Volume 198 #{149}Number 3 Radiology #{149}903

Automatic Motion Correction for Breast MR Imaging’

Chun S. Zuo, PhDAiping Jiang, PhDBill L. Buff, MDThomas G. Mahon, MDTerence Z. Wong, MD, PhD

In 29 gadolinium-enhanced breast mag-netic resonance (MR) examinations,breast motion prevented accurate andefficient image processing. To compen-sate for global rotations and transla-tions, an automatic motion correctionmethod with a ratio-variance minimiza-tion algorithm was used to align imagesat multiple time points through an it-erative process. This method reducedbreast motion and improved the accu-racy and efficiency of lesion detection.

Index terms: Breast, MR. 00.12143 #{149}Breast

neoplasms, MR. 00.12143, 00.121416 #{149}Gadotin-

ium, 00.12143 #{149}Magnetic resonance (MR), mo-

tion correction, 00.12143

Radiology 1996; 198:903-906

I From the Department of Radiological Sci-

ences, Deaconess Hospital, One Deaconess Rd,Boston, MA 02215 (C.S.Z., B.L.B., T.G.M.,T.Z.W.); and the Department of Radiology, Mas-sachusetts General Hospital and Harvard Medi-

cal School, Boston, Mass (A.J.). From the 1994RSNA scientific assembly. T.Z.W. supported inpart by an RSNA Research Resident Award. Re-ceived May 12, 1995; revision requested June 30;revision received October 23; accepted October31. Address reprint requests to C.S.Z.

C RSNA, 1996

T � detect breast lesions, magneticresonance (MR) imaging is cur-

rently performed to depict the differ-

ence in enhancement between normaltissues and lesions (both benign and �

malignant) during the dynamic uptakeof gadopentetate dimeglumine (1-3). Inthis process, the breast is imaged at aseries of time points before and after the

intravenous administration of the con-

trast agent. When the unenhanced and

contrast material-enhanced Ti-weightedimages are compared, enhanced tissues

are recognized as regions with incneas-ing signal intensity. In the case of malig-

nant tumors, their tendency to sproutnew vessels and to exhibit angioneo-genesis (4-6) and their increased capil-lary permeability result in rapid earlyenhancement followed by a period ofslower enhancement (7,8). Benign le-sions, on the other hand, tend to show

a slower rate of enhancement (7,8). Fi-

broadenomas may demonstrate an in-termediate enhancement pattern (7,8).The observation of contrast enhance-ment over time may, therefore, helpdistinguish benign from malignantbreast lesions.

This contrast enhancement process ispromising, but imaging of the breast at

a series of time points during the up-take process results in acquisition of alarge number of images in clinical breastMR imaging examinations. For instance,at our institution, a typical breast MRimaging examination generates 350-400images. The examination of such a large

data set is very time-consuming anddetails are difficult to assimilate fromthis large data set with use of standard

evaluation procedures (ie, placement ofthe images on a light box, review in se-rial order). Once a questionable area isdetected, regional signal intensity mustbe measured manually on a computerconsole; this procedure necessitates even

more time. In addition, the variations insignal intensity in the relatively small le-sions may not be obvious at visual exami-nation, and false condusions may be de-rived under certain circumstances. Forthese reasons, automatic image processingis desirable for efficient and proper imagereview and analysis.

In the process of automatically ana-lyzing the dynamic contrast material-

enhanced breast MR images, we werehindered by breast motion in the im-

ages at different time points during the

baseline and dynamic scanning. Thesebreast motions caused the location ofbreast lesions in the images to vary over

the time series; hence, lesion detectionresults were sometimes false. Further-

more, these motions prevent efficiencyin image processing.

The purpose of this article is to intro-duce a technique for automatic detec-tion and correction of breast motion in

breast MR images and, ultimately, toprovide a new strategy for efficient ex-amination of large numbers of dynamiccontrast-enhanced MR images.

Materials and Methods

MR imaging protocol-The breast MRimaging examinations were performedon a 1.0-T system (SP-42; Siemens Medi-cal Systems, Erlangen, Germany), witha bilateral breast coil. Twenty-nine pa-tients (aged 23-60 years [mean, 46years]) with abnormal results at mam-mography were referred to the MR im-aging unit at our institutions. Writteninformed consent was obtained fromeach patient. During the MR imagingexamination, the patient lay prone withthe breast suspended in the coil. Blan-kets and pillows were provided asneeded for patient comfort. No firmbreast packing or immobilization tech-nique was used. Unenhanced and gado-linium-enhanced two-dimensional andthree-dimensional fast low-angle shot(FLASH) images were acquired. Gado-pentetate dimeglumine (Magnevist;Schering, Berlin, Germany) was then

administered intravenously in a dose of

0.2 mmol per kilogram of body weight.Immediately after the contrast agentwas injected, 10 two-dimensional

FLASH image sets (20 sections per set,54 seconds per set) and a three-dimen-

sional FLASH image set were acquired.Typical imaging parameters in two-di-mensional FLASH imaging includedrepetition time msec/echo time msecof 280/6; acquisition matrix, 192 x 256;field of view, 30 cm; and 20 5-mm-thicksections, with i-mm intersection gap.

Relatively large breast motions (dis-placement > 2 pixels) were detected inthe time series images of five of the 29patients, and motion correction wasperformed on those images.

Motion correction-The approach formotion detection and correction isbased on the ratio-variance mimmiza-tion algorithm (9,10) to reduce breastlocation change in time series breast im-ages. The algorithm aligns two imagevolumes by minimizing the variance onthe ratio images of the two image vol-umes. An iterative process is used tosearch the six-parameter transformationspace (three rotation angles and threetranslations), with use of a modifiedNewton-Raphson method (11).

a. b.

Figure 1. (a) Subtraction image before motion correction. (b) Subtraction image after motion

correction. Both a and b were generated by subtracting the unenhanced image from the last

contrast-enhanced image. Motion-induced edge artifacts were observed when the subtraction

algorithm was applied to non-motion-corrected images (in a). The region of interest (ROT) of

the lesion was depicted more clearly when motion correction was applied before image sub-

traction (in b).

rID

a.

Figure 2. (a) One-dimensional profile at location y� (in Fig la) along the x direction. Motion

correction reduced the motion-induced edge artifacts at the breast boundary and helped re-

cover the signal intensity lost due to motion in the region of interest of the lesion. (b) Signal-

to-noise ratio (SNR) of the region of interest of the lesion in the subtraction images as a func-

lion of time. The region of interest (ROT) comprised 30 pixels (arrows in Fig 1). Greater enhancement

of the signal-to-noise ratio was obtained on the subtraction images after motion correction

(Post Correction). Pre Correction = before correction.

80

Pixel Index Time (mm)b.

904 #{149}Radiology March 1996

Breast motions in dynamic contrast

material-enhanced bneast MR imagesconsist of rotations, translations, and

some deformations. The breast motionsin images were further complicated by

the nonuniformity of motions acrossvarious regions of the imaged body sec-tion. To remedy the nonuniform com-plication, a rectangular subvolume that

enclosed the breast was extracted fromthe raw images with simple image edit-ing prior to motion correction. After the

subvolume was extracted, we foundthat global rotations and translationswere the dominant motion componentsin the motion-contaminated images inthe five patients. This was the basis for

use of the ratio-variance minimizationalgorithm to remove the primary por-

tion of the breast motions.The first image volume (baseline im-

ages in this study), V0, was selected as

the reference image volume. Each sub-sequent image volume, Vk where kequals 1, 2, . . . . , was aligned with thereference image volume in a pairwise

fashion. Initial values of zero were usedfor the rotations about and translationsalong each of the three coordinate axes

in this study. The first and second par-tial derivatives of the variance in theratio images with respect to each of therotation angles and displacements werecalculated in each iteration. The conver-gence was defined as occurring when

the absolute values of all the first partialderivatives are less than 0.0057 mm� fortranslations and 0.005 rad’ for rotations.Finally, a new time series of image setswas created by resectioning each of the

original image volumes after the identi-fled transformation was applied. This

motion correction method has beentested extensively with simulations and

functional brain images (9,10).The overall processing time for the

alignment of a set of time series imagevolumes is a function of the motion

complexity and the size of the subvol-ume. Our current implementation ne-cessitates less than 1 minute with use ofa single 100-MHz central processingunit (Onyx; Silicon Graphics, MountainView, Calif) to process a time series con-sisting of 11 subvolumes of 116 x 78 x20 voxels. Finally, subtraction algo-rithms (12) were applied to the re-sec-tioned, corrected images to segment theabnormally enhanced regions. The vari-

ance across the breast boundaries, a

quantity that is highly sensitive tobreast motion, was computed for the

subtraction images to evaluate the re-sults of motion correction (in the case of

successful alignment, the variance de-creases dramatically compared with thevariance before correction).

Results

Two of the five cases in which motion

connection was applied depicted MR

imaging findings of carcinoma, whichwas confirmed at pathologic examina-tion. In the remaining three examina-tions, no areas of abnormal enhance-ment were observed.

A significant reduction in breast mo-

tion was seen in cine display of images

before and after application of this mo-tion correction technique (12). The re-

sults of motion correction can be dem-onstrated by comparing the followingthree factors before and after motion

correction: (a) the subtraction images,(b) the signal-to-noise ratio versus timein the region of interest of the lesions,

and (c) the magnitude of the motion-induced edge artifacts and variance

across the breast boundary.

Figure 1 shows the subtraction im-

ages before and after motion correction

in a breast containing carcinoma. Beforemotion correction (Fig ia), the lesion isnot well resolved and the motion-in-duced edge artifacts are clearly seen.

After motion correction (Fig ib), the re-

gion of interest of the lesion is well re-solved and the motion-induced edge

artifacts have been dramatically ne-duced. To semiquantitatively examinethe results, we obtained a one-dimen-sional profile at point y’o along the x di-rection (Fig 1), and we took a 30-pixelregion of interest of the lesion and cal-culated the signal-to-noise ratio through-out the time series (Fig 2). The one-di-mensional profile (Fig 2a) illustrated

that motion correction recovered part ofthe signal intensity of the lesion thatwas lost due to motion, and the motion-

induced edge artifacts were reducedsignificantly across the breast boundaryarea (the maximum amplitude was re-

duced by about 50%). The plot of thesignal-to-noise ratios in the 30-pixel re-

gion of interest versus time (Fig 2b) con-firmed the recovery of the lost signal

intensity and indicated that the en-

hancement pattern was that of a malig-nant lesion-a dramatic increase insignal intensity followed by slower en-

Volume 198 #{149}Number 3 Radiology #{149}905

hancement. Finally, 15 14-pixel regions

of interest were selected across the mo-tion-induced edge artifacts along the

border of the breast (Fig 1), and the van-ance was calculated before and aftermotion correction. The motion-correc-tion technique reduced the variance byapproximately 78%.

Reductions in breast motion artifactson the subtraction images were observedafter motion correction in three of the ne-maining four cases (12). After motion con-

nection in the motion-contaminated im-

ages of the five patients, the varianceacross the breast boundaries in the subtrac-

lion images was reduced by more than

50% (range, 50%-82%).

Discussion

Angioneogenesis, a characteristic of

both invasive and noninvasive cancino-mas and of some benign tumors, is pre-

sumed to be a prerequisite for tumorgrowth beyond a diameter of 2 mm(13,14). At MR imaging in a region ofangioneogenesis, a lesion may have adiameter of 3-4 mm, which correspondsto a region of interest 2-3 pixels in di-ameter (the in-plane resolution of the

breast images was approximately 1.4 x1.8 mm). To resolve such a lesion, the

magnitude of the breast motions in theMR image has to be less than 2 pixels.The breast motions commonly encoun-tered in contrast-enhanced breast MRimages, however, are often larger than a

few pixels (motions of this magnitudecould completely obscure small lesionson could create artifactual areas of in-creased signal intensity that result infalse-positive and false-negative find-ings). This explains the importance of

minimizing the effect of breast motionsat dynamic breast MR imaging to detect

small, potentially cancerous lesions.

There are two ways to reduce breastmotions in the time series images: (a) im-mobilize the breast in one position dur-ing the examination (breast packing) or(b) perform motion correction in post-processing. Some of the motion prob-lems in breast MR images may be ame-lionated with breast packing, but the

breast motions may not be eliminated

completely. Algorithms for motion cor-nection (or image registration) have been

introduced (15-17). In the past, motioncorrection was performed by superim-posing images two at a time in the im-age display system and by notating andshifting the object in the image in thehorizontal and/or vertical directionsuntil an optimal visual registration wasachieved manually (15), which necessi-tated explicit identification of particularlandmarks in both image sets (reference

and aligned). The major disadvantagesof this manual registration method arethat results are subjective and very time-consuming, especially when a largenumber of data sets are evaluated. Fun-

thenmore, only two-dimensional motion(ie, in-plane but not out-of-plane mo-

lion) can be registered, which presentsa problem when the breast motions areencountered in three-dimensional im-

ages. The ratio-variance minimizationtechnique introduced herein aligns thebreast in three-dimensional space in arobust fashion that reduces the time

necessary to prepare the data. In addi-

tion, the whole data analysis (motioncorrection, lesion detection, statisticalanalysis) could be performed in three-

dimensional space automatically, which

would make breast image analysis effi-

cient and accurate.Practical limitations exist to the adop-

tion of the ratio-variance minimizationtechnique to detect motion and correct

dynamic breast images. The nonunifor-mity of breast motions (relative motions

across different regions in the image)

complicate the process, and the defor-mation of the breasts violates, to somedegree, the necessity for rigidity that isintrinsic to the alignment method. The

relative motions (a) between the breastsand (b) between the breasts and thechest prevent application of the align-

ment algorithm directly on the breastMR imaging data. Simple editing to

separate the breast from the chesthelped remedy this problem (the edit-ing can be implemented in a semiauto-

matic fashion that necessitates selection

of the region at only the first time pointin the time series). After editing, transla-

tions and rotations, the dominant com-

ponents of the breast motions oven thetime series, were successfully detectedand corrected with use of the ratio-van-

ance minimization technique. Deforma-tion caused by the compressibility of the

breast could not be corrected with thismethod. In addition, each volume inthe time series is treated as if it wereacquired simultaneously. In this study,

the multisection acquisitions acquired

the 20-section volume evenly over theacquisition time, 54 seconds. Therefore,motion that is slow compared with the

acquisition time is treated adequately,but motion that is faster than the acqui-

sition time (such as respiratory motion)propagates through the sections in a way

that is not compensated. The breast wouldappear nonrigid, and motion in the image

could not be corrected with this method.This problem may be reduced as image

acquisition time decreases.Theoretically, the uptake of gadopen-

tetate dimeglumine by lesions, whichresults in a dramatically temporal

change in signal intensity, may affectthe results of motion detection and con-

rection. Practically, because the regionof interest of the lesion is much smaller

than the breast, the technique gave thecorrected results regardless of the en-

hancement of the small lesions. A simi-lan situation is the stimulated activation

in functional MR images that has beendiscussed and tested extensively with

simulations and human images (10). In

our study we simulated a “lesion” re-

gion of 200 voxels in the breast; the up-take of contrast agent was simulated byincreasing the signal intensity 100%

above the reference level and no actualmotion was introduced. As a result ofmotion detection, we had parameterswith notation angles of less than 0.05#{176}and translations of less than 0.02 mm.Regional lesion enhancement has a

minimal effect on the results of motiondetection and correction.

Use of this motion correction tech-

nique reduces breast motions in dy-

namic contrast-enhanced breast MR im-

ages and improves the accuracy and

efficiency of tumor detection. Morecomplete visualization of a given lesionis provided, as well as a better statistical

map, which should improve the accu-racy of lesion detection. This technique

addresses motion(s) in three dimensions

and can be implemented in a robustfashion, which is more useful in breastMR imaging than were previous manualin-plane motion correction methods. Mor-phologic deformation cannot be removed

with this technique, and more sophisti-

cated algorithms must be applied to re-duce these motions. Nonetheless, motioncorrection with this technique simplifiesimage review and analysis and shouldimprove lesion detection, which would be

particularly beneficial at breast MR imag-ing to detect small potentially cancerouslesions. #{149}

Acknowledgments: We thank Julie Barder forher assistance in preparation of the manuscriptfor this article and Melvin Clouse, MD, for hiscontinued enthusiasm.

References

1. Kaiser WA, Zeitler E. MR Imaging of thebreast: fast imaging sequences with andwithout GdDTPA. Radiotogy 1989; 170:681-686.

2. Heywang SH, Hahn D, Schimd H, et at.MR imaging of breast using GdDTPA.Comput Assist Tomogr 1986; 10:199-204.

3. WeinerJl, Chako AC, Merten CW, et at.Breast and axillary tissue MR imaging: cor-

relations of signal intensities and relaxationtimes with pathologic findings. Radiology1986; 160:299-305.

4. Blood CH, Zetter BR. Tumor interactions

with the vasculature: angiogenesis and tu-mor metastasis. Biochem Biophys Acta1990; 1032:89-118.

5. Jensen HM, Chen J, De Vault MR. Lewis

AE. Angiogenesis induced by “normal”human breast tissue: a probable marker forprecancer. Science 1982; 218:293-295.

6. Gullino PM. Natural history of breastcancer. Cancer 1977; 39:2697-2703.

7. StackJP, Redmond OM, Codd MB, DervanPA, Ennis JT. Breast disease: tissue char-acterization with GdDTPA enhancementprofiles. Radiology 1990; 174:491-494.

8. Harms SE, Flamig DP. MR imaging of thebreast. JMRI 1993; 3:277-283.

9. Woods RP, Cherry SR, Mazziotta JC.Rapid automated algorithm for aligning

906 #{149}Radiology March 1996

and reslicing PET images. J Comput AssistTomogr 1992; 16:620-633.

10. Jiang A, Kennedy D. Motion detection

and correction in functional MR imaging.Human Brain Mapping (in press).

11. Press WH, Flannery BP, Teukolsky SA, Vet-terling WT. Numerical recipes in C, theart of scientific computing. New York, NY:

Cambridge University Press, 1988; 323.12. Zuo CS, Jiang A, Mahon T, Buff BL, Wong

TZ. Automatic motion correction methodfor MR imaging of the breast (abstr). Radi-

otogy 1994; 193(P):330.

13. Gimbrone MA, Leapman SB, Cotran RS,Folkman J. Tumor dormancy in vivo byprevention of neovascularization. J Exp

Med 1972; 73:461-473.14. Tannock JF. The relation between cell

proliferation and the vascular system trans-planted mouse mammary tumor. Br J Can-cer 1986; 22:258-273.

15. Adams AH, Brookeman EL, Merickel MB.Breast lesion discrimination using statisti-

cat analysis and shape measures on mag-netic resonance imagery. Comput Med Im-

aging 1991; 15:339-349.

16. Gohagan JL, Spitznagel EL, Murphy WA,

et at. Multispectral analysis of MR imagesof the breast. Radiology 1987; 163:703-709.

17. Buckley DL, Kerslake RW, Btackband SJ,Horsman A. Quantitative analysis of

multi-slice GdDTPA enhanced dynamicMRI using an automated simplex minimi-zation procedure. Magn Reson Med 1994;

32:646-651.

Reference Lines for Oblique Axial MR Imagingofthe Brain’

Shoichiro Otake, MDDaigo Yamana, MDHirokazu Mizutani, MDSatoru Ohba, MD

Reference lines that correspond to thethree reference lines used at computedtomography were determined on 50normal midsagittal magnetic resonancebrain images . Lines were drawn fromthe mammillary body to the posteriortentorial attachment to the sinus (orbi-tomeatal), from the center of the pitu-itary gland to the posterior tentorialattachment to the sinus (Reid baseline),from the mammillary body to the poste-nor edge of the fourth ventricle (supra-orbitomeatal).

Index terms: Brain, anatomy #{149}Brain, MR.10.1214 #{149}Brain,CT,10.1211

Radiology 1996; 198:906-907

I From the Department of Radiology, NaritaMemorial Hospital, Toyohashi, Japan (S. Otake,DY.); and the Department ofRadiotogy, NagoyaCity University Medical School, Nagoya, Japan(H.M., S. Ohba). From the 1994 RSNA scientificassembly. Received July 7, 1995; revision re-quested August 4; revision received October 2;accepted October 5. Address reprint requests toS. Otake, Department of Radiology, NagoyaCity Higashi Municipal Hospital, 1-2-23 Waka-mizu, Chikusa-ku, Nagoya, Aichi 464 Japan.

© RSNA, 1996

XIAL images of the brain are rou-

timely acquired during magneticresonance (MR) imaging examinations.There is no standard, however, for the

degree of angulation in axial images.This lack of a standard makes it difficultto compare axial MR imaging examina-tions with subsequent MR imaging ex-

aminations or with computed tomo-graphic (CT) examinations.

For CT of the brain, there are three

standard reference lines: the orbitome-atal line, the Reid baseline, and the su-praorbitomeatal line (1). The onbitome-

atal line runs through the external canthusand the center of the external auditorymeatus. This is the most widely used line,

because acquisition of fewer sections isnecessary to cover the entire brain. The

Reid baseline runs though the inferior or-bital wall and the superior border of the

external auditory meatus. This line is par-

allel to the optic nerve and provides the

best demonstration of the orbital contents.The supnaonbitomeatal line runs throughthe superior orbital wall and the center of

the external auditory meatus. This lineapproximately parallels the skull base (1,2).

We developed reference lines for useat MR imaging that are analogous to thethree standard reference lines used at

CT, on the basis of anatomic landmarks

that are visible on midsagittal MR im-ages. These lines can be used to pre-scribe subsequent oblique axial se-quences.

Materials and Methods

We prospectively performed MR im-aging in 50 healthy subjects: 21 menand 29 women (mean age, 60 years;range, 23-84 years). All studies were

performed on a 1.0-T MR imager (Mag-

netom; Siemens Medical Systems, Enlan-

gen, Germany). In each subject, sagittalTi-weighted imaging (repetition time,520 msec; echo time, 15 msec) was per-

formed with 7-mm-thick sections, 0.7-mmintersection gap, 192 x 256 matrix, andtwo signals acquired.

On the basis of anatomic structuresdepicted on the midsagittal image, tworadiologists (S. Otake, DY.) plotted MRimaging reference lines to correspond to

the standard reference lines on CT scans.

To maximize reproducibility, well-definedstructures were selected as landmarks

for the reference lines rather than con-tinuous curves on lines. The line similarto the orbitomeatal line was drawn fromthe mammillany body to the posteriortentorial attachment to the sinus. The

line similar to the Reid baseline wasdrawn from the center of the pituitarygland to the posterior tentonial attach-ment to the sinus. The line similar to thesupraorbitomeatal line was drawn from

the mammillany body to the posterioredge of the fourth ventricle (Figure).

To compare these MR imaging refer-ence lines with the standard CT refer-

ence lines, the position and orientationof the CT lines were calculated for each

of the 50 subjects in the following man-ner: The position of the center of thelens, the superior and inferior orbital

walls, and the center and the superior

borders of the external auditory meatuswere identified on the parasagittal im-ages and were superimposed onto the

midsagittal images. The three standard

CT reference lines were plotted on themidsagittal image with use of these su-

perimposed anatomic structures.The MR imaging reference lines were

then compared with the standard CTreference lines on images obtained in