-
7/28/2019 AAC99a
1/18
Lehrstuhl fr Bildverarbeitung
Institute of Imaging & Computer Vision
Digital Image Acquisition and Processing inMedical X-Ray
Imaging
Til Aach and Ulrich Schiebel and Gerhard Spekowius
in: Journal of Electronic Imaging. See also BIBTEX entry
below.
BIBTEX:
@article{AAC99a,
author = {Til Aach and Ulrich Schiebel and Gerhard
Spekowius},
title = {Digital Image Acquisition and Processing in Medical
X-Ray Imaging},
journal = {Journal of Electronic Imaging},
publisher = {SPIE},
volume = {8},
number = {Special Section on Biomedical Image
Representation},
year = {1999},
pages = {7--22}}
Copyright (c) 1999 The Society for Imaging Science and
Technology and The Society of Photo-Optical Instrumentation
Engineers. Copying of material in this journal for internal or
personaluse, or the internal or personal use of specific clients,
beyond fair use provisions granted by theU.S. Copyright Law is
authorized by SPIE and IS&T and subject to the payment of
copying fees.
document created on: December 20, 2006
created from file: jei97coverpage.tex
cover page automatically created with CoverPage.sty
(available at your favourite CTAN mirror)
-
7/28/2019 AAC99a
2/18
A reprint rom
Electronic IrnagingtssN 0l7-9909January 999
Digital image acquisition and processing in medical x-ray
imagingTil AachUlrich SchiebelGerhard Spekowius
-
7/28/2019 AAC99a
3/18
Journal of Electronic maging 8(1), 7-22 (January1999).
Digital image acquisition and processing nmedical x-ray
imaging*Til AachTLIlrich SchiebelGerhard SpekowiusPhilips GmbH
ResearchLaboratoriesWeisshausstrasseD-52O66Aachen, GermanyE-mail:
[email protected]
Abstract. This contribution discusses a selection of today's
tech-niques and futurc concepts for digital x-ray imaging in
medicine.Advantages of digital imaging over conventional analog
methodsinclude the possibility to archive and transmit images in
digital intor-mation systems as well as to digitally process
pictures before dis-play, for example, to enhance low contrast
details. After reviewingtwo digital x-ray radiography systems for
the capture of sll x-rayimages, we examine the real time
acquisition of dynamic x-ray im-ages (x-ray fluoroscopy). Here,
particular attention is paid to theimplications of introducing
charge-coupled device cameras. Wethen present a new unified
radiography/fluoroscopy solid-state de-tector concept. As digital
image quality is predominantly determinedby the relation of signal
and noise, aspectsof signal ransfer,noise,and noise-related quality
measures like detective quantum effi-ciency feature prominently in
our discussions. Finally, we descibe adigital image processing
algorithm for the reduction of noise in im-ages acquired with low
x-ray dose. @ 19ss SptE and tS&T.[s1 1 -e909(99)004018]
1 lntroduction an d OverviewIn this paper, we discuss selected
current topics of digitalimage acquisition and processing n
medicine, focusing onx-ray projection imaging. A key feature of
digital imagingis the inherent separationof image acquisition and
display.Whereasanalog screen/film combinations (Fig. 1) use filmas
a medium for both image recording and viewing, digi-tally acquired
images can be processed n order to correctaccidental over- or
underexposure,or to enhancediagnos-tically relevant information
before display. Also, digital im-ages can be stored and fransmitted
via picture archiving andcommunication systems PACS),' and be
presentedon dif-ferent output devices, ike film printers or cathode
ay tube(CRT) monitors (softcopyview ng).*Partly presentedm m
inyited paper at th Intemational Symposiumon ElecuonicPhotography
(ISEP: a PHOTOKINA event), Cologne, Gemmy, Sept. 21-22,1996.iT.
Aacb is now witb rhe Medical University of Luebeck, hstitute for
Sipal Pro-cesshg md hmess Control, Rataburger Allee 160, D-23538
Luebeck, Gemmy.
Paper 97010 reeived Apr. 1, 1997; revised mmuscript rrceived May
i, 1998; ac-cepred for publication June 1, 1998.1017-9909/99/510.00
1999SPIE an d IS&T.
The separation of image acquisition and display in adigital
system s illustrated by comparing analogand digitalacquisition of
silgle high resolution projection images (x-ray radiograpy). The
principle of the imaging serup issketched n Fig. 2. X radiation
passes hrough the patientbefore exposing a detector. Widely used
for image detec-tion are analog screen/film combinations as shown
in Fig.1, which consist of a fiIm sheet sandwichedbetween
thinphosphor intensifying screens.The phosphor screenscon-vert the
incoming x radiation into visible light blackeningthe film, which,
after developing, is examined by viewingon a
lightbox.Well-established digital alternatives include
storagephosphorsystems SPS),'* alsoknown as computed adi-ography
(CR) systems,and a selenium-detectorbaseddigi-ta l chest
adiographysystem (DCS), "Thoravision"].''o InCR systems, he image
receptor s a photostimulable phos-phor plate, which absorbsand
storesa significant portion ofthe incoming x-ray energy by trapping
electrons and holesin elevatedenergy states.The storedenergy
pattern can beread out by scanning he plate with a laser beam.The
emit-ted luminescence s detectedby a photomultiplier and
sub-sequentlydigitized. Common plate sizes are 35X35 cm2sampledby a
1760X 1760matrix, 24x30 cm2 sampledbya 7576X1976 matrix, and for
high resolutions 18x24 cmz sampledby a 1770x2370 matrix. The
resultingNyquist frequenciesare between 2.5 and 5 lp/mm. An
ex-ample CR image is given in Fig. 3.The detector of a DCS
consistsof an amorphous sele-nium layer evaporatedonto a
cylindrical aluminum drum.Exposure of the drum to x radiation
generatesan electro-static charge mage, which is read out by
electrometer sen-sors. Maximum size of the sampled mage matrix is
2166x2448 pixels, with a Nyquist frequencyof 2.7 lp/mm.In analog as
well as in digital systems, he acquired ra-diographs are degraded
by nonideal system properties.These nclude limitations of contrast
and resolution, and aredescribedfor instanceby the modulation
transfer function(MTF). Other undesiredeffects are spatially
varying detec-tor sensitivity and unwanted offsets. Additional
degrada-tions can be introduced by accidental over- or
underexpo-Journal of Electronic maging January 1999 Vol. B(1) 7
-
7/28/2019 AAC99a
4/18
Aach, Schiebel,and Spekowiusx-roy film
iT,::'Y.nFig,1 Principlef conventional-ray mage etectiony a
screen/film ombination.he ighfsensitiveilm
ssandwichedetweenwophosphorntensifyingcreens hich onverthe ncoming
radia-t ion nto isibleight.sure. Unlike screen/film
systems,however, digital systemsenable the compensationof such
known degradationsbysuitableprocessing ike gain and offset
correctionand MTFrestoration.Furthermore,the problem of over- or
underex-posures s virtually eliminated by the wide latitude of
theSPS and DCS image receptors(about four orders of mag-nitude) and
the possibility to digitally adjust the displayedintensity range.
Finally, methods like "unsharp masking"and "harmonization" can be
employed o enhance elevantdetail with respect o diagnos,ticallyess
mportant informa-tion, and to optimize image presentation n the
selectedoutput device.'-'" Figure 4 shows the result of
applyingsuch enhancement echniques o the radiograph n Fig. 3.As we
will see, he predominant factor limiting the ex-tent up to which
MTF restoration and optimization of im-agepresentationcan be
successful s the noise evel presentin a digital image. Rather than
by traditional quality mea-sures ike MTF or radiographic speedof
screen/film com-binations, the imaging performanceof
electronicsystems stherefore described by measurescapturing the
signal-to-noise transfer properties, like signal-to-noise ratio
(SNR),noiseequivalent guanta(NEO and
detectivequantumeffi-ciency(DQE).r'o'r' An imaging-inherent
oisesource s thediscrete nature of x radiation, generating
so-called x-rayquantumnoise in the acquired mages. This fype of
noise isparticularly relevant for images recorded with very
lowx-radiation doses,and affectsboth analogand digital imag-ing
systems.Clearly, noise introduced within the imagingsystem during
later processing stages, e.g., quantizationnoise during
analog-to-digital (A/D) conversion, can fur-ther deteriorate the
imaging performance.Noise behaviortherefore featuresprominently in
our discussions.In the next sectionwe review x-ray image qualiry
mea-sures needed later in the paper. We then consider digitalreal
time dynamic x-ray imaging, known as x-ray fluoros-copy. Here, we
pay particular attention to differences in
,Fig. 2 Principle f x-rayprojectionadiographyview
romabove,1:x-ray ube, 2'.xray beam,3: patient, : detector).8 /
Joumal of Electronic maging January 1999 Vol.8(1)
Fig.3 Portion f size700x1846pixelsroma radiographf a
foot(dorso-plantar)cquiredy a CR system.noise behavior of
electronic camera tubes (Sec. 3.3) andsolid-state charge-coupled
device (CCD) cameras (Sec.3.4). This is followed by a discussion of
a new flat solid-state x-ray sensor for both digital x-ray
fluoroscopy andhigh resolution radiography. Finally, in Sec. 5, we
describea recently developed quantum noise reduction filter.2 X-Ray
lmage DetectionAn x-ray tube generatesx radiation by accelerating
elec-fions i,nan electric field towards a tungstenanode.On hit-ting
the anode, about lVo of the electrons generatex-rayquanta,which
leave the tube through an x-ray transparentwindow. The x-ray beam
consists of a discrete number ofx-ray quantaof varyilg energy, with
the maximum energybeing limited by the applied tube voltage.
Typical valuesfor the tube voltage range between 60 and 150 kV.
Theenergydistribution of the x-ray quantadetermiaes he beamqwlity.
A thin aluminum plate about 3 mm thick, whichabsorbs ow-energy
x-ray quantaunable to pass hrough thepatient, is integrated
directly into ttre tube window. These
-
7/28/2019 AAC99a
5/18
Digital x-ray imaging
quantawould only addto the absorbedpatient
dosewithoutcontributing to the imaging process.In the following,
thethus reduced ange of energies s approximatedby a single,average
nergy, .e., we assumemonoenergetic radiation.For tube voltagesof
150and 60 kV, theseaverage nergiesare about63 and 38 keV,
respectively.Owing to the discrete nafure of x radiation only a
lim-ited, potentially small number of x-ray quanta contributesto
the imagingprocessat eachpixel. For instance, n x-rayfluoroscopy he
typical x-ray dose for an image is about 10nGy at a beam quality of
60 keV. This results n a quantumflow q6 of roughly qs:300
quanta.imm2. -ray quanrumnoise is causedby random fluctuationsof
the quantumflow, which obey a Poissondistribution.l2Therefbre,
hestandarddeviation o of quantum noise is proportional toJqo. er
the detectedsignal S is proportionalto qs, the
Fig,5 Schematiciagram fan efficiencytage f an x-ray
etec-tor,whichabsorbs nlya fraction ol the ncominguantumlowQ o 'SNR
S/o varies with 1,Q0,u"d decreaseswith decreasingx-ray dose.A real
detector absorbsonly a fraction of the incomingx-ray quanta given
by the detector absorption fficiency a(Fig. 5). The
squaredsignal-to-noiseratio at the output ofthis fficiency stage
obeys
^ 51... o2a7SNR1,,,:-#1u, aQ oand is equal to the effective
number of absorbed x-rayquanta. The squaredSNR is therefore also
referred to asnoise equivalent quantd (NEQ). Normalizing SNRI,, by
4syields the so-called detective quantum fficiency (DQE).For the
efficiency stage n Fig. 5, the DQE is identical tothe absorption
efficiency a. As q6 corresponds to thesquared nput SNR, the DQE can
also be interpretedas theratio SNRl,r/SNR',.The so far simplified
treatrnentof NEQ and DQE con-sidered only homogeneousexposureswith
a spatially con-stant quantum flow 4s, and neglected noise
generatedbythe detectorsystem. n the following, we presentmore
gen-eral definitions of spatial frequency-dependentNEQ andDQE for
electronic imaging systems, ncluding effects of anonideal detector
MTF and detector noise. The originaldefinitions of NEQ and DQE for
photograp,hicsystemscanbe found in Refs. 13 and 14 (cf. also Ref.
11).Figure 6 shows the diagram of our detector model. Theefficiency
stage s followed by a gain factor G, which de-scribes the
conversion of x-rgy quanta into other informa-tion carriers, e.g.,
electrons or the solid-statedetector dis-cussed n Sec. 4. This is
followed by a stage of spatiallyscatterixg he information
carriers,which results n a spatiaiblur. This is describedby a
linear modulation transfer func-tion (MTF) I/(a), which
decreasesover the spatial fre-quency u. Finally, system noise with
power spectrumNlr,(u) is added.The power specrrumNl of the
incomingquantum noise is flat,r) and its power spectral density
isshown in Appendix A to be equal to the quantum flow 40.Clearly,
at zero spatial frequency, i.e., for a homoge-neous exposure with
quantumflow qo, the output signal isgiven by
e? dct -{tH Htu)--$y-:etNi' ------rY r'-'r
Y N'."C N ; "Fig, 6 Schematicof an x-ray detector comprisingan
etficiencystage ollowed y a conversion ithgainG, a linearMT FH(u),
andaddit ive ystemnoisewith NPS N!"".
Fig. 4 Enhancedversionof Fig. 3. First,middleand high
spatialfrequencieswere amplified elative o very low ones in order
tomakesuchdetailsbettervisible (harmonization). n a secondstage,the
imagewas givena sharperappearance y additional mplifica-tion of
highspatial frequenciesby unsharp masking.
Journalof Electronicmaging/January 1999/Vol.B(l)/9
-
7/28/2019 AAC99a
6/18
Aach, Schiebel,and SpekowiusS, , , (0 ) 4odG,since11(0): 1.
Noise at the detectoroutputconsistsof theoriginally white
quanfumnoise filtered by the system rans-fer function and of system
noise. Its power spectrumtt!,,1u1 sN7,,@):qoaczH2(u)+lr ] r , (a) .
(3)We now consider an almost homogeneousexposure with asinusoidally
varying quantum flow q(u,x):eoll* e stn(2mrx)], where x is a
spatial coordinate, and 0
-
7/28/2019 AAC99a
7/18
Digital x-ray imaging
Fig, Detectorrontendof a fluoroscopyystem1 XRll ube,2:input
creen ndphotocathode,: electronptics, :output indow,5: andemens,
:TV camera,: amplifier).into visible photons, which in turn
reachthe photocathode.The CsI screen s a layer approximately 400
;r.mthick andevaporatedonto an aluminum substrate.The absorption
ofthesescreenss about 607o-707o.16'18n addition,ai shownin Fig. 9,
CsI is grown in a needle-like structure such thatthe individual
needles act as optical guides for the gener-ated ight photons.This
preventsundesired ateral propaga-tion within the CsI layer, and
thus ensuresa relatively goodscreenMTF (seeFig. 13).The
generatedphotoelectronsare acceleratedwhen pass-ing through the
electron optics and focusedonto the outputwindow, where they
generate uminescencephotons. Theresulting visible pictures are then
picked up by the camera.The diameter of the circular XRII entrance
screen rangesbetween 15 and 40 cm, dependingon the application,
whilethe diameter of the XRII output window is between 25 and50 mm.
Apart from image intensification, the electron op-tics allow
demagnification, and zooming by projecting asmall subfield of the
entrancescreenonto the output win-dow.To pick up the images from
the XRII output window,electronic camera tubes Iike plumbicons,
vidicons or sati-cons are still in wide use today. In Europe,
camera readoutis mostly with 625 lines/image, with the full frame
rate of25 images per second n interlaced or
progressiveformat.However, specialhigh resolutionmodes 1250
ines/image)are also often available. In applications where
temporalresolution is less critical, the frame rate can be
reduceddown to only a single image per second(Iow frame ratepulsed
luoroscopy) in order to savex-ray dose. The analog
Fig, 9 Electron-microscopicmage of the cross section of a
Csllaver.
Fig.10 Samplemage oma fluoroscopyequence,artly howinga
patient'spineanda guidewire.he circularmage oundaryscaused y he
cylindricalhape f he XRll.video signal is then amplified and fed to
an 8 bit-10 bitA./D converter. In case of digitization by 8 bit,
the gain ofthe amplifier is higher for smaller signal amplirudes
thanfor larger ones n order to enhancedark partsofthe images.This
is commonly referred to as analog white compression.An example of a
fluoroscopy image is depicted in Fig. 10.3.2 Main Noise Sources in
Fluoroscopy lmageDetectionThe SNR attainable n fluoroscopy is
inherently limited bythe low x-ray quantum low 4s, which,
asdiscussedn Sec.2, generates elatively strong quantum noise in
relation tosignal.System-internal noise sources include
so-calledfixedpattenx noise, arrdsignal shot noise. Fixed pattern
noise ismainly caused by inhomogeneities of the XRII
outputscreen,which are stableover time. Signal shot noise
isgeneratedby the discrete nature of the conversion of infor-mation
carriers, e.g., from luminescencephotons nto elec-trons in the XRII
photocathode and the camera. As thepower spectrumof signal shot
noise is approximately flat,while the spectrum of quantum noise is
lowpass shaped,shot noise can affect the SNR and the DQE mostly for
highspatial frequencies. Still, signal shot noise is often
negli-gible compared o x-ray quantumnoise.3.3 Noise in Camera
TubesThe visible images areprojected from the XRII output win-dow
onto the photoconductive target layer of the camerarube,which for
plumbicons,consistsof lead oxide (PbO).The resulting
electrostaticcharge mage is then read out byscanning the target
layer linewise with an electron beam(scanneddevice). Line-by-line
scanningmeans hat sam-pling in the vertical direction is carried
out directly on thephotoconductivetarget, with the size of the
electron beamspot being sufficiently large to prevent alias.
Horizontalsampling is done later at the A,/D converter.
Journal of Electronic maging January 1999 Vol. 8(1) 1 1
-
7/28/2019 AAC99a
8/18
\ horiz. PS'\ \ .,.r\., .\ \ \ \
Aach, Schiebel,and Spekowius
0.=c-ci(Eaz
0 0.5 1 1.5 2 2.5 3spatialrequencylp/mmlFig.11 Horizontalross
ectionfan NPS oranXRll/cameraubesystem efore orizontalampling.he
iseof heNPS etween.5and1.0 p/mms due o thehighpassharacteristict
heamplifier,which ompensateshe owpassransferunction f
theplumbicontargetayer. he NPSdrops ff againor
requenciesargerhan1lp/mm ue o bandwidthimitationsnd
heantialiasilter.
Unavoidable capacitancesof the target layer itself aswell as of
the subsequent irst amplifier stageact as a low-passfilter, which
attenuateshigh frequency signal compo-nents. This attenuation has
to be compensated by ahighpass-like transfer function of the
amplifier, which,however, also amplifies high-frequency components
oforiginally white ele-ctronicnoise. This effect is referred toas
triangular noise,t" because andwidth limitations and anantialias
filter before the A,/D converter decrease he noiseamplification
again for higher spatial frequencies. Theoverall effect is a
triangle-shapedNPS as llustrated n Fig.11, giving this type of
noise its name. Due to the linewisescanning, triangular noise
increases only with horizontalspatial frequencies, and is
independent of vertical spatialfrequencies n the two-dimensional
specaldomain.A typical overall Fourier amplitude spectrumof a
homo-geneous x-ray exposure acquired by an XRlVplumbiconchain is
shown in Fig. 12, where the gray level representsthe modulus of the
Fourier coefficients. The origin (0,0) ofthe spatial frequency axes
u,u is in the centerof the plot.To avoid an excessivepeakat the
origin, the average mageintensity was subtracted prior to the
Fourier transform.Hence, the shown amplitude specffum corresponds o
theobserved ealization of noise n the homogeneous xposure.First, a
concentration of high Fourier amplitudes can beobserved around the
origin, i.e., for low spatial requencies.This is caused by x-ray
quantum noise, and reflects thedrop of the overall system MTF
toward higher spatial fre-quencies. Along the horizontal direction,
spectral ampli-tudes increase again toward higher spatial
frequencies, e-flecting triangular noise. As discussed in the
previousparagraph, riangular noise does not occur along the
verti-cal spatial requencyaxis.
3.4 Noise in CCD-CamerasAithough today's XRlVcamera fube
combinationsachievea good fluoroscopic image quality, it is
intended to intro-duce high resolution solid-state CCD sensors nto
futuresystems n order to further improve image quality. A
CCDconsists f a two-dimensional2D) anay of discrete ensorelements
rather than of a "continuous" target plate. Thearraysize s usually
5I2x5l2 or I kXl k sensor lements,each of which corresponds o a
pixel. The benefits of using1 Journal f ElectronicmagingJanuary1999
Vol.8(1
Fig.12 Two-dimensionalourier mplitudepectrum f a homoge-neous
xposureecorded ithan XRll/plumbiconhain. heoriginof the spatial
requency xes s in the centerof the plot.Thelowpass-shapeduantum
oise pectrum,nd, n horizontalirec-tion, riangularoise replainly
vident.
a CCD include greater geometrical stability and the absenceof
readout itter. (In an electronic camera tube, readout it-ter is
causedby instability of the electron readout beam.)First, this
improves the performanceof subtraction fluoros-copy modalities,
like digital subtraction angiography,where a background image is
subacted from live imagesto increase detail contrast. Second, he
increasedgeometri-cal stability facilitates the correction of XRtr
fixed patternnoise. Note that inhomogeneities of the CCD sensor
ele-ments may also require a fixed pattern noise
correction.Additionally, CCD cameras exhibit less temporal lag
thanpickup tubes. fAmong the mentioned pickup tubes (plum-bicons,
vidicons, saticons),plumbiconshave lowest lag.]This results in less
blurring of moving objects, but ia-creased noise levels due to the
reduced temporalintegration.20The fact that spatial sampling is
inherent in a CCD cam-era causes ts noise behavior to be
fundamentally differentfrom that of pickup tubes. The Nyquist
frequency u1scot-responding to the sampling raster is commonly
related tothe XRII entrance plane, and depends on the
resolution(512x512 or 1 kX 1 k pels), and he selectedXRII zoom.It
rangesbetween 0.7 and 4 lp/mm. Apart from the XRIIand tandem lens,
the presampling system MTF is deter-mined by the apertureof
eachsensorelement, which, for anideal sensor, s sinc shaped Fig.
13). However, as can beseen from the MTF for a CCD-pixel aperture
in Fig. 13,integrationover the sensorelement aperture s
generallynotsufficient to reduce the MTF for spatial
frequenciesbeyondthe Nyquist frequency to prevent alias. Hence, f
the systemMTF is not low (about57oor less)at uN,
noise(andsignal)from beyond a1,,s mirrored back to lower spatial
frequen-cies, where it increases he observed noise
contributions.
-
7/28/2019 AAC99a
9/18
Digital x-ray imaging
totat t\ Csl1 k . 1 khai: \ccDu [p/mm]
Fig.13 Sinc-shapedTFof he inite perturef a CCDpixel. lsoshown re
heMTFs f heCslscreen ndot he otal maginghainior a Nyquistrequencyf
2.2 plmm.At this requency,he pixelMTF sstillquite igh.This effect
is depicted in Fig. 14. In a pickup rube, suchaliasing is
preventedby the size of the electron beam spotand by the antialias
filter before sampling the video signal.Triangular noise does not
occur in a CCD camera.A particular alias-baseddistortion can occur
when anantiscattergrid is used n connection with a CCD "ameru.2lAn
antiscattergrid consistsof thin lead stripes separated yx-ray
transparentspacingmaterial, and s mounted close tothe XRtr enance
screen o prevent x-ray quanta scatteredby the patient from reaching
the CsI layer. In small XRIImodes, i.e., when zooming to a small
subfield of the en-trance screen, the CCD-pixel size-as related to
the en-tranceplane-approaches the anfiscattergrid spacing. As
aresult, Moirt! patternsmay occur.Further CCD-cameranoise
sourcesnclude electrondarkcurrent and electronic amplifier
noise.The power spectrumof these noise sources s approximately flat
up to the Ny-quist frequency.Similar as n Fig. 12 for pickup
tubes,Fig. 15 shows theFourier amplitude spectrum of a
homogeneousexposurerecordedby an XRIVCCD chain. The influence of
the low-pass shapedquantum noise spectrum s clearly evident, asis
the absenceof triangular noise. Quantum noise extendsto higher
spatial frequenciesthan in Fig. 12, this is mostlydue to the
significantly better MTF of a CCD as comparedto a pickup tube.
=!liased
Fig. 14 NPS aliasing ffects nduced y samplingn a CCD.
Thisfiguredepicts he continuousNPS and its shifted ersion
ocatedatthe sampling requency ury. The shadedportionbelow he
Nyquistlrequencyu,yadds o the sampledNPS. Alsodepicted s
whitesig-na l shot noise.
Fig. 15 Fourier mplitudepectrum f a homogeneousxposurerecorded
ithan XRll/CCD-camerahain.Finally, Fig. 16 shows DQE curyes for a
400 pm CsIentrancescreen,an entfte 23 cm image intensifier
equippedwith such a screen, and a complete imaging chain with a
1 kX I k CCD camera. The Nyquist frequency a7yof thissystem s
2.2 lp/mm. Below 2lplmm, the DQEs of both theXRII and the entire
chain are almost exclusively deter-mined by the CsI screen. Above 3
lp/mm, the XRII-DQEfalls below the CsI DQE. This is a result of the
increasedweight of flat signal shot noise of the
photoeiectronsoverlowpass-filtered quantumnoise and signal for high
frequen-cies. The rather steepdrop of the DQE for the enre imag-ing
chain between 2 lpimm and the Nyquist frequency iscaused by
sampling and the aliased noise componentsshown in Fig. 14. Still,
due,to the absenceof triangularnoise, the DQE of a CCD-based
imaging chain is muchbefter than that one of a
plumbicon-basedchain. Thus, con-siderable MTF restoration is
possible without introducingunacceptablyhigh noise levels. As shown
in Fig. 13, thetotal system MTF has fallen to about 0.2 at 2 lp/mm.
Ac-
1 ,00,8
|J. 0,6FE o,oo,20,0
0.70.60.5
U 5 I -xRil -------\ - ' . ^ XRl l+ CCD , , .
2q1i.l
Lrr 0.4(1a \ n 2
0.20 .1
spatial requency p/mmlFig. 16 DQE curves for 60 keV averagebeam
energy of a Cslscreen, n XRll,an da total maging hainwitha 1 kx1
k-pixel CDcamera.
Journal of Electronic maging January 1999 Vol. 8(1 / 13
-
7/28/2019 AAC99a
10/18
Aach, Schiebel, and Spekowiuscording to Eq. (8), and with DQE
valuesof 0.65 at zerospatial frequency and 0.37 at 2 lp/mm, the MTF
can berestored o about0.75 at 2 lp/mm.In summary, mage quality can
be improved with respectto three major points by the innoduction of
CCD cameras:first, the excellent geomelrical stability of the
sensor arrayimproves modalities like subtraction angiography,
whereaccurate egistration of several mages with respect o eachother
is important. This enablesalso better correction offixed pattern
noise. Second, he MTF of a CCD camera sconsiderably better than the
MTF of a pickup tube. Due tothe physical separationof pixels in a
CCD, this holds alsofor low spatial frequencies. n a pickup
tube,transfer of lowspatial frequency information can be reduced
by, amongother effects, large-areaequalization of the charge
imageon the target during readout.Third, ttre absenceof triangu-lar
noise improves the SNR and DQE particularly forhigher spatial
frequencies, hus allowing a higher degree ofdigital MTF restoration
and edge enhancement.To stillkeepquantizationnoisenegligible
compared o the reducednoise evels particularly at higher spatial
requencies,12 bitA./D conversion may be advisable rather than the
previ-ously mentioned8 or 10 bit conversion.Further advantagesof a
CCD camera nclude its low sensitivity to electromag-netic
interferences,and a mechanicalconstruction which ismuch more
compact than that of pickup tubes.4 Solid-State Large Area Flat
Dynamic X-Raylmage Detector (FDXD)Despite the excellent image
qualiry today's XRIfIV-camera chains achieve,there are some
drawbacksof thesesystems.One is the bulkiness,size and weight
particularlyof the XRII, which hampers especially bedside
imag-gwith mobile fluoroscopy systems.Furthermore, he systemssuffer
from geometric distortion, veiling glare and vignett-ing. Veiling
glare refers to disrurbing light in the outputimagesof an XRII
causedby scatterof x-ray quanta, pho-toelectrons and light photons
inside the XRII, which re-ducescontrast.Vignetting denotes he drop
in output imageintensity toward the circular image boundary, which
iscausedby the convex shapeof the XRtr input screen. Al-though both
veiling glare and vignetting can be compen-sateddigitally, it is
preferable to prevent them in the firstplace.This can be
accomplishedwith an all solid-state magesensorwhich does not rely
on electron and light-opticalcomponentsand therefore promises
negligible vignettingand veiling glare, zero geometric distortion
due to its rigidpixel matrix, and a flat input screen. n addition
the devicescan be made compact and light-weight even for very
largequadraticor rectangular ield sizes up to 50X 50 cmz. Witha pel
pitch about 100-200 pcm,and a very large dynamicrange, such a
device is very well suited for high qualitydigital radiography. At
the sametime it can be used as afluoroscopy imager using a readout
mode with reducedpixel count, obtained by either zooming or pixel
binning.The fabrication of large area solid-state mage sensorshas
now becomefeasibleby the progressmade n thin-filmeiectronics
technology which has been developed for flatdisplays. A detector
array is realized by a 2D matrix ofsensorelementsas
schematicallydepicted n Fig. 17. Eachsensoreiement consistsof an
amomhous silicon (a-Si)14 Journal f Electronicmaging January1999
Vol.8(1)
Fig.17 Schematicf thesolid-stateetector.photodiode and a
thin-fiim transistor (TFI).22 The array iscoveredby a
thallium-doped Csl-scintillator screenmakingit sensitive to x
radiation."The main issueswe will addressare signal ansfer
anddetectornoise. The results show that the SNR of the devicecan be
made sufficiently Iarge even at very low dose rates,despitethe fact
that there is no photoelectronamplificationstep nvolved.4.1
Structureof the DetectorFigure 17 shows the detector setup:each
pixel-formed bya photodiode and a TFT-is connectedvia a data line
to acharge sensitive readout amplifier, and is linked by a gateline
to a row driver. The photodiodesensitivity is spectrallymatched to
the thallium-doped CsI layer, i.e., is highestbetween 550 and 600
nm, where thallium-doped CsI lightemission is strong. Via an analog
multiplexer each readoutamplifier is connected to an A/D converter.
The chargegeneratedafter illuminating the detector s read out by
suc-cessive activation of the detector rows via the row drivers.The
charge of an activated row is transferred o the readoutamplifiers,
and subsequentlydigitized.4.2 Signal Transfer and NoiseTo
analyzesignal and noise behavior, we focus on fluoros-copy, which,
due to the Iow doses used, is more criticalthan medium or high-dose
radiography. The target is toachievea fluoroscopy performancewhich
is comparableorbetter than that of XRIVTV-camera chains. This means
hatespecially for low doseselectronic detector noise must
besignificantly lower thanquantumnoise, which, aqsqldingtoSec. 2,
is also ow at low doses.In order to present quantitative figures on
SNR perfor-manceachievedso far, we considera l kXl kpixel detec-tor
with 2OOp,m pitch and a corresponding Nyquist fre-quency of 2.5
lp/mm as described n Ref. 22.
Signal transfer is mainly characterizedby the detectorgain G
quantifying the conversion of x radiation into elec-
-
7/28/2019 AAC99a
11/18
Digital x-ray imaging
0 1 2 3 4spatial equencypimmlFig. 18 Presampling MTF ol the
solid-state detector.
ffon chargewithin eachphotodiode,and by the MTF. For agiven,
typical fluoroscopy x-ray beam quality, the gain ismeasured n
electrons generated n each pixel per nGy ofradiation incident on
the anay. With an optical reflectorcoating on top of the CsI
screen,which prevents he escapeof generated luminescence photons,
values of G:700 electrons/nGywere measured or a protoq?e detec-tor
in Ref. 22. In the meantime this value has been im-proved to about
1500 electrons/nGy by increasing the ac-tive size of the
plgtodiodes and by optimizing the CsIdepositionprocess."The
presampling MTF for the analog detector compo-nents before
sampling, i.e., the CsI screen and the finitepixel pitch, is shown
in Fig. 18. As the presampling MTFdoes not vanish beyond the
Nyquist frequency, some alias-ing of quantum noise will occur,
similarly as described orCCD sensors n Sec. 3.4. With the MTF at
Nyquist fre-quency having dropped to approximately I5Vo, the
influ-ence of aliased noise is appreciablebut not too severe.The
main electronic noise sourcesof this detector are thereadout
amplifier noise and the reset noise of the pixel ca-pacitances. n
order to relate the noise measurements o thesignal evel, the noise
standarddeviation is in the followingalso expressed n electrons,
and refers to a single pixel.The effective amplifier noise depends
on the readouttiming pattern. Our protofype is read out by
so-calledcor-relatid-double sampling (CbS) depicted in Fig.
19.22Thegateof eachTFT in a row is activated by the
corresponding
H-gfr*tus"--|-o"tu L
Fig. 19 Timingof correlated ouble ampling. ample1 takenbe -fore
eadout s subtractedrom sample2 which s takenaftercapaci-tive
eedthrough as been eversed. he samplingaperture s aboutI lrs.
row driver through a positive pulse, which enables low ofthe
signal charge o the readout amplifier. To avoid distor-tion of the
sampledcharge signal by additional charge fedcapacitively through
the TFT gate during the positive slopeof the activation pulse,
sampling of the amplified chargesignal occurs only after the TFTs
are deactivatedagain, .e.,after the capacitive feedthrough has been
reversedby thenegative slope of the activation pulse. From this
sample,another sample s subtractedwhich was t*en
beforechargereadout.This readout timing schemeacts as a highpass
il-ter which efficiently suppresses ffsets and a Iowpass
noisecomponent of the amplifiers, which is known as 1f
noise.Simultaneously,the finite sampling aperture of about 8
,u.sacts as a lowpass filter attenuating white amplifier noise.With
this timing scheme he pixel capacitances'noise sgiven by ,lTkrc|,
where I is the absolute temperature,kthe Boltzmann constant, and C,
the pixel capacitancewhich is typically on the order of 2 pF.4.2.1
Fluoroscopy pertormanceLet us now examine the potential
signal-to-noise perfor-mance of a 1kxl k-pixel array with 2O0 p,m
pitch at atypical fluoroscopic dose rate of 10 nGy/frame and an
av-erage beam energy of 60 keV. At a gain G of 1500electrons/nGy,
this generatesa sign^alof 15 000 electronsper pel. With 4e:300
quanta/mm', the number of x-rayquanta Q absorbedper pixel is about
10. Hence, after de-tection the standarddeviation of x-ray quantum
noise perpixel is givenby C1Q:+l+O electrons.When using correlated
double sampling, the standarddeviation of readout amplifier noise
is about 600 electrons.The pixel capacitances'noise is about 800
electrons.Thesefigures result in an overall electronic noise with a
standarddeviation of 1000 electrons.Hence the SNR of the detector
is clearly determinedbyquantum noise at typical fluoroscopic dose
rates. Even indark image areas,where quantum counts are as Iow as
oneper pixel, the quantum noise still is at 1500
elecffons,ex-ceeding the overall electronia noise.Assuming a pixel
size of 200X200 pmz, the corre-sponding values for XRII/TV-camera
chains are a gain ofabout 10 000 electrons per 10 nGy and pixel, at
an elec-tronic noise level of approximately 200 electrons.
Thesefigures show that the electronic SNR performanceof
XRIVTV-camera systemswill not quite be reached by the solid-state
detector,but the difference should in practice be in-significant,
due to the dominant x-ray quantum noise. Inaddition for the
solid-state detector the absorption effi-ciency for the x-ray
quanta can be further increased,henceincreasing the overall SNR for
a given entrance dose.4.3 Digital ProcessingTo achieve optimal
detectorperformance, t is necessary ocompensate ixed pattern
effects like variations in offsetand gain of the sensorelements by
suitable digital process-ing. An additional effect which should be
conected digi-tally is the memory effect, which denotes he
remaining ofan undesired residual image after image readout. This
iscausedby the incomplete flowing off of electrons from
thephotodiodes. The memory effect is intrinsic to amorphous
Journal of Electronic maging January 1999 Vol. 8(1 / 15
-
7/28/2019 AAC99a
12/18
Aach, Schiebel, and Spekowiussilicon, and can be described by an
appropriate model.2aBasedon this model, digital compensation f the
memoryeffect is feasible.In conclusion, when taking into account
the above-mentionedadvantages f the solid-state etectorof no
geo-metric distortion, no veiling glare, and no vigneffing,
weexpect that the overall image quality of thesefuture x-rayimagers
wiil be superior, provided that the best perfor-mance achieved in
laboratory models so far can be trans-ferred to the production of
clinical imaging devices.5 Digital Quantum Noise
ReductionRegardlessof whether one employs the described solid-state
detectoror the discussedXRII/TV-camera chain, fluo-roscopy images
are always afflicted by relatively highquantum noise levels
compared to signal due to the lowdoserates used.One possibility to
reducequanrumnoise inthe observed mages s by appropriatedigital
noisefiltering.In fuIl frame rate fluoroscopy with 25 or more
images persecond, his can be done by,recursive temporal
averaging,what can be interpretedas digitally introducing an
intendedlag. To prevent blurring of moving objects, filtering is
re-duced or switched off entirely in areaswhereobject motionis
detected(mo ion adaptive' iIterin g).25'26For low frame rate
fluoroscopymentioned n Sec. 3.1,however, temporal filtering is
often not feasible,so that theonly option which remains is spatial
filtering within singleimages. Such an algorithm is described n the
following.To reduce noise, the filtering algorithm has to
exploitstructuraldifferencesbetweensignal contentof images
andnoise. Modelling of noise can be basedon our
previousdiscussionsn Secs.2 and3. However, appropriatemodel-ing of
the signal contentof medical images s difficult if notimpossible.
The often used Markov random field models,for example,do not
capture sufficient medical detail if keptmathematically tractable.
It is therefore desirable to keepthe assumptions on the signal as
weak as possible. Wehencerely here on a noise model as starting
point and as-sume those input observations as containing image
signal(in addition to noise) that cannotbe explainedwell by
noiseonly.5.1 Noise Reduction by SpectralAmplitudeEstimationWe have
aiready seen that quantum noise exhibits alowpass-shaped ower
spectrum n the acquired mages.Arelatively high proportion of the
overall noise power ishence contributed from low spatial
frequencies' Standardspatial window-basedfilters, like lowpas-s
onvolution ker-nels, nonstationaryWiener approaches,'' or order
statistic-based ilters,28-30'5o*"u"r, end to attenuatemainly
highspatial frequency components.This has the twofold short-coming
that, first, the full noise reduction potenrial is notexploited.
Second, he filtering operation shifts the spectralcomposition of
quantum noise even more towards low spa-tial frequencies,what is
often visualiy unpleasilg despiteareduction of the overall noise
power. Additionally, orderstatistic-basedilters tend to
generatepatchesor streaks ofconstant ntensity,3o'31 hich add to the
unnatural appear-anceof suchprocessedmages.
To avoid such artifacts without sacrificing noise reduc-tion
performance,and in particular to be able to tailor our16 Journalof
Electronicmaging January1999 Vol.8(1)
20 40 60 lnl"n.n, oo 1:Fig. 20 Noisepowervs signal ntensityor
the fluoroscopye-quencen Fig.10. Alsoshowns the noisepowerwhich
emainsafterilteringyDFT- ndDCT-basedpectral mplitudestimationwa
sapplied.
filters to spatially colourednoise, we apply here the conceptof
so-calledspectral amplitude estimation, where noise at-tenuation is
carried out in the spectral domain.32Spectralamplirude estimation
-is a widely reported approach tospeech estoration,"-ro but there
are only few reported m-age processingapplications." Starting with
a decomposi-tion of the input images into overlapping blocks which
aresubjected to a standard block transform [discrete
fouriertransform (DFT) or discrete cosine transform (DCT)],
thecentral dea is to compareeach ransform coefficient to
thecorresponding noise only" expectation,.e., to its coun-terpart
from the NPS. Each coefficient is then aftenuateddependingon how
likely it is that it contzins only noise'The purpose of the block
decomposition is to make thefilter algorithm adaptiveto small image
detail as well as tononstationaritiesof noise.5.1.1 Quantum noise
modelWe have already seen hat, due to its Poissoniannature,
hequantum noisepower depends inearly on the quantum flow4e, that
is, on signal. For the digitized fluoroscopy imageshown in Fig. 10,
this dependenceover image intensity isdepicted in Fig. 20. Over low
to medium intensities, thiscurve exhibits (approximately) the
predicted linear rise.The drop-off toward high intensities is
caused by the re-duced amplifier gain for high signat amplitudes
(whitecompression,Sec. 3.2). Second,as already discussedandshown n,
e.g., Fig. 15, quantum noise exhibits a lowpassshapedpower spectrum
n the acquired mages. For a givenfluoroscopy system, both the
signal dependenceand theNPS of quantum noise are assumed o be
known. Further-more, we assume that quantum noise is the
dominatingnoise source (quanrum-limited imaging), and that the
sys-tem MTF is signal independent. Signal dependence ofquantum
noise then affects only the absolute scale of theNPS, but notits
shape.We therefore separatesignal depen-dence and spatial frequency
dependenceof the observedI.[PSNz(S,&) by
o3o 3 0oLc
1 0
l r21S,a)oz(S)r ' (u ) , (e)
-
7/28/2019 AAC99a
13/18
Digital x-ray imaging
Fig. 21 1D llustrationf the useof overlappingindowedlocksover
spatial oordinateto prevent lockingrtifactsnprocessedimages.
heperiodicallyepeated indowunction (k)addsup ounity, husallowing
erfect econstructionf theoriginalmage.ftheDFT s
used,windowingerveshe doubleunctionf allowingblock verlaps
ndpreventingeakage.
where (S; is the noisepower which depends n signalSas llustrated
in, e.g., Fig. 20. The spatial frequencydepen-dence or NPS shape is
captured by the reference NPSn'(u), which ntegrateso unity.As our
spectral amplitude estimation filter is based onblock transforms
like DFT or DCT, we estimate he NPSby well-known periodogram
averaging echniquesbasedonthe same block size and analysis window
fype as the oneused later in the noise reduction algorithm. For a
givensystem,the described estimation is done a priori from
ho-mogeneousexposures ike the ones used for Figs. 12 and15.
Windowing serves here the double purposeof prevent-ing DFT leakage
and enabling the use of overlappingblocks in order to avoid
blocking artifacts n the processedimages (see Fig. 21). Signal
dependence 1S; ana meNPS shapenz1u1 arc stored n look-up
tables.
5.1.2 Spectral amplitude estimation filterIn the following, we
denote the observed, noisy specalcoefficients by Y u) . To compare
these observations towhat we would expect if only noise were
present,we cal-culate he so-callednstantaneous NR r(z), which
relatesthe instantaneors power of each observation l(rz) to
theexpectednoise power iD(a). The squared nstantaneousSNR is given
by
Fig.22 Block iagramJ hespectralmplitudestimationilter ornoise
eduction.After image decomposifion and DFT, the correspondingNPS
coefficientN'(S,a) for each observation s read outfrom this box,
where the mean block gray level Y(0) isused as an estimate for the
signal intensity S needed toaccess he appropriate scaling factor
fwe make here theapproximating assumptionthat the
signal-dependentnoiseis stationary within a block (short space
station*ity)].o2(S). The SNR r(u) can now be calculated,and the
cor-respondingattenuation actor hlr(u)] is read out from
the"attenuation function" box. Y(u) is then multiplied byhlr(u)1,
before the noise reduced mage is reconstructedby taking the inverse
DFT, and reassembling the imageblocks.The precise shape of the
attenuation function is deter-mined by the chosenobjective function
and the underlyingmodels ior noise and signal.3a'3?'3e'40ur filter
algorithm iibasedon the conceptof minimum mean square error
esti-mation of spectral amplitudes.32The resulting
attenuarionfunction is derived in Appendix B, and depicted in Fig.
23.5.2 Processing ResultsFigure 24 shows he enlargedcentral porfion
of the fluoros-copy image in Fig. 10, and the filtered version is
depictedin Fig. 25. This result was obtained using the DFT in
con-nection with a blocksize of 64X.64 pixel and an overlap of16
pixel. The DFT window was a modified separable2D. . l Y t r ) l tr
- \u t : o ( r ) (10)
where (an estimate for) @(a) is easily obtained from theNPS in
Eq. (9): estimation of the NPS by periodogramaveraging nvolvel
normalization by the squaresum f win-dow coefficients."''o Simply
dropping this normalizationyields the desired estimatefor O(n).
Noise reducfion sachieved by attenuatingeach observation I(u)
dependingon r27u1 according oS ( r ) : Y ( u ) h \ r ( u ) ) . ( 1
1 )The attenuationfunction r(r) variesbetween ero andone,and
increasesmonotonically over r. The total effect is anattenuationof
spectral coefficients which are ikely to rep-resent mainly noise.
Generally, the attenuafion unction isreal valued. Equation (11)
therefore s a zero-phaseilter,hence the name spectral amplitude
estimation.Figure 22 shows the block diagram of the noise
filter.The box termed "noise model" stores ie signal depen-denceof
quantumnoise and the referenceNPS of Eq. (9).
0. 80. 70. 6
tIooo6
3 4SN R r)Fig. 23 Attenuation as functionof the instantaneousSNR
r, de-rivedbasedon the conceptof minimummean square error
estima-tion.
Journal of Electronic maging January 1999 Vol. 8(1) 17
-
7/28/2019 AAC99a
14/18
Aach, Schiebel, and Spekowius
Fig.24 Enlargedortionf he magen Fig.10 .Hanning window, whose
cosine-shapeddrop-off was re-stricted to a 16 pixel wide border
region. The noise powerremaining after filtering is still signal
dependent,and quan-titatively evaluated n Fig. 20, showing a noise
power re-duction by about 607o.DCT basedprocessing esults are
hardly distinguishablefrom those obtained using DFT. As shown in
Fig. 20, quan-titative noise reduction performance is also similar
to theDFT-based processing.One advantage of the DCT is
itsnegligible leakage.Windowing before taking the transformcan
hencebe omitted. Block overlap is now only necessaryto avoid the
block raster from becoming visible, what canbe ensured by overlaps
as low as two pixels, reducing thecomputational load by about
4OVa.The window operationrequired by block overlap is now performed
within the im-age econstruction ox inFig.22.A processing esult for
another mage (Fig. 26) is shownin Fig. 27. This result was obtained
by a modified spectral
Fig, 25 Noise-reduced ersionof Fig.24.18 Joumal of Electronic
maging January 1999 Vol. 8(1
Fig. 26 Partof a lowdosex-ray mage, howing
thinguidewireinsertedntoa patienfsascularystem.
amplitude estimation algorithm which explicitly
detectsandexploits perceptually important oriented structures,
likeguide wires, catheters,or edgesof bones.alWhen using theDFT,
the occurrence of an oriented structure in an imageblock results in
the spectral domain in a concentration ofenergy along the line
perpendicular to the spatial orienta-tion and passing hrough the
origin. For each mage block,our modified algorithm uses an inertia
matrix-basedmethodto detect the line along which concentration of
energy isstrongest.*""' The filter then explicitly adapts to the
de-tected orientation by applying less attenuation to---or byeven
enhancing-coefficients along this line, with this be-havior beine
the more pronounced. the more distinct thelocal orienttion.ot
Cor."rpondingly, the processed mageshows both strong suppressionof
noise and enhancementofIines and edges, ike the guide wire. Another
example of afluoroscopy image is shown in Fig. 28, and the
processingresult in Fig. 29. Unlike the previous image, this
example
Fig. 27 Figure26 processed y orientation-dependentoise educ-t
ionand enhancement.
-
7/28/2019 AAC99a
15/18
Digital x-ray imaging
o - ' -E 3tr1 2.5I
1 . 51
Fig.28 Partof a fluoroscopymage epictingntestines.contains no
sharp guide wires, but is dominated bymedium-sharp anatomic
strucfures like fissures and thetransition from intestines to
background, which are alsoenhanced.To relate the achieved
noisepower reduction to potentialdegradations of the MTF during
filtering, we have alsomeaswed the SNR before and after filtering.
Our filter be-ing adaptive implies, of course, hat its performance
s de-pendent on the input signal. For our measurements,wesimulated
different versionsof a "difficult" image of a thinguidewire
embedded n quantum noise. correspondingtoacquisitions t 10, 30, and
100 nGy, respectively.For eachsimulated acquisition dose, the ratio
of the SNR after fil-tering to the SNR before filtering is depicted
over spatialfrequency in Fig. 30. fl-oosely speaking, his ratio
could be
Fig.29 Figure28 processed y orientation-dependentoise educ-tion
and enhancement.
0 0.2 0.4 0.6 0.8 1 1"2spatialrequencylpimm)Fig.30 Ratio f
SNRafter rocessingo SNRbefore rocessingsa function f
spatialrequency.easurementsased n 30 realiza-tionsof a simulatedest
mage ontaining thinguidewirendquantum oise orrespondingo
acquisitionst 10,30, and 100nGy.interpreted as a (signal dependent)
DQE of the filter.] Themeasurementswere based on 30 realizations of
each testimage. Evidently, the SNR is indeed improved over a
widerangeof spatial requencies,.e., potential ossesof MTFare
outweighed by the reduction of noise power.5.3 DiscussionThe
strength of our spectraldomain approach to quantumnoise reduction
is that it can be tailored specifically to theknown properties of
quantum noise, while only very gen-eral assumptionsabout the
behavior of the unlrrrown signalunder orthogonal transforms are
required.It might appear as a shortcoming of the discussed
algo-rithms that processings basedon an unnaturalblock struc-ture.
In the presentcontext,however, blockwise processinghas the
significant advantage hat inevitable "decision" er-rors, especially
mistaking noise for signal, are dispersedover entire blocks as
sine-like gratings. The occasional ap-pearanceof thesegratings can
easily be concealed by re-taining a low wideband noise floor. The
visually muchmore unpleasingpatchJike artifacts of spatial domain
fil-ters are thus completely avoided. The block raster itself
isprevented from becoming visible through the use of over-lapping
blocks. The advantageof the DCT-based filter isthat it keeps the
computational overhead to a minimum,whereas he DFT provides a
framework well suited for theexploitafionof local orientation.6
Concluding RemarksThis article described selected opics of medical
x-ray im-age acquisition and processingby digital techniques,
someof which are already well established,while othersare
pres-ently emerging. By first comparing digital radiography
sys-tems to analog ones, t was shown that a key advantage ofdigital
imaging lies in the inherent separationof image ac-quisition and
display media, which enablesone to digitallyrestore and enhance
acquired images before they are dis-played. It turned out that the
amount of restoration andenhancementwhich can be applied is
fundamentally lim-ited by noise.The performance f digital imaging
systemswas therefore characterizedbv the sisnal-to-noise ratio
and
Journal of Electronic maging January 1999 Vol. B(1 / 1
-
7/28/2019 AAC99a
16/18
Aach, Schiebel, and Spekowiusrelated measures.Expressing he SNR
in terms of x-rayquantum flow resulted in the two pnncipal
performancemeasures, iz., noise equivalentquanta(NEQ and detec-tive
quantumefficiency(DQE) Essentially, hesemeasuresquantify how
effectively the incoming quantum flow is ex-ploited by an imagrng
system at different spatial frequen-cies.
The relation between signal and noise transfer was em-phasized
in our subsequentdiscussions, starting withpresent-day luoroscopy
systems based on x-ray imageintensifier/camerachains. We
highlighted the fundamentaldifferences in noise behavior between
vacuum cameratubes and CCD cameras, and discussed he major
pointswhere image quality can be improved by the introductionof CCD
cameras.We then presenteda concept for a flatsolid-state x-ray
detector based on amorphous silicon,which is intended to be used
for both high resolution radi-ography and fluoroscopy. Here, we
analyzedsignal transferand noise properties for the more critical
application tolow-dose fluoroscopy and showed that this concept can
in-deed be a viable alternativerto XRIVTV systems.
Presentdevelopment efforts, however, focus on the introduction
ofthis and other flat detector tvpes into the market for
disitalradiography.a3'aOth". nat etector types include
a-Siae-tectors with direct diode switching without using
TFTs,a5'46and detectorsusing amorphousselenium(a-Se) to detect
xradiation, which are read out by TFT matrices.4T'48inally,we
described a new noise reduction algorithm specificallytailored to
the reduction of x-ray quantum noise in low-dose x-ray images,where
the SNR is especially ow. As apurely spatial filter, this algorithm
is particularly suited forlow frame ratesoften used in
connectionwith pulsed fluo-roscopy, where temporal filtering is
often not feasible.Appendix ATo see hat the power spectraldensityN|
of quantum noiseis given by the quantumflow qe, consideran ideal
detectorwhich countsquanta mpinging on an areaA. The detectedsignal
hen obeysS:qsA. Due to the Poissonian atureofquantum noise,S is
aiso identical to the noise power o2 atthe detector output. The
impulse response (x) of this de-tector is
I t i f x e A ,f("): I o else,where x is the spatial coordinate.
The noisepower can alsobe calculated from
(42)Hence,Ni:eo.Appendix BThe MMSE estimateSia; of the
noise-free pectralcoef-ficient S(u) is given by the conditional
mean S1r;:E[S(a)lY(u)), and explicit ly exploits he
well-knownsignal energy compaction properties of both the DFT and20
Joumalof Electronicmaging January1999 Vol.8(1)
the DCT. Assuming for eachblock that the undistorted im-age
signal appearsn only a few coefficients, each observedcoefficient
represents either noise only (null hypothesis116),or signal and
noise(alternativehypothesisF11).Splirt ing up A[S(r)lY(r.r)]
according o thesehypotheses, eclearly have E[S(a)ly(u),Ao]:0. The
conditionalmeancan then be rewritten tcrS(u)r ls( , ) lv( r ) l: r
[ s (a ) lY (u ) ,H ] . (1 -P {g lY ( " ) l ) , (B l )where
Prff/sly(r)] is the probabiliryfor the null hypoth-esisgiven Y(a).
Furthennore,basedon the single observa-tion I(a) comrptedby
zero-meannoise, and with no fru-ther prior knowledge available, we
haveE[s(a) lY(u),H]:Y(u). Using the Bayes rule,P.[HolY(u)] cu be
rewritten oPr[Hs lY(u) ] :plrfu)lrsl.k(Ho)pLvtu)lwith p[f
(a)1116]denoting he probabilitydensity function(pdf) for the noisy
observation when signal is absent.Theunconditionalpdf p[y(a)] can
be split up intoplY u) l p lY u) lHslPr( o
(82)
+ p lY (u ) lH1 l ( I - P r (o ) ) .Equation (B1) can now be
rewritten to. I a f r ( a ) l a o J . P r ( H o ) l ( ' )s (a ) r
fu ) } * o l ra lWr l .P r ( r ) l
(Ar) pty(lnst:;;*{ +#]
^ l ( l v t u ) l 2 l- ls ( u ) : r t a ) [ t + t tx n l -. ( r
, i j ,with
Since each spectral coefficient is a weighted sum of thegray
levels in the processedblock, we assume he coeffi-cients as complex
Gaussian distributed according to thecenal limit theorem.For the
null hypothesis, .e., when theobservation (u) is caused by noise
only, this complexGaussian df is given by
(B3)
(84)
^ ^ f *t ' : Ni I _ f2(xtdx:N24.
(Bs)where (a) is the noise variancedefined n Sec. 5.1. ForY(a)
given hypothesisHy, i.e., when signal is present,another complex
Gaussian pdf can be assumed with un-known variance.Typically,
however, he variance P(u) ofcoefficients containing both signal and
noise is much largerthan
-
7/28/2019 AAC99a
17/18
Digital x+ay imaging
. P(u)Pr(H6) : -
-
7/28/2019 AAC99a
18/18
Aach, Schiebel, and Spekowius43 . "Digital x-ray capture," in
Supplement o: Diagnostic maging,D3-D2 5 (Nov. 1996).44 . "On the
horizon: Digital radiography," in Med. Imaging, 100-107(Nov.
i996).45. J. Chabbal, C. Chaussat,T. Ducourant,L. Fritsch, J.
Michailos, V.Spinnler, G. Vieux, M. Arques, G. Hahm, M. Hoheisel,
H. Horb-aschek, R. Schuiz, and M. Spahn, "Amorphous silicon X-ray
imagesensor," in Medical Imaging 1996: Physics of Medical
Imaging,Proc. SPIE2708,499-510 (1996).46. T. Graeve, Y. Li, A.
Fabans, and W. Huang, "High-resoiution amor-
phous silicon image sensor," in Medical Imaging 1996:
PhysicsoJMedical Imaging, Proc. SPIE 2708, 494-498 (1996).4'7. D.L.
Lee, L. K. Cheung,E. F. Palecki,and L. S. Jeromin,"A dis-cussion on
resolution and dynamic range of c-Se-TFT direct digitalradiographic
detector," in Medical Imaging 1996: Physicsof MedicalImaging, Proc.
SPIE 2708,511-522 (1996).48. W. Zrao, I. Blevis, S. Germann, and J.
A. Rowlands, "A flat-paneldetector for digital radiology using
active matrix readout of amor-phous selenium," in Medical Imaging
1996: Physics of Medical Im-aging, Proc- SPIE 2708,523-531
(1996).49. J. S. Lim, Two-Dimensional Signal and Image Processing,
Prentice-Hall, Englewood Cliffs, NJ (1990).Til Aach received he
dipl oma and Doc -toral degree n electrical ngineeringromAachen
University f Technology RWTH)in 1987and 1993, espectively.rom1987to
1993,he was with he Instituteor Com-municationsEngineering,Aachen
Univer-sity of Technology,where he was respon-sible for a numberof
researchoroiects nmedical image processing,anlyiis andcompressionof
stereoscopicmages,andia ' image sequence analysis.From 1993 to1998,
he was a project eaderwith PhilipsResearchLaboratories,Aachen,
where he was responsibleor projects n medical mage
processing.n 1996,he was als o a lecturerwith
Otto-von-GuerickeUniversity,Magdeburg.As ol February 1998,he was
appointedafull professor f computerscienceand head of the lnstitute
or Sig-nal Processing nd ProcessControl,MedicalUniversity f
Luebeck,Germany.He s also a consultant o industry.Ulrich Schiebel
received is PhD n phys-ics from
Justus-Liebig-University,iessen,Germany n 1975.Since1978 he is
withPhilips Research Laboratories,Aachen,Germany.For more han 15
yearshe hasbeen working n the ield of electronic -raydetectors or
digital adiography, eliveringmajor research contributions to
theselenium-basedPhilips Thoravision sys-tem and a-Si:H
flat-panelx-ray imagers.Currentlvhe is a department ead n Phil-ips
Research, esponsibleor the ieldsof luminescentmaterials
ndelectronemitters.Gerhard Spekowius studied physics atthe
University f Hannover Germany) ndreceived PhD n 1991 His
majorfields fworking have been multiplemolecular on-ization
processes, photionization, aserspectroscopy, nd
chargedparticledetec-tors. In 1992 he joined the Phil ips Re-search
Laboratoriesn Aachen.Since henhe has been workingas project eader
ordigital x-ray imaging with XRlI/CCD cam-era chainsand x-ray
magequalitymodel-on medicaldisolavsand soft-copv maqeng. His
current ocus s on medicaldisplaysand soft-copy
magepresentation.
22 / Journal of Electronic maging January 1999 Vol.8(1)
