Comparison of extended field-of-view reconstructions in C-arm flat-detector CT using patient size, shape or attenuation information.

Abstract

In C-arm-based flat-detector computed tomography (FDCT) it frequently happens that the patient exceeds the scan field of view (SFOV) in the transaxial direction because of the limited detector size. This results in data truncation and CT image artefacts. In this work three truncation correction approaches for extended field-of-view (EFOV) reconstructions have been implemented and evaluated. An FDCT-based method estimates the patient size and shape from the truncated projections by fitting an elliptical model to the raw data in order to apply an extrapolation. In a camera-based approach the patient is sampled with an optical tracking system and this information is used to apply an extrapolation. In a CT-based method the projections are completed by artificial projection data obtained from the CT data acquired in an earlier exam. For all methods the extended projections are filtered and backprojected with a standard Feldkamp-type algorithm. Quantitative evaluations have been performed by simulations of voxelized phantoms on the basis of the root mean square deviation and a quality factor Q (Q = 1 represents the ideal correction). Measurements with a C-arm FDCT system have been used to validate the simulations and to investigate the practical applicability using anthropomorphic phantoms which caused truncation in all projections. The proposed approaches enlarged the FOV to cover wider patient cross-sections. Thus, image quality inside and outside the SFOV has been improved. Best results have been obtained using the CT-based method, followed by the camera-based and the FDCT-based truncation correction. For simulations, quality factors up to 0.98 have been achieved. Truncation-induced cupping artefacts have been reduced, e.g., from 218% to less than 1% for the measurements. The proposed truncation correction approaches for EFOV reconstructions are an effective way to ensure accurate CT values inside the SFOV and to recover peripheral information outside the SFOV.

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Comparison of extended field-of-view reconstructions in C-arm flat-detector CT using patient
size, shape or attenuation information
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IOP PUBLISHING
PHYSICS IN MEDICINE AND BIOLOGY
Phys. Med. Biol. 56 (2011) 39–56
doi:10.1088/0031-9155/56/1/003
Comparison of extended field-of-view reconstructions
in C-arm flat-detector CT using patient size, shape or
attenuation information
Daniel Kolditz1, Michael Meyer, Yiannis Kyriakou and Willi A Kalender
Institute of Medical Physics, University of Erlangen-N¨ urnberg, Germany
E-mail: daniel.kolditz@imp.uni-erlangen.de
Received 28 June 2010, in final form 30 August 2010
Published 30 November 2010
Online at stacks.iop.org/PMB/56/39
Abstract
In C-arm-based flat-detector computed tomography (FDCT) it frequently
happens that the patient exceeds the scan field of view (SFOV) in the transaxial
direction because of the limited detector size. This results in data truncation
and CT image artefacts. In this work three truncation correction approaches
forextendedfield-of-view(EFOV)reconstructionshavebeenimplementedand
evaluated. An FDCT-based method estimates the patient size and shape from
thetruncatedprojectionsbyfittinganellipticalmodeltotherawdatainorderto
apply an extrapolation. In a camera-based approach the patient is sampled with
anopticaltrackingsystemandthisinformationisusedtoapplyanextrapolation.
InaCT-basedmethodtheprojectionsarecompletedbyartificialprojectiondata
obtained from the CT data acquired in an earlier exam. For all methods the
extended projections are filtered and backprojected with a standard Feldkamp-
type algorithm. Quantitative evaluations have been performed by simulations
of voxelized phantoms on the basis of the root mean square deviation and
a quality factor Q (Q = 1 represents the ideal correction). Measurements
with a C-arm FDCT system have been used to validate the simulations and to
investigate the practical applicability using anthropomorphic phantoms which
caused truncation in all projections. The proposed approaches enlarged the
FOV to cover wider patient cross-sections. Thus, image quality inside and
outside the SFOV has been improved. Best results have been obtained using
the CT-based method, followed by the camera-based and the FDCT-based
truncation correction. For simulations, quality factors up to 0.98 have been
achieved. Truncation-induced cupping artefacts have been reduced, e.g., from
218%tolessthan1%forthemeasurements. Theproposedtruncationcorrection
approaches for EFOV reconstructions are an effective way to ensure accurate
1Author to whom any correspondence should be addressed.
0031-9155/11/010039+18$33.00 © 2011 Institute of Physics and Engineering in MedicinePrinted in the UK39

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40D Kolditz et al
CT values inside the SFOV and to recover peripheral information outside the
SFOV.
(Some figures in this article are in colour only in the electronic version)
1. Introduction
C-arm-based flat-detector computed tomography (FDCT) is primarily intended to be used
in special applications, e.g., interventional FDCT, intra-operative FDCT or image-guided
radiotherapy, since it offers higher flexibility in comparison to conventional CT (Jaffray et al
2002, Siewerdsen et al 2005, Daly et al 2006, Kalender and Kyriakou 2007). However, a
current problem and challenge in FDCT is the limited size of the scan field of view (SFOV)
(Kalender2005, KalenderandKyriakou2007)whichisdeterminedbythesizeofthex-rayflat
detector and the C-arm geometry. In a standard configuration of currently available systems,
e.g.,fortherobot-drivenC-armsystemArtiszeego(SiemensAG,HealthcareSector,Erlangen,
Germany), the size of the SFOV is 240 mm in diameter for standard circular scans, which
is smaller than the typical patient diameters of about 300 to 500 mm. If the patient exceeds
the detector in the lateral direction, projections are truncated and the filtered backprojection
introduces nonlinear image artefacts in the reconstructed CT images, so-called truncation
artefacts (Kalender 2005, Kalender and Kyriakou 2007). It is known from clinical CT that
truncationartefactsreduceCTvalueaccuracyinsidetheSFOVbytruncation-inducedcupping,
cause a bright white ring at the edge of the SFOV and produce streak-type artefacts outside
the SFOV. In general, the CT values inside the SFOV are overestimated and the CT values
outside the SFOV are underestimated. Hence, anatomic information inside and outside the
SFOV is suppressed (Ohnesorge et al 2000, Hsieh et al 2004, Sourbelle et al 2005, Zamyatin
and Nakanishi 2007, Maltz et al 2007).
OnemethodtorestoreimagequalitywithintheSFOVistoemployatruncationcorrection
before the filtered backprojection which extrapolates the truncated projections and leads
the attenuation values to zero. Different extrapolation methods for conventional CT were
mentionedintheliterature. Examplesareasymmetricmirroringofprojectiondata(Ohnesorge
et al 2000), a water-cylinder fitting (Hsieh et al 2004), a square-root extrapolation (Sourbelle
et al 2005), a sinogram interpolation (Zamyatin and Nakanishi 2007) or an approximation of
the patient as a water ellipse (Maltz et al 2007). Some of these algorithms can be extended to
cone-beamCT(Sourbelleetal2005,Hsiehetal2004,Maltzetal2007)andotherextrapolation
methods were developed for cone-beam geometry (Starman et al 2005, Anoop and Rajgopal
2007). The extrapolation methods were proposed in order to restore the image quality in
the SFOV and to reconstruct small areas outside the SFOV when the degree of truncation is
low. However, they have some significant disadvantages, e.g., an undefined behaviour if both
sides of the projections are truncated, consistency criteria requiring at least one non-truncated
projection, a time-consuming rebinning from 3D to 2D geometry, or they are optimized to
recover the areas inside the SFOV and cannot handle severe data truncation. Other methods
change the reconstruction algorithm to exact region-of-interest (ROI) reconstruction on one
side of the object (Noo et al 2004, Cho et al 2007), but these algorithms are only useful
for ROI reconstructions and not for extending the FOV. Moreover, iterative and statistical
reconstruction techniques are available (Fu et al 2006, Snyder et al 2006, Zhang and Zeng
2007, Yu and Wang 2009) which can in some cases obtain useful information outside the
SFOV but are computationally intensive and time-consuming. Also a combination of two

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Comparison of EFOV reconstructions in C-arm FDCT 41
scans was investigated (Ruchala et al 2002, Wiegert et al 2005) to handle truncated data by
using a previous dataset.
However, none of the mentioned algorithms handles severe truncation in C-arm FDCT or
satisfactorily reconstructs areas outside the SFOV. Moreover, truncation corrections are built
into the most FDCT systems, but generally there is no exact information available on the
method used, and for the most cases only the corrected SFOV is provided to the user. In this
work, truncation correction algorithms for extended field-of-view (EFOV) reconstructions in
FDCT were implemented and evaluated. It was the effort to restore the image quality inside
the SFOV and to recover the patient structure and shape outside the SFOV especially for
severe truncated data on both sides of the projections and for datasets where all projections
are truncated. The EFOV should be at least 500 mm in diameter comparable to the SFOV of
clinical CT. It was our goal to restore the patient circumference in the reconstructed volumes
and to provide information outside the SFOV to support existing and new applications in
interventional FDCT or image-guided radiation therapy.
2. Materials and methods
2.1. Extended field-of-view reconstructions in C-arm FDCT
In this work three truncation correction approaches for EFOV reconstructions in C-arm FDCT
were implemented and evaluated—an FDCT-based, a camera-based and a CT-based method.
The FDCT-based method estimates the patient size and shape from the truncated projections
and uses this information for an extrapolation. In the camera-based method extrapolation is
applied as well, buttheextrapolation lengths are determined fromthe patient outlinemeasured
by an optical tracking system. In the CT-based method the projections are completed, instead
of extrapolated, by artificial projection data obtained from an a priori CT volume. For all
methods the extended projections are filtered and backprojected with a standard Feldkamp-
type (Feldkamp et al 1984) algorithm. The described FDCT geometry is represented in
figure 1, and the abbreviations and symbols used throughout this paper are listed in table 1.
2.2. FDCT-based truncation correction
The FDCT-based algorithm uses the truncated FDCT projections to estimate the patient size
and shape by fitting an elliptical model to the raw data. The resulting patient contour estimate
is used to extrapolate the truncated projections (see figure 2).
2.2.1. Patient size and shape estimation.
method which was previously developed for scatter correction in FDCT (Meyer et al 2010).
With the original, measured projection values po(nu,o, nv) we calculate mean projection curves
p(nu,o) of several detector rows nv. An ellipse is fitted to each mean projection curve using a
least-squares minimization method (Hal´ ıˇ r and Flusser 1998). The ellipse equation is
?nu,o− nu,c
where 2·ruand 2·rpdenote the size and attenuation of the elliptical cylinder, respectively, and
nu,cdenotes the shift of the elliptical cylinder from the centre of the detector in the u direction.
Since the attenuation profile of a patient is not exactly an ellipse and to get rid of beam
hardening and scatter effects, ruand rpare matched with an a priori database. This database
We extended and optimized the size estimation
ru
?2
+
?p(nu,o)
rp
?2
= 1,
(1)

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42D Kolditz et al
(a)
Focus
x
y
SFOV
x
z
y
o
s
u
v
Focus
(b)
Figure 1. Definition of the C-arm FDCT geometry in (a) a two-dimensional view and (b) a three-
dimensional view. In (a) it is demonstrated that large parts of the patient may exceed the small
SFOV of 240 mm in diameter causing truncated projections.
Table 1. Abbreviations and symbols.
Abbreviations
FDCT
EFOV
SFOV
ROI
RMSD
Symbols
e
na
nu,A, nu,B, ..., nu,H
nu
nu,s
nu,t
Nu
nv
p
pLo, pHi
p?Lo, p?Hi
P
Q
r, ϑ, z
rLo, rHi
s, o, u, v
ATB
w
x, y, z
Flat-detector computed tomography
Extended field of view
Scan field of view
Region of interest
Root mean square deviation
Extrapolation function
Projection number
Positions on the extended detector
Pixel number in the u direction
Shift width in the u direction
Transition width in the u direction
Number of pixels in the u direction
Pixel number in the v direction
Projection value
Mean projection values in the transition regions
Mean slope values in the transition regions
Patient outline
Quality factor
Cylinder coordinates of the patient outline
Object radius on the left and right side on the detector
Source and detector coordinates
Transformation from A to B coordinate system
Weighting function
Cartesian coordinates of the patient outline