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a Shape Decomposition Technique in Electrical Impedance Tomography 1999

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Journal of Computational Physics 155, 75–95 (1999) Article ID jcph.1999.6330, available online at http://www.idealibrary.com on A Shape Decomposition Technique in Electrical Impedance Tomography David K. Han∗ and Andrea Prosperetti† ∗ Applied Physics Laboratory, The Johns Hopkins University, Laurel, Maryland 20723; †Department of Mechanical Engineering, The Johns Hopkins University, Baltimore, Maryland 21218 and Department of Applied Physics, Twente Institute of Mechanics, and Burgerscentrum, U
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  Journal of Computational Physics  155,  75–95 (1999)Article ID jcph.1999.6330, available online at http://www.idealibrary.com on A ShapeDecomposition Techniquein ElectricalImpedanceTomography David K. Han ∗ and Andrea Prosperetti † ∗  Applied Physics Laboratory, The Johns Hopkins University, Laurel, Maryland 20723;  †  Department of Mechanical Engineering, The Johns Hopkins University, Baltimore, Maryland 21218 and  Department of Applied Physics, Twente Institute of Mechanics, and Burgerscentrum,University of Twente, AE 7500 Enschede, The Netherlands E-mail: David.Han@jhuapl.edu, prosperetti@jhu.eduReceived December 28, 1998; revised June 15, 1999 Consideratwo-dimensionaldomaincontainingamediumwithunitelectricalcon-ductivity and one or more non-conducting objects. The problem considered here isthatofidentifyingshapeandpositionoftheobjectsonthesolebasisofmeasurementson the external boundary of the domain. An iterative technique is presented in whicha sequence of solutions of the direct problem is generated by a boundary elementmethod on the basis of assumed positions and shapes of the objects. The key newaspect of the approach is that the boundary of each object is represented in terms of Fouriercoefficientsratherthanapoint-wisediscretization.TheseFouriercoefficientsgeneratethefundamental“shapes”mentionedinthetitleintermsofwhichtheobjectshape is decomposed. The iterative procedure consists in the successive updating of the Fourier coefficients at every step by means of the Levenberg–Marquardt algo-rithm. It is shown that the Fourier decomposition—which, essentially, amounts to aform of image compression—enables the algorithm to image the embedded objectswith unprecedented accuracy and clarity. In a separate paper, the method has alsobeen extended to three dimensions with equally good results.  c  1999 Academic Press Key Words:  electrical impedance tomography; inverse problems; image compres-sion. 1. INTRODUCTION Thegeneralproblemofelectricalimpedancetomographyconsistsinthereconstructionof an unknown impedance distribution in a spatial region on the basis of measurements on theboundary. The technique, srcinally developed for biomedical and geological applications,uses an array of electrodes placed on the boundary of the domain of interest (see, e.g.,Refs. [1, 2] for recent reviews). A sequence of prescribed voltages (or currents) is applied 75 0021-9991/99 $30.00Copyright c  1999 by Academic PressAll rights of reproduction in any form reserved.  76  HAN AND PROSPERETTI to these electrodes, and the resulting currents (or voltages) are measured. The problem thatarisesinthiswayfallsinthecategoryofso-calledinverseproblemsasthesolutionsoughtisnot the calculation of currents (or voltages) given voltages (or currents) and the parametersof the domain—as in the direct problem—but the characterization of the domain itself. It iswell known that problems of this type are ill posed so that small amounts of measurementnoise are sufficient to render a faithful resolution impossible. It is therefore essential tostabilize the solution against the instability resulting from noisy data.In the present paper we address a special class of problems of this type, in which theregionofinterestistwo-dimensionalandtheunknownelectricalconductivityhasaconstantvalue of unity except in the interior of one or more objects where it vanishes. We considermeasurements at very low frequency so that the impedance is purely real and reduces to theresistivity. In Ref. [3] some encouraging preliminary results in which the present method isextended to three dimensions were shown.Situationsofthetypewestudymayariseforexampleintwo-phaseflow,wherelongbub-blesriseintubesintheso-calledslugflowregime,thedetectionofburiedcables,theimagingof bones or vessels in limbs, of lungs in the chest, non-destructive evaluation, and others.In general, the approaches developed to date to determine an unknown impedance distri-bution fall into two classes. One is the so-called back-projection method, which is basicallyan adaptation of the technique developed for medical CAT-scans. Barber and Brown [4, 5]were the first to produce the image of a human forearm using this method, although thesharpness of the image was limited. Santosa and Vogelius [6] later improved the techniqueby using the conjugate residual method. Guardo  et al.  [7] also used the back-projectionmethod in their study and gave an experimental demonstration in a three-dimensional case.So far, the back-projection method has been applied only to situations in which the conduc-tivity contrast is small. It is not clear whether it can be extended to the problem consideredhere where, on the contrary, it is large.The other approach, called “model based,” consists in the generation of a sequence of solutions of the direct, or forward, problem, in which the currents (or voltages) predictedon the basis of an assumed impedance distribution are compared with those measured. Ateach step the assumed impedance distribution is refined in such a way as to decrease themismatchbetweentheforwardsolutionandmeasurement(see,e.g.,Refs.[1,2,8–11]).Thisis the path that we follow in the present paper. In our implementation we use the boundaryintegral method for the forward problem (see, e.g., Refs. [12, 13]), and the Levenberg–Marquardt algorithm (see, e.g., Ref. [14]) for the inverse problem. The key new feature thatwe introduce—and that results in a remarkable improvement over existing methods—isthe description of the boundary of the objects in terms of a Fourier series, rather than apoint-wise discretization. In this way, we are plagued far less than previous investigatorsby the instability of the solution with respect to measurement noise. 2. MATHEMATICAL MODEL We consider a medium with uniform electric conductivity occupying a two-dimensionalplane region    bounded externally by a circle  C   and internally by one or more curves    j with  j = 1 , 2 ,..., m .  The electrical conductivity vanishes inside the internal boundaries.The objective of the tomographic reconstruction is to deduce the shape of the internalboundaries from measurements on the external boundary of    .  SHAPE DECOMPOSITION METHOD FOR EIT  77 FIG. 1.  Computational domain with 3 non-conducting objects. The inset shows the boundary with gaps andelectrodes. Thisexternalboundaryconsistsofanumberofequal,evenlyspaced,perfectlyconductingelectrodes  E  k   separated by perfectly insulating gaps  G  l  as shown in Fig. 1. In practice, of course, there will be some contact resistance that would, however, be highly dependenton the particular experimental set-up and conditions [15, 16]. Since this effect cannot bemeaningfully modeled in general terms, we do not attempt to include it although it may, inpractice, have quantitatively significant effects.In principle the data needed for the tomographic image reconstruction can be acquiredeither by imposing a current pattern on the electrodes and measuring the resulting voltagesor, reciprocally, by imposing voltages and measuring currents. The latter alternative leadsto a somewhat simpler modeling as, in practice, electrodes consist of highly conductivematerial throughout which the voltage can be assumed to be spatially uniform. When thetotal current into an electrode is specified, on the other hand, the current density is notuniform but needs to be determined from the solution of a boundary value problem. Forthis reason, for the sake of simplicity, we consider here a situation in which voltages areprescribed and currents measured.As in other model-based algorithms, our method consists of the solution of a sequenceof forward problems in which a better and better approximation to the internal boundaries   j  is progressively constructed.  78  HAN AND PROSPERETTI The mathematical formulation of the forward problem is the following. The electricpotential  V   inside the region    satisfies Laplace’s equation ∇  2 V   = 0 ,  (1)subjecttotheconditionofanimposedvoltage  V  k   onthe k  thelectrode E  k   andofzerocurrentin the  l th gap  G  l . Mathematically, this latter condition is expressed by n · ∇ V   = 0 over  G  l .  (2)The same condition applies at the inner boundaries    j . Here and in the following we setthe electrical conductivity of the material to 1 for convenience.The normal current density  n · ∇ V   on the  k  th electrode and the total current  I  k   throughit are related by  I  k   =   E  k  n · ∇ V d  E  k  .  (3)A comparison of these calculated currents with the measured ones gives a measure of theaccuracy of the reconstruction and a means to refine it.The ill-posed nature of tomographic reconstruction manifests itself in an ill-conditioningofthematrixofthesystemthesolutionofwhichgivestheparametersdefiningtheimage.Inthepastthedifficultyduetothisill-conditioninghasbeenmitigatedbytheuseoftechniquessuch as the singular value decomposition, but at the expense of a significant sacrifice inimage quality (see, e.g., Ref. [17]). The degree of ill-conditioning grows as the number of unknownsusedtoparameterizetheimageisincreasedforagivennumberofmeasurements.Thisremarksuggeststhatadesirablefeatureofaninversionmethodwouldbetheuseofadescription of the object in terms of a number of parameters as small as possible. From thisperspective it is clear that a point-wise description of the object boundaries, such as the oneused, for example, by Murai and Kagawa [18], is rather inefficient. For example, 4 points(i.e., 8 parameters) can only approximate a quadrilateral. A more complex shape wouldrequire a significantly larger number of parameters even for a very coarse representation.We take a different approach, namely we try to reduce the number of parameters nec-essary for an acceptable approximation of the image by superposing fundamental shapes,each one characterized by a small number of parameters, whence the denomination “shapedecomposition” of the present technique. One may interpret this idea as attempting to re-construct a compressed version of the image of the srcinal object. Such an approach isparticularly valuable when some general information as to the general shape of the objectsis available a priori. For example, circles can be described in terms of 3 parameters only,the position of the center, and the radius.While there is of course a great latitude in the choice of the fundamental shapes, here weuse, for each object, a Fourier decomposition of the type | x − x C  |= 12  A 0 + ∞  k  = 2 (  A k   cos k  θ   +  B k   sin k  θ).  (4)Here  x C  ≡ (  x C  ,  y C  )  is the centroid of the object defined so that the term  k  = 1 is not present
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