Optical Deflectometric Tomography (ODT) provides an accurate characterization of transparent materials whose complex surfaces present a real challenge for manufacture and control. In ODT, the refractive index map (RIM) of a transparent object is reconstructed by measuring light deflection under multiple orientations. We show that this imaging modality can be made "compressive", i.e., a correct RIM reconstruction is achievable with far less observations than required by traditional Filtered Back Projection (FBP) methods. Assuming a cartoon-shape RIM model, this reconstruction is driven by minimizing the map Total-Variation under a fidelity constraint with the available observations. Moreover, two other realistic assumptions are added to improve the stability of our approach: the map positivity and a frontier condition. Numerically, our method relies on an accurate ODT sensing model and on a primal-dual minimization scheme, including easily the sensing operator and the proposed RIM constraints. We conclude this paper by demonstrating the power of our method on synthetic and experimental data under various compressive scenarios. In particular, the compressiveness of the stabilized ODT problem is demonstrated by observing a typical gain of 20 dB compared to FBP at only 5 of 360 incident light angles for moderately noisy sensing.
In differential phase-contrast tomography, the refractive index distribution is recovered from phase-shifts measurements. These are composed by the derivative of the refractive index map, inducing the apparition of the frequency @math when using the FST, as it happens in the ODT sensing model (Sec. ). In this application, @cite_16 have used the FBP algorithm to reconstruct the refractive index map from a fully covered set of projections. @cite_38 @cite_13 have used different iterative schemes based on the minimization of the TV norm to reconstruct the refractive index distribution over a region of interest. These methods are accurate and provide similar results, but the iterative scheme based on the TV norm has proved to be better than FBP when the amount of acquisitions decreases.
@cite_38 @cite_16 @cite_13
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Differential phase-contrast interior tomography allows reconstruction of a refractive index distribution over a region of interest (ROI) for visualization and analysis of structures inside a large biological specimen. In the imaging mode, x-ray scanning only targets an ROI in an object and a narrow beam passes through the object, allowing a significant reduction of both radiation dose and system cost. Inspired by recently developed compressive sensing theory, in a numerical analysis framework we show that accurate interior reconstruction can be achieved on an ROI from truncated differential projection data through the ROI via the total variation minimization, assuming a piecewise constant distribution of the refractive indices in the ROI. Then, we develop a practical iterative algorithm for such an interior reconstruction and perform numerical experiments to demonstrate the feasibility of the proposed approach. We report on a method for tomographic phase contrast imaging of centimeter sized objects. As opposed to existing techniques, our approach can be used with low-brilliance, lab based x-ray sources and thus is of interest for a wide range of applications in medicine, biology, and nondestructive testing. The work is based on the recent development of a hard x-ray grating interferometer, which has been demonstrated to yield differential phase contrast projection images. Here we particularly focus on how this method can be used for tomographic reconstructions using filtered back projection algorithms to yield quantitative volumetric information of both the real and imaginary part of the samples's refractive index High radiation dose in computed tomography (CT) scans increases the lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with total variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data acquired at low mAs levels in order to reduce the imaging dose. Nonetheless, the low-contrast structures tend to be smoothed out by the TV regularization, posing a great challenge for the TV method. To solve this problem, in this work we develop an iterative CT reconstruction algorithm with edge-preserving TV (EPTV) regularization to reconstruct CT images from highly undersampled data obtained at low mAs levels. The CT image is reconstructed by minimizing energy consisting of an EPTV norm and a data fidelity term posed by the x-ray projections. The EPTV term is proposed to preferentially perform smoothing only on the non-edge part of the image in order to better preserve the edges, which is realized by introducing a penalty weight to the original TV norm. During the reconstruction process, the pixels at the edges would be gradually identified and given low penalty weight. Our iterative algorithm is implemented on graphics processing unit to improve its speed. We test our reconstruction algorithm on a digital NURBS-based cardiac-troso phantom, a physical chest phantom and a Catphan phantom. Reconstruction results from a conventional filtered backprojection (FBP) algorithm and a TV regularization method without edge-preserving penalty are also presented for comparison purposes. The experimental results illustrate that both the TV-based algorithm and our EPTV algorithm outperform the conventional FBP algorithm in suppressing the streaking artifacts and image noise under a low-dose context. Our edge-preserving algorithm is superior to the TV-based algorithm in that it can preserve more information of low-contrast structures and therefore maintain acceptable spatial resolution. Tomographic image reconstruction, such as the reconstruction of computed tomography projection values, of tomosynthesis data, positron emission tomography or SPECT events, and of magnetic resonance imaging data is computationally very demanding. One of the most time-consuming steps is the backprojection. Recently, a novel general purpose architecture optimized for distributed computing became available: the cell broadband engine (CBE). To maximize image reconstruction speed we modified our parallel-beam backprojection algorithm [two dimensional (2D)] and our perspective backprojection algorithm [three dimensional (3D), cone beam for flat–panel detectors] and optimized the code for the CBE. The algorithms are pixel or voxel driven, run with floating point accuracy and use linear (LI) or nearest neighbor (NN) interpolation between detector elements. For the parallel-beam case, 512 projections per half rotation, 1024 detector channels, and an image of size 512 2 was used. The cone-beam backprojection performance was assessed by backprojecting a full circle scan of 512 projections of size 1024 2 into a volume of size 512 3 voxels. The field of view was chosen to completely lie within the field of measurement and the pixel or voxel size was set to correspond to the detector element size projected to the center of rotation divided by 2 . Both the PC and the CBE were clocked at 3 GHz . For the parallel backprojection of 512 projections into a 512 2 image, a throughput of 11 fps (LI) and 15 fps (NN) was measured on the PC, whereas the CBE achieved 126 fps (LI) and 165 fps (NN), respectively. The cone-beam backprojection of 512 projections into the 512 3 volume took 3.2 min on the PC and is as fast as 13.6 s on the cell. Thereby, the cell greatly outperforms today’s top-notch backprojections based on graphical processing units. Using both CBEs of our dual cell-based blade (Mercury Computer Systems) allows to 2D backproject 330 images s and one can complete the 3D cone-beam backprojection in 6.8 s .