivalent to that
of a single chest radiograph. Over the last ⬃5 years, clinical
DE systems based on FPDs have become available,15,19 renewing interest in a broad spectrum of DE imaging applications and raising the need for investigation of optimal acquisition techniques in the context of this new technology.
Further, such renewed interest in DE imaging using FPDs
extends beyond chest imaging to include real-time DE
fluoroscopy20 共e.g., vascular and cardiac interventions兲 and
DE computed tomography.21,22 In each case, a careful examination of optimal imaging techniques is important to maximizing DE imaging performance.
Several previous studies have investigated DE technique
optimization in the context of mammography,23–26 and to a
lesser extent, single-shot DE imaging of the chest.27 Such
work provides a valuable basis for investigation of optimal
image acquisition techniques in the current context of dualshot FPD imaging. The work reported below details the optimization of DE image acquisition techniques for a chest
imaging prototype under development. A combination of theoretical studies 共cascaded systems analysis28兲 and experimental measurements 共in chest phantoms兲 were performed to
identify the optimal DE filtration, kVp pair, and allocation of
dose between low- and high-kVp projections. The objective
in each case is the maximization of soft-tissue visibility of
lung nodules in DE soft-tissue images. Three phantom thicknesses corresponding to “thin,” “average,” and “thick” adult
chest thicknesses were investigated, with total radiation dose
equivalent to that of a single chest radiograph. Novel aspects
of the work reported below include: 共i.兲 identification of optimal added filtration based on theoretical analysis of image
signal and noise; 共ii.兲 selection of optimal kVp pair for FPDbased systems; and 共iii.兲 investigation of optimal dose allocation between low- and high-energy images. Taken together,
Medical Physics, Vol. 34, No. 10, October 2007
these constitute a fairly complete characterization of technique factors, suitable for definition of an optimal “technique
chart” for use in clinical studies.
II. MATERIALS AND METHODS
II.A. High-performance, cardiac-gated dual-energy
imaging system
A DE imaging system has recently been developed in our
laboratory29,30 and translated to patient imaging trials designed to test the sensitivity and specificity of lung nodule
diagnosis. The system is illustrated in Fig. 1. Based on
a Kodak RVG-5100 digital radiography chest stand
共Carestream Health, Inc., Rochester, NY兲, the system includes a high-frequency, three-phase generator 共Indico 100,
CPI, Georgetown, Ontario兲, a 400 kHU x-ray tube 共Varian
Rad-60, Salt Lake City, UT兲, and a 10:1 antiscatter Bucky
grid 共Advanced Instrument Development Inc., Melrose Park,
NJ兲. Modifications to the RVG-5100 platform include: 共1兲 a
collimator 共Ralco R302 ACS/A, Biassono, Italy兲 incorporating a computer-controlled filter wheel; 共2兲 a highperformance FPD 共Trixell Pixium-4600, Moirans, France兲;
共3兲 a custom-built cardiac gating system based on a fingertip
pulse oximeter 共Nonin Ipod, Plymouth, MN兲; and 共4兲 the
associated image acquisition and processing/display workstations. The filter wheel supports four positions for differential
filtration of low- and high-kVp beams. The selection of
added filtration for DE imaging is described in detail below,
with results suggesting low-kVp filtration equivalent to
2.5 mm Al 共equal to the inherent filtration of the x-ray tube
and collimator兲 and high-kVp filtration by an additional
2 mm Al+ 0.6 mm Ag. The added filtration in the two remaining filter wheel positions are used for conventional DR
image acquisition 共1 mm Al+ 0.2 mm Cu兲 and quality assurance tests 共2 mm Al兲. The Pixium-4600 is a large-area
共⬃43⫻ 43 cm2兲 indirect-detection 共250 mg/ cm2 CsI: Tl兲
FPD composed of 3121⫻ 3121 pixels 共143 ␮m pitch兲 with a
68% fill-factor based on an a-Si: H photodiode plus doublediode pixel readout architecture.31 To minimize the misregistration associated with cardiac motion between low- and
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Shkumat et al.: Dual-energy optimization for chest imaging
high-kVp projections, a cardiac gating system was implemented to trigger x-ray exposure within the quiescent phase
of the heart cycle.29,30
DE
DE
DE soft-tissue and bone-only images 共Isoft
and Ibone
, respectively兲 were decomposed by weighted log-subtraction:
DE
ln共Isoft
兲
= ln共I 兲 − ws ln共I 兲
H
共1a兲
L
DE
ln共Ibone
兲 = − ln共IH兲 − wb ln共IL兲,
共1b兲
where IL represents the low-energy image, IH the highenergy image, and ws and wb are weighting parameters for
soft-tissue and bone, respectively. Weighted log-subtraction
was employed throughout this study, chosen due to its applicability to cascaded systems modeling and computational
simplicity, with weighting parameters chosen either theoretically 共from the ratio of attenuation coefficient at low- and
high-kVp兲 or experimentally 共iteratively selected to cancel a
given material兲.
II.B. Dosimetry and imaging performance metrics
II.B.1. Imparted energy
Radiation dose was characterized in terms of the imparted
energy:
␧=
冕
E
qE共E兲 · ␩共E;t兲dE,
共2兲
0
where ␧ has units of ␮J / cm , qE共E兲 is the incident x-ray
energy fluence, and ␩共E ; t兲 is the fraction of energy absorbed
as a function of x-ray energy, E, and patient 共water兲 thickness, t.32 The imparted energy associated with typical DR
chest imaging was determined by computing x-ray spectra
for typical clinical techniques33 共kVp, mAs, and filtration兲
integrated over the absorption fraction for patient 共water兲
thickness approximating various body habitus and for a
given source-to-patient distance. Throughout this work, unless stated otherwise, the total imparted energy for a DE
acquisition 共␧Total = ␧L + ␧H兲 was equal to that of a single DR
radiograph 共within ±5%兲 for the same chest thickness. For
example, for an average-sized chest 共24 cm兲 ␧Total
= 0.91 ␮J / cm2, consistent with the mean DR imaging dose
reported in the Nationwide Evaluation of X-Ray Trends
共NEXT兲 Survey of 2001.33 In comparison to alternative detector technologies, the dose for a DR image is slightly lower
than for a computed radiograph 共CR兲 or film-screen 共400
speed兲 acquisition. Therefore, fixing the total DE imaging
dose to that of a DR radiograph represents a conservative
operating point for the studies described below. To quantify
differences in dose across detector types, the entrance surface dose 共ESD兲 was computed using the f-factor34 共f water兲
and backscatter fraction35 共BSF兲 averaged over the incident
x-ray spectrum:
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the TASMIP algorithm37兲, and 共q / X兲共E兲 is the fluence per
unit exposure. ESD was computed to ensure consistency
with the values reported in the NEXT Survey.33
II.B.2. Dose allocation
An important technique factor in DE imaging is the proportion of total dose imparted by the low- and high-kVp
projections, referred to as dose allocation. For a fixed total
imparted energy, ␧Total, the dose allocation, A␧, is
ESD =
冋冕
0
册
qo共E兲
dE BSF · f water ,
共q/X兲共E兲
共3兲
where ESD has units of mGy, qo共E兲 is the incident x-ray
spectrum 共computed using the SPEKTR36 implementation of
Medical Physics, Vol. 34, No. 10, October 2007
共4兲
L
where ␧L and ␧H are the energies imparted in low- and highkVp projections, respectively. Dose allocation ranges from 0
共all dose allocated to the high-kVp projection兲 to 1 共all dose
allocated to the low-kVp projection兲.
II.B.3. Dual-energy image signal, noise, and SDNR
II.B.3.a. Dual-energy image signal. A simple metric
used below to characterize DE imaging performance in the
visualization of soft-tissue structures is the signal-differenceto-noise ratio 共SDNRDE兲 in a lung nodule relative to background 共lung兲. For the soft-tissue image, henceforth denoted
IDE, the signal in the DE image may be written
IDE =
2
E
␧L
,
␧ + ␧H
A␧ =
IH
.
共IL兲ws
共5兲
The relative signal difference between the nodule and
background was measured as the difference in mean signal
between the two regions, normalized by the mean signal
level
DE
=
SDrel
DE
DE
Inodule
− Ibackground
DE
Imean
,
共6兲
DE
DE
where Inodule
and Ibackground
are the mean signal in nodule and
background regions of a DE image, respectively, and the
mean signal is simply
DE
DE
DE
= 21 共Inodule
+ Ibackground
兲.
Imean
共7兲
Signal difference was used as a measure of contrast in both
experimental 共phantom兲 measurements and theoretical calculations.
Cascaded systems analysis provides an analytical description of signal and noise propagation in an imaging system,
has been applied successfully to several imaging
systems,38–45 and was employed in this work to compute the
DE signal and noise across a broad range of energy, dose,
filtration, etc. The detector signal in either the low- or highenergy image is proportional to the linear combination of
gain factors associated with the imaging chain:
I=X
冉冊
qo 2 ¯ ¯ ¯
a g 1g 2g 4 ,
X pix
共8兲
where detector signal, I, has units of electrons per pixel.
X-ray spectra were computed using SPEKTR,36 X is the exposure at the detector, qo is the mean fluence of 共Poisson-
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Shkumat et al.: Dual-energy optimization for chest imaging
q
distributed兲 incident x rays, and Xo is the mean fluence per
2
unit exposure. The sensitive area of the pixel aperture is apix
共including the fill factor兲. The gain parameters, g1 共quantum
detection efficiency兲, g2 共scintillator gain兲, and g4 共coupling
efficiency of secondary quanta兲 were computed as described
previously.46,47
II.B.3.b. Dual-energy image noise. Noise in DE images
was measured in terms of the variation in pixel values in
regions of the nodule and background, with relative noise
given by the mean standard deviation divided by the mean
signal:
DE
␴rel
=
1
DE
DE
2 共␴nodule + ␴background兲
,
DE
Imean
DE
␴nodule
共9兲
DE
␴background
where
and
are the standard deviations in
signal in the nodule and background regions.
Theoretically, the noise in DE images was computed using the noise-power spectrum 共NPS兲 for the low- and highkVp projections, combined to yield the dual-energy relative
NPS as47
DE
H
L
NPSrel
= NPSrel
+ ws2NPSrel
.
共10兲
The NPS was computed using cascaded systems analysis,
including effects such as K fluorescence, scintillator blur,
noise aliasing, and electronic noise.46,47 The subscript “rel”
indicates relative NPS 共i.e., the absolute NPS divided by the
square of the mean signal兲. The pixel variance was computed
by integrating the NPS over the Nyquist region of the 2D
Fourier domain, yielding the relative DE pixel noise:
DE 2
H 2
L 2
兲 = 共␴rel
兲 + ws2共␴rel
兲 ,
共␴rel
共11兲
H 2
L 2
兲 and 共␴rel
兲 are the relative variances in highwhere 共␴rel
and low-kVp images, respectively, and ws is the weighting
parameter for bone cancellation calculated from the ratio of
the effective low- and high-kVp linear attenuation coefficients:
ws =
=
H
H
¯ bone
¯ bone
␮
xbone ␮
=
L
L
¯ bone
¯ bone
␮
xbone ␮
H
H
/Ibone,0
ln共Ibone
兲
ln共
L
L
Ibone
/Ibone,0
兲
,
共12兲
DE
SDrel
DE
␴rel
.
共13兲
共14兲
Similarly for theoretical calculations, SDNRDE was computed as the ratio of relative signal difference and noise as
computed by cascaded systems analysis 关Eqs. 共5兲, 共8兲, 共10兲,
and 共11兲兴.
Medical Physics, Vol. 34, No. 10, October 2007
II.C. Filtration
The effect of differential added filtration between lowand high-kVp projections was examined as a function of the
material type 共atomic number, Zfilter兲 and thickness 共sfilter兲 of
added filtration. Performance was evaluated in terms of
SDNR as well as patient dose and tube loading characteristics.
The contrast between nodule and lung in a DE image was
calculated from the difference in attenuation coefficients at
low- and high-kVp:
冋
H
H
− ␮lung
兲−
CDE = 共␮nodule
册
H
␮bone
L
L
共␮nodule
− ␮lung
兲 dnodule ,
L
␮bone
共15兲
where ␮ is the effective attenuation coefficient for nodule,
lung, or bone averaged over the low- or high-kVp spectra,46
and dnodule is the thickness of the nodule. This equation indicates that increasing the spectral separation improves nodule
contrast, accomplished by hardening the high-kVp beam or
softening the low-kVp beam 共e.g., with a K-edge filter兲. Previous studies30 indicate that effects of the low-kVp filter
共e.g., softening the beam with a ⬃0.1– 0.2 mm Ce兲 are fairly
small due to subsequent hardening of the beam by the patient. The results below focus on the high-kVp filter, keeping
the low-kVp filter fixed at 2.5 mm Al.
Calculations were performed on the basis of a hypothetical chest model composed of 10 cm water and 10 cm inflated
lung.34 Ribs were modeled as 5 mm cortical bone and pulmonary nodules as 9.5 mm polyethylene.34 The signal difference, noise, and SDNR in DE images were calculated analytically as in Sec. II B 3 as a function of the atomic number
共Zfilter = 1 – 92兲 and thickness 共sfilter = 0 – 2.5 g / cm2兲 of added
filtration. For each filter selection, the exposure at the detector was fixed at 1 mR, and patient dose was calculated in
terms of the imparted energy. As typical of clinical practice,
therefore, ␧Total was allowed to vary in these calculations
such that the detector exposure was 1 mR.
II.D. Optimal acquisition techniques
II.D.1. Imaging phantom
where Ibone,0 denotes the signal without bone attenuation.
II.B.3.c. Dual-energy image SDNR The SDNR was
measured in DE images of a chest phantom 共detailed below兲
as the ratio of relative signal difference and noise 关Eqs. 共6兲
and 共9兲, respectively兴:
SDNRDE =
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Optimal acquisition techniques, including kVp pair and
dose allocation were investigated experimentally using a
chest phantom modeled after the ANSI patient-equivalent
phantom,48 as illustrated in Fig. 2共a兲. Lung nodules 共9.5 mm
right-circular cylinders兲 were simulated using materials ranging from micro-bubble-infused polyurethane 共−500 HU兲 to
nylon 共⬃ + 75 HU兲. Ribs were simulated by Al slats 共3 and
6 mm thick兲. The correspondence between phantom thickness and patient thickness was established by measuring the
transmitted exposure 共i.e., the exposure at the surface of the
Bucky grid兲 for “thin,” “average,” and “thick” DR technique
stations, varying the thickness of acrylic such that the transmitted exposure was ⬃1 mR in each case. The phantom
共acrylic兲 thicknesses corresponding to “thin” 共18 cm兲, “average” 共24 cm兲, and “thick” 共30 cm兲 patient thicknesses were
7.5, 10, and 12 cm acrylic, respectively. The selection of
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Shkumat et al.: Dual-energy optimization for chest imaging
FIG. 2. 共a兲 Schematic of the chest “slab” phantom containing simulated lung
nodules 共9.5 mm diameter right-circular polyethylene cylinders兲 and simulated ribs 共3 and 6 mm thick Al slats兲. Chest thickness is variable through
the addition or removal of acrylic slabs. 共b兲–共d兲 Example low-energy 共LE兲,
high-energy 共HE兲, and DE images of a simulated lung nodule obscured by a
3 mm rib. ROIs for SDNRDE analysis are shown as squares superimposed in
共d兲, six within the background 共cancelled bone兲 and one within the nodule.
three sizes was motivated by clinical technique charts typically classifying patients in this manner. As the central lung
field is the main area of interest when imaging for the detection of solitary lung nodules, the phantom thickness corresponds to this region. Therefore, the phantom thickness 共e.g.,
7.5– 12 cm acrylic兲 corresponds to the effective thickness in
the region of the lung for a given patient thickness 共e.g.,
18– 30 cm chest兲 as measured from the spine to the sternum.
II.D.2. DR technique factors and dose
DR technique factors for “thin,” “average,” and “thick”
patient sizes were obtained from a review of the literature
and clinical technique charts at our institution. The resulting
kVp and mAs are shown in Table I, along with the transmitted exposure measured behind the corresponding thickness
of acrylic 共XDetector兲 and total imparted energy 共␧Total兲. Other
factors relating to x-ray scatter and glare were held fixed at
nominal selections for the prototype system—e.g., use of a
10:1 bucky grid and a fixed geometry 共⬃10 cm objectdetector air gap兲.
II.D.3. Dose allocation and kVp pair
Measurements of SDNRDE were performed using the
phantom of Fig. 2 across a range of low-kVp 共60– 90 kVp兲,
high-kVp 共120– 150 kVp兲, dose allocation 共A␧ = ⬃ 0 – 1兲, and
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patient dose 共␧Total = 0.20– 1.73 ␮J / cm2兲. Beam filtration was
fixed at 2 mm Al ⫹ 0.6 mm Ag for high-kVp projections and
the equivalent of 2.5 mm Al 共inherent兲 for low-kVp projections. To acquire DE images at various low-kVp, high-kVp,
and allocation but at the same total dose, imparted energies
were computed at all available kVp and mAs stations permitted by the x-ray generator. For each patient thickness and
kVp pair, combinations of ␧L共mAs兲 and ␧H共mAs兲 were identified that yielded a given total dose, ␧Total, within ±5%. For
example, at a kVp pair of 关70/ 130兴 kVp, mAs settings of
关3.2/ 16兴 mAs give ␧Total = 0.88 ␮J / cm2 with an allocation of
A␧ = 0.29, whereas mAs settings of 关10/ 2兴 mAs deliver the
same total dose 共␧Total = 0.90 ␮J / cm2兲, but with allocation of
A␧ = 0.91. In this manner, ⬃ten stations were identified for
each patient thickness, kVp pair, and total dose that resulted
in allocation in the range A␧ ⬃ 0.1– 0.9.
SDNR was evaluated in soft-tissue DE images of the
phantom, with the tissue weighting parameter, 共ws兲 determined automatically to minimize the signal difference between regions of simulated rib and background, ensuring optimal bone cancellation in the DE soft-tissue images. As
illustrated in Fig. 2共d兲, seven ROIs 共41⫻ 41 pixels兲 were
DE
identified, one within the polyethylene nodule 共Inodule
兲 and
DE
six in the adjacent background 共Ibackground兲. Signal difference,
noise, and SDNR were computed as in Eqs. 共6兲, 共11兲, and
共14兲, respectively. The mean and standard deviations in each
measurement were determined from ten repeat image acquisitions. Measurements were performed for a total of 16 kVp
pairs and three phantom thicknesses. In addition, measurements were performed as a function of imparted energy 共viz.,
11 dose levels ranging from about one fifth to twice that of a
conventional DR chest exam, 0.20– 1.73 ␮J / cm2兲 at
关70/ 130兴 kVp. Slight variations in the dose 共constant to
within ±5% for fixed patient thickness and kVp pair兲 were
corrected by normalizing the measured noise by the square
root of the ratio of calculated and target level of ␧Total.
Curves of SDNRDE versus dose allocation 共for a given
kVp pair and ␧Total兲 were fit using a three-parameter empirical function. Curve fits were intended to guide the reader’s
eye in the results below and to identify optimal dose allocation, denoted A*␧, as indicated by the maximum of the fitted
curve. Fits were found to give a better representation of the
data under a change of variables, where a modified independent variable, A␧⬘, was defined as A␧⬘ = A␧ / 共1 − A␧兲. Nonlinear
TABLE I. Summary of DR technique factors for thin, average, and thick patient sizes.
Patient Thickness
tchest
tacrylic
kVp
Added Filtration
mAs
XDetector
␧Total
Thin
Average
Thick
18 cm
7.5 cm
120 kVp
1 mm Al+ 0.2 mm Cu
2.0 mAs
共1.10± 0.004兲 mR
0.44 ␮J / cm2
24 cm
10 cm
120 kVp
1 mm Al+ 0.2 mm Cu
3.2 mAs
共1.14± 0.005兲 mR
0.91 ␮J / cm2
30 cm
12 cm
120 kVp
1 mm Al+ 0.2 mm Cu
6.4 mAs
共1.34± 0.003兲 mR
2.08 ␮J / cm2
Medical Physics, Vol. 34, No. 10, October 2007
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FIG. 3. Effect of added filtration on DE imaging performance. Calculations are shown as a function of high-kVp filter material 共Zfilter兲 and thickness 共sfilter兲.
共a兲 Tissue weighting parameter, ws, for decomposition of a soft-tissue-only image. 共b兲 DE image signal difference 共nodule contrast兲 computed as in Eqs.
共5兲–共8兲. 共c兲 DE image signal-difference to noise ratio, SDNRDE. 共d兲 Tube mAs required to deliver an exposure of 1 mR to the detector 共in the high-kVp
projection兲. Note the logarithmic scale. 共e兲 Imparted energy. 共f兲 SDNRDE per unit patient dose 共imparted energy兲. All results are for an average patient
thickness.
fitting using the Levenberg–Marquardt method was used to
minimize the ␹2 value between fitted data and measurement.
II.E. Anthropomorphic phantom
An anthropomorphic chest phantom 共Model 55-8PL, Radiology Support Services, Long Beach, CA兲 was imaged as a
function of dose allocation 共A␧ = 0.06, 0.30, 0.63, and 0.91兲 at
关70/ 130兴 kVp to illustrate the effect of allocation on image
quality. As in the experiments described above, the total dose
delivered to the phantom was fixed, and only the dose allocation was varied. The phantom was imaged at techniques
corresponding to an average patient, and images were interpreted by an expert chest radiologist 共NSP兲 on a diagnostic
workstation 共dual-head, 1536⫻ 2048 pixel, 8-bit grayscale
displays; AXIS III, National Display Systems, Morgan Hill,
CA兲.
eter, ws 共Zfilter, sfilter兲, as filter thickness and atomic number
increase 共up to Zfilter ⬃ 65兲, corresponding to reduced bone
contrast for harder beams. The increase in ws in the region
Zfilter = 65– 80 is due to the filter K-edge falling close to the
mean energy of the high-kVp beam, effectively softening the
beam. A sharp decrease in ws occurs as the K-edge increases
at higher atomic numbers, Zfilter ⬎ 80.
The effect of filtration on SDDE is similar, as shown in
Fig. 3共b兲. A harder beam results in increased spectral separation, giving increased DE signal difference at Zfilter
III. RESULTS
III.A. Differential beam filtration
The dependence of DE imaging parameters and performance metrics on beam filtration 共described in Sec. II C兲 is
illustrated in Fig. 3. In each case, calculations are shown as a
function of high-kVp filter material type 共Zfilter兲 and thickness 共sfilter兲, with the low-kVp beam fixed at 70 kVp
共+2.5 mm Al added filtration兲 and a high-kVp of 130 kVp.
Figure 3共a兲 shows the reduction in tissue weighting paramMedical Physics, Vol. 34, No. 10, October 2007
FIG. 4. Dual-axis plot displaying peak SDNRDE and required filter thickness
共converted to millimeters兲 as a function of filter material. The plateau in the
range Zfilter ⬃ 25– 50 suggests a range of filters providing nearly equivalent
peak SDNRDE.
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Shkumat et al.: Dual-energy optimization for chest imaging
3910
FIG. 5. DE image SDNR measured as a function of dose allocation for 共a兲 thin, 共b兲 average, and 共c兲 thick phantom thicknesses. The results plotted here
correspond to a fixed high kVp 共130 kVp兲, with the low-energy technique varied from 60– 90 kVp. Curve fits are as described in the text. For each patient
thickness, an optimal allocation of A␧ ⬃ 0.3 is suggested.
⬃ 42– 63 and Zfilter ⬎ 84 at thicknesses greater than
1.5 g / cm2. The K-edge effect at Zfilter = 65– 80 significantly
reduces SDDE at all thicknesses due to softening of the highkVp beam. While a harder high-kVp beam increases nodule
contrast, the trade-off in image noise and SDNRDE is illustrated in Fig. 3共c兲, suggesting optimal filtration in the region
Zfilter = 25– 50 共depending on filter thickness兲, and a second
region of even higher SDNRDE above Zfilter ⬎ 77.
The filters thus implied were considered in relation to
tube loading and patient dose as in Figs. 3共d兲 and 3共e兲. Figure 3共d兲 shows the mAs required to deliver 1 mR to the
detector as a function of high-kVp filtration, implying an
enormous heat load for thick, high-Z filters. Such loading
effectively rules out the upper-right quadrant of 共Zfilter, sfilter兲
for which mAsH ⬎ ⬃ 100 mAs. The patient dose 共imparted
energy兲 for the high-kVp beam is shown in Fig. 3共e兲, showing increased dose for softer beams and suggesting a region
in the range Z ⬃ 30– 65 consistent with lower patient dose.
Because the calculations were performed with a fixed detector exposure of 1 mR, the patient dose varies significantly
over the range of 共Zfilter, sfilter兲 investigated.
The SDNRDE per unit dose 共imparted energy兲 is shown in
Fig. 3共f兲. Similar to Fig. 3共c兲, the results illustrate the degradation in performance at low atomic number 共Zfilter ⬍ 20兲, the
influence of the K-edge 共Zfilter = 65– 80兲, and the enhancement at very high atomic number 共Zfilter ⬎ 80兲. The effects
within the optimal range Zfilter ⬃ 25– 50 implied by Fig. 3共c兲
exhibit a shift toward higher filter thickness 关due to reduced
patient dose, as in Fig. 3共e兲兴. Such is consistent with the
generally recognized notion that increasing filter thickness
improves SNR per unit dose, but at the cost of tube loading
关Fig. 3共d兲兴. A realistic, optimal filter selection must therefore
account for the trade-offs among SDNRDE, tube loading, and
patient dose. For example, considering SDNRDE 关Fig. 3共c兲兴,
an optimal filter selection of Zfilter = 47 共Ag兲 and sfilter
= 0.5 g / cm2 共0.48 mm兲 is implied, and such is consistent
with reasonable tube output 关mAsH = 17 mAs in Fig. 3共d兲兴.
Considering SDNRDE per unit dose, on the other hand 关Fig.
3共f兲兴, the optimal filter thickness increases to 0.85 g / cm2
共0.81 mm Ag兲 with an increased high-kV tube output of
Medical Physics, Vol. 34, No. 10, October 2007
35 mAs. A reasonable compromise among competing factors
of SDNRDE, tube output, and dose, for example, is
0.63 g / cm2 共0.6 mm兲 Ag, corresponding to an acceptable
tube output 共mAsH = 25 mAs兲 without significant trade-off in
SDNRDE or SDNRDE / ␧Total.
FIG. 6. DE soft-tissue images of a polyethylene lung nodule. Images were
acquired at a fixed kVpH = 130 kVp and various kVpL for three phantom
thicknesses. Nodule contrast is highest at lower kVp 共60 kVp兲 and for the
thin phantom. Reduced contrast and SDNRDE in thicker phantoms is offset
in part by increased dose 共reduced noise兲 as in Table II.
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Shkumat et al.: Dual-energy optimization for chest imaging
3911
FIG. 7. Optimal kVp pairs. 共a兲 Tissue weighting parameter, ws, for optimal bone cancellation. 共b兲 Relative signal difference 共contrast兲, 共c兲 image noise, 共d兲
peak SDNRDE, and 共e兲 optimal dose allocation in the resulting soft-tissue DE images. All results are shown for the average patient thickness.
The results of Fig. 3 imply a fairly broad range of filter
materials that, given an appropriate thickness, represent
equivalently “optimal” filter selections. To illustrate this
point, the peak SDNRDE from Fig. 3共c兲 and the associated
DE
is
filter thickness are shown in Fig. 4. A plateau in SDNRpeak
found in the range Zfilter = 25– 50, suggesting a fairly broad
range of choices for high-kVp filtration. The increase in
DE
at Zfilter ⬃ 80 was ruled out due to unacceptably
SDNRpeak
high tube loading. For filters in the range Zfilter ⬃ 45– 52, optimal filtration is achieved with filter thickness less than
⬃1 mm, aiding practical implementation. Reasonable filter
selections include ⬃2.1 mm Cu 共Zfilter = 29兲, ⬃1.2 mm Zr
共Zfilter = 40兲, ⬃0.7 mm Mo 共Zfilter = 42兲, ⬃0.4 mm Pd 共Zfilter
= 46兲, and ⬃0.5 mm Ag 共Zfilter = 47兲.
III.B. Optimal acquisition techniques
III.B.1. Dose allocation and kVp pair
Varying the proportion of dose between low- and highkVp images had a substantial effect on SDNRDE. Figures
5共a兲–5共c兲 show SDNRDE as a function of A␧ at a fixed highkVp 共130 kVp兲 for three phantom thicknesses. The four
curves in each figure correspond to low kVp of 60, 70, 80,
and 90 kVp, respectively, each corresponding to the same
total dose level ±5%. As guided by the optimal filter results
of Fig. 3, added filtration in these experiments was fixed at
2 mm Al ⫹ 0.6 mm Ag for the high-kVp projection and
2.5 mm Al 共inherent兲 for low-kVp projections. For each
curve, the peak SDNRDE is found at an allocation of A␧
⬃ 0.3, suggesting optimal image quality when one third of
the total dose is imparted by the low-kVp beam. A significant
Medical Physics, Vol. 34, No. 10, October 2007
increase in SDNRDE is observed with increasing spectral
separation 共i.e., reduced low-kVp兲. These results are qualitatively illustrated in Fig. 6, showing DE images of a simulated 共polyethylene兲 nodule acquired at optimal allocation
共denoted A*␧兲 for each of the 12 curves shown in Fig. 5. For a
given phantom thickness, nodule contrast is seen to improve
with reduced low-kVp. The reduction in nodule contrast for
thicker phantoms is attributed to x-ray scatter, offset somewhat by a reduction in noise 共an increase in total dose兲 such
that SDNRDE is similar for each phantom thickness.
Measurements as in Fig. 5 were repeated for all 16 kVp
pairs, summarized in Fig. 7, where each parameter plotted
corresponds to the peak SDNRDE 共i.e., optimal allocation兲.
As shown in Fig. 7共a兲, the weighting parameter giving optimal bone cancellation decreases with increasing high-kVp
共reduced bone contrast兲. Figure 7共b兲 illustrates the trend toward lower low- and high-kVp, suggesting maximum DE
soft-tissue signal difference at 关60/ 120兴 kVp. The results
suggest a trade-off between spectral separation 共i.e., increased contrast for lower low-kVp兲 and x-ray scatter 共i.e.,
reduced nodule contrast at higher high-kVp兲. As shown in
Fig. 7共c兲, image noise was highest at 90 kVp 关likely due to
decreased quantum detection efficiency 共QDE兲兴. Taken together, the effects of kVp selection on nodule contrast and
noise are shown in Fig. 7共d兲 where SDNRDE is found to be
highest at 关60/ 120兴 kVp, reduces sharply with increasing
low-kVp 共reduced spectral separation兲, and reduces slightly
with increasing high-kVp 共increased x-ray scatter兲.
Finally, as shown in Fig. 7共e兲, the selection of kVp pair
was found to have a small effect on the optimal dose alloca-
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Shkumat et al.: Dual-energy optimization for chest imaging
3912
FIG. 8. Effect of dose on DE imaging
performance. 共a兲 DE image SDNR
measured as a function of dose allocation for four total dose levels 共␧1
= 0.20, ␧2 = 0.45, ␧3 = 0.86, and ␧4
= 1.73 ␮J / cm2兲. 共b兲 DE images of a
polyethylene nodule acquired at condiDE
for
tions corresponding to SDNRpeak
␧1, ␧2, ␧3, and ␧4. 共c兲 Peak SDNRDE as
a function of total imparted energy,
plotted in comparison to a square-root
fit. 共d兲 Optimal dose allocation measured as a function of total imparted
energy with a linear fit superimposed
and suggesting a slight decrease with
higher total dose. All results were acquired at 关70/ 130兴 kVp.
tion, with A␧ ⬃ 0.3 presenting a smooth optimum across all
conditions. Although the trends are comparable to the experimental error, higher allocation was required for reduced low
and high-kVp, suggesting: 共i兲 adequate transmission through
the patient required a larger proportion of dose at the lower
low-kVp; and 共ii兲 increasing the high-kVp necessitates lower
allocation to reduce quantum noise associated with reduced
QDE at higher kVp. When low-kVp increases from 80 to
90 kVp, A␧ increases, indicating a trade-off between imparted energy, transmitted exposure, and quantum noise. In
particular, the increased noise at 90 kVp combined with the
larger weighting parameter suggest an increase in the optimal
dose allocation.
III.B.2. Dose allocation and total dose
For a fixed kVp pair and patient thickness, the behavior of
ws, SDDE, ␴DE, SDNRDE, peak SDNRDE, and optimal dose
allocation was investigated as a function of the total imparted
energy. SDNRDE measured as a function of A␧ for imparted
energy ranging from approximately one fifth to double that
of a conventional DR radiograph are shown in Fig. 8共a兲. DE
images of the polyethylene nodule acquired at optimal allocation are shown in Fig. 8共b兲. The tissue weighting parameter and signal difference did not appreciably vary with dose,
although image noise decreased in proportion to the inverse
square-root of dose as expected, resulting in the square-root
DE
shown in Fig. 8共c兲. Reduction of
dependence in SDNRpeak
Medical Physics, Vol. 34, No. 10, October 2007
DE
␴rel
was the driving factor for the increase of peak SDNRDE.
The optimal dose allocation decreased slightly with dose as
shown in Fig. 8共d兲.
III.B.3. Dual-energy imaging technique chart
The optimal DE imaging techniques identified above
guided the formation of a technique chart for use of the DE
imaging prototype in patient studies, including optimal filtration, kVp, and mAs for low- and high-kVp projections as
well as dose allocation. Table II summarizes the optimal
techniques along with energy imparted and entrance surface
dose for three patient thicknesses. Interestingly, the optimal
technique parameters 共Table II兲, including kVpL, kVpH, and
A*␧ show little dependence on patient thickness across the
range investigated.
III.C. Anthropomorphic phantom
Figure 8 illustrates the effect of dose allocation on DE
imaging performance. In each case, a magnified view of the
right lung of an anthropomorphic phantom is shown for DE
soft-tissue images acquired across a broad range in dose allocation. The results are generally consistent with Fig. 5共b兲,
suggesting strong degradation in image quality at extreme
values of allocation 共e.g., A␧ = 0.06 and A␧ = 0.91兲, with a
fairly weak dependence in the range A␧ ⬃ 0.2– 0.6. Interpretation by an expert thoracic radiologist indicates that the vis-
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Shkumat et al.: Dual-energy optimization for chest imaging
3913
FIG. 9. DE soft-tissue images of an
anthropomorphic phantom acquired at
four levels of dose allocation. Images
were acquired at 关70/ 130兴 kVp and at
equivalent total dose 共⬃0.9 ␮J / cm2,
corresponding to the energy imparted
for an average patient兲. Optimal image
quality is obtained at A␧ = 0.30. A noticeable increase in image noise is evident at very low 共A␧ = 0.06兲 and very
high 共A␧ = 0.91兲 allocation.
ibility of spherical nodules in the lung is highest for the case
A␧ = 0.30, slightly reduced at A␧ = 0.63, and significantly degraded at allocation extremes.
IV. DISCUSSION AND CONCLUSIONS
DE imaging can reduce the contribution of anatomical
clutter within a chest radiograph, which has shown to be a
significant impediment in the visualization of soft-tissue
structures.7 To achieve maximum DE image quality, careful
consideration of trade-offs in soft-tissue contrast and image
noise must be taken into account. The analysis presented
above points to DE imaging techniques that maximize softtissue visibility in DE soft-tissue images, specifically in the
context of chest imaging. The results pertain to DE image
decomposition by log-weighted subtraction, with future work
to include investigation and management of x-ray scatter,
optimization in association with various postprocessing techniques 共e.g., noise reduction兲,49 and alternative imaging tasks
共e.g., visualization of bony detail in the bone-only image兲.
Previous work investigated the important role of differential filtration between low- and high-kVp beams,20,27 showing that strong filtering of the high-kVp beam is important to
technique optimization. The work presented above is consistent with these findings, demonstrating further the trade-offs
between increased spectral separation 共improved nodule con-
trast兲 and image noise. Optimal filter material types and
thickness emerge that balance the trade-offs between contrast
and noise, presenting techniques that are achievable at acceptable tube loading and patient dose. A range of high-kVp
filters providing comparable imaging performance is
suggested—e.g., as shown in Figs. 3 and 4, metals in the
range Zfilter ⬃ 40– 47 with thickness less than 1 mm.
The optimal kVp pair in DE imaging has been shown to
be task dependent20,46,50 with optima ranging from 关60/120兴
to 关80/ 110兴 kVp. The results above indicate an optimal softtissue imaging performance at a kVp pair of 关60/ 120兴 kVp
for all patient thicknesses investigated and with total dose
equivalent to that of a single chest radiograph. Low-kVp
exhibited a stronger effect on SDNRDE, with 60 kVp providing improved nodule contrast and higher detector efficiency.
The effect of high-kVp was less significant, suggesting competing effects among energy separation 共contrast兲, image
noise, and x-ray scatter in relation to soft-tissue visibility. As
expected, the tissue weighting parameter was observed to be
dependent on phantom thickness—slightly larger for thinner
phantoms. This effect was accounted for in the experimental
studies by selecting ws independently in each DE image to
automatically minimize the signal difference 共contrast兲 between simulated bone and soft-tissue background. The softtissue DE images therefore exhibit optimal bone cancellation
TABLE II. Dual-energy technique chart describing optimal acquisition techniques for three patient thicknesses.
Patient Thickness
Thin 共18 cm chest兲
kVp
mAs
␧ 共␮J / cm2兲
ESD 共mGy兲
A*␧
Average 共24 cm chest兲
Thick 共30 cm chest兲
Low
High
Total
Low
High
Total
Low
High
Total
60
3.2
0.13
0.03
–
120
16
0.30
0.04
–
–
–
0.43
0.07
0.31
60
5
0.25
0.05
–
120
25
0.64
0.07
–
–
–
0.89
0.12
0.28
60
10
0.54
0.11
–
120
50
1.54
0.13
–
–
–
2.08
0.24
0.26
Medical Physics, Vol. 34, No. 10, October 2007
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Shkumat et al.: Dual-energy optimization for chest imaging
共i.e., ws selection兲 in all cases. The optimal dose allocation
for this imaging task was also found to be fairly constant
共A*␧ ⬃ 0.3兲 for all patient thicknesses investigated. The majority of patient dose is allotted to the high-kVp image to reduce
noise associated with the high-kVp image.
Conventionally, DE imaging has been somewhat constrained by the need for increased total imaging dose,51,52 but
the optimal techniques investigated above correspond to a
total dose equivalent to that of a single chest radiograph.
Such studies will facilitate deployment of DE imaging systems at clinically accepted dose levels. Furthermore, the insensitivity of certain optima 共e.g., kVp pair and dose allocation兲 to patient thickness is desirable from the standpoint of
simplified system implementation—i.e., the optima are applicable to a fairly broad range of patient body types. This
allowed the technique factors 共mAsH and mAsL兲 to be simply
interpolated for any patient thickness within the range of
measurements—e.g., at 1 cm increments in the range
18– 30 cm for the technique chart developed for clinical trials with this prototype.
It is important to consider how DE imaging systems such
as the one described above could be implemented clinically
with respect to conventional PA and lateral 共LAT兲 radiographic exams. It is unlikely that a PA DE image would
replace the conventional two-view chest exam—e.g., to visualize the retro-hepatic lung. Moreover, the imaging performance and diagnostic value of a LAT DE image remains to
be fully investigated. Hence, a likely short-term clinical
implementation would involve a PA DE image, followed by
a conventional LAT DR. However, to the extent that a truly
equivalent DR image can be decomposed from the low- and
high-kVp projections 共e.g., by log-weighted addition兲, both
the PA and LAT views could be acquired as DE images. In
such implementation, the PA and LAT views could be rendered at the clinician’s discretion as either an equivalent radiograph or a soft-tissue/bone-only decomposition. Such potential implementations should, of course, be considered with
respect to further clinical research. Furthermore, it is interesting to consider the extent to which DE imaging could
provide a useful modality for the numerous functions of general radiography—e.g., assessment of heart failure, line
placement, foreign objects, and rib fractures. For these applications and others beyond thoracic imaging 共e.g., musculoskeletal and interventional imaging兲, the potential role of DE
imaging depends on its performance in PA and LAT views,
the capacity to decompose an equivalent DR image from the
low- and high-energy projections, and the ability to provide a
high degree of material discrimination. Such questions are
the subject of future investigation and preclinical trials.
ACKNOWLEDGMENTS
The authors extend thanks to R. Asento and M. Haines
共Carestream Health, Inc., Rochester, NY兲 for assistance with
the implementation of the clinical prototype. N. A. Shkumat
was supported by scholarships from the Canadian Institutes
of Health Research 共CIHR兲, the University of Toronto, and
the Ontario Student Opportunity Trust Fund 共OSOTF兲. This
Medical Physics, Vol. 34, No. 10, October 2007
3914
research was conducted in collaboration with Carestream,
Inc. 共Rochester, New York兲 and funded in part by National
Institutes of Health 共NIH兲 Grant No. R01-CA112163-01.
a兲
Author to whom correspondence should be addressed. Electronic mail:
[email protected]. Telephone: 416-946-4501共x5516兲; Fax:
416-946-6529.
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