892 research outputs found
Swiss journal of geosciences (1888-2006) / Entwurf zu einer tektonischen Gliederung der Betischen Cordilleren des centralen und südwestlichen Andalusien
No full textentworfen nach eigenen Aufanhmen und solchen von R.W. van Bemmelen (Granada - Motril), R. Douvillé (Prov. Jaén), J. Gavala (Prov. Cadiz), D. de Orueta (Serrania de Ronda) durch Moritz M. Blumenthal, August 1927Am linken oberen Kartenrand: "M. Blumenthal: Betische Cordilleren
Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models
No full textPurpose
We introduce a framework that enables efficient sampling from learned probability distributions for MRI reconstruction.
Method
Samples are drawn from the posterior distribution given the measured k-space using the Markov chain Monte Carlo (MCMC) method, different from conventional deep learning-based MRI reconstruction techniques. In addition to the maximum a posteriori estimate for the image, which can be obtained by maximizing the log-likelihood indirectly or directly, the minimum mean square error estimate and uncertainty maps can also be computed from those drawn samples. The data-driven Markov chains are constructed with the score-based generative model learned from a given image database and are independent of the forward operator that is used to model the k-space measurement.
Results
We numerically investigate the framework from these perspectives: (1) the interpretation of the uncertainty of the image reconstructed from undersampled k-space; (2) the effect of the number of noise scales used to train the generative models; (3) using a burn-in phase in MCMC sampling to reduce computation; (4) the comparison to conventional
-wavelet regularized reconstruction; (5) the transferability of learned information; and (6) the comparison to fastMRI challenge.
Conclusion
A framework is described that connects the diffusion process and advanced generative models with Markov chains. We demonstrate its flexibility in terms of contrasts and sampling patterns using advanced generative priors and the benefits of also quantifying the uncertainty for every pixel
Kosciusko [music] /
No full textFor voice and piano.; Cover title.; "Introduced & sung by Miss Nella Webb."; Cover carries portraits of Nella Webb (by Rudolph Buchner), Charles Vaude and Moritz Lutzen.; Words printed as text on p. [4].; "During Moritz Lutzen's visit to Australia he offered a prize for the best lyric, by an Australian author to be set to music by himself. The prize was awarded to Charles Vaude, for his lyric 'Kosciusko,' and Miss Nella Webb produced this song with instantaneous success."--P. [4].; Also available online http://nla.gov.au/nla.mus-an8393500; 1913, by Victor J. Draper, Sydney.; NLA's NL copy from the collection of Keith Watson. ANL
Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models
No full textWe introduce a framework that enables efficient sampling from learned probability distributions for MRI reconstruction. Different from conventional deep learning-based MRI reconstruction techniques, samples are drawn from the posterior distribution given the measured k-space using the Markov chain Monte Carlo (MCMC) method. In addition to the maximum a posteriori (MAP) estimate for the image, which can be obtained with conventional methods, the minimum mean square error (MMSE) estimate and uncertainty maps can also be computed. The data-driven Markov chains are constructed from the generative model learned from a given image database and are independent of the forward operator that is used to model the k-space measurement. This provides flexibility because the method can be applied to k-space acquired with different sampling schemes or receive coils using the same pre-trained models. Furthermore, we use a framework based on a reverse diffusion process to be able to utilize advanced generative models. The performance of the method is evaluated on an open dataset using 10-fold undersampling in k-space
Swiss journal of geosciences (1888-2006) / Geologische Karte des Gebietes beiderseits des mittleren Rio Guadalhorce (Provinz Malága)
No full textvon M. BlumenthalAm linken oberen Kartenrand: "M. Blumenthal: Geologie der betischen Cordilleren
Letter containing inquiry regarding the ethnic identity of the descendents of Georg Moritz Oppenheim.
No full textLetter from Wilhelm Gehlig to Rabbi Dr. Freudenthal in Nuremberg with a genealogical question regarding Georg Moritz Oppenheim. Of particular interest to the author is to determine whether Oppenheim's descendents are "rein jüdischen Blutes (=of pure Jewish blood)."Robert Singermandigitize
Conventional and circular economy compliant modification strategies for recycled polypropylene
No full textAuthor Moritz MagerMasterarbeit Universität Linz 2021Arbeit gesperr
Conventional and circular economy compliant modification strategies for recycled polypropylene
No full textAuthor Moritz MagerMasterarbeit Universität Linz 2021Arbeit gesperr
Self‐supervised learning for improved calibrationless radial MRI with NLINV‐Net
Full text linkAbstract Purpose To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. Methods NLINV‐Net is a model‐based neural network architecture that directly estimates images and coil sensitivities from (radial) k‐space data via nonlinear inversion (NLINV). Combined with a training strategy using self‐supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We validated the method for (1) real‐time cardiac imaging and (2) single‐shot subspace‐based quantitative T1 mapping. Furthermore, region‐optimized virtual (ROVir) coils were used to suppress artifacts stemming from outside the field of view and to focus the k‐space‐based SSDU loss on the region of interest. NLINV‐Net‐based reconstructions were compared with conventional NLINV and PI‐CS (parallel imaging + compressed sensing) reconstruction and the effect of the region‐optimized virtual coils and the type of training loss was evaluated qualitatively. Results NLINV‐Net‐based reconstructions contain significantly less noise than the NLINV‐based counterpart. ROVir coils effectively suppress streakings which are not suppressed by the neural networks while the ROVir‐based focused loss leads to visually sharper time series for the movement of the myocardial wall in cardiac real‐time imaging. For quantitative imaging, T1‐maps reconstructed using NLINV‐Net show similar quality as PI‐CS reconstructions, but NLINV‐Net does not require slice‐specific tuning of the regularization parameter. Conclusion NLINV‐Net is a versatile tool for calibrationless imaging which can be used in challenging imaging scenarios where a ground truth is not available.National Institutes of Health https://doi.org/10.13039/100000002Volkswagen Foundation https://doi.org/10.13039/501100001663Deutsches Zentrum für Herz-Kreislaufforschung https://doi.org/10.13039/10001044
Free-Breathing Liver Fat and R2 Mapping: Multi-Echo Radial FLASH and Model-based Reconstruction (MERLOT)
No full text- …
