This paper discusses impact of a novel registration algorithm for dynamic MRI data on diagnosis of rheumatoid arthritis. The algorithm is based on a hybrid Euclidean-Lagrangian approach. It was applied to data acquired with low and higheld MRI scanners. The scans were processed with region-of-interest based and voxel-by-voxel approaches before and after the egistration. In this paper, we demonstrate that diagnostic parameters extracted from the data before and after the registration vary dramatically, which has a crucial effect on diagnostic decision. Application of the the proposed algorithm signicantly reduces artefacts incurred due to patient motion, which permits reduction of variability of the enhancement curves, yielding more distinguishable uptake, equilibrium and wash-out phases and more precise quantitative data analysis

Boesen, M.

Boyle, R.D.

Kubassova, O.

English

White Rose Research Online

promoting access to White Rose research papers    White Rose Research Online   Universities of Leeds, Sheffield and York http://eprints.whiterose.ac.uk/    White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/5394/    Published conference paper Kubassova, O., Boyle, R. and Boesen, M. (2008) Registration of dynamic MRI data and its impact on diagnostic process. In: Hamarneh, G. and Abugharbieh, R., (eds.) Proceedings of the First Workshop on Analysis of Functional Medical Images. 11th International Conference on Medical Image Computing & Computer Assisted Intervention, September 10th 2008, New York. , New York, pp. 57-64. http://bisicl.ece.ubc.ca/functional2008/art/miccai_func2008_proc.pdf  eprints@whiterose.ac.uk  Registration of Dynamic MRI Data and itsImpact on Diagnostic ProcessOlga Kubassova1, Roger Boyle1, Mikael Boesen2, Marco A. Cimmino3, andHenning Bliddal21 School of Computing, University of Leeds, United Kingdom,olga@imageanalysis.org.uk, roger@comp.leeds.ac.uk2 The Parker Institute Frederiksberg Hospital, Frederiksberg, Denmark,mikael.boesen@dadlnet.dkh; Henning.bliddal@frh.regionh.dk3 University of Genoa, Genoa, Italy, cimmino@unige.it ?Abstract. This paper discusses impact of a novel registration algorithmfor dynamic MRI data on diagnosis of rheumatoid arthritis. The al-gorithm is based on a hybrid Euclidean-Lagrangian approach. It wasapplied to data acquired with low and high-field MRI scanners. Thescans were processed with region-of-interest based and voxel-by-voxelapproaches before and after the registration. In this paper, we demon-strate that diagnostic parameters extracted from the data before andafter the registration vary dramatically, which has a crucial effect ondiagnostic decision. Application of the the proposed algorithm signifi-cantly reduces artefacts incurred due to patient motion, which permitsreduction of variability of the enhancement curves, yielding more dis-tinguishable uptake, equilibrium and wash-out phases and more precisequantitative data analysis.1 IntroductionRheumatoid arthritis (RA) is an inflammatory disease which affects more than0.3-1% of the adult population [1]. RA patients are often examined with DynamicContrast Enhanced Magnetic Resonance Imaging (DCE-MRI), where joints areimaged over time resulting in a 4D dataset. Temporal changes of signal intensityduring and immediately after a bolus injection of a contrast agent reflect un-derlying changes in local concentration of the contrast agent, which are relatedto the extent of tissue inflammation. Typically, DCE-MRI data is processed ona voxel-by-voxel basis, where enhancement curves are extracted from temporalslices and evaluated using pharmacokinetic [2], heuristic [3], or region of interest(ROI) based [4] methods.These approaches for analysis of DCE-MRI data assume that signal intensityvs. time changes at each voxel can be attributed to the contrast leakage. However,patient movement can introduce artefactual enhancement with implications to? We thank Leeds General Infirmary, UK and The Parker Institute, DK, University ofGenoa, Italy for providing data and assisting us with evaluation of the techniques.the extracted measurements. Hands of active RA patients shiver and the rangeof motion is approximately 5-10mm translations and 5−150 rotations in 2D and3D planes.Alignment of images is complicated by the non-uniformity of motion acrossvarious regions of the imaged joints and non-homogenous contract and brightnessvariations in later volumes.Early registration methods [5, 6] described motion using rigid transformationsthat attempted to minimise the variance ratio between a pair of images. Recently,motion was modelled using optical flow and affine transformation [7, 8]. However,some optical flow techniques rely on the assumption that the contrast of thetarget and source images remains constant while positions of objects change,which is untrue for DCE-MRI datasets.To align DCE-MRI data, we have modified the algorithm presented in [9],which was designed to deal with partial occlusions. We augmented the registra-tion model with a Euclidean-Lagrangian incremental approach, which was moti-vated by the fact that some tissue significantly changes its intensity and bright-ness in the post-contrast images and therefore alignment of the post-contrastsource to the pre-contrast target may not be accurate.In this article, we will present the registration model and demonstrate itsvalue for further quantitative analysis of dynamic data.2 DataA total of 37 datasets acquired from patients with low-field (0.2T, ESAOTE,Italy) and high-field (1.5T, Phillips, The Netherlands) scanners were processed.10 high-field datasets were acquired from metacarpophalangeal joints (MCPJs)in the axial direction using T1 weighted spoiled gradient-echo sequence; TR/TE:14/3.8; FOV/imaging matrix: 100× 200mm / 128× 256, slice thickness 3mm, 6slices. Acquisition time was 142s. 27 low-field datasets were acquired from thehand, wrist, and tendon using gradient-echo and spin-echo sequences, TR/TE:100/16 and 60/6, imaging matrix 256×256, resulting in 3 temporal slices, 22-30images each. Acquisition time was 300s.3 MethodsThe transformation between source f(x, y, z, t) and target f(x, y, z, t−1) volumestakes the following form:m13f(x, y, z, t− 1) + m14 =f(m1x + m2y + m3z + m10,m4x + m5y + m6z + m11,m7x + m8y + m9z + m12, t),where (m1, ...,m9) represent affine and (m10, ..., m12) translation parameters;m13 and m14 are spatially varying parameters which explicitly account for con-trast and brightness variations. m = (m1, ..., m14) are estimated locally for eachsmall neighbourhood, but for the sake of clarity their spatial notation is dropped.To avoid making a decision on the optimal size of the neighbourhood, theassumption that the parameters m do not change within the neighbourhood isreplaced with a smoothness assumption, which implies that physical properties inthe neighbourhood of a space or within the time interval do not change abruptly.A least square measure has been employed to deduce parameters m via costminimisation. The error function is approximated by a 1st order Taylor seriesexpansion and differentiated to its unknowns m. The result is set to zero, andthe solution takes the following form:m =[ ∑x,y,z∈Ωc c T]−1[ ∑x,y,z∈Ωc k], (1)where Ω denotes a neighbourhood of the current pixel, c and scalar k are definedby Eq. 2, fx(·), fy(·), fz(·), and ft(·) are spatial and temporal derivatives of f(·).k = ft − f + xfx + yfy + zfzc = (xfx, yfx, zfx, xfy, yfy, yfy, xfz, yfz, zfz, fx, fy, fz,−f,−1)T (2)A smoothness constraint is then imposed on the model parameters m andthe error function is augmented by this, which penalises solutions proportionalto the local change in each parameter across a small spatial neighbourhood. Theerror function, defined in such a way, allows for a locally smooth, but globallynon-rigid transformation. Minimisation of the error function was done throughdifferentiating, setting the result to zero and solving for m.The entire procedure is built upon a differential multiscale framework [10],that permits the capture of both large- and small-scale transformations.3.1 Euclidean-Lagrangian ExtensionLet UAB denote a transformation between target A and source B obtained withthe algorithm described above. For a sequence of volumes I1, ..., IN , we can deriveU I1I2 , U I1I3 , ..., U I1IN . Let =k, k = 1..N denote the output: =k = U I1Ik [Ik] ≈ I1.Geometric differences between pre- and post-contrast image might not be sig-nificant, however contrast and brightness variations, especially in the datasetsacquired from the patients severely affected by RA, are dramatic. Thus, align-ment of the post-contrast images to the first one of a dynamic series might notbe accurate.To minimise the registration error the transformation could be performed inan incremental rather than pair-wise fashion (a.k.a. Euclidian approach). How-ever, if the alignment between the volumes at the beginning was not accurate,the error would further propagate. To overcome this, the output of this sequentialtransformation is used as an initial solution for the basic registration algorithm.Such an approach is often referred to as Lagrangian.So, for a DCE-MRI dataset, we firstly determine transform between neigh-bouring volumes, where the contrast and brightness variations are less significant:V Ik = U I1I2 ⊗ ...⊗ U Ik−2Ik−1 ⊗ U Ik−1Ik (3)6789MONo reg U−transf. W−transf.Fig. 1. Left: Bone interiors outlined in source and superimposed on target before (solid)and after (dashed) registration with W -transformation. Right: MO between bone re-gions in source and target in 200 images before registration, and after images werealigned with the registration with basic U - and proposed W -transformations.This transformation applied to the kth volume yields the volume Jk, Jk =V Ik [Ik] ≈ I1. However, if the alignment of the first volumes was not perfect,registration error would propagate when registering volumes from later in thestudy. To compensate for this possible error, a sequence of volumes registeredin the sequential manner {Jk}k=1...N is taken as an initial solution for the basicregistration algorithm.Then, the final transform is defined as W Ik = U I1Jk⊗V Ik , and being appliedto the kth volume yields the volume Lk, Lk = W Ik [Ik]. With this approach aDCE-MRI study is considered as a whole, which permits reduction of the trans-formation error and allows compensation for contrast and brightness variationsbetween the images.4 DiscussionTo evaluate registration with U -, V -, and W -transformations, we measured mu-tual overlap (MO) [11] between manually outlined rigid bone interiors in 200source and target images before and after the registration. Fig. 1 illustrates po-sitions of bone interiors before and after registration in a sample image (left).True bone interiors outlined in the target are shown in white. Contours of thebone interiors from the source image before (solid line) and after (dashed) reg-istration were superimposed on the target. MO here has increased from 0.76 to0.92. The same experiment was performed on 200 images and the results areshown in Fig. 1 (right).MO before registration was on average 0.74 with the minimum at 0.53 andstandard deviation, σ = 0.07. After registration with the U -transformation, itbecame 0.8 with σ = 0.06; after registration with the V -transformation – 0.81with σ = 0.04. Finally, when the W -transformation was applied, the mutualoverlap became on average 0.92 with σ = 0.03.The appearance of the images before and after registration with the proposedtransformation is visibly different and medical observers’ judgement was posi-tive. The algorithm has not failed on any of the images. Registration permitsreduction of the noise artefacts and significant improvement in the location ofFig. 2. Pre-contrast (a), post-contrast (b) images of the MCPJs (bottom) and tendon(top) and subtractions between pre- and post- contrast images before (c) and after (d)registration. After registration, artefacts have been reduced; shape and location of theinflamed areas are clearer.the blood vessels, bone interiors, skin, but most importantly synovial tissue. Thisis especially prominent on the data acquired with the low-field scanner (Fig. 2,top), where the signal to noise ratio is significantly lower and acquisition timesare longer. Fig. 2 illustrates pre- and post-contrast images and their subtractionbefore and after registration.To evaluate the improvement in visual appearance, we subtracted sourcebefore and after registration from the target and estimated the mean squareerrors (MSE) on intensity values of subtracted images.Ideally, in the absence of patient motion and contrast agent MSE betweenthe registered images should be zero. However, due to the effect of the contrastagent, MSE is always greater than zero, reflecting the magnitude of the enhance-ment. Figures 3 illustrates MSE computed before and after the registration withvarious transformations applied to 100 images.The shape of the MSE graphs corresponds to the expected change in theintensity. The major variations of the intensity occur at the wash-in and wash-out phases. At the baseline and plateau phases (1-3 and 7-16 time instants,respectively), where no significant intensity changes are expected, reduction inMSE can be attributed mostly to the effect of the registration. This experimentdemonstrate that on average for the high-field data MSE has decreased from0.35 to 0.2 and for low-field data from 0.25 to 0.08.4.1 Estimation of enhancement curvesNormally, to perform quantitative analysis of DCE-MRI data, a clinical expertextracts from a small ROI signal intensity vs. time curves. Parameters describingtheir shape (maximum of intensity and a slope) are then extracted. Curves cor-responding to the inflamed synovial tissue exhibit steep wash-in, high maximum5 10 15 2000.10.20.30.40.5T imeMSE5 10 15 2000.10.20.3T imeMSEFig. 3. MSE between 100 target and source images acquired with the high-field (left)and low-field (right) scanners and aligned with the registration with U -(bold) and W -(thin) transformations. The length of the error bars is 1.96σ. Time - number of framesper acquisition.intensity values, and a wash-out phase. Curves corresponding to tissue which isnot affected by RA normally exhibit no enhancement.Patient motion can change the shape of the enhancement curves. Fig. 4 il-lustrates signal intensity vs. time curves before (left) and after (right) the reg-istration. A comparison of these two sets suggests that significant movementoccurred between 5th and 10th time instants. Evidently, enhancement curves ex-tracted from the registered images show much less variation during the wash-inand equilibrium phases.The bold black curve (the mean) is the one used by the radiologist to evaluatethe nature of the enhancement. Without registration, the curve shows contin-ues increase and no wash-out phase, despite the fact that enhancement of theinflamed synovium is expected to peak around the 15th time instance. Afterregistration, the behaviour of the enhancement curves corresponds much betterto the nature of the enhancement: with more pronounced wash-in and wash-outphases, evaluation of the inflammation can be performed more accurately. Thisexperiment illustrates that registration significantly improves the accuracy ofthe estimation.Fig. 5 illustrates parametric maps of maximum enhancement (ME), com-puted with the automated quantitative approach [3], superimposed on low-fieldpost-contrast image of the wrist (top) and MCPJs (bottom), extracted from ac-tive RA patient and healthy control, respectively. In the top row, it is expectedthat synovial tissues and erosions will exhibit high ME (yellow-white colours),intermediately active tissues will be coloured in dark red, and non-enhancingtissue have no colour. Before the registration (mid) the most significant enhance-ment corresponds to skin area; the joint with an erosion in the center is shownin darker red colours. After registration, a quantitative map of ME reflects ex-pected activation events (pixels within an erosion are in white-yellow). In thebottom row, the ME map for a healthy control should only show enhancementwithin the blood vessels. Before registration, we observe some activation aroundthe joints, after the registration, the inflammation map corresponds precisely toclinical expectation. This demonstrates that the algorithm allows elimination ofthe artifactual enhancement.5 ConclusionThe paper discussed registration algorithm for alignment of non-rigid multi-sliceDCE-MRI data, which accounts for significant variations in contrast and bright-ness that occur in the post-contrast volumes. The approach was demonstratedon data acquired from active RA patients using low and high field scanners.Enhancement curves extracted from the registered images demonstrate less vari-ability and more distinguished wash-in and wash-out phases. The registrationalgorithm has significant impact on the accuracy of quantitative analysis tech-niques and allows for significant reduction of the artefactual enhancement. Thiscontributes to the data fidelity for diagnosis of RA.The scheme presented here is yet to be tested with other similarity mea-sures such as local normalised cross-correlation, correlation ratio or mutual in-formation [7] and in application to other DCE-MRI studies. Our preliminaryexperience suggests that it is acceptable for a wider application.References1. Silman, A.J., Pearson, J.E.: Epidemiology and genetics of rheumatoid arthritis.Journal of Arthritis Research and Therapy 4(3) (2002) S265–S2722. Tofts, P.S.: Modelling tracer kinetics in dynamic Gd-DTPA MR imaging. Journalof Magnetic Resonance Imaging 7(1) (1997) 91–1013. Kubassova, O., Boesen, M., Boyle, R.D., Cimmino, M.A., Jensen, K.E., Bliddal,H., Radjenovic, A.: Fast and robust analysis of dynamic contrast enhanced MRIdatasets. In: MICCAI. Volume 2. 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Registration of dynamic MRI data and its impact on diagnostic process

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Registration of dynamic MRI data and its impact on diagnostic process

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