Evaluation of noise removal algorithms for imaging and reconstruction of vascular networks using micro-CT

Micro-computed tomography systems are widely used for high-resolution, non-destructive analysis of internal microvascular networks. When the scale of the targeted vessel approaches the imaging resolution limit, the level of noise becomes a limiting factor for accurate reconstruction. Denoising algorithms provided by vendors are often suboptimal for enhancing SNR of fine (vessel) features. Furthermore, the performance of existing methods has not been systematically analyzed in the context of final network reconstruction and graph model extraction. This work evaluates several standard and state-of-the-art noise reduction techniques using both in silico and physical phantoms, and ex vivo rat coronary data for their ability to improve vascular network analysis. We compared five noise reduction approaches, including vendor-supplied (Gaussian smoothing), conventional (median filter) and advanced (i.e. wavelet filter with soft thresholding, block-matching collaborative filtering (BM3D), and isotropic and anisotropic total variation denoising) techniques. The latter two methods were chosen for their reported ability to preserve fine details, a prerequisite for a successful microvascular extraction. The full evaluation pipeline included the reconstruction from projection images, denoising, vascular segmentation and graph model extraction to be performed on all simulated and real image data sets. SNR, CNR and 3D NPS were quantified from denoised images, and where the ground truth was known, Sørensen–Dice coefficients, Jaccard index metrics were calculated as measures of segmentation error. The performance of the image denoising algorithms where the ground-truth was available has been assessed by computing the correlation coefficients between the residual images (obtained between the noise-free data and the denoised data) and the first derivative of the noise-free data were computed. Overall, simpler denoising techniques including the median and wavelet filters and the vendor-supplied implementations have been found to perform inadequately for segmentation of fine vessel features, particularly on real images. BM3D technique performed well in most of our tests, however isotropic total variation (ITV) was the optimal choice for noise reduction and feature preservation in real data as shown by the extracted network models. Globally, ITV increased the SNR from 10.2 to 31.7 dB in a Shepp Logan phantom, doubled SNR and CNR values in a scanned physical phantom compared with BM3D, enabled the smallest vessels to be fully recovered in an in silicon phantom and achieved a near-ideal outcome in the rat coronary data

Validation of diffusion tensor MRI measurements of cardiac microstructure with structure tensor synchrotron radiation imaging.

Background Diffusion tensor imaging (DTI) is widely used to assess tissue microstructure non-invasively. Cardiac DTI enables inference of cell and sheetlet orientations, which are altered under pathological conditions. However, DTI is affected by many factors, therefore robust validation is critical. Existing histological validation is intrinsically flawed, since it requires further tissue processing leading to sample distortion, is routinely limited in field-of-view and requires reconstruction of three-dimensional volumes from two-dimensional images. In contrast, synchrotron radiation imaging (SRI) data enables imaging of the heart in 3D without further preparation following DTI. The objective of the study was to validate DTI measurements based on structure tensor analysis of SRI data. Methods One isolated, fixed rat heart was imaged ex vivo with DTI and X-ray phase contrast SRI, and reconstructed at 100 μm and 3.6 μm isotropic resolution respectively. Structure tensors were determined from the SRI data and registered to the DTI data. Results Excellent agreement in helix angles (HA) and transverse angles (TA) was observed between the DTI and structure tensor synchrotron radiation imaging (STSRI) data, where HADTI-STSRI = −1.4° ± 23.2° and TADTI-STSRI = −1.4° ± 35.0° (mean ± 1.96 standard deviation across all voxels in the left ventricle). STSRI confirmed that the primary eigenvector of the diffusion tensor corresponds with the cardiomyocyte long-axis across the whole myocardium. Conclusions We have used STSRI as a novel and high-resolution gold standard for the validation of DTI, allowing like-with-like comparison of three-dimensional tissue structures in the same intact heart free of distortion. This represents a critical step forward in independently verifying the structural basis and informing the interpretation of cardiac DTI data, thereby supporting the further development and adoption of DTI in structure-based electro-mechanical modelling and routine clinical applications

Non-linear iterative phase retrieval based on Frechet derivative and projection operators

International audiencePhase retrieval from Fresnel diffraction patterns is a nonlinear ill-posed inverse problem of paramount importance in various areas of applied physics. Recently a new non-linear iterative algorithm based on a Landweber method with an analytic calculation of the Frechet derivative adjoint has been proposed. In this work, we refine this scheme by introducing Fienup projectors in the algorithm. The new method was tested on noisy simulated data. Our method outperforms the approaches based on the linearization of the relation between the phase shift induced by the object and the diffracted intensity and the former non linear algorithm with no projectors

absorption and phase retrieval with tikhonov and joint sparsity regularizations

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Reconstruction non linéaire itératif des images de phase en rayons X

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In-line phase tomography using nonlinear phase retrieval

International audienceWith hard X-rays synchrotron beams, phase contrast for in-line phase tomography can be obtained with the measurement of the Fresnel diffraction intensity patterns associated to a phase shift induced by the object. We have studied the resolution of this inverse problem with an iterative nonlinear method. The phase retrieval algorithm was tested for a 3D Shepp-Logan phantom in the presence of noise. The nonlinear scheme outperforms the linear method. Both the high and low frequency ranges of the phase retrieved are improved and the method is less sensitive to noise. In future work, the method will be tested on experimental data. The method is expected to open new perspectives for the study of biological samples

Nonlinear approaches for the single-distance phase retrieval problem involving regularizations with sparsity constraints

International audienceThe phase retrieval process is a nonlinear ill-posed problem. The Fresnel diffraction patterns obtained with hard x-ray synchrotron beam can be used to retrieve the phase contrast. In this work, we present a convergence comparison of several nonlinear approaches for the phase retrieval problem involving regularizations with sparsity constraints. The phase solution is assumed to have a sparse representation with respect to an orthonormal wavelets basis. One approach uses alternatively a solution of the nonlinear problem based on the Fréchet derivative and a solution of the linear problem in wavelet coordinates with an iterative thresholding. A second method is the one proposed by Ramlau and Teschke which generalizes to a nonlinear problem the classical thresholding algorithm. The algorithms were tested on a 3D Shepp-Logan phantom corrupted by white Gaussian noise. The best simulation results are obtained by the first method for the various noise levels and initializations investigated. The reconstruction errors are significantly decreased with respect to the ones given by the classical linear phase retrieval approaches

Non-linear iterative phase retrieval based on Frechet derivative

International audienceSeveral methods of phase retrieval for in-line phase tomog-raphy have already been investigated based on the linearization of the relation between the phase shift induced by the object and the diffracted intensity. In this work, we present a non-linear iterative approach using the Frechet derivative of the intensity recorded at a few number of propagation distances. A Landweber type iterative method with an analytic calculation of the Frechet derivative adjoint is proposed. The inverse problem is regularized with the smoothing L 2 norm of the phase gradient and evaluated for several different implementations. The evaluation of the method was performed using a simple phase map, both with and without noise. Our approach outperforms the linear methods on simulated noisy data up to high noise levels and thanks to the proposed analytical calculation is suited to the processing of large experimental image data sets

Non-Linear Phase Retrieval Combined with Iterative Thresholding in Wavelet Coordinates

International audienceWith hard X-rays synchrotron beams, phase contrast can be obtained with the measurement of the Fresnel diffraction intensity patterns associated to a phase shift induced by the object. We have studied the resolution of this inverse problem with an iterative thresholding algorithm in wavelet coordinates combined with an iterative nonlinear method with a Tikhonov regularization. The phase retrieval algorithm was tested for a 3D Shepp-Logan phantom in the presence of noise. The results show that the combined approach outperforms the Mixed, the CTF and the nonlinear methods

Absorption and phase retrieval in phase contrast imaging with non linear Tikhonov regularization

International audienceThe X-ray phase contrast imaging technique relies on the measurement of the Fresnel diffraction intensity patterns associated to a phase shift induced by the object. The simultaneous recovery of the phase and of the absorption is an ill-posed non linear inverse problem. In this work, we investigate the resolution of this problem with non linear Tikhonov regularization and with the Iterative Gauss Newton method. The algorithm is evalutated using simulated noisy data