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Träfflista för sökning "WFRF:(Persson Fredrik) ;pers:(Grönberg Fredrik)"

Sökning: WFRF:(Persson Fredrik) > Grönberg Fredrik

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1.
  • da Silva, Joakim, et al. (författare)
  • Resolution characterization of a silicon-based, photon-counting computed tomography prototype capable of patient scanning
  • 2019
  • Ingår i: Journal of Medical Imaging. - USA : SPIE - International Society for Optical Engineering. - 2329-4302 .- 2329-4310. ; 6:4
  • Tidskriftsartikel (refereegranskat)abstract
    • Photon-counting detectors are expected to bring a range of improvements to patient imaging with x-ray computed tomography (CT). One is higher spatial resolution. We demonstrate the resolution obtained using a commercial CT scanner where the original energy-integrating detector has been replaced by a single-slice, silicon-based, photon-counting detector. This prototype constitutes the first full-field-of-view silicon-based CT scanner capable of patient scanning. First, the pixel response function and focal spot profile are measured and, combining the two, the system modulation transfer function is calculated. Second, the prototype is used to scan a resolution phantom and a skull phantom. The resolution images are compared to images from a state-of-the-art CT scanner. The comparison shows that for the prototype 19 lp∕cm are detectable with the same clarity as 14 lp∕cm on the reference scanner at equal dose and reconstruction grid, with more line pairs visible with increasing dose and decreasing image pixel size. The high spatial resolution remains evident in the anatomy of the skull phantom and is comparable to that of other photon-counting CT prototypes present in the literature. We conclude that the deep silicon-based detector used in our study could provide improved spatial resolution in patient imaging without increasing the x-ray dose.
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  • Eguizabal, Alma, et al. (författare)
  • A deep learning post-processing to enhance the maximum likelihood estimate of three material decomposition in photon counting spectral CT
  • 2021
  • Ingår i: Proceedings of SPIE. - : SPIE-Intl Soc Optical Eng.
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • Photon counting detectors in x-ray computed tomography (CT) improve the decomposition of the CT scans into different materials. This decomposition is however not straightforward to solve, both in terms of computation expense and Photon counting detectors in x-ray computed tomography (CT) are a major technological advancement that provides additional energy information, and improve the decomposition of the CT image into material images. This material decomposition problem is however a non-linear inverse problem that is difficult to solve, both in terms of computation expense and accuracy. The most accepted solution consists in defining an optimization problem based on a maximum likelihood (ML) estimate with Poisson statistics, which is a model-based approach very dependent on the considered forward model and the chosen optimization solver. This may make the material decomposition result noisy and slow to be computed. To incorporate data-driven enhancement to the ML estimate, we propose a deep learning post-processing technique. Our approach is based on convolutional residual blocks that mimic the updates of an iterative optimization process and consider the ML estimate as an input. Therefore, our architecture implicitly considers the physical models of the problem, and in consequence needs less training data and fewer parameters than other standard convolutional networks typically used in medical imaging. We have studied a simulation case of our deep learning post-processing, first on a set of 350 Shepp-Logan -based phantoms, and then in a 600 human numerical phantoms. Our approach has shown denoising enhancement over two different ray-wise decomposition methods: one based on a Newton’s method to solve the ML estimation, and one based on a linear least-squares approximation of the ML expression. We believe this new deep learning post-processing approach is a promising technique to denoise material-decomposed sinograms in photon-counting CT.
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  • Grönberg, Fredrik, et al. (författare)
  • Dimensionality and Background Cancellation in Energy Selective X-Ray Imaging
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Purpose: The set of linear attenuation coefficients that belong to materials in the human body is commonly assumed to be spanned by two basis functions in the range of clinical x-ray energies, even though there is evidence that the dimensionality of this set is greater than two. It has not yet been clear that the use of a third basis function could be beneficial in absence of contrast agents.Approach: In this work, the choice of the number of basis functions used in the basis decomposition method is studied for the task of producing an image where a third material is separated from a background of two other materials, in a case where none of the materials have a K-edge in the range of considered x-ray energies (20-140 keV). The case of separating iron from mixtures of liver and adipose tissue is studied with a simulated phantom which incorporates random and realistic tissue variability.Results: Inclusion of a third basis function improves the quantitative estimate of iron concentration by several orders of magnitude in terms of mean squared error in the resulting image.Conclusions: The inclusion of a third basis function in the basis decomposition is essential for the studied imaging task and could have potential application for quantitative estimation of iron concentration from material decomposed images.
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5.
  • Grönberg, Fredrik, et al. (författare)
  • Image reconstruction based on energy-resolved image data from a photon-counting multi-bin detector
  • 2015
  • Patent (populärvet., debatt m.m.)abstract
    • There is provided a method of image reconstruction based on energy-resolved image data from a photon-counting multi-bin detector or an intermediate storage. The method comprises processing (S1) the energy-resolved image data by performing at least two separate basis decompositions using different number of basis functions for modeling linear attenuation, wherein a first basis decomposition is performed using a first smaller set of basis functions to obtain at least one first basis image representation, and wherein a second basis decomposition is performed using a second larger set of basis functions to obtain at least one second basis image representation. The method also comprises reconstructing a first image based on said at least one first basis image representation obtained from the first basis decomposition, and combining the first image with information representative of said at least one second basis image representation.
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6.
  • Grönberg, Fredrik (författare)
  • Spectral Photon-Counting Computed Tomography with Silicon Detectors: New Models and Applications
  • 2022
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • X-ray computed tomography (CT) is a widely used imaging modality that enables visualization of nearly every part of the human body. It is used for diagnosis of disease and injury as well as medical treatment planning. The vast majority of CT scanners in clinical use today have energy-integrating x-ray detectors, which measure the total incident energy in a given measurement.Spectral photon-counting detectors operate by counting individual photons and measuring their energy, and are expected to yield the next major advance in CT, with improvements in spatial resolution, dose efficiency, material differentiation and quantitative imaging capabilities compared to the current state-of-the-art.In this Thesis, a set of new models and applications for a spectral photon-counting silicon detector developed for CT is investigated. The first part of the Thesis is dedicated to the modeling of spectral photon-counting silicon detectors. A new statistical model for the effects of pulse pileup is presented. Also, the effects on image quality from intra-detector Compton scatter in silicon detectors are investigated via spatio-energetic modeling. In the second part of the Thesis, potential applications for spectral photon-counting detectors are investigated. An experimental study of ex vivo CT imaging of an excised human heart with calcified plaque is presented. It demonstrates the feasibility of unconstrained projection-based three-material decomposition with iodine as a third basis material and explores the potential improvements in spatial resolution and material differentiation that can be achieved with a spectral photon-counting silicon detector compared to a conventional dual-energy CTsystem. Two other applications are investigated with simulations: a method for reconstructing CT images from spectral photon-counting CT data that accurately mimic conventional CT images; and a method for estimating iron concentration in mixtures of liver and adipose tissue when using three basis functions instead of only two to describe the linear attenuation coefficient of tissues in the human body. Although the methods presented in this Thesis have been specifically developed for a spectral photon-counting silicon detector, they are also applicable for other types of photon-counting detectors.
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  • Persson, Mats, 1987-, et al. (författare)
  • Bias-variance tradeoff in anticorrelated noise reduction for spectral CT
  • 2017
  • Ingår i: Medical physics (Lancaster). - : WILEY. - 0094-2405. ; 44:9, s. E242-E254
  • Tidskriftsartikel (refereegranskat)abstract
    • Purpose: In spectral CT, basis material decomposition is commonly used to generate a set of basis images showing the material composition at each point in the field of view. The noise in these images typically contains anticorrelations between the different basis images, which leads to increased noise in each basis image. These anticorrelations can be removed by changing the basis functions used in the material decomposition, but the resulting basis images can then no longer be used for quantitative measurements. Recent studies have demonstrated that reconstruction methods which take the anticorrelations into account give reduced noise in the reconstructed image. The purpose of this work is to analyze an analytically solvable denoising model problem and investigate its effect on the noise level and bias in the image as a function of spatial frequency. Method: A denoising problem with a quadratic regularization term is studied as a mathematically tractable model for such a reconstruction method. An analytic formula for the resulting image in the spatial frequency domain is presented, and this formula is applied to a simple mathematical phantom consisting of an iodinated contrast agent insert embedded in soft tissue. We study the effect of the denoising on the image in terms of its transfer function and the visual appearance, the noise power spectrum and the Fourier component correlation coefficient of the resulting image, and compare the result to a denoising problem which does not model the anticorrelations in the image. Results: Including the anticorrelations in the noise model of the denoising method gives 3-40% lower noise standard deviation in the soft-tissue image while leaving the iodine standard deviation nearly unchanged (0-1% difference). It also gives a sharper edge-spread function. The studied denoising method preserves the noise level and the anticorrelated structure at low spatial frequencies but suppresses the noise and removes the anticorrelations at higher spatial frequencies. Cross-talk between images gives rise to artifacts at high spatial frequencies. Conclusions: Modeling anticorrelations in a denoising problem can decrease the noise level in the basis images by removing anticorrelations at high spatial frequencies while leaving low spatial frequencies unchanged. In this way, basis image cross-talk does not lead to low spatial frequency bias but it may cause artifacts at edges in the image. This theoretical insight will be useful for researchers analyzing and designing reconstruction algorithms for spectral CT.
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