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\pNone Ba=c@=Z?N*8X"1Arial1Arial1Arial1Arial1Arial1Arial1Arial1ArialGeneral `1Search Results wtitletitlealternativecreatorcontributor programmefaculty
departmentprojectsubjectdescriptiondatedatetypelanguage
identifierclassificationmetisididentifierbibliographiclatitude longitude publisherrelationrelationtyperightssourcetypeformatsetuuid filenames4Simultaneous DeNoising in Phase Contrast TomographyKoehler, T.; Roessl, E.
20120320en MS 33.175Univ. Tokyo(c) Univ. Tokyok2012 International Workshop on Xray and neutron phase imaging with gratings, Tokyo, Japan, March 57, 2012Conference paperphilips
MS33.175.pdf)A New Method for Metal Artifact Reduction%Koehler, T.; Brendel, B.; Brown, K.M.Aartifact reduction; computed tomography; metal artifact reduction
20120716 MS 33.229(c) The AuthorsThe Second International Conference on Image Formation in Xray Computed Tomography, June 2427, 2012, Salt Lake City, Utah, USA; authors version
MS33.229.pdf@Iterative Reconstruction for Differential Phase Contrast Imaging$Koehler, T.; Brendel, B.; Roessl, E.Xblobs; compressed sensing; differential phase contrast imaging; iterative reconstruction=Purpose: The purpose of this work is to combine two areas of active research in tomographic xray imaging. The first one is the use of iterative reconstruction techniques. The second one is differential phase contrast imaging (DPCI).
Method: We derive an SPS type maximum likelihood (ML) reconstruction algorithm with regularization for DPCI. Forward and backprojection are implemented using spherically symmetric basis functions (blobs) and differential footprints, thus completely avoiding the need for numerical differentiation throughout the reconstruction process. The method is applied to the problem of reconstruction of an object from sparsely sampled projection.
Results: The results show that the proposed method can handle the sparely sampled data efficiently. In particular no streak artifacts are visible which are present images obtained by filtered backprojection (FBP).
Conclusion: Iterative reconstruction algorithms have a wide spectrum of proven advantages in the area of conventional computed tomography. The present work describes for the first time, how a matched forward and backprojection can be implemented for DPCI, which is furthermore free of any heuristics. The newly developed ML reconstruction algorithm for DPCI shows that for the case of sparsely sampled projection data, an improvement in image quality is obtained that is qualitatively comparable to a corresponding situation in conventional xray imaging. Based on the proposed operators for forward and backprojection, a large variety of iterative reconstruction algorithms is thus made available for DPCI.
20110818 MS32.3744AAPM (American Association of Physicist in Medicine)=(c) 2011 AAPM (American Association of Physicist in Medicine)Medical Physicsarticle
MS32.374.pdf:NonScatter Contributions to the Dark Field Signal in DPCI8Koehler, T.; Martens, G.; Van Stevendaal, U.; Roessl, E.
invited paper
20111221 MS33.171University of Tokyo(c) University of TokyodInternational Workshop on Xray and Neutron Phase Imaging with Grating, Tokyo, Japan, 57 March 2012
MS33.171.pdf\Incorporation of Bone Beam Hardening Correction into Statistical Iterative CT Reconstruction0Brendel, B.; Koehler, T.; Yagil, Y.; Thomson, R.Aartifact reduction; computed tomography; iterative reconstructionA number of different methods for post reconstruction bone beam hardening (BBH) correction are available for conventional FBP reconstruction and are used in commercially available products. An incorporation of these existing methods into statistical iterative reconstruction for CT is desired for several reasons. There are two ways imaginable to incorporate the BBH correction into iterative reconstruction:The first option is to use the beam hardening corrected projectionsas input for the statistical iterative reconstruction. For this ithas to be considered that the noise level in the projection data changes due to the correction. The second option is to incorporate theinverse of the beam hardening correction into the forward projectionof the cost function, and derive an update equation from this modified cost function. Both methods are implemented and compared based on simulated data with respect to artifact suppression, image noise,and speed of convergence. MS 33.254
MS33.254.pdfPRobust Automated Regularization Factor Selection for Statistical Reconstructions>Bergner, F.; Brendel, B.; Noel, P.B.; Dobritz, M.; Koehler, T.Statistical, iterative reconstruction techniques have become a major research topic in the CT sector. These techniques promise a better system model, which is used for the inversion of the tomographic problem, and therefore better reconstruction results. Due to the ill posedness of these problems, regularization is required in the cost functions in order to stabilize the algorithm and to reduce the noise in the resulting images. The strength of the regularization is usually changed by using an appropriate multiplicative factor, which in most cases has to be determined empirically with major efforts. This paper describes a new automated selection of this factor by using a quality criterion and a regulator, which controls the multiplicative factor over the iterations to a desired level. The method is light weight, robust and also applicable for other iterative methods like de noising.
20120624 MS 33.251(c) 2012 The Author(s)r2nd International Conference on Image Formation in XRay Computed Tomography, Salt Lake City, USA, 2427 June 2012
MS33.251.pdfQProjector and Backprojector for Iterative CT Reconstruction with Blobs using CUDAKBippus, R.D.; Koehler, T.; Bergner, F.; Brendel, B.; Hansis, E.; Proksa, R.OAiming at modeling the systems geometry correctly accounting for the major effects influencing image quality within an iterative reconstruction framework we want to achieve this within reasonable processing times. This principle objective led us to using blobs for imagerepresentation and a dedicated GPU hardware implementation. Making extensive use of the texture interpolation capabilities of CUDA and implementing an asymmetric projector/backprojector pair we achieve reasonable processing times and good system modeling at the same time.We conclude from the above results that using GPUs and adequate implementations of the projectors, iterative reconstruction using blobsfor image representation becomes feasible. This, along with avoiding resampling, will allow us to apply detailed system modeling for enhanced resolution/noise tradeoff.
20110825 MS 32.211(c) The Author(s)Fully 3D 2011: 11th International Meeting on Fully ThreeDimensional Image Reconstruction in Radiology and Nuclear Medicine, Potsdam, Germany, 1115 July 2011
MS32.211.pdf>Contrasttonoise in Xray differential phase contrast imagingZEngel, K.J.; Geller, D.; Koehler, T.; Martens, G.; Schusser, S.; Vogtmeier, G.; Roessl, E.contrasttonoise ratio; differential phase contrast; digital mammography; mammography; phase contrast; xray mammography; xray phase imagingmA quantitative theory for the c<
ontrasttonoise ratio (CNR) in differential phase contrast imaging (DPCI) is proposed and compared to that of images derived from classical absorption contrast imaging (ACI). Most prominently, the CNR for DPCI contains the reciprocal of thespatial wavelength to be imaged, the fringe visibility, and a tunable factor dependent on the system geometry. DPCI is thus potentiallybeneficial especially for the imaging of small object structures. We demonstrate CNR calculations for mammography, finding optimal imaging energies between 15 and 22 keV for ACI, and between 20 and 40 keV for DPCI.
20110728 MS 31.490Elsevier(c) ElsevierDNuclear Instruments and Methods in Physics Research; authors version
MS31_490.pdfRComparing implementations of penalized weighted least squares sinogram restorationWForthmann, P.; Koehler, T.; Defrise, M. (Univ. Brussel); La Riviere, P. (Univ. Chicago)sinogram restorationXWe have explored and compared two implementation strategies for PWLS sinogram restoration:(1)A direct matrixinversion strategy based on the closedform solution to the PWLS optimization problem and(2) an iterative approach based on the conjugategradient algorithmObtaining optimal performance from each strategy required modifyingthe naive offtheshelf implementations of the algorithms to exploitthe particular symmetry and sparseness of the sinogramrestorationproblem. For the closedform approach, we subdivided the large matrixinversion into smaller coup led problems and exploited sparsenessto minimize matrix operations. For the conjugate gradient approach,we exploited sparseness and preconditioned the problem to speed convergence. Despite the acceleration strategies, the direct matrixinversion approach was found to be uncompetitive with iterative approaches,with a computational burden an order of magnitude or more higher.The iterative conjugategradient approach, however, does appear promising, with computation times half that of our previous penalizedlikelihood implementation.
20110727 MS31.483#AIP (American Institute of Physics),(c) 2010 AIP (American Institute of Physics)
MS31.843.pdf>Image Fusion Algorithm for Differential Phase Contrast ImagingRRoessl, E.; Koehler, T.; Van Stevendaal, U. ; Hauser, N.; Wang, Z.; Stampanoni, M.<differential phase contrast; image fusion; xray mammographyDifferential phasecontrast imaging in the xray domain provides three physically complementary pieces of information: the attenuation,the differential phasecontrast, related to the refractive index, and the darkfield signal, related to the total amount of radiation scattered into very small angles. In medical applications, it is of the utmost importance to present to the radiologist all clinically relevant information in as compact a way as possible. Hence, the needarisis for a method to combine two or more of the above mentioned images into one image containing all information relevant for diagnosis. We present an image composition algorithm that fuses the attenuation image and the differential phase contrast image into a composite image. The composition is performed in a noise optimal way such that the composite image is characterized by minimal noisepower at each frequency component.
20111116 MS 32.7364SPIE (International Society for Optical Engineering)=(c) 2012 SPIE (International Society for Optical Engineering)>SPIE Medical Imaging 2012, San Diego, CA, USA; authors version
MS32.736.pdf
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