Abstract

For medical applications of the in-line phase-contrast x-ray imaging, phase retrieval is a crucial step for quantitative imaging such as reconstructing the 3-D distribution of tissue linear attenuation coefficients and refraction indices. The conventional phase retrieval algorithms, such as the transport of intensity equation (TIE) based algorithms, are not robust against the quantum noise that appears in acquired images due to the radiation dose constraints in medical imaging. In this work a new attenuation-partition based iterative phase retrieval algorithm is proposed. This new algorithm takes advantage of the correlation between the attenuation and phase-shift, and is much robust against noise in acquired images. Phase retrieval results from experimental images show that this new iterative algorithm is fast and robust, and it has good potential for medical x-ray imaging applications.

© 2008 Optical Society of America

1. Introduction

X-ray imaging is ubiquitously utilized in modern medicine, science and technology. The phasecontrast x-ray imaging is getting more attention recently for its great potentials for medical imaging applications [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. Phase-contrast x-ray imaging is based on the highly sensitive phase shifts of transmitted x-ray wave field from imaged objects. The amount of phase shift is determined by the refractive index of the tissue. The refractive index n for x-ray is complex and is given by

n=1δ+iβ

where δ, the refractive index decrement, is responsible for x-ray phase shift, and β is the imaginary part of n and it is responsible for x-ray attenuation. The δ is given by [1, 2, 12]:

δ=(reλ22π)kNk(Zk+fkr)

where re, Nk, Zk and frk are the classical electron radius, atomic density, atomic number and real part of the anomalous atomic scattering factor of the element k, respectively. Note that tissue’s δ is much larger than β. For example, for the infiltrating ductal carcinoma (IDC) in breast, we found that the cancer tissue’s δ(10-6–10-8) is about 1000 times greater than β (10-9–10-11) for x-ray of 10 keV to 150 keV [6]. In a word, human tissue’s phase shift-shift cross sections are about 1000 times greater than that of attenuation for x-ray in above energy range [1, 2, 5, 6, 7]. Hence phase-contrast imaging has great potential of improving x-ray imaging sensitivity and tremendously reducing radiation doses to patients for clinical imaging. The in-line phase contrast x-ray imaging is the simplest among all phase contrast x-ray imaging techniques, since no x-ray optics is needed. In this imaging technique, x-ray propagates freely from the imaged object’s exit to the imaging-detector. Phase-shifted x-ray diffracts and generates the dark-bright interference fringes at tissue’s boundary and interfaces [1, 2, 3, 4, 5, 6, 7]. In the inline phase contrast imaging setting, as the x-ray traverses an object, not only its intensity is attenuated, but x-ray undergoes phase-shifts as well. Propagating from the object to the detector a distance downstream, the x-rays with different phase-shifts diffract and interfere to form the dark-bright fringes along the tissue interfaces and edges. It should be noted that x-ray diffraction process from object’s exit to the detector plays the role of x-ray optics to convert x-ray phase-shifts to xray intensity variation for detection. In this way tissue’s boundary and interfaces get enhanced. Furthermore, recovering the phase shift, or the phase retrieval, plays crucial roles in exploring tissue phase contrast. The importance of phase retrieval can be summarized in, and not limited to, the following categories:

  • It can disentangle tissue phase-contrast from mixed contrast in a phase-contrast image for phase-contrast enhancement. Tissue’s phase-shift differences are more sensitive than attenuation differences. Enhanced images can be expected based on tissue’s phase-shift compared to that based on attenuation change. This can potentially reduce the x-ray radiation dose, a critical issue for medical imaging fields [5].
  • It can provide quantitative maps of tissue projected electron densities. In fact when x-ray traverses an object, the phase shift accrued is given by ϕ(r)=2πλreδ(r,z)dz, , where the integral is over the ray-path. From the formula for δ, Eq. (2), one obtains ϕ(r⃗)=-λre∫ρe(r⃗, z)dz=-λreρe,p(r⃗), where r⃗ is the position vector at the object plane, λ the x-ray wavelength, ρe(r⃗, z) denotes the object’s electron density, ρe,p(r⃗) the integral of tissue electron densities over the ray path, or called the projected electron density as well, and re is the classical electron radius.
  • It is the key step for obtaining 3-D Tomograms of tissue linear attenuation coefficients and refraction indices. This is because that the “direct” phase-contrast tomography without phase-retrieval generates tomograms representing a mixed sum of tissue’s linear attenuation plus a contribution from the 3-D Laplacian of tissue refractive indices, as well as a contribution of artifacts depending on the global distribution of attenuation coefficients and refractive indices. The reconstructed tomographs with this “direct” approach hinders quantitative tissue characterization, and are susceptible to image-artifacts [13]. Therefore, a correct approach should perform the phase retrieval together with filtered backprojection for reconstructing tomograms of tissue linear attenuation coefficients, and tomograms of tissue refraction indices, respectively. Note that tomograms of tissue refraction indices are equivalently tomograms of tissue electron densities [14, 15, 16, 17].

Phase retrieval is to reconstruct the object phase-shift map from multiple phase-contrast images [4, 8, 18, 19]. Phase retrieval is based on x-ray propagation equation derived either from the Fresnel diffraction or the Wigner distribution based phase-space formalism [3, 4, 5, 8, 20]. To be specific, we model the imaged object by a two-dimensional transmission function T 0(r⃗)=A 0(r⃗)e(r⃗), where ϕ(r⃗) denotes the x-ray phase-shift caused by an imaged object, and A 0(r⃗) denotes the x-ray transmission, or the attenuation-map of the object. For the imaging geometry, we assume that the source-object distance is R 1, and the object-detector distance is R 2, hence the magnification factor for the object image is M=(R 1+R 2)/R 1. We stress again no x-ray-optics such as gratings or zone-plates are used in imaging. A general formula for x-ray intensity at the detector was derived as [5]:

I˜(uM;R1+R2)=Iin{cos(πλR2Mu2)·𝓕̂(A02)+
+[2sin(πλR2Mu2)(2πλR2Mu2)·cos(πλR2Mu2)]·𝓕̂(A02ϕ)
cos(πλR2Mu2)·λR22πM·𝓕̂(·(A02ϕ))},

where,

I˜(u)=2ei2πx·uI(x)dx,

I in is the incident x-ray intensity at R 1, λ the wavelength of the monochromatic point x-ray source and 𝓕^ (·) is the Fourier transform. Here πλR 2 u⃗ 2/M is the FT-Space Fresnel propagator’s phase. When the FT-Space propagator’s phase is small

πλR2umax21,

Eq. (5) imposes the moderate variation on the object and imaging geometry. In other words, Eq. (5) imposes conditions such that the imaged object’s phase variation should be moderate or the distance from the object to detector cannot be too large. In clinical imaging, the spatial resolution is limited (<20–30 mm/lp) and object-detector distance <3 m, so the condition Eq. (5) is satisfied. But for synchrotron-based phase contrast imaging experiments the Eq. (5) can be violated [20].

Equation (3) can be simplified to the common known Transport of Intensity Equation (TIE), [21, 18, 5]

I(r;R1+R2)=IinM2{A02(rM)λR22πM·(A02ϕ)(rM)}.

So, TIE (6) is a special case of Eq. (3). It is valid only for moderate resolution images. For high resolution images, i.e. (5) not satisfied, one has to use (3) for phase retrieval. From Eq.(3) or (6), multiple but at least two images (such as A 2 0 and I) should be known. For clinical applications the phase retrieval based on just two acquired images are much preferable than that based on multiple images because of the speed and radiation dose consideration. There are two ways to acquire two images of the imaged object. One way is to make two exposures with different object-detector distances. Another way is to make just one exposure for two images with two detectors in downstream, and we called this approach the dual detector approach [8].

Note (6) is a second order elliptic partial differential equation about phase ϕ. The solution ϕ, represented in the form of pseudo-differential operator, can be approximated by (see [19] for details about the implementation of the algorithm)

ϕ(r)=2πMλR22{·[1A02(2(M2IinIA02))]},

where ∇ is the gradient operator. Since ∇-2 is singular at zero-frequencies, the algorithm based on (5) is unstable near zero-frequencies with noisy data, since the low frequency noise components will get amplified in this case. In medical imaging applications the noise embedded in images is intrinsic because of the radiation dose constraints. Therefore, the algorithms based on Eq. (7) is not suitable for clinical imaging applications [22]. To overcome this difficulty, some kind of regularization can be used. The most widely used regularization is Tikhonov regularization:

2=2(2)2+α2,

where α is called the Tikhonov regularization parameter [23, 24, 25, 22]. In essence Tikhonov regularization seeks the minimum-norm, least squares solution of Eq. (7). However the performance of Tikhonov regularization is highly dependent on the level of noise. Note that in Eq. (6), phase ϕ appears only in the term contains λR 2/2πM. Since the x-ray’s short wavelength λ, and clinical restriction (small R 2), the contribution of tissue’s phase ϕ to intensity is small. A small amount of noise compared to intensity will render Tikhonov regularization useless for phase retrieval [22]. Another disadvantage of Tikhonov regularization is that α is not a priori parameter which makes it difficult for practical applications.

 

Fig. 1. Flow chart of APBA

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As we mentioned earlier that the x-ray phase-shift by tissues arises from the x-ray coherent scattering by tissues. In contrast x-ray attenuation arises from three x-ray-tissue interactions: the photoelectric absorption, coherent scattering and incoherent scattering in the x-ray energy range employed for medical imaging. For high energy x-rays of 60 keV to 500 keV x-ray attenuation by soft tissues is determined by the incoherent x-ray scattering. Therefore, we observed that both the x-ray phase-shift and attenuation by soft tissues are all determined by the projected electron density for these high energy x-rays. We called this complementary relationship between phase-shift and attenuation for soft tissues as the phase-attenuation duality.

Looking for more effective and robust phase retrieval algorithms, we found a phase retrieval approach based on just a single acquired phase-contrast image. This approach, which is based on the phase-attenuation duality, is applicable for soft tissue (or light element objects) imaging with x-rays of 60 keV or higher [12]. This phase retrieval approach is very much robust against image noise [22]. In this work, we extend the notion of phase-attenuation duality for light element objects to the correlation between phase shift and attenuation for general objects and general x-ray energy range. Based on this observation of the correlation between phase shift and attenuation, we develop an iterative phase retrieval algorithm, called Attenuation-Partition Based Algorithm (APBA) for general objects. Before we are going to detailed analysis in following sections, we briefly describe how this new algorithm works. Figure 1 represents the flow chart of the algorithm. In the chart, I 0 and A 0 represent the acquired phase-contrast image intensity at the source to imaging-detector distance SID=R 1+R 2 and the attenuation data from the acquired image at SID=R 1. The new algorithm consists of the following four simple steps: (1) compute A n+1 and ϕ n+1 by assuming In satisfy the phase-attenuation duality Eq. (4) of [12]; (2) compute the residue δ A n+1=A n+1-A 0; (3) propagate δAn+1·eiϕn+1 using Fresnel propagation to get δ In+1; (4) replace In with In+1=(I0+δIn+1)2 . The phase retrieval is complete through about 10 iteration steps as is shown below.

2. The Attenuation-Partition Based Algorithm (APBA)

In the diagnostic x-ray imaging, the tissue’s attenuation change arises from three x-ray–tissue interactions: the photoelectric absorption, the coherent scattering, and the incoherent scattering [26, 27, 5, 12]. If we denote the x-ray wave amplitude attenuated by incoherent scattering by A incoh, and the amplitude attenuated by photoelectric absorption and coherent scattering by A pe,coh, the total attenuated x-ray amplitude A 0 can be written as

A0(r)=Aincoh(r)·Ape,coh(r).

The reason for factoring out the incoherent scattering contribution is that both A incoh and the phase-shift ϕ are all determined by tissue’s electron density. In fact, for light elements with atomic numbers Z<10, x-ray attenuation is dominated by the Compton scattering for x-rays of 60 keV or higher. We noted that in these cases attenuation and phase are both determined by object’s electron densities, we call this notion as the phase-attenuation duality [12]. With the help of the duality notion, we found a phase retrieval approach based on just a single acquired phase-contrast image [12]. Surprisingly this phase retrieval approach is very much robust against image noise [22]. For imaging tasks with general objects containing high z-elements such as iodinated-contrast media, or imaging tasks with x-rays of lower energies such as in mammography, the duality condition will not hold any more, but the correlation between phase shift and attenuation for general objects and general x-ray energy range can be used for phase retrieval through the attenuation-partition as is shown in follows.

First let’s denote the amplitude attenuated by Compton scattering only as A KN. As we pointed out in [12], A KN has a relation with tissue’s phase-shift, ϕ, in the form:

AKN2(r)=exp(μKN(r,s)ds)=exp[σKNλreϕ(r)],

where µKN is the tissue linear attenuation coefficient from the Compton scattering, λ the x-ray wavelength, re the classic electron radius and σ KN is the total cross section for x-ray photon Compton scattering from a single free electron, which can be determined by the time-honored Klein-Nishina formula:

σKN=2πre2{1+ηη2[2(1+η)1+2η1ηlog(1+2η)]+12ηlog(1+2η)1+3η(1+2η)2}.

Here η=E photon/mec 2, with E photon denotes the photon energy of the primary x-ray beam and mec 2 the resting electron energy. When the phase-attenuation duality holds, combining (10) and (6), we get the duality reversion formula [12]:

AKN2(r)=𝓕ˇ(𝓕̂(M2IinI)1+4π2ku2),

where I is the intensity of phase-contrast image acquired at SID=R 1+R 2,

k=λR22πMλreσKN,

and 𝓕^ (·), 𝓕ˇ (·) represent the Fourier and Inverse Fourier transforms. And phase can be retrieved from equations (10) and (12). For imaging tasks with general objects and using x-rays of lower energies, Eq. (10) will not hold any more. In order still to take advantage of the correlation between phase shift and attenuation, we take a strategy of attenuation-partitioning and iterative improvement of approximation. Specifically we rewrite Eq. (9) in the form

A0(r)=A~(r)δA(r),

where

A˜(r)=AKN(r)=Aincoh(r),δA(r)=AKN(r)·(1Ape,coh(r)).

Now we construct an iterative algorithm as follows. (1) First, we regard the acquired phasecontrast image as if it would receive all contribution from the Compton scattering. In other words, the amplitude √I of the phase-contrast image is contributed all from Ã(r⃗)=A KN(r⃗). Using the duality inversion formula (12) compute à and the approximated phase-map ϕ=λreσKNln(A~2) ; (2) second, we compute the error in estimation of the object’s attenuation map from this approximation. This error is apparently δA=Ã-A 0, where A 0 is the object’s attenuation obtained from the image acquired at SID=R 1; (3) third, we want to improve our previous estimate on Ã(r⃗)=A KN(r⃗) by incorporating the error δA. To do so we consider a hypothetical object δ T 0(r⃗)=δA(r⃗e (r⃗), and compute the transmitted x-ray wave field arriving at the detector at SID=R 1+R 2. The computation is according to the paraxial Fresnel diffraction theory, for a monochromatic x-ray point source of wavelength λ at the origin, the diffracted spherical x-ray wave field arriving at r⃗ on the detector plane, can be modelled by the Fresnel-Kirchhoff integral [5]

E(r;R1,R2)=IinλR2·exp(2πiR1+R2λ)2exp[iπλ(ξ2R1+(ξr)2R2)]A0(ξ)eiϕ(ξ)dξ

where i=1 ,r⃗ is the position in the imaged object, R 1 the distance from point x-ray source to object, R 2 the distance from object to detector plane, I in the incident x-ray intensity at R 1. For convenience we denote

h(r)=(M1)r22R1R2,ω=λR22πM
E1(r)=MIinE(Mr;R1,R2)·ei2ωh(r),

where M=(R 1+R 2)/R 1 is the geometric magnification factor. Note in above E 1(r⃗) is the normalized x-ray wave field at detector entrance, and its intensity forms the phase-contrast image. From Eq. (15) a simple computation leads to

E1(r)=12πω·2ei(rξ)22ωT0(ξ)dξ=12πω.G*T0(r),

with G(r⃗)=exp[i r⃗ 2/2ω],∗ represents the convolution operator, and E1=MIinI . In the rest of the paper, M2IinI will be called the normalized intensity and still be denoted by I. Rewrite E1(r)=I(r)eiψ(r) , we found by applying stationary phase formula [28], that

ψ(r)=π2+ϕ(r)+ω2[2A0(r)A0(r)ϕ(r)2].

Eq. (17) provides a general formula for the x-ray wave eikonal transport. Now for given object phase ϕ, denoting two virtual objects, 0=à e and δ T 0=δAeiϕ located at the object plane. After the paraxial Fresnel free propagation to position SID=R 1+R 2, i.e. Eq. (16), the corresponding propagated wave fields at the detector-entrance are denoted by 1=√Ĩeiψ˜, and δE1=δIeiδψ respectively. Then by (16), E 1= 1-δ E 1. This gives

I(r)=I~(r)+δI(r)2I~(r)·δI(r)·cos(ψ~(r)δψ(r)).

From (17)

ψ~(r)δψ(r)=ω2(2A~(r)A~(r)2δA(r)δA(r)) .

Thus

cos(ψ~(r)δψ(r))=cos(ω2(2A~(r)A~(r)2δA(r)δA(r)))1

when we assume

[ω2(2A~(r)A~(r)2δA(r)δA(r))]21 .

This gives

I(r)I~(r)δI(r),

by assuming Ĩ(r⃗)>δI(r⃗). In other words, the square root of the (normalized) phase-contrast image intensity data I, generated by the object, is the square root of the intensity Ĩ, generated by the virtual object 0=Ãe, minus the square root of the intensity δI, generated by the virtual object δT 0=δAe. This elaborated analysis leads to the 4th step of the algorithm, that is, (4). update √Ĩ with I+δI . A new iteration step can then be started. Summarizing the above analysis, we arrive at the following new phase-retrieval algorithm:

Algorithm 1. The Attenuation-Partition Based iterative phase retrieval Algorithm (APBA). Assume the attenuation A0 and the normalized intensity I=M 2/I in·|E(r⃗;R 1,R 2)|2 are known inputs. The initial value of δI, generally 0, is also set from the input.

1. Compute Ĩ from (19).

2. Compute Ã=AKN from Eq. (12) with M2Iin I in (12) substituted with Ĩ. Then compute ϕ from (10).

3. Compute δA=Ã-A 0.

4. Compute the improvement of the iteration:

δ=δδAoldδA,

where ‖·‖ denotes some kind of norm, that can effectively reflect the accuracy of the retrieved data as well as the visual perception of the data as an image, and δA old is the δA computed in the previous iteration step. If δ is less than a predefined threshhold value β, or the iteration step exceeds a predefined maximum number of iteration steps, then ϕ is the retrieved phase and the iteration stops; otherwise, go to the next step.

5. Compute δI=|E1|2 from (16), with T 0=δA·e and go back to step (1).

Remarks:

  • Due to space limitation a detailed mathematical proof about this algorithm will be present in another paper. It can be shown that this algorithm converges whenever the parameter k in (12) is greater than zero.
  • The extent of correlation between A 0 and ϕ is another important factor affecting the speed of convergence. In other words, the relative portion of incoherent scattering contribution in the total attenuation can affect the speed of convergence. In real situations, if k is chosen correctly, an accurate result can be reached within 10 iteration steps.

3. Phase retrieval with experimental images

 

Fig. 2. x-ray edge images and the recovered phase map. (a) Attenuation image A 2 0 ; (b) Phase-contrast image I; (c) phase map recovered using APBA (with 10 iteration steps)

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In this section, we compare the performance of APBA with the TIE algorithm [19] for lab experimental images. Two groups of data are tested. In the first group, the two images are that of an acrylic plate of 1.42 mm. Acrylic is a clear plastic of C5H8O2 that resembles glass, and is with commercial names such as Lucite and Plexi glass. Its mass density ranges from 1.16–1.20 g/cm3. The laser-cut sharp edge of the acrylic plate was imaged with a micro-focus x-ray tube (UltraBright Microfocus X-ray source, Oxford Instruments, CA) operating at 40 kVp. The focal spot used was of 14 µm. For the imaging detector a Computed Radiography(CR)-system was used, which includes a CR-plate (the photostimulable phosphor) and a reader of a 43.75 µm sampling pitch (Konica REGIUS 190, Konica Minolta, Japan). Figure 2(a) and Fig. 2(b) are the central parts of the acquired images. Figure 2(a) is a conventional radiograph of the plate acquired with a source-detector distance SID=66 cm, and an object-detector distance R 2≈0. Figure 2(b) is a phase-contrast image acquired with SID=132 cm, and an object-detector distance R 2=66 cm. In Fig. 2(b) the phase-contrast manifests itself as the dark-bright fringe along the acrylic plate-edge. The dark-bright fringe pair is the signature enhancement-pattern of phase-jumps. It forms as a result of the interference of the diffracted x-ray with different phase-shifts along the plate edge. As a comparison, the corresponding image acquired with R 2≈0, that is, the conventional radiograph of the plate (Fig. 2(a)), showed no dark-bright fringe. This is because x-rays exiting from the plate need to diffract a certain distance to generate interference between waves of different phase-shifts [6, 10]. We cut a piece with 1500×1500 pixels from the original image data (SID=132 cm, R 2=66 cm) and treat it as the intensity data I. Based on the magnification factor M, we cut a corresponding part from the image with (SID=66 cm, R 2≈0) and treat it as the attenuation data A 2, and interpolate it to the same size as I. Care is taken in the process of aligning the two images. Figure 2(c) is the recovered phase map using APBA after 10 iteration steps. The corresponding phase maps recovered using TIE algorithm [19], with different Tikhonov regularization parameter α, are shown in Fig. 3. We can see that the result using APBA is much better, in visual perception, than that using TIE algorithm [19]. The true phase shifts of the acrylic plate is about 101 radian, which is estimated from the estimated average x-ray wavelength, the plate’s composition and mass density for acrylic, and the plate thickness (1.5 mm). We found the retrieved phase shifts for 10 iteration steps is 57.3, about half the estimated “intrinsic” phase-shift. The error is attributed to the image alignment error, and the error in mass-density estimation of the plate. On the other hand, the estimated phase-shifts of the plate from the TIE-based retrieval algorithm (Fig. 3) are 22941, 36.1 and 2.6 when α=0, 10Δ2, and 50Δ2 respectively, where Δ is the sampling step-size in FT-space. Hence it is clear that the phase-maps by TIE based retrieval are way off the true phase-shift values.

 

Fig. 3. Recovered phase maps using TIE algorithm, with different Tikhonov regularization parameters α: (a) α=0; (b) α=10Δ2; (c) α=50Δ2. Δ is the sampling step-size in FT space.

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In the second group, two images of a breast cancer patient’s lumpectomy specimen were used for testing the phase retrieval algorithms. As the details of image acquisition were described in [29], here we just give a brief description. The source used was a micro-focus x-ray tube (Model L8121-01, Hamamastu Photonics, Japan) operating at 40 kVp. The focus spot was set to 7µm. The detector is an amorphous-selenium based flat panel detector (DirectRay, Hologic, DE) with a pixel pitch of 139µm. Figure 4(a) is the conventional radiograph of the lumpectomy specimen acquired with SID=66 cm, R 2≈0. Figure 4(b) is the phase-contrast image of the specimen acquired with SID=184.9 cm and R 2=118.5 cm. We used these two images as the input for testing the phase retrieval algorithms. Compared to Fig. 4(a), Fig. 4(b) shows the enhanced dark-bright fringes at tissue boundaries. The recovered phase map using APBA (after 10 iteration steps) is shown in Fig. 4(c). However the phase recovery using TIE based algorithm, as shown in Fig. 5, was completely failed. This case shows again our new algorithm is much more robust than TIE algorithm.

 

Fig. 4. x-ray lumpectomy images and the recovered phase map. (a) Attenuation image A 2 0 ; (©[2008] IEEE);(b) Phase-contrast image I; (©[2008] IEEE);(c) phase map recovered using APBA with 10 iteration steps.

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Fig. 5. Recovered lumpectomy phase maps using TIE algorithm, with different Tikhonov regularization parameters α: (a) α=0; (b) α=10Δ2; (c) α=50Δ2. Δ is the sampling step-size in FT space.

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4. Conclusions

Phase retrieval is a crucial step for quantitative imaging such as reconstructing the 3-D distribution of tissue linear attenuation coefficients and refraction indices. However, images acquired in medical applications are relatively noisy due to the radiation dose constraints. The conventional phase retrieval algorithms, such as the transport of intensity equation (TIE) based algorithms, are not robust against the quantum noise that appears in acquired images. In this work a new attenuation-partition based iterative phase retrieval algorithm is proposed. This new iterative algorithm is derived from our previous notion of the phase-attenuation duality, and it takes advantage of the correlation between the attenuation and phase-shift. In this work, we analyzed the speed of convergence of this algorithm and conclude that the converge speed is proportional to the order of the correlation between the objects attenuation and phase. The phase retrieval with the experimental images showed that the new algorithm has much better performance than the TIE-based algorithm ([21]). We expect that this new iterative phase retrieval will find many applications in medical x-ray imaging.

Acknowledgments

The authors would like to thank Mr. Da Zhang for his help with the experimental images.

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19. L. Allen and M. Oxley, “Phase retrieval from series of images obtainedbe defocus variation,” Opt. Commun. 199, 65–75 (2001). [CrossRef]  

20. X. Wu and H. Liu, “Phase-space evolution of x-ray coherence in phase-sensitive imaging,” Appl. Opt. 47, E44–E52 (2008). [CrossRef]   [PubMed]  

21. M. Teague, “Deterministic phase retrieval: a Green’s function solution,” J. Opt. Soc. Am. 73, 1434–1441 (1983). [CrossRef]  

22. X. Wu and H. Liu, “Robustness of a phase-retrieval approach based on phase-attenuation duality,” J. X-ray Sci. and Tech. 15, 85–95 (2007).

23. A. Tikhonov and V. Arsenin, Solution of Ill-posed Problems (Winston & Sons, Washington, 1977).

24. I. Schelokov, T. Weitkamp, and A. Snigirev, “Reconstruction of an object phase transmission function from inline X-ray holograms,” Opt. Commun. 213, 247–258 (2002). [CrossRef]  

25. T. Gureyev, A. Pogany, D. Paganin, and S. Wilkins, “Linear algorithms for phase retrieval in the Fresnel region,” Opt. Commun. 231, 53–70 (2004). [CrossRef]  

26. N. Dyson, X-Rays in Atomic and Nuclear Physics (Longman Scientific and Technical, Essex, UK, 1973).

27. X. Wu, A. Dean, and H. Liu, Biomedical Photonics Handbook, chap. 26, pp. 26-1–26-34 (CRC Press, Tampa, Fla., 2003).

28. L. Hörmander, The Analysis of Linear Partial Differential Operators I (Springer, New York, 1983). [CrossRef]  

29. D. Zhang, M. Donvan, L. Fajardo, A. Archet, X. Wu, and H. Liu, “Preliminary Feasibility study of an in-line phase contrast x-ray imaging prototype,” IEEE Trans Biomedical Engineering 55, (in press) (2008).

References

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  1. A. Snigirev, I. Snigireva, V. Kohn, S. Kuznetsov, and I. Shelokov, "On the possibilities of x-ray phase contrast microimaging by coherent high-energy synchrotron radiation," Rev. Sci. Instrum. 66, 5486 - 5492 (1995).
    [CrossRef]
  2. S. Wilkins, T. Gureyev, D. Gao, A. Pogany, and A. Stevenson, "Phase-contrast imaging using polychromatic hard X-rays," Nature 384, 335 - 338 (1996).
    [CrossRef]
  3. A. Pogany, D. Gao, and S. Wilkins, "Contrast and resolution in imaging with a microfocus x-ray source," Rev. Sci. Instrum. 68, 2774 (1997).
    [CrossRef]
  4. D. Paganin and K. Nugent, "Noninterferometric Phase Imaging with Partially Coherent Light," Phy. Rev. Lett. 80, 2586 - 2589 (1998).
    [CrossRef]
  5. X. Wu and H. Liu, "A general theoretical formalism for X-ray phase contrast imaging," J. X-ray Sci. Technol. 11, 33 - 42 (2003).
  6. X.  Wu and H. Liu, "Clinical implementation of x-ray phase-contrast imaging: Theoretical foundations and design considerations," Med. Phys. 30, 2169 - 2179 (2003).
    [CrossRef] [PubMed]
  7. E. Donnelly, R. Price, and D. Pickens, "Dual focal-spot imaging for phase extraction in phase-contrast radiography," Med. Phys. 30, 2292 - 2296 (2003).
    [CrossRef] [PubMed]
  8. X. Wu and H. Liu, "A dual detector approach for X-ray attenuation and phase imaging," J. X-ray Sci. Technol. 12, 35-42 (2004).
  9. X. Wu and H. Liu, "An experimental method of determining relative phase-contrast factor for x-ray imaging systems," Med. Phys. 31, 997 - 1002 (2004).
    [CrossRef]
  10. X. Wu and H. Liu, "A new theory of phase-contrast x-ray imaging based on Wigner distributions," Med. Phys. 31, 2378 - 2384 (2004).
    [CrossRef] [PubMed]
  11. X. Wu and H. Liu, "A reconstruction formula for soft tissue X-ray phase tomography," J. X-ray Sci. Technol. 12, 273 - 279 (2004).
  12. X. Wu, H. Liu, and A. Yan, "X-ray phase-attenuation duality and phase retrieval," Opt. Lett. 30(4), 379 - 381 (2005).
    [CrossRef] [PubMed]
  13. X. Wu, H. Liu, and A. Yan, "Phase-Contrast X-Ray Tomography: Contrast Mechanism and Roles of Phase Retrieval," Eur. J. Radiol. (to be published) (2008).
    [CrossRef] [PubMed]
  14. A. Bronnikov, "Theory of quantitative phase-contrast computed tomography," J. Opt. Soc. Am. A 19, 472 - 480 (2002).
    [CrossRef]
  15. X. Wu and H. Liu, "X-Ray cone-beam phase tomography formulas based on phase-attenuation duality," Opt. Express 13, 6000 - 6014 (2005).
    [CrossRef] [PubMed]
  16. T. Gureyev, D. Paganin, G. Myers, Y. Nesterets, and S. Wilkins, "Phase-and-amplitude computer tomography," Appl. Phys. Lett. 89, 034,102 (2006).
    [CrossRef]
  17. R. M. P. Cloetens, M. Schlenker, and S. Lerbs-Mache, "Quantitative phase tomography of Arabidopsis seeds reveals intercellular void network," PNAS 103, 14,626 - 14,630 (2006).
    [CrossRef]
  18. K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, "Quantitative Phase Imaging Using Hard X Rays," Phy. Rev. Lett. 77, 2961 - 2964 (1996).
    [CrossRef]
  19. L. Allen and M. Oxley, "Phase retrieval from series of images obtainedbe defocus variation," Opt. Commun. 199, 65 - 75 (2001).
    [CrossRef]
  20. X. Wu and H. Liu, "Phase-space evolution of x-ray coherence in phase-sensitive imaging," Appl. Opt. 47, E44 - E52 (2008).
    [CrossRef] [PubMed]
  21. M. Teague, "Deterministic phase retrieval: a Green�??s function solution," J. Opt. Soc. Am. 73, 1434 - 1441 (1983).
    [CrossRef]
  22. X. Wu and H. Liu, "Robustness of a phase-retrieval approach based on phase-attenuation duality," J. X-Ray Sci. Technol. 15, 85 - 95 (2007).
  23. A. Tikhonov and V. Arsenin, Solution of Ill-Posed Problems (Winston & Sons, Washington, 1977).
  24. I. Schelokov, T. Weitkamp, and A. Snigirev, "Reconstruction of an object phase transmission function from inline X-ray holograms," Opt. Commun. 213, 247 - 258 (2002).
    [CrossRef]
  25. T. Gureyev, A. Pogany, D. Paganin, and S. Wilkins, "Linear algorithms for phase retrieval in the Fresnel region," Opt. Commun. 231, 53 - 70 (2004).
    [CrossRef]
  26. N. Dyson, X-Rays in Atomic and Nuclear Physics (Longman Scientific and Technical, Essex, UK, 1973).
  27. X. Wu, A. Dean, and H. Liu, Biomedical Photonics Handbook, (CRC Press, Tampa, Fla., 2003), Chap. 26, pp. 26-1 - 26-34
  28. L. Hörmander, The Analysis of Linear Partial Differential Operators I (Springer, New York, 1983).
    [CrossRef]
  29. D. Zhang, M. Donvan, L. Fajardo, A. Archet, X. Wu, and H. Liu, "Preliminary feasibility study of an in-line phase contrast x-ray imaging prototype," IEEE Trans. Biomed. Eng. 55, (in press) (2008).

2008 (3)

X. Wu, H. Liu, and A. Yan, "Phase-Contrast X-Ray Tomography: Contrast Mechanism and Roles of Phase Retrieval," Eur. J. Radiol. (to be published) (2008).
[CrossRef] [PubMed]

X. Wu and H. Liu, "Phase-space evolution of x-ray coherence in phase-sensitive imaging," Appl. Opt. 47, E44 - E52 (2008).
[CrossRef] [PubMed]

D. Zhang, M. Donvan, L. Fajardo, A. Archet, X. Wu, and H. Liu, "Preliminary feasibility study of an in-line phase contrast x-ray imaging prototype," IEEE Trans. Biomed. Eng. 55, (in press) (2008).

2007 (1)

X. Wu and H. Liu, "Robustness of a phase-retrieval approach based on phase-attenuation duality," J. X-Ray Sci. Technol. 15, 85 - 95 (2007).

2006 (2)

T. Gureyev, D. Paganin, G. Myers, Y. Nesterets, and S. Wilkins, "Phase-and-amplitude computer tomography," Appl. Phys. Lett. 89, 034,102 (2006).
[CrossRef]

R. M. P. Cloetens, M. Schlenker, and S. Lerbs-Mache, "Quantitative phase tomography of Arabidopsis seeds reveals intercellular void network," PNAS 103, 14,626 - 14,630 (2006).
[CrossRef]

2005 (2)

2004 (5)

X. Wu and H. Liu, "A dual detector approach for X-ray attenuation and phase imaging," J. X-ray Sci. Technol. 12, 35-42 (2004).

X. Wu and H. Liu, "An experimental method of determining relative phase-contrast factor for x-ray imaging systems," Med. Phys. 31, 997 - 1002 (2004).
[CrossRef]

X. Wu and H. Liu, "A new theory of phase-contrast x-ray imaging based on Wigner distributions," Med. Phys. 31, 2378 - 2384 (2004).
[CrossRef] [PubMed]

X. Wu and H. Liu, "A reconstruction formula for soft tissue X-ray phase tomography," J. X-ray Sci. Technol. 12, 273 - 279 (2004).

T. Gureyev, A. Pogany, D. Paganin, and S. Wilkins, "Linear algorithms for phase retrieval in the Fresnel region," Opt. Commun. 231, 53 - 70 (2004).
[CrossRef]

2003 (3)

X. Wu and H. Liu, "A general theoretical formalism for X-ray phase contrast imaging," J. X-ray Sci. Technol. 11, 33 - 42 (2003).

X.  Wu and H. Liu, "Clinical implementation of x-ray phase-contrast imaging: Theoretical foundations and design considerations," Med. Phys. 30, 2169 - 2179 (2003).
[CrossRef] [PubMed]

E. Donnelly, R. Price, and D. Pickens, "Dual focal-spot imaging for phase extraction in phase-contrast radiography," Med. Phys. 30, 2292 - 2296 (2003).
[CrossRef] [PubMed]

2002 (2)

A. Bronnikov, "Theory of quantitative phase-contrast computed tomography," J. Opt. Soc. Am. A 19, 472 - 480 (2002).
[CrossRef]

I. Schelokov, T. Weitkamp, and A. Snigirev, "Reconstruction of an object phase transmission function from inline X-ray holograms," Opt. Commun. 213, 247 - 258 (2002).
[CrossRef]

2001 (1)

L. Allen and M. Oxley, "Phase retrieval from series of images obtainedbe defocus variation," Opt. Commun. 199, 65 - 75 (2001).
[CrossRef]

1998 (1)

D. Paganin and K. Nugent, "Noninterferometric Phase Imaging with Partially Coherent Light," Phy. Rev. Lett. 80, 2586 - 2589 (1998).
[CrossRef]

1997 (1)

A. Pogany, D. Gao, and S. Wilkins, "Contrast and resolution in imaging with a microfocus x-ray source," Rev. Sci. Instrum. 68, 2774 (1997).
[CrossRef]

1996 (2)

S. Wilkins, T. Gureyev, D. Gao, A. Pogany, and A. Stevenson, "Phase-contrast imaging using polychromatic hard X-rays," Nature 384, 335 - 338 (1996).
[CrossRef]

K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, "Quantitative Phase Imaging Using Hard X Rays," Phy. Rev. Lett. 77, 2961 - 2964 (1996).
[CrossRef]

1995 (1)

A. Snigirev, I. Snigireva, V. Kohn, S. Kuznetsov, and I. Shelokov, "On the possibilities of x-ray phase contrast microimaging by coherent high-energy synchrotron radiation," Rev. Sci. Instrum. 66, 5486 - 5492 (1995).
[CrossRef]

1983 (1)

Allen, L.

L. Allen and M. Oxley, "Phase retrieval from series of images obtainedbe defocus variation," Opt. Commun. 199, 65 - 75 (2001).
[CrossRef]

Archet, A.

D. Zhang, M. Donvan, L. Fajardo, A. Archet, X. Wu, and H. Liu, "Preliminary feasibility study of an in-line phase contrast x-ray imaging prototype," IEEE Trans. Biomed. Eng. 55, (in press) (2008).

Barnea, Z.

K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, "Quantitative Phase Imaging Using Hard X Rays," Phy. Rev. Lett. 77, 2961 - 2964 (1996).
[CrossRef]

Bronnikov, A.

Cloetens, R. M. P.

R. M. P. Cloetens, M. Schlenker, and S. Lerbs-Mache, "Quantitative phase tomography of Arabidopsis seeds reveals intercellular void network," PNAS 103, 14,626 - 14,630 (2006).
[CrossRef]

Cookson, D.

K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, "Quantitative Phase Imaging Using Hard X Rays," Phy. Rev. Lett. 77, 2961 - 2964 (1996).
[CrossRef]

Donnelly, E.

E. Donnelly, R. Price, and D. Pickens, "Dual focal-spot imaging for phase extraction in phase-contrast radiography," Med. Phys. 30, 2292 - 2296 (2003).
[CrossRef] [PubMed]

Donvan, M.

D. Zhang, M. Donvan, L. Fajardo, A. Archet, X. Wu, and H. Liu, "Preliminary feasibility study of an in-line phase contrast x-ray imaging prototype," IEEE Trans. Biomed. Eng. 55, (in press) (2008).

Fajardo, L.

D. Zhang, M. Donvan, L. Fajardo, A. Archet, X. Wu, and H. Liu, "Preliminary feasibility study of an in-line phase contrast x-ray imaging prototype," IEEE Trans. Biomed. Eng. 55, (in press) (2008).

Gao, D.

A. Pogany, D. Gao, and S. Wilkins, "Contrast and resolution in imaging with a microfocus x-ray source," Rev. Sci. Instrum. 68, 2774 (1997).
[CrossRef]

S. Wilkins, T. Gureyev, D. Gao, A. Pogany, and A. Stevenson, "Phase-contrast imaging using polychromatic hard X-rays," Nature 384, 335 - 338 (1996).
[CrossRef]

Gureyev, T.

T. Gureyev, D. Paganin, G. Myers, Y. Nesterets, and S. Wilkins, "Phase-and-amplitude computer tomography," Appl. Phys. Lett. 89, 034,102 (2006).
[CrossRef]

T. Gureyev, A. Pogany, D. Paganin, and S. Wilkins, "Linear algorithms for phase retrieval in the Fresnel region," Opt. Commun. 231, 53 - 70 (2004).
[CrossRef]

K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, "Quantitative Phase Imaging Using Hard X Rays," Phy. Rev. Lett. 77, 2961 - 2964 (1996).
[CrossRef]

S. Wilkins, T. Gureyev, D. Gao, A. Pogany, and A. Stevenson, "Phase-contrast imaging using polychromatic hard X-rays," Nature 384, 335 - 338 (1996).
[CrossRef]

Kohn, V.

A. Snigirev, I. Snigireva, V. Kohn, S. Kuznetsov, and I. Shelokov, "On the possibilities of x-ray phase contrast microimaging by coherent high-energy synchrotron radiation," Rev. Sci. Instrum. 66, 5486 - 5492 (1995).
[CrossRef]

Kuznetsov, S.

A. Snigirev, I. Snigireva, V. Kohn, S. Kuznetsov, and I. Shelokov, "On the possibilities of x-ray phase contrast microimaging by coherent high-energy synchrotron radiation," Rev. Sci. Instrum. 66, 5486 - 5492 (1995).
[CrossRef]

Lerbs-Mache, S.

R. M. P. Cloetens, M. Schlenker, and S. Lerbs-Mache, "Quantitative phase tomography of Arabidopsis seeds reveals intercellular void network," PNAS 103, 14,626 - 14,630 (2006).
[CrossRef]

Liu, H.

X. Wu, H. Liu, and A. Yan, "Phase-Contrast X-Ray Tomography: Contrast Mechanism and Roles of Phase Retrieval," Eur. J. Radiol. (to be published) (2008).
[CrossRef] [PubMed]

X. Wu and H. Liu, "Phase-space evolution of x-ray coherence in phase-sensitive imaging," Appl. Opt. 47, E44 - E52 (2008).
[CrossRef] [PubMed]

D. Zhang, M. Donvan, L. Fajardo, A. Archet, X. Wu, and H. Liu, "Preliminary feasibility study of an in-line phase contrast x-ray imaging prototype," IEEE Trans. Biomed. Eng. 55, (in press) (2008).

X. Wu and H. Liu, "Robustness of a phase-retrieval approach based on phase-attenuation duality," J. X-Ray Sci. Technol. 15, 85 - 95 (2007).

X. Wu and H. Liu, "X-Ray cone-beam phase tomography formulas based on phase-attenuation duality," Opt. Express 13, 6000 - 6014 (2005).
[CrossRef] [PubMed]

X. Wu, H. Liu, and A. Yan, "X-ray phase-attenuation duality and phase retrieval," Opt. Lett. 30(4), 379 - 381 (2005).
[CrossRef] [PubMed]

X. Wu and H. Liu, "A reconstruction formula for soft tissue X-ray phase tomography," J. X-ray Sci. Technol. 12, 273 - 279 (2004).

X. Wu and H. Liu, "An experimental method of determining relative phase-contrast factor for x-ray imaging systems," Med. Phys. 31, 997 - 1002 (2004).
[CrossRef]

X. Wu and H. Liu, "A new theory of phase-contrast x-ray imaging based on Wigner distributions," Med. Phys. 31, 2378 - 2384 (2004).
[CrossRef] [PubMed]

X. Wu and H. Liu, "A dual detector approach for X-ray attenuation and phase imaging," J. X-ray Sci. Technol. 12, 35-42 (2004).

X.  Wu and H. Liu, "Clinical implementation of x-ray phase-contrast imaging: Theoretical foundations and design considerations," Med. Phys. 30, 2169 - 2179 (2003).
[CrossRef] [PubMed]

X. Wu and H. Liu, "A general theoretical formalism for X-ray phase contrast imaging," J. X-ray Sci. Technol. 11, 33 - 42 (2003).

Myers, G.

T. Gureyev, D. Paganin, G. Myers, Y. Nesterets, and S. Wilkins, "Phase-and-amplitude computer tomography," Appl. Phys. Lett. 89, 034,102 (2006).
[CrossRef]

Nesterets, Y.

T. Gureyev, D. Paganin, G. Myers, Y. Nesterets, and S. Wilkins, "Phase-and-amplitude computer tomography," Appl. Phys. Lett. 89, 034,102 (2006).
[CrossRef]

Nugent, K.

D. Paganin and K. Nugent, "Noninterferometric Phase Imaging with Partially Coherent Light," Phy. Rev. Lett. 80, 2586 - 2589 (1998).
[CrossRef]

K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, "Quantitative Phase Imaging Using Hard X Rays," Phy. Rev. Lett. 77, 2961 - 2964 (1996).
[CrossRef]

Oxley, M.

L. Allen and M. Oxley, "Phase retrieval from series of images obtainedbe defocus variation," Opt. Commun. 199, 65 - 75 (2001).
[CrossRef]

Paganin, D.

T. Gureyev, D. Paganin, G. Myers, Y. Nesterets, and S. Wilkins, "Phase-and-amplitude computer tomography," Appl. Phys. Lett. 89, 034,102 (2006).
[CrossRef]

T. Gureyev, A. Pogany, D. Paganin, and S. Wilkins, "Linear algorithms for phase retrieval in the Fresnel region," Opt. Commun. 231, 53 - 70 (2004).
[CrossRef]

D. Paganin and K. Nugent, "Noninterferometric Phase Imaging with Partially Coherent Light," Phy. Rev. Lett. 80, 2586 - 2589 (1998).
[CrossRef]

K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, "Quantitative Phase Imaging Using Hard X Rays," Phy. Rev. Lett. 77, 2961 - 2964 (1996).
[CrossRef]

Pickens, D.

E. Donnelly, R. Price, and D. Pickens, "Dual focal-spot imaging for phase extraction in phase-contrast radiography," Med. Phys. 30, 2292 - 2296 (2003).
[CrossRef] [PubMed]

Pogany, A.

T. Gureyev, A. Pogany, D. Paganin, and S. Wilkins, "Linear algorithms for phase retrieval in the Fresnel region," Opt. Commun. 231, 53 - 70 (2004).
[CrossRef]

A. Pogany, D. Gao, and S. Wilkins, "Contrast and resolution in imaging with a microfocus x-ray source," Rev. Sci. Instrum. 68, 2774 (1997).
[CrossRef]

S. Wilkins, T. Gureyev, D. Gao, A. Pogany, and A. Stevenson, "Phase-contrast imaging using polychromatic hard X-rays," Nature 384, 335 - 338 (1996).
[CrossRef]

Price, R.

E. Donnelly, R. Price, and D. Pickens, "Dual focal-spot imaging for phase extraction in phase-contrast radiography," Med. Phys. 30, 2292 - 2296 (2003).
[CrossRef] [PubMed]

Schelokov, I.

I. Schelokov, T. Weitkamp, and A. Snigirev, "Reconstruction of an object phase transmission function from inline X-ray holograms," Opt. Commun. 213, 247 - 258 (2002).
[CrossRef]

Schlenker, M.

R. M. P. Cloetens, M. Schlenker, and S. Lerbs-Mache, "Quantitative phase tomography of Arabidopsis seeds reveals intercellular void network," PNAS 103, 14,626 - 14,630 (2006).
[CrossRef]

Shelokov, I.

A. Snigirev, I. Snigireva, V. Kohn, S. Kuznetsov, and I. Shelokov, "On the possibilities of x-ray phase contrast microimaging by coherent high-energy synchrotron radiation," Rev. Sci. Instrum. 66, 5486 - 5492 (1995).
[CrossRef]

Snigirev, A.

I. Schelokov, T. Weitkamp, and A. Snigirev, "Reconstruction of an object phase transmission function from inline X-ray holograms," Opt. Commun. 213, 247 - 258 (2002).
[CrossRef]

A. Snigirev, I. Snigireva, V. Kohn, S. Kuznetsov, and I. Shelokov, "On the possibilities of x-ray phase contrast microimaging by coherent high-energy synchrotron radiation," Rev. Sci. Instrum. 66, 5486 - 5492 (1995).
[CrossRef]

Snigireva, I.

A. Snigirev, I. Snigireva, V. Kohn, S. Kuznetsov, and I. Shelokov, "On the possibilities of x-ray phase contrast microimaging by coherent high-energy synchrotron radiation," Rev. Sci. Instrum. 66, 5486 - 5492 (1995).
[CrossRef]

Stevenson, A.

S. Wilkins, T. Gureyev, D. Gao, A. Pogany, and A. Stevenson, "Phase-contrast imaging using polychromatic hard X-rays," Nature 384, 335 - 338 (1996).
[CrossRef]

Teague, M.

Weitkamp, T.

I. Schelokov, T. Weitkamp, and A. Snigirev, "Reconstruction of an object phase transmission function from inline X-ray holograms," Opt. Commun. 213, 247 - 258 (2002).
[CrossRef]

Wilkins, S.

T. Gureyev, D. Paganin, G. Myers, Y. Nesterets, and S. Wilkins, "Phase-and-amplitude computer tomography," Appl. Phys. Lett. 89, 034,102 (2006).
[CrossRef]

T. Gureyev, A. Pogany, D. Paganin, and S. Wilkins, "Linear algorithms for phase retrieval in the Fresnel region," Opt. Commun. 231, 53 - 70 (2004).
[CrossRef]

A. Pogany, D. Gao, and S. Wilkins, "Contrast and resolution in imaging with a microfocus x-ray source," Rev. Sci. Instrum. 68, 2774 (1997).
[CrossRef]

S. Wilkins, T. Gureyev, D. Gao, A. Pogany, and A. Stevenson, "Phase-contrast imaging using polychromatic hard X-rays," Nature 384, 335 - 338 (1996).
[CrossRef]

Wu, X.

D. Zhang, M. Donvan, L. Fajardo, A. Archet, X. Wu, and H. Liu, "Preliminary feasibility study of an in-line phase contrast x-ray imaging prototype," IEEE Trans. Biomed. Eng. 55, (in press) (2008).

X. Wu and H. Liu, "Phase-space evolution of x-ray coherence in phase-sensitive imaging," Appl. Opt. 47, E44 - E52 (2008).
[CrossRef] [PubMed]

X. Wu, H. Liu, and A. Yan, "Phase-Contrast X-Ray Tomography: Contrast Mechanism and Roles of Phase Retrieval," Eur. J. Radiol. (to be published) (2008).
[CrossRef] [PubMed]

X. Wu and H. Liu, "Robustness of a phase-retrieval approach based on phase-attenuation duality," J. X-Ray Sci. Technol. 15, 85 - 95 (2007).

X. Wu and H. Liu, "X-Ray cone-beam phase tomography formulas based on phase-attenuation duality," Opt. Express 13, 6000 - 6014 (2005).
[CrossRef] [PubMed]

X. Wu, H. Liu, and A. Yan, "X-ray phase-attenuation duality and phase retrieval," Opt. Lett. 30(4), 379 - 381 (2005).
[CrossRef] [PubMed]

X. Wu and H. Liu, "A reconstruction formula for soft tissue X-ray phase tomography," J. X-ray Sci. Technol. 12, 273 - 279 (2004).

X. Wu and H. Liu, "A new theory of phase-contrast x-ray imaging based on Wigner distributions," Med. Phys. 31, 2378 - 2384 (2004).
[CrossRef] [PubMed]

X. Wu and H. Liu, "A dual detector approach for X-ray attenuation and phase imaging," J. X-ray Sci. Technol. 12, 35-42 (2004).

X. Wu and H. Liu, "An experimental method of determining relative phase-contrast factor for x-ray imaging systems," Med. Phys. 31, 997 - 1002 (2004).
[CrossRef]

X. Wu and H. Liu, "A general theoretical formalism for X-ray phase contrast imaging," J. X-ray Sci. Technol. 11, 33 - 42 (2003).

Wu, X.

X.  Wu and H. Liu, "Clinical implementation of x-ray phase-contrast imaging: Theoretical foundations and design considerations," Med. Phys. 30, 2169 - 2179 (2003).
[CrossRef] [PubMed]

Yan, A.

X. Wu, H. Liu, and A. Yan, "Phase-Contrast X-Ray Tomography: Contrast Mechanism and Roles of Phase Retrieval," Eur. J. Radiol. (to be published) (2008).
[CrossRef] [PubMed]

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[CrossRef] [PubMed]

Zhang, D.

D. Zhang, M. Donvan, L. Fajardo, A. Archet, X. Wu, and H. Liu, "Preliminary feasibility study of an in-line phase contrast x-ray imaging prototype," IEEE Trans. Biomed. Eng. 55, (in press) (2008).

Appl. Opt. (1)

Appl. Phys. Lett. (1)

T. Gureyev, D. Paganin, G. Myers, Y. Nesterets, and S. Wilkins, "Phase-and-amplitude computer tomography," Appl. Phys. Lett. 89, 034,102 (2006).
[CrossRef]

Eur. J. Radiol. (1)

X. Wu, H. Liu, and A. Yan, "Phase-Contrast X-Ray Tomography: Contrast Mechanism and Roles of Phase Retrieval," Eur. J. Radiol. (to be published) (2008).
[CrossRef] [PubMed]

IEEE Trans. Biomed. Eng. (1)

D. Zhang, M. Donvan, L. Fajardo, A. Archet, X. Wu, and H. Liu, "Preliminary feasibility study of an in-line phase contrast x-ray imaging prototype," IEEE Trans. Biomed. Eng. 55, (in press) (2008).

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A (1)

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X. Wu and H. Liu, "A reconstruction formula for soft tissue X-ray phase tomography," J. X-ray Sci. Technol. 12, 273 - 279 (2004).

X. Wu and H. Liu, "A general theoretical formalism for X-ray phase contrast imaging," J. X-ray Sci. Technol. 11, 33 - 42 (2003).

X. Wu and H. Liu, "A dual detector approach for X-ray attenuation and phase imaging," J. X-ray Sci. Technol. 12, 35-42 (2004).

X. Wu and H. Liu, "Robustness of a phase-retrieval approach based on phase-attenuation duality," J. X-Ray Sci. Technol. 15, 85 - 95 (2007).

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X. Wu and H. Liu, "An experimental method of determining relative phase-contrast factor for x-ray imaging systems," Med. Phys. 31, 997 - 1002 (2004).
[CrossRef]

X. Wu and H. Liu, "A new theory of phase-contrast x-ray imaging based on Wigner distributions," Med. Phys. 31, 2378 - 2384 (2004).
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X.  Wu and H. Liu, "Clinical implementation of x-ray phase-contrast imaging: Theoretical foundations and design considerations," Med. Phys. 30, 2169 - 2179 (2003).
[CrossRef] [PubMed]

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[CrossRef] [PubMed]

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[CrossRef]

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Opt. Lett. (1)

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Figures (5)

Fig. 1.
Fig. 1.

Flow chart of APBA

Fig. 2.
Fig. 2.

x-ray edge images and the recovered phase map. (a) Attenuation image A 2 0 ; (b) Phase-contrast image I; (c) phase map recovered using APBA (with 10 iteration steps)

Fig. 3.
Fig. 3.

Recovered phase maps using TIE algorithm, with different Tikhonov regularization parameters α: (a) α=0; (b) α=10Δ2; (c) α=50Δ2. Δ is the sampling step-size in FT space.

Fig. 4.
Fig. 4.

x-ray lumpectomy images and the recovered phase map. (a) Attenuation image A 2 0 ; (©[2008] IEEE);(b) Phase-contrast image I; (©[2008] IEEE);(c) phase map recovered using APBA with 10 iteration steps.

Fig. 5.
Fig. 5.

Recovered lumpectomy phase maps using TIE algorithm, with different Tikhonov regularization parameters α: (a) α=0; (b) α=10Δ2; (c) α=50Δ2. Δ is the sampling step-size in FT space.

Equations (25)

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n = 1 δ + i β
δ = ( r e λ 2 2 π ) k N k ( Z k + f k r )
I ˜ ( u M ; R 1 + R 2 ) = I in { cos ( π λ R 2 M u 2 ) · 𝓕 ̂ ( A 0 2 ) +
+ [ 2 sin ( π λ R 2 M u 2 ) ( 2 π λ R 2 M u 2 ) · cos ( π λ R 2 M u 2 ) ] · 𝓕 ̂ ( A 0 2 ϕ )
cos ( π λ R 2 M u 2 ) · λ R 2 2 π M · 𝓕 ̂ ( · ( A 0 2 ϕ ) ) } ,
I ˜ ( u ) = 2 e i 2 π x · u I ( x ) d x ,
π λ R 2 u max 2 1 ,
I ( r ; R 1 + R 2 ) = I in M 2 { A 0 2 ( r M ) λ R 2 2 π M · ( A 0 2 ϕ ) ( r M ) } .
ϕ ( r ) = 2 π M λ R 2 2 { · [ 1 A 0 2 ( 2 ( M 2 I in I A 0 2 ) ) ] } ,
2 = 2 ( 2 ) 2 + α 2 ,
A 0 ( r ) = A incoh ( r ) · A pe , coh ( r ) .
A KN 2 ( r ) = exp ( μ KN ( r , s ) ds ) = exp [ σ KN λ r e ϕ ( r ) ] ,
σ KN = 2 π r e 2 { 1 + η η 2 [ 2 ( 1 + η ) 1 + 2 η 1 η log ( 1 + 2 η ) ] + 1 2 η log ( 1 + 2 η ) 1 + 3 η ( 1 + 2 η ) 2 } .
A KN 2 ( r ) = 𝓕 ˇ ( 𝓕 ̂ ( M 2 I in I ) 1 + 4 π 2 k u 2 ) ,
k = λ R 2 2 π M λ r e σ KN ,
A 0 ( r ) = A ~ ( r ) δ A ( r ) ,
A ˜ ( r ) = A KN ( r ) = A incoh ( r ), δ A ( r ) = A KN ( r ) · ( 1 A pe ,coh( r )).
E ( r ; R 1 , R 2 ) = I in λ R 2 · exp ( 2 π i R 1 + R 2 λ ) 2 exp [ i π λ ( ξ 2 R 1 + ( ξ r ) 2 R 2 ) ] A 0 ( ξ ) e i ϕ ( ξ ) d ξ
h ( r ) = ( M 1 ) r 2 2 R 1 R 2 , ω = λ R 2 2 π M
E 1 ( r ) = M I in E ( M r ; R 1 , R 2 ) · e i 2 ω h ( r ) ,
E 1 ( r ) = 1 2 π ω · 2 e i ( r ξ ) 2 2 ω T 0 ( ξ ) d ξ = 1 2 π ω . G * T 0 ( r ) ,
ψ ( r ) = π 2 + ϕ ( r ) + ω 2 [ 2 A 0 ( r ) A 0 ( r ) ϕ ( r ) 2 ] .
I ( r ) = I ~ ( r ) + δ I ( r ) 2 I ~ ( r ) · δ I ( r ) · cos ( ψ ~ ( r ) δ ψ ( r ) ) .
I ( r ) I ~ ( r ) δI ( r ) ,
δ = δ δ A old δ A ,

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