Mannudeep K. Kalra, MDConrad Wittram, MB, ChBMichael M. Maher, MD, FFR

(RCSI), FRCRAmita Sharma, MDGopal B. Avinash, PhDKelly Karau, PhDThomas L. Toth, AASElkan Halpern, PhDSanjay Saini, MDJo-Anne Shepard, MD

Index terms:Computed tomography (CT), radiation

exposureFilters, radiographicThorax, CT, 60.12112, 60.12115

Published online before print10.1148/radiol.2281020606

Radiology 2003; 228:257–264

Abbreviations:CNR � contrast-to-noise ratioMTF � modulation transfer function

1 From the Department of Radiology,Founders 202, Massachusetts GeneralHospital and Harvard Medical School,32 Fruit St, Boston, MA 02114 (M.K.K.,C.W., M.M.M., A.S., E.H., S.S., J.S.), andGE Medical Systems, Waukesha, Wis(G.B.A., K.K., T.L.T.). Supported in partby a grant from GE Medical Systems.Received May 23, 2002; revision re-quested July 16; final revision receivedOctober 11; accepted October 23. Ad-dress correspondence to J.S. (e-mail:jshepard@partners.org).

Author contributions:Guarantors of integrity of entire study,J.S., S.S.; study concepts, M.K.K., T.L.T.,G.B.A., C.W.; study design, M.K.K.,C.W.; literature research, M.K.K., G.B.A.;clinical studies, M.K.K., J.S., C.W.,M.M.M., A.S.; experimental studies,K.K., G.B.A., T.L.T.; data acquisition,M.K.K.; data analysis/interpretation,E.H., M.K.K., M.M.M.; statistical analy-sis, E.H., M.K.K., M.M.M.; manuscriptpreparation, M.K.K., M.M.M., C.W.,G.B.A., K.K., S.S.; manuscript definitionof intellectual content, M.K.K., T.L.T.;manuscript editing, M.K.K., M.M.M.,C.W., G.B.A., K.K.; manuscript revision/review and final version approval,M.K.K., M.M.M., C.W., K.K.© RSNA, 2003

Can Noise Reduction FiltersImprove Low-Radiation-DoseChest CT Images? Pilot Study1

Effect of noise reduction filters on chestcomputed tomographic (CT) imagesacquired with 50% radiation dose re-duction was evaluated. Two sets of im-ages were acquired with multi–detec-tor row CT at standard (220–280 mA)and 50% reduced (110–140 mA) tubecurrent at the level of the carina. Afterpostprocessing with six noise reduc-tion filters, images were comparedwith baseline standard-dose imagesfor noise, sharpness, and contrast inlungs, mediastinum, and chest wall.Quantitative image noise was mea-sured in descending thoracic aorta.Modulation transfer functions werecalculated from CT images of 50-�mwire. Noise reduction filters reducedimage noise on low-radiation-dosechest CT images, with some compro-mise in image sharpness and contrastassessed qualitatively, and slightly al-tered modulation transfer function athigher spatial frequencies.© RSNA, 2003

In U.S. medical facilities, the annualnumber of computed tomographic (CT)examinations increased from approxi-mately 3.6 million in 1980 to 13.3 mil-lion in 1990 and to 33 million in 1998(1,2). Findings of a recent study in theNetherlands revealed that the annual av-erage effective radiation dose from diag-nostic medical exposures has increasedby 26% to 0.59 mSv per capita since thelast inventory of medical radiation expo-sure was obtained 1 decade ago (3). In1998, the annual population-averagedeffective dose was attributed to x-ray pro-cedures in hospitals (87%), nuclear med-icine examinations (11%), and mammo-graphic screening (1.5%). In the UnitedKingdom, Crawley and colleagues re-ported a higher patient radiation dosefrom CT in comparison with that from

other radiologic procedures, with CTcontributing more than 40% of the esti-mated collective effective dose to thepopulation from medical x-rays (fromboth CT and radiography) in 1999 (4).Reflecting a similar trend, Mettler et al (5)reported that although CT representsabout 1/10 of all examinations in whichionizing radiation is used in the UnitedStates, it contributes more than two-thirds of the total radiation dose. Withthe advent of improved CT technology,including the development of multi–de-tector row scanners, the use of CT in di-agnostic radiology is likely to increasefurther in the future (6).

Currently, there is an emerging con-sensus for a reduction of the radiationdose associated with CT scanning. How-ever, modulation of scanning parame-ters, such as tube current and tube volt-age, to decrease radiation exposure islimited by compromised image quality.Thus, the purpose of our study was toassess the effect of noise reduction filterson chest CT images acquired with 50%radiation dose reduction.

Materials and Methods

Subjects and Imaging Techniques

The Human Research Committee ofthe institutional review board approvedthe study protocol. All the patients gavetheir written informed consent to partic-ipation. The study cohort included fourconsecutive subjects who were older than65 years, had a known history of malig-nancy, and had been referred for routinechest CT. There were two women andtwo men with a mean age of 67 years (agerange, 65–71 years). All studies were per-formed with a multi–detector row CTscanner (LightSpeed QX/i; GE MedicalSystems, Waukesha, Wis) with four de-tector rows. Two sets of an additionalfour images were acquired in the equilib-rium phase with the following scanningparameters: 140 kVp, detector configura-tion of 2.5 mm, beam pitch of 1.5:1, table

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speed of 15 mm per gantry rotation, andgantry rotation time of 0.8 second. Five-millimeter images were reconstructed at5-mm intervals with a standard reconstruc-tion filter algorithm. The first data set ofbaseline standard-dose images was ac-quired at 220, 240, 240, 280 mA tube cur-rent. These parameters were identical tothose used for the diagnostic examinationin the dynamic phase. For the second set ofbaseline images acquired at a reduced dose,tube current was reduced by 50%, and theimages were acquired at 110, 120, 120, 140mA, with all of the other scanning param-eters remaining constant.

Both sets of these standard- and re-duced-dose baseline images were post-processed with six noise reduction filters(GE Medical Systems, Milwaukee, Wis) toreduce image noise while preserving thequalitative appearance of the noise with-out perceptible loss of anatomic struc-ture. In comparison with tube attenua-tion filters, these noise reduction filtersrepresent image processing filter algo-rithms that are applied to postrecon-structed images. According to coding ofthe manufacturer, these filters were des-ignated as follows: filter A, normal-low;filter B, normal-medium; filter C, normal-high; filter D, special-low; filter E, special-medium; filter F, special-high. The Ap-pendix includes the mechanism of noisereduction provided by the filters.

For each of the four patients, two sets

(without and with 50% reduced dose) offour baseline images were generated (n �2 � 4 � 4 � 32). These baseline images(n � 32) were postprocessed with six noisereduction filters (n � 192). Subsequently,the postprocessed images were combinedwith the baseline images (n � 192 � 32 �224) and randomized. Thus, 224 imageswere evaluated by four experienced chestradiologists (C.W., M.M.M., A.S., J.S.) whowere unaware of the image order and theparameters.

Qualitative Analysis

To facilitate blinded evaluation, imagespresented to the chest radiologists did notinclude patient demographics and scan-ning protocol information (ie, details ofkilovolt peak, milliampere, and reconstruc-tion algorithm used for scanning). Foursubspecialty radiologists (C.W., M.M.M.,A.S., J.S.) with expertise in thoracic imag-ing compared the low–radiation-dose post-processed images with the baseline stan-dard-dose images obtained in the samepatient at a similar level in a side-by-sidemanner by using a digital picture-archiv-ing and communication system diagnosticworkstation (Impax RS 3000 1K; AGFATechnical Imaging Systems, Richfield Park,NJ). All four radiologists independently re-viewed the images at a constant windowwidth and window level to simulate bothlung window settings (window width,

1,500 HU; window level, �600 HU) andsoft-tissue window settings for mediasti-num and chest wall (window width, 350HU; window level, 50 HU). Images wereassessed for lung noise, lung contrast,sharpness of central lung vessels and air-ways, sharpness of peripheral lung vesselsand airways (ie, within 2 cm of the parietalpleura), mediastinal noise, mediastinalcontrast, mediastinal sharpness, chest wallnoise, chest wall contrast, and chest wallsharpness (n � 10 factors). The graininessof the image was the main factor in decid-ing the degree of noise in lung fields, me-diastinum, or chest wall. In comparisonwith the corresponding baseline images,image noise was graded as better than,equal to, or worse than that on the baselineimages. Contrast was scored on the basis ofrelative ability to discern various anatomicstructures with differential densities.Sharpness of the lung vessels, mediastinalstructures, and chest wall was assessed onthe basis of visually sharp reproduction ofthese structures on the given image.Hence, 10 factors in 224 images (n � 10 �224 � 2,240 qualitative factors) were re-viewed by four radiologists, which resultedin 8,960 qualitative rating factors (n �2,240 � 4 � 8,960). Each factor was as-sessed by using a three-point scale (score 1,worse than that of the corresponding stan-dard-dose CT image; score 2, equal to thatof the corresponding standard-dose CT im-age; score 3, better than that of the corre-

TABLE 1Results of Qualitative Assessment of Four Radiologists for 10 Factors

FilterLungNoise

LungContrast

CentralLung

Sharpness

PeripheralLung

SharpnessMediastinal

NoiseMediastinal

ContrastMediastinalSharpness

ChestWall

Noise

ChestWall

Contrast

ChestWall

Sharpness

None,reduced

1.95 �0.19

2.50 �0.16

2.75 �0.14

2.73 �0.09

1.09 �0.06

2.61 �0.39

2.77 �0.17

1.13 �0.13

2.69 �0.33

2.78 �0.19

current (�.05) (�.05) (�.05) (�.05) (.001)* (�.05) (�.05) (.001)* (�.05) (�.05)A 2.13 �

0.152.25 �0.07

2.47 �0.19

2.61 �0.14

1.19 �0.07

2.61 �0.20

2.70 �0.24

1.36 �0.14

2.59 �0.23

2.75 �0.15

(�.05) (�.05) (�.05) (�.05) (�.05) (�.05) (�.05) (�.05) (�.05) (�.05)B 2.16 �

0.252.09 �0.24

2.22 �0.08

2.28 �0.13

1.36 �0.08

2.44 �0.22

2.56 �0.14

1.50 �0.19

2.48 �0.25

2.69 �0.05

(�.05) (�.05) (�.05) (�.05) (�.05) (�.05) (�.05) (�.05) (�.05) (�.05)C 2.31 �

0.141.94 �0.35

1.97 �0.31

1.95 �0.27

2.28 �0.25

2.05 �0.25

2.28 �0.18

2.27 �0.29

1.91 �0.30

2.14 �0.39

(.007)* (.005)* (.02)* (.02)* (.03)* (.02)* (.01)* (.005)* (.006)* (.01)*D 2.13 �

0.141.97 �0.19

2.26 �0.43

2.26 �0.41

1.28 �0.21

2.58 �0.25

2.70 �0.14

1.44 �0.25

2.49 �0.22

2.72 �0.15

(�.05) (�.05) (�.05) (�.05) (�.05) (�.05) (�.05) (�.05) (�.05) (�.05)E 2.21 �

0.221.81 �0.18

2.00 �0.22

1.94 �0.32

1.51 �0.25

2.45 �0.27

2.51 �0.19

1.55 �0.16

2.42 �0.27

2.59 �0.14

(.03)* (.03)* (.03)* (.02)* (�.05) (�.05) (�.05) (�.05) (�.05) (�.05)F 2.52 �

0.191.41 �0.28

1.38 �0.208

1.28 �0.17

2.39 �0.19

1.92 �0.19

2.09 �0.28

2.36 �0.16

1.88 �0.34

2.11 �0.32

(.003)* (.001)* (.001)* (.001)* (.01)* (.03)* (.01)* (.03)* (.005)* (.005)*

Note.—Values are the mean scores � standard error of the mean. Numbers in parentheses are P values.* Value shows a statistically significant difference with a two-sided P value of less than .05, compared with the value of the corresponding baseline

standard-dose CT image.

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sponding standard-dose CT image). In ad-dition, conspicuity of vascular structures(ie, identical visualization of tiny periph-eral vascular structures) in the peripheral 2cm of the lungs was also evaluated with thesame three-point scale. The standard-doseand postprocessed images were identicallymagnified for this purpose.

In addition, standard-dose images post-processed with noise reduction filters werealso assessed in an identical manner.

Quantitative Analysis

One author (M.K.K.) obtained thequantitative measurements of attenua-tion values (in Hounsfield units) and im-age noise (SD of attenuation coefficients,n � 2 � 2 � 224 � 896 measurements) inthe descending thoracic aorta and chestwall at a constant position with a regionof interest of a constant size (45 squarepixels) and shape on all 224 images.Background image noise was also mea-sured on all images (n � 896 � 224 �1,120) for calculation of the contrast-to-noise ratio (CNR) of the descending tho-racic aorta with respect to chest wall mus-cles for each image. The CNR wascalculated by subtracting the attenuationvalue of the chest wall muscles from theattenuation value of the thoracic aorta.

Background Image Noise

The effect of noise reduction filters onthe spatial resolution of images was de-termined for baseline images and post-processed images. The modulation transferfunction (MTF) was used to mathemati-cally quantify the influence of the vari-ous filters on image spatial resolution.The MTF was computed as the angularaverage of the two-dimensional Fouriertransform of the point spread functionmeasured from a CT image of a phantomthat comprised a 50-�m tungsten wirecentered in a 2-inch hole through a Plexi-glas block. The images were acquiredwith a multi–detector row CT scanner, asnoted previously, at 200 mA and 120 kVand were reconstructed at a section thick-ness of 2.5 mm. In addition, image noisewas measured objectively as the SD of theattenuation value from the originalphantom CT image, as well as from post-processed images.

Statistical Analysis

For each subset of baseline and postpro-cessed images, qualitative image noise,sharpness, and contrast scores for all 10factors were reported as the mean � stan-dard error of the mean. Values for qualita-

tive image factors of low–radiation-doseCT images postprocessed with noise re-duction filters were compared with thoseof baseline images acquired at a standardtube current. Individual scores of quali-tative image factors were compared byusing the Wilcoxon signed rank test andstatistical software (SAS/STAT; SAS, Cary,NC). Similarly, objective image noise oflow–radiation-dose CT images postpro-cessed with noise reduction filters wascompared with that of standard-dosebaseline images. Statistical differences be-tween these two groups were determinedby using the paired t test and software(Excel; Microsoft, Redmond, Wash).

The correlation between subjective im-age noise and quantitative image noisewas determined for all four readers byusing the Spearman correlation test. Sig-nificant correlation was defined as a dif-ference with a two-sided P value of lessthan .05. The Cohen test was used toassess the degree of interobserver agree-ment between the readers. P values wereconsidered exploratory in nature, andtherefore no Bonferroni correction wasmade (12). The coefficient values forinterobserver agreement were consideredas follows: slight, with a value less than0.20; fair, with a value between 0.21 and0.40; moderate, with a value between0.41 and 0.60; substantial, with a valuebetween 0.61 and 0.80; or almost perfect,with a value between 0.81 and 1.00 (12).

Results

Qualitative Data

Mean qualitative image noise, sharp-ness, and contrast scores at standard-doseCT and seven subsets of images acquiredat 50% reduced tube current (ie, six sub-sets of postprocessed images and one sub-set of baseline images obtained at scan-ning with reduced tube current) for thefour readers are summarized in Table 1.All four readers rated the baseline low–radiation-dose CT images as inferior tothe baseline standard-dose CT images (P �.05) with respect to image noise in the me-diastinum and chest wall. For the lung, allfour radiologists found the baseline stan-dard-dose images less noisy than the base-line reduced-dose images; however, thesedifferences were not statistically significant(P � .05). A statistically significant (P � .05)improvement in qualitative image noisewas noted in the mediastinum and thechest wall with two of the six filters, that is,filters C and F (Fig 1).

There was a decrease in the overallsharpness on the postprocessed images in

Figure 1. Contrast material–enhanced transverse CT images of the chest obtained with 140kVp, 110 mA, and 0.8-second gantry rotation time in a 65-year-old man. (a) Baseline image.(b) Same baseline image postprocessed with filter F. Note deterioration of sharpness of lungvascular marking and conspicuity of peripheral vascular markings (arrows) on b in comparisonwith a.

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comparison with that on the baseline im-ages. This was statistically significantwith filters C, E, and F for sharpness ofvascular structures and airways in thecentral and peripheral lung fields (P �.05) (Table 1). The reduction in sharpnesswas associated with loss of visualizationof details of the small vascular structuresin the peripheral 2 cm of the lung fieldswith filter F, the filter that caused maxi-mum improvement in image noise (Fig2). Similarly, filters C and F markedly de-

creased the visual sharpness of the medi-astinal vascular structures and the chestwall. Image contrast was affected in asimilar fashion by the noise reductionfilters. There was a decrease in contrast inthe lung fields, mediastinum, and chestwall, in comparison with the correspond-ing baseline images. This decrease in con-trast was statistically significant with fil-ters C, E, and F in the lung fields and withfilters C and F in the mediastinum andchest wall (P � .05). Filters A, B, and D

caused minimal reductions in image con-trast and sharpness, which were not sig-nificant (P � .05) with respect to thebaseline images.

The standard-dose postprocessed im-ages showed improvement in imagenoise with four filters (filters B, C, E, F),with compromise in image sharpnessand contrast.

Quantitative Data

Mean objective image noise and CNRfor the corresponding subsets of imagesare presented in Table 2. The objectivedata showed a statistically significant dif-ference in objective image noise betweenthe standard-dose and low–radiation-dose CT images (P � .05), with standard-dose images being less noisy than werereduced-dose images. A statistically sig-nificant reduction in objective imagenoise was noted with filters C and F (P �.05). Images of the line-wire phantomalso demonstrated reduction of imagenoise with all filters, with respect to thebaseline standard-dose CT images (Table2, Fig 3). In comparison with the baselinestandard-dose images, a statistically sig-nificant improvement of CNR in the re-duced-dose postprocessed CT images was

Figure 2. Contrast-enhanced transverse CT images of the chest ob-tained with 140 kVp, 120 mA, and 0.8-second gantry rotation time ina 70-year-old man. (a) Baseline image. (b) Same baseline image post-processed with filter C. (c) Same baseline image postprocessed withfilter F. Note improvement of mediastinal and chest wall noise anddeterioration of chest wall sharpness (arrow) in b and c in comparisonwith a.

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noted with four of the six noise reductionfilters (P � .05).

Results of spatial resolution for thenoise reduction filters are illustrated inFigures 4 and 5. It is evident that all filterconfigurations, with the exception of filterB, boosted the MTF at low spatial fre-quencies (Fig 4). The 50% MTF demon-strated a resolution enhancement for low-frequency objects of approximately 3%for filter C and 8% for filter F. Also, allfilter configurations reduced the MTFslightly at higher spatial frequencies (Fig5). The 10% MTF showed a reduction inresolution of high-frequency objects ofapproximately 4% for filter C and 5% forfilter F. CT images of the phantom post-processed with noise reduction filtersshowed a marked decrease in image noisewith filters B, C, E, and F (Fig 1).

Interobserver Agreement

The Cohen test of agreement amongthe four radiologists was statistically sig-nificant (moderate interobserver agree-ment, simple coefficient, 0.57; two-sided P value, �.05). Additionally wefound a significant correlation betweenthe subjective image noise assessment offour readers and the quantitative imagenoise with the Spearman correlation test(Spearman correlation coefficient, 0.6;P � .05).

Discussion

In 1996, in an American College ofRadiology publication, the risk of cancerdeath for those undergoing CT was re-ported to be 12.5 per 10,000 populationfor each single-phase CT scan of the ab-

domen (13). This risk was compared with12.0 cancer deaths per 10,000 populationthat occurred as a result of 1 year ofsmoking in a similar population. In thispublication, the American College of Ra-diology suggested that the CT radiationdose be reduced, especially in studies per-formed on children and small adults.Subsequently, Brenner et al (14) reportedan association between CT radiation doseand increased lifetime radiation risks inchildren relative to adults. On the basisof their calculations that approximately600,000 children younger than 15 yearsundergo abdominal and head CT exami-nations annually in the United States,approximately 500 individuals might ul-timately die from cancer attributable toCT radiation. These concerns about po-tential cumulative harmful effects of ion-izing radiation from CT are aptly re-flected in the increasing number ofscientific reports in which associated po-tential radiation hazards are evaluated.However, CT is an extremely valuable di-agnostic tool, and in most cases whenclinically indicated, the risk-versus-bene-fit ratio is probably acceptable (15).

Findings in CT radiation dose studieshave indicated promising results for a re-duction in radiation exposure from achest CT examination (16–19). The rec-ommended stratagem includes limita-tion of CT examinations to carefully jus-tified indications, avoidance of needlessmultiphase protocols, judicious use of re-peat or follow-up examinations, and ap-propriate adjustment of technical scan-ning parameters on the basis of apatient’s attributes (20,21). Findings ofseveral studies (22–24) about use of chest

CT for cancer screening indicated thatthere was no significant difference innodule detection at low–radiation-dosechest CT. Mayo et al (24) recommendedthat a twofold reduction in tube current(ie, 400–140 mAs) and resultant radia-tion dose do not cause a significantchange in subjective image quality or indetection of mediastinal or lung abnor-malities with conventional chest CT.

In addition, various technical ad-vances to decrease radiation dose fromCT have been developed or are in an ex-perimental stage (25–29). Kachelriess etal (29) investigated the use of multidi-mensional generalized adaptive filters forCT image noise reduction and reductionin the radiation dose to the patient. Theyreported a 30%–60% noise reduction inimage noise, typically along the directionof the highest attenuation in noncylin-drical body regions, such as the shoulder.A novel technique for noise reductionwith use of a nonlinear wavelet filter inwhich the filter thresholds are calculatedindividually from the “measured” projec-tion data has been described (30). Alvarezand Stonestrom (31) evaluated spatialresolution and noise properties of CT im-ages altered by two-dimensional linearfiltering of the initial image. They docu-mented that the use of their filter func-tions could reduce the noise variance by17% in comparison with the reductionwith conventional filters. Use of nonlin-ear image processing techniques, in par-ticular smoothing that is based on theunderstanding of the image, has been re-ported for creating CT images of goodquality by using less radiation (32). Theseresults have shown that newer nonlinearimage processing techniques, in particu-lar smoothing that is based on the under-standing of the image, may help to create

Figure 3. Graph shows image noise on corresponding original andpostprocessed images of line-wire phantom. Note the similar trendswith qualitative and quantitative noise reduction, compared withpatients’ data shown in Tables 1 and 2.

Figure 4. Graph shows MTF with line-wire phantom for originalimages and images postprocessed with filters A–F. lp/cm � line pairsper centimeter.

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CT images of good quality by using lessradiation.

A fundamental objective of this studywas to determine whether use of noisereduction filters could improve the imagenoise with 50% reduction in CT tube cur-rent. In addition, we also aimed to assesswhether these filters have sufficientpromise in facilitating radiation dose re-duction to allow their use in CT scannersin the general medical community. Toour knowledge, our assessment of 8,960qualitative and 1,120 objective factors(total factors evaluated, 10,080) of imagenoise, sharpness, and contrast representsthe largest study of low–radiation-doseCT image quality reported in the chestradiology literature. Three of the six fil-ters (filters C, E, and F) analyzed in thepresent study showed improvement inimage noise on low–radiation-dose CTimages in comparison with image noiseon images acquired at the standard cur-rent of 240–280 mA and reconstructedby using standard reconstruction algo-rithms. There was a significant decreasein image sharpness and contrast with thefilters that caused the most significantdecrease in image noise, that is, filters Cand F. We compared the images acquiredwith a reduced radiation dose with thoseacquired with a standard radiation dose,and we noted improvement in the imagenoise with all noise reduction filters.With the exception of filters C, E, and F,there was no significant decrease in im-age contrast and sharpness with otherfilters. The quantitative analysis sup-ported subjective data and demonstratedthat the MTF of filtered images is notconsiderably altered from the originalimage. From such analysis, it is notablethat resolution of structured objects ispreserved and, in most cases, improvedwhile noise reduction in nonstructuredregions is achieved.

Interestingly, although filter F resultedin maximum improvement in imagenoise, it also caused a decrease in theconspicuity of small vessels in the pe-ripheral lung fields. A possible explana-tion for this phenomenon may be thatthis filter may have “filtered out” the pix-els representing these small vessels, be-cause to the filter they may have repre-sented noise. Alternatively, a decrease inimage contrast and sharpness associatedwith this particular filter may have beenresponsible for decreased conspicuity.Regardless, this observation raises con-cerns about the possibility of nonvisual-ization of subtle or low-contrast lesionswith filter F. Overall, the readers pre-ferred filters C and F to view the soft

tissues, because these filters caused max-imum reduction of noise. Findings in thepresent study suggest that filters C and Fwere most effective in reducing mediasti-nal and chest wall noise and could beuseful in “very noisy” images. For lessnoisy images, filters B and E may be usedfor reducing noise with less compromisein sharpness and contrast. The use of fil-ters C and F resulted in reduced conspi-cuity of small peripheral vessels; thus,their use in general practice may result infailure to appreciate small nodules. Fur-ther clinical trials with lesion detectionwill be essential to address this issue andto establish the validity and actual appli-cation of noise reduction filters for radi-ation reduction.

There were several limitations in ourstudy. The issue of potential compromisein diagnostic sensitivity of postprocessedCT images acquired with a reduced radi-ation dose for detection and characteriza-tion of lesions was not addressed. How-

ever, as an initial pilot study, ourobjective was to obtain preliminary dataabout the effectiveness of noise reduc-tion filters in reducing image noise inimages obtained with substantial radia-tion dose reduction and to ascertain theeffect of the filters on image sharpnessand contrast with low–radiation-doseCT. An important limitation of our studywas that a small number of patients wereincluded, and there existed a consequentinterdependency of data and statisticalanalysis, which resulted from a largenumber of images being obtained from asmall patient cohort. However, this inter-dependence was accounted for by the useof the Wilcoxon signed rank test (ie, thenonparametric equivalent of the paired ttest), which explicitly incorporates theinterdependence of the test values to ob-tain a more powerful test. As noted in ourmethods, the interdependence of the testresults in which the low–radiation-dosepostprocessed filtered images were com-

TABLE 2Mean Objective Image Noise in Descending Thoracic Aorta, CNR,and Signal-to-Noise Ratio in All 224 Images

FilterMediastinal

Noise CNRSignal-to-Noise

Ratio

NoneStandard current 7.35 7.62 161.16Reduced current 10.41* 7.03 110.67*

A 9.70* 9.37 120.43B 8.75 10.01 132.93C 7.57 12.53* 152.46D 9.30 13.29* 126.21E 8.20 15.21* 142.84F 6.87 18.94* 170.10

* Value shows a statistically significant difference with a two-sided P value of less than .05,compared with the value of the corresponding baseline standard-dose CT image.

Figure 5. Graph shows spatial resolution at 10% (Œ) and 50% (F)MTF for original images and images postprocessed with filters A–F.lp/cm � line pairs per centimeter.

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pared with the baseline images was notcorrected with Bonferroni adjustment,because the P value was exploratory. Asmall study size is an issue when the studyresults are “negative,” because they mayreflect inadequate power, due to too smalla sample size. However, a small sample sizedoes not make positive results more likely.On the contrary, larger differences are re-quired for a positive result in a small studythan are required for a positive result in alarge study. Radiation safety concerns andconsequent procedural difficulties in ob-taining consent for acquisition of extra im-ages, along with elaborate labor-intensivepostprocessing with noise reduction filters,contributed substantially to the small pa-tient numbers, and we could not fully ex-plore the entire range of biovariability ofthe entire patient population.

Objective noise and CNR data ob-tained on all images, as well as noise andspatial resolution (ie, MTF) obtained withthe wire phantom, supported the quali-tative data. The qualitative improvementof image noise with two of the six filters(filters C and F) correlated with the ob-jective measurement of noise on the im-ages obtained in the patients and on theimages of the line-wire phantom thatalso demonstrated maximum reductionof image noise with filters C and F.

Our initial findings with reduced-doseCT images processed with noise reduc-tion filters are encouraging. Noise reduc-tion filters successfully reduced imagenoise on low–radiation-dose chest CT im-ages, with some compromise in imagesharpness and contrast assessed qualita-tively, and slightly altered MTF at higher

spatial frequencies. Furthermore, theseresults can be confirmed with those ofstudies for lesion detection in a largercohort of patients with a wider modula-tion of tube current when noise reduc-tion filters become commercially avail-able for clinical and research purposes.

In conclusion, postprocessing of re-duced-dose CT images with noise reduc-tion filters resulted in improved qualitativeimage noise in the lung fields, mediasti-num, and chest wall with all six filters. Allthe reduced-dose postprocessed imagesshowed better CNR and signal-to-noise ra-tio in comparison with those of the base-line low–radiation-dose CT images. How-ever, there was some compromise in imagesharpness and contrast.

APPENDIX

The most fundamental noise reductionstrategy, both computationally and theoret-ically, is the application of a simple smooth-ing filter. In the early days of dual-energyCT, Rutherford et al (7) first suggested usinga low-pass filter to smooth the noisy recon-structed images. Driven by the use of dual-energy in computed radiography, noise re-duction strategies in the late 1980s and1990s focused on more sophisticated algo-rithms. Subsequently, Kalender et al (8) de-veloped an algorithm called correlatednoise reduction in which knowledge aboutthe anticorrelation in noise between boneand tissue images was used. An iterativenoise reduction method to improve thenoise magnitude, edges, and sharpness wasproposed by Kido et al (9). Several othermethods were also introduced that focusedon improvement of the sharpness and noisetexture, including noise forcing and noiseclipping (10,11). In many algorithms, reso-lution decomposition, which decomposesthe image into various frequency bands,processes each band separately, and thenregroups all the frequency bands togetherto reconstitute the image, is used. Anotherclass of filters is segmentation based, andthese filters decompose the image on thebasis of structures and nonstructures, pro-cess structures and nonstructures sepa-rately, and then recombine the processedstructures and nonstructures to form thefinal filtered image.

On an image, a group of structural pix-els representative of structures of interestand a group of nonstructural pixels repre-sentative of nonstructural regions on theimage are present. The structural pixelsare identified by determining the gradientvalues for each pixel and by identifyingthe pixels that have a desired relationshipto the gradient threshold value. In thepresent study, the noise reduction filtertechnique involves isotropic filtering of

Figure A1. Schematic depicts main steps of filtering algorithms used inthis study. I1 � input image, I2 � intermediate image, I3 � filtered image,I4 � image formed by expansion by factor x, I5 � final filtered output image.

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nonstructured regions with a low-pass fil-ter and directional filtering of the struc-tured regions with a smoothing filter,which operates parallel to the edges andwith an enhancing filter that operates per-pendicular to the edges. A blending pa-rameter regulates the recombination ofthe structured and nonstructured seg-ments (Fig A1). The six filters (filters A-F)analyzed in this study were designed toachieve varying levels of segmentation,blending, and sharpening to provide a rangeof varying visual effects in noise reductionand structure enhancement. These may beseparated into two groups: Filters A, B, C areincluded in one group and filters D, E, F areincluded in the other. The former filters apply33% more aggressive sharpening to thesmoothed-structure pixels than do the latterfilters. In general, the three filters in eachgroup were designed to incorporate differentlevels of smoothing. Filters A and D are para-metrically twice smoother than are filters Band E and four times smoother than are filtersC and F. Filters A and D are the least aggres-sive in terms of level of filtering, whereas fil-ters C and F are the most aggressive.

In the present algorithm (Fig A1), theinput image (I1) is first shrunk by a pre-specified factor x to form an intermediateimage (I2) by means of neighborhood av-eraging. The size of the image is aug-mented to prevent loss of data while im-ages are shrunk. The amount of shrinkingis set by a prespecified factor x. Image I2 isfiltered with a segmentation-based noisereduction filter to obtain the filtered im-age (I3). With this class of techniques, theimage is decomposed on the basis of struc-tures and nonstructures that are processedseparately and then recombined to formthe final filtered image. With the segmen-tation algorithm, gradient magnitude andgradient direction essentially are used toautomatically arrive at an initial segmen-tation mask for a class of images. Most ofthe salient edges are broken up because ofthe noisiness of the direction measure-ment. A connectivity analysis is used toeliminate “islands” in the mask, and thetotal number of remaining edge points areused to select the gradient-based thresh-old. The gradient threshold value is iden-tified by comparing gradient values forthe pixels with a desired value and bycomparing gradient directions for the pix-els with those of one another. This is fol-lowed by iteratively filtering the structurewith an anisotropic smoothing kernelalong the dominant direction in a givenneighborhood, which would be the direc-tion of the majority of the local minimumvariances. Sharpening of the anisotropicallysmoothed structure pixels, the gradients ofwhich are greater than a prespecified limit,is performed. Next, I3 is expanded by the

same factor x to form I4 by using a suitableinterpolation function. Finally, I4 is blendedwith I1 to form the final filtered output I5.

Acknowledgments: The authors acknowl-edge Karen Procknow and Holly McDaniel, GEMedical Systems, who provided their clinical ap-plications expertise and worked with one of theauthors (G.B.A.). Significant contributions tothis study were also made by other personnel asfollows: Barry Black, Sherry Hao, Andrew Hussli,Daniel Biank, Tim Deller, and Clare Zhang.

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