A first-order adjoint and a second-order hybrid method for an energy output least-squares elastography inverse problem of identifying tumor location
© Cahill et al.; licensee Springer. 2013
Received: 14 August 2013
Accepted: 5 November 2013
Published: 2 December 2013
In this paper we investigate the elastography inverse problem of identifying cancerous tumors within the human body. From a mathematical standpoint, the elastography inverse problem consists of identifying the variable Lamé parameter μ in a system of linear elasticity where the underlying object exhibits nearly incompressible behavior. This problem is subsequently posed as an optimization problem using an energy output least-squares (EOLS) functional, but the nonlinearity that arises makes the computation of the EOLS functional’s derivatives challenging. We employ an adjoint method for the computation of the gradient, something shown to be an efficient method in recent studies, and also give a parallelizable hybrid method for the computation of the EOLS functional’s second derivative. Detailed discrete formulas and nontrivial computational examples are provided to show the feasibility of both the adjoint and hybrid approaches. Furthermore, all results are given in the framework of a general saddle point problem allowing easy adaptation to numerous other inverse problems.
is the linearized strain tensor. The resulting stress tensor σ in the stress-strain law (1b) is obtained under the assumption that the elastic object is isotropic and the displacement is small enough so that a linear relationship holds. The Lamé parameters μ and λ quantify the elastic properties of the material. (In the following, for simplicity we set .)
In this work our objective is to investigate the elastography (also known as elasticity imaging) inverse problem of locating cancerous tumors within the human body. This inverse problem consists of identifying the variable parameter μ in (1a)-(1d) from a measurement of the displacement field u. Conversely, the direct problem for (1a)-(1d) is to find the displacement u when function h, the variable coefficients μ and λ, and the body force f are all known. The underlying idea is that differences in molecular makeup as well as microscopic and macroscopic structure result in significant differences in the stiffness of living soft tissue (see ). Moreover, changes in tissue stiffness generally correlate with changes in pathological state, with many cancers appearing as hard nodules within the surrounding softer tissue. In a clinical setting, measurements of displacement in human tissue can be obtained using ultrasound and this can then serve as data in the context of the elastography inverse problem. By solving this inverse problem and recovering μ, tumor locations can be identified using the marked differences in elastic properties between the healthy and unhealthy tissue. Additionally, we note that in the elastography inverse problem the human body is treated as a nearly incompressible object where the parameter λ is significantly large and hence only the parameter μ is sought.
Although numerous authors have contributed to using the elasticity properties of soft tissue as a tool to differentiate between normal and cancerous tissue, Raghavan and Yagle  were among the first authors to realize that this study can be best done in an inverse problem framework using measured strains and the equations of equilibrium to recover elasticity (cf. (1a)-(1d)). Since then, many studies have been devoted to investigating various aspects of the elastography inverse problem and the interested reader is referred to [3–8] and the cited reference therein. Additionally, a detailed account of the recent developments in elastography inverse problem can be found in the survey article by Doyley . See also [9–21] and the cited references therein for more details.
One of the main technical challenges in the study of this inverse problem stems from the fact that the human body is treated as a nearly incompressible object. That is, the elasticity modulus λ is significantly large (and particularly ), rendering classical finite element methods ineffective due to the so-called locking effect. In the literature, several approaches have been proposed to overcome the locking effect, and in this work we employ the mixed finite elements strategy.
In the following, we provide the necessary details for the transformation of system (1a)-(1d) into a saddle point problem to which the mixed finite element approach can be applied.
where the pressure p is also an unknown.
where and .
For the saddle point formulation, the Babuška-Brezzi condition provides guidance in the choice of finite element spaces necessary for a stable numerical approximation (see ).
The primary objective of this work is to develop an efficient computational framework for the elastography inverse problem. For this we employ an adjoint approach for the derivative computation of a recently proposed energy output least-squares (EOLS) functional . We recall that Oberai et al.  used the adjoint approach to compute efficiently the gradient of the output least-squares functional. Inspired by Tortorelli and Michaleris , we also devise a hybrid method for an efficient computation of the second-order derivative of the EOLS functional. In this direction, we would also like to draw attention to an interesting paper by Cioacaa, Alexea, and Sandua  where a second-order adjoint method is studied. All the results and formulas given are for a general saddle point problem and hence can easily be adapted to a wide range of inverse problems for variational problems (see ). In the derivation of the adjoint formulas, we do not include the regularization functional while considering the EOLS functional. However, we use a smooth regularizer for the identification of a smooth parameter and a BV regularizer for the identification of discontinuous coefficients.
2 Optimization approach for inverse problems in saddle point problems
Remark 2.1 We remark that for the subsequent development of our approach, it suffices to assume that A is a closed and convex set of admissible parameters. Most commonly, it is chosen as the set of box-constraints. In some works, the space in which A resides is required to be compactly embedded in the solution space (see [28–31]). In our discrete examples, we have used linear elements to approximate the imposed box-constraints.
Given all the data, the direct problem in this setting is to find . However, our focus is on the inverse problem of finding a parameter that makes (8a)-(8b) true for a measurement of .
where , is the measured data, and is the solution of (8a)-(8b) corresponding to ℓ.
where is the measured data and is the solution of (8a)-(8b) corresponding to ℓ.
Clearly, to solve an optimization problem with the above objective functional, we need to compute its derivative which, in turn, requires us to compute the derivative of the solution map. It is well known that one of the most challenging aspects in the study of inverse problems is in finding an efficient computation of the derivative of the solution map. We will now develop an adjoint method for the computation of the first derivative of the EOLS functional and then a new hybrid method for the computation of the functional’s second derivative.
For every , the map is well defined and single-valued. The following result for the differentiability of S, which was announced in  without a proof, will be needed.
- 1.Given u, the first derivative is the unique solution of the saddle point problem:(12a)(12b)
- 2.The second-order derivativeis the unique solution of the saddle point problem(13a)(13b)
By [, Proposition 4], the map is an isomorphism. Therefore, using the implicit function theorem, the map is infinitely differentiable at any ℓ in the interior of A.
which is (12b). Consequently, (12a) and (12b) characterize the first derivative.
which in conjunction with (13b) forms the corresponding saddle point whose unique solution characterizes the second derivative . □
3 An adjoint and a hybrid method for the energy output least squares
where stands for the partial derivative with respect to ℓ.
which exists, by standard arguments, since the above problem is just (8a)-(8b) with .
Compute u by (16).
Compute w by (21a)-(21b).
Compute by (22).
Let us now develop the hybrid method for the computation of the second-order derivative. In the hybrid method proposed below, the derivative δu is computed directly while the computation of the second derivative is avoided by using an adjoint method. We will follow the same general scheme that was used above, but here we will use derivative formula (12a)-(12b).
Compute by (16).
Compute by (12a)-(12b).
Compute by (25a)-(25b).
Compute by (26).
4 Discretization formulas for the adjoint and the hybrid method
In this section, we collect discrete formulae for saddle point problem (8a)-(8b) and the associated inverse problem. We begin, therefore, with a triangulation on Ω, is the space of all piecewise continuous polynomials of degree relative to , is the space of all piecewise continuous polynomials of degree relative to , and is the space of all piecewise continuous polynomials of degree relative to .
In order to represent the discrete saddle point problem in a computable form, we proceed as follows. We represent bases for , , and by , , and , respectively. The space is then isomorphic to and for any , we define by for , where the nodal basis corresponds to the nodes . Conversely, each corresponds to defined by . Similarly, will correspond to , where , , and , where are the nodes of the mesh defining . Finally, will correspond to , where , , and , where are the nodes of the mesh defining . (The spaces , , and are defined relative to the same elements, but the nodes will be different if .)
4.1 Computation of the gradient by using the adjoint method
- 1.We compute by solving the linear system(30)
- 2.We compute by solving the linear system(31)
- 3.The gradient can be calculated by using the adjoint stiffness matrix. From (22), we have(32)a direct discretization gives the following:and therefore the gradient is given by(33)
4.2 Computation of the Hessian by using a hybrid method
Compute by solving linear system (30).
Compute by solving linear system (31).
Compute by solving m linear systems.
Compute by using formula (35).
We note that to compute the Hessian using the hybrid method requires the solution of linear systems.
5 Numerical experiments
We consider here two representative examples of elastography inverse problems for the recovery of a variable μ on a two-dimensional isotropic domain with boundary . In the first example, a smooth coefficient is recovered using both the adjoint and hybrid gradient calculation methods. For the second example, we examine the recovery of a discontinuous coefficient using the adjoint method.
All examples are solved on a quadrangular mesh with 5,476 quadrangles and 16,576 total degrees of freedom. Example 1 uses a smooth Tikhonov-type regularization method, whereas the discontinuities in Example 2 necessitate the use of a BV-regularization scheme (see  for a more thorough discussion of regularization).
5.1 Example 1
5.2 Example 2
where and .
6 Concluding remarks
One issue not addressed in depth was the comparative performance of these methods, measured both against existing schemes and against one other. In short, we note that the hybrid method requires the solution of linear systems with m scaling along with the size of the mesh. However, the m systems remain entirely independent, allowing for the parallelization of parts of the computation and thus granting significant performance gains and potential advantages over other strategies. In a future work, we look to extend our study here into just such a thorough analysis and carefully consider the performance of the adjoint and hybrid derivative computation methods.
The work of AA Khan is supported by RIT’s COS D-RIG Acceleration Research Funding Program 2012-2013 and a grant from the Simons Foundation (#210443 to Akhtar Khan). The work of M Sama is partially supported by Ministerio de Ciencia (Spain), project (MTM2012-30942).
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