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International Journal of Biomedical Imaging logoLink to International Journal of Biomedical Imaging
. 2007 Mar 8;2007:12839. doi: 10.1155/2007/12839

The Formula of Grangeat for Tensor Fields of Arbitrary Order in n Dimensions

T Schuster 1,*
PMCID: PMC1906704  PMID: 17713588

Abstract

The cone beam transform of a tensor field of order m in n ≥ 2 dimensions is considered. We prove that the image of a tensor field under this transform is related to a derivative of the n-dimensional Radon transform applied to a projection of the tensor field. Actually the relation we show reduces for m = 0 and n = 3 to the well-known formula of Grangeat. In that sense, the paper contains a generalization of Grangeat's formula to arbitrary tensor fields in any dimension. We further briefly explain the importance of that formula for the problem of tensor field tomography. Unfortunately, for m > 0, an inversion method cannot be derived immediately. Thus, we point out the possibility to calculate reconstruction kernels for the cone beam transform using Grangeat's formula.

1. INTRODUCTION

The cone beam transform for a symmetric covariant tensor field f of order m reads as

Df(a,ω)=0f(a+tω),ωmdt, (1)

where a is the source of an X-ray, ωS n−1 is a direction, and ωm denotes the m-fold tensor product ωm = ω ⊗ ⋯ ⊗ ω. If m = 0, this is the classical X-ray transform of functions which represents the mathematical model for the cone beam geometry in computerized tomography. For m = 1, the operator D is the longitudinal X-ray transform of vector fields. A lot of numerical algorithms have been developed in recent years to solve the inverse problem D f = g in case m = 0 and m = 1; see, for example, Louis [1], Katsevich [2], Schuster [3], Derevtsov and Kashina [4], Sparr et al. [5] among others. But also for tensor fields of order m > 1, this transform is of interest in various applications such as photoelasticity and plasma physics. Solution approaches for the tensor tomography problem are found in Derevtsov [6], and Kazantsev and Bukhgeim [7]. A further important transform in computerized tomography is given by the Radon transform

Rf(s,ω)=ωf(sω+y)dy,s, (2)

which maps a scalar function to its integrals over hyperplanes.

An important connection between D and R is given by the formula of Grangeat:

sRf(ω,s=a,ω)=S2Df(a,θ)δ(θ,ω)dθ, (3)

which is valid for differentiable scalar fields f with compact support; see Grangeat [8]. In this paper, we prove a generalization of Grangeat's formula to arbitrary tensor fields. More explicitly, we show that

(n2)s(n2)Rfa(ω,s=a,ω)=(1)(n2)Sn1Df(a,θ)δ(n2)(θ,ω)dθ, (4)

where δ is Dirac's delta distribution and fa are projections of the tensor field f.

In Section 2, we prove that D is a bounded linear mapping between suitable L2-spaces and give a representation for its adjoint D*. In Section 3, we prove formula (4) using a duality argument for D and R. We finish this paper by pointing out the importance of this result for research in the area of tensor field tomography.

2. THE CONE BEAM TRANSFORM OF TENSOR FIELDS

We consider the Euclidean space ℝn. A covariant tensor of order m in ℝn is given by

f=fi1imdxi1dxim,xn, (5)

where fi1im ∈ ℝ, 1 ≤ ijn for j = 1, … , m and dxi, i = 1, … , n, is the basis of covectors in (ℝn)*,

dxi(v)=vi,i=1,,n,vn. (6)

As in (5), we use Einstein's summation convention throughout the paper, that means we sum up over equal indices. A tensor (5) of order m is symmetric if

fiσ(1)iσ(m)=fi1im, (7)

where σ runs over all m! permutations of {1, … ,m}. The set of all symmetric tensors of order m is denoted by 𝒮m. A scalar product on 𝒮m is given by

f,g=fi1imgi1im,f,g𝒮m, (8)

where gi1im are the contravariant components of the tensor g. We write f=f,f for the norm on 𝒮m. If m = 1, this is the Euclidean norm. A symmetric covariant tensor field of order m in ℝn maps a point x ∈ ℝn to an element of 𝒮m,

xf(x)=fi1im(x)dxi1dxim,xn, (9)

where fi1im (x) ∈ 𝒮m for fixed x.

Let further Ωn = {x ∈ ℝn : |x| < 1} be the open unit ball in ℝn. We introduce an inner product for tensor fields defined on Ωn by

f,gL2=Ωnf(x),g(x)dx=Ωnfi1im(x)gi1im(x)dx, (10)

which turns L 2n, 𝒮m) := {f𝒮m : fL2=f,fL21/2 < ∞} to a Hilbert space. Assume that Γ ⊂ (n\Ωn¯) is the path representing the curve of sources of the X-ray beams. Examples for Γ which are used in practice are a circle, two perpendicular circles, or a helix. The cone beam transform of a symmetric tensor field f of order m is then defined by

Df(a,ω)=0f(a+tω),ωmdt=0fi1im(a+tω)ωi1ωimdt, (11)

, where ωS n−1 = Ωn is the direction and a ∈ Γ the source of the beam and ωm = ω ⊗ ⋯ ⊗ ω means the m-fold tensor product of ω. As an arrangement, we extend f(x) = 0 in n\Ωn¯. Hence, integrals like (11) are well defined. Finally, we denote D a f(ω) := D f(a, ω). We note that D coincides with the longitudinal ray transform in the book of Sharafutdinov [9]. The operators D and D a are linear and bounded between L 2-spaces.

Theorem 1. —

Let a ∈ Γ. The mappings D a : L 2n, 𝒮m) → L 2(S n−1) and D : L 2n, 𝒮m) → L 2(Γ × S n−1) are linear and bounded if

Γ(|a|1)1nda<. (12)

Proof. —

For fL 2n, 𝒮m) and a ∈ Γ, we have

Sn1|Daf(ω)|2dω=Sn1|0f(a+tω),ωmdt|2dω2Sn-10f(a+tω)2dtdω=2Ωnf(x)2|xa|1ndx2(|a|1)1nfL22, (13)

where we used the substitution x = a + and the fact that f(x) = 0 in n\Ωn¯. This shows the continuity of D a. The continuity of D follows then by using D f(a, ω) = D a f(ω) and

ΓSn1|Df(a,ω)|2dωda2fL22Γ(|a|1)1nda. (14)

Theorem 1 implies that D a and D have bounded adjoints Da and D*.

Lemma 1. —

The adjoints Da : L 2(S n−1) → L 2n, 𝒮m) and D* : L 2(Γ × S n−1) → L 2n, 𝒮m) have the following representations:

Dag(x)=|xa|1nmg(xa|xa|)(xa)m, (15)
Dg(x)=Γ{|xa|1nmg(xa|xa|)(xa)m}da. (16)

In (15), (16), the power m again is to be understood as the m-fold tensor product

(xa)m=(xa)(xa). (17)

Proof. —

Let fL 2n, 𝒮m), gL 2(S n−1). Then

Sn1Daf(ω)g(ω)dω=Sn10fi1im(a+tω)ωi1ωimg(ω)dtdω=Ωn|x-a|1nfi1im(x)(xa)i1(xa)im|xa|m×g(xa|xa|)dx=f,DagL2. (18)

Here, again we substituted x = a + tω. This shows the representation of Da. Equation (16) follows easily from (15) by an integration over Γ.

For m = 0, n = 3, D* is the backprojection operator in classical 3D cone beam tomography. If m = 1, n = 3, we obtain the adjoint of the cone beam transform in vector field tomography

Dg(x)=Γ|xa|3g(a,xa|xa|)(xa)da. (19)

Remark 1. —

Note that the integrals (12) and (16) are well defined since Γ has a positive distance from Ωn¯.

To prove formula (4), we will also need the adjoint of the Radon transform. The following lemma summarizes basic results of the Radon transform (2) which can be found, for example, in the book of Natterer [10].

Lemma 2. —

The transforms R : L 2n) → L 2([−1, 1] × S n−1) and R ω : L 2n) → L 2([−1, 1]) where R ω f(s) = R f(s, ω) are linear and continuous with bounded adjoints R* : L 2([−1, 1] × S n−1) → L 2n) and Rω : L 2([−1, 1]) → L 2n) represented by

Rωg(x)=g(x,ω),Rg(x)=Sn1g(x,ω,ω)dω. (20)

3. A CONNECTION BETWEEN RADON AND CONE BEAM TRANSFORM

The proof of (4) essentially relies on the duality of the pairs (R ω, Rω), (D a, Da) on the one side and the fact that δ (k), where δ denotes Dirac's delta distribution, is homogeneous of degree −k −1 on the other side. To see the latter property, we take φ𝒞0 (ℝ), λ > 0 and compute

φ(s)δ(k)(λs)ds=λ1φ(λ1s)δ(k)(s)ds=λ1(1)kksk{φ(λ1s)}|s=0=λk1(1)kφ(k)(0)=φ(s)λk1δ(k)(s)ds. (21)

For a tensor field fL 2n, 𝒮m) and a ∈ Γ, we furthermore define

fa(x)=f(x),|xa|m(xa)m=fi1im(x)|xa|m(xa)i1(xa)im,1ijn,j=1,,m. (22)

Using the Cauchy-Schwartz inequality, we easily get

Ωn|fa(x)|2dxΩnf(x)2dx. (23)

Thus, faL 2n), when fL 2n, 𝒮m).

We are now able to state the main result of this paper.

Theorem 2. —

Let n ≥ 2 and f𝒞0(n2)n, 𝒮m). Then

(n2)s(n2)Rfa(ω,s=a,ω)=(1)(n2)Sn1Df(a,θ)δ(n2)(ω,θ)dθ, (24)

where a ∈ Γ, ωS n−1.

Proof. —

We follow the proof of Grangeat's classical formula as outlined in Natterer and Wübbeling [11, Section 2.3]. For ψL 2([−1, +1]), we have from lemma 2 that

1+1Rωfa(s)ψ(s)ds=Ωnfa(x)ψ(x,ω)dx=Ωnf(x),|xa|m(xa)mψ(x,ω)dx. (25)

Using (15), we obtain in the same way for hL 2(S n−1),

Sn1Daf(θ)h(θ)dθ=Ωnf(x),|xa|1nm(xa)mh(xa|xa|)dx. (26)

Assertion (24) is then proved when setting h(θ) = δ (n−2)(〈θ, ω〉), ψ(s) = δ (n−2)(s − 〈a, ω〉) and taking into account that δ (n−2) is homogeneous of degree 1 − n.

Remark 2. —

Obviously, δ (n−2) is not in L 2([−1, +1]). But since δ (n−2) ∈ (𝒞 (n−2)([−1, +1]))′ and the cone beam transform D f(a, y) can be extended homogeneously to ℝn with respect to the second variable for any m according to m = 1 (see [11, Section 2.3]), the integrals in the proof of Theorem 2 are well defined by the smoothness requirement for f. The expression on the right-hand side of (24) is to be understood as

(1)(n2)Sn1Df(a,θ)δ(n2)(ω,θ)dθ=Sn1ωd(n2)Df(a,y=θ),ω(n2)dθ, (27)

where dm = d ⊗ ⋯ ⊗ d means the m-fold inner derivative with respect to the second variable in D f(a, y). We have that d 1 = ∇ is the gradient, d 2 is the Hessian.

If n = 3, m = 0, (24) is just the classical formula of Grangeat (3). For m = 1, we get an extension of Grangeat's formula to vector fields, where

fa(x)=f(x),|xa|1(xa)〉. (28)

The benefits of formula (24) can barely be anticipated. Let us consider the scalar case, that is, m = 0. If there exists to each s ∈ [−1, 1] a source point a ∈ Γ such that 〈a, ω〉 = s, then the derivative s(n2) R f(ω, s) can be obtained for arbitrary ωS n−1, s ∈ [−1, 1] by integrating a corresponding derivative of the data D f (a, θ) over the manifold S n−1ω . This condition is well known as Tuy's condition (see, e.g., [10, Section VI.5]) and means that every hyperplane passing through Ωn has to intersect the source curve Γ in at least one point. The situation changes decisively for m > 0 since the projections fa depend on the source point a. Even if we found to every s a source a satisfying 〈a, ω〉 = s, this would not help since the object function fa of R changes with a. Thus applying formula (24) would give us R fa (ω, s) for a single s, namely, s = 〈a, ω〉. Tuy's condition is not sufficient for m > 0. Moreover, we have to take into account that there is a nontrivial null space for m > 0 anyway. To see this, we note that D f = 0 if f is a potential field, that means f = d p for pH01n, 𝒮 m−1). We refer to the book of Sharafutdinov [9] for a characterization of the null space of D. Denisjuk [12] suggested a generalization of Tuy's condition for higher order tensor fields. He obtained similar formulas as (24) and showed that every plane through Ωn has to intersect Γ at least m − 1 times.

If it is possible to compute fa with the help of formula (24), the curve Γ additionally has to satisfy the requirement that f(x) can be computed from the projections

f(x),|xa|m(xa)m. (29)

This is possible, if the curve Γ fulfills the condition, that for each x ∈ Ωn there exist dim(𝒮m) = nm source points a 1,…,anm such that the tensors |x − ai|−m (x − ai)m are linearly independent for fixed x and 1 ≤ inm. The tensor field f(x) can then be recovered from the projections (29). In case of three-dimensional vector fields (n = 3, m = 1), we need three linearly independent vectors x − ai to each x. Hence, this condition is not fulfilled when, for example, Γ = {a ∈ ℝ3 : |a − a 0| = r, a 3 = 0} is a single circle since we find no such vectors for x in {|x − a 0| < 1, x 3 = 0}.

Formula (24) could be used to calculate reconstruction kernels for D, that is we could try to solve

Dνi1imγ(x)=Ei1imγ(x,) (30)

using that relation to the Radon transform, where Ei1imγ (x, y) ≈ δ(x − y)dxi1 ⊗ ⋯ ⊗ dxim for small γ > 0 is an approximation to the delta distribution. Reconstruction kernels are necessary to cope the problem of tensor tomography with the method of approximate inverse; see, for example, Louis [13], Schuster [3], Rieder and Schuster [14]. It is clear that

Df(a,ω)ω+α1(a,ω,ω1)ω1+α2(a,ω,ω2)ω2=0f(a+tω)dt (31)

for certain coefficients α 1, α 2, where {ω, ω 1, ω 2} forms an orthonormal basis of ℝ3. Unfortunately, α 1, α 2, are unknown. An idea to apply the method of approximate inverse to D might be to approximate

Df(a,ω)ω0f(a+tω)dt, (32)

and to use methods for 3D cone beam CT to solve the problem. If νγ(x) denotes a reconstruction kernel for D in case m = 0, then νiγ (x) := νγ (x) · ei represents a reconstruction kernel for the right-hand side of (32). This approach is subject of current research. Hence, relation (24) might be of large interest in the area of tensor tomography problems.

ACKNOWLEDGMENT

The author is supported by the Deutsche Forschungsgemeinschaft (DFG) under Schu 1978/1-5.

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