92 research outputs found
Modelle zur Veränderung der Lernkultur - Demokratie als Leitbild für die Veränderung der Lernkultur. Wilhelm-Busch-Grundschule, Berlin
Entwicklung einer Schule für den ganzen Tag, Aufbau einer Lernwerkstatt, Umgestaltung des Schulgeländes - die Wilhelm-Busch-Grundschule in Berlin beschließt: Wir verändern uns selbst! Durch die von Pädagog/-innen, Schüler/-innen und Eltern gemeinsam entwickelten Vorhaben soll die Lernkultur der einzigen Grundschule des Wohngebiets verändert und die Schule zum sozialen und kulturellen Mittelpunkt für Kinder und Eltern in dem Wohngebiet werden
Uncertainty quantification for sparse Fourier recovery
One of the most prominent methods for uncertainty quantification in
high-dimen-sional statistics is the desparsified LASSO that relies on
unconstrained -minimization. The majority of initial works focused on
real (sub-)Gaussian designs. However, in many applications, such as magnetic
resonance imaging (MRI), the measurement process possesses a certain structure
due to the nature of the problem. The measurement operator in MRI can be
described by a subsampled Fourier matrix. The purpose of this work is to extend
the uncertainty quantification process using the desparsified LASSO to design
matrices originating from a bounded orthonormal system, which naturally
generalizes the subsampled Fourier case and also allows for the treatment of
the case where the sparsity basis is not the standard basis. In particular we
construct honest confidence intervals for every pixel of an MR image that is
sparse in the standard basis provided the number of measurements satisfies or that is sparse with respect to
the Haar Wavelet basis provided a slightly larger number of measurements
A Plug-and-Play Approach To Multiparametric Quantitative MRI:Image Reconstruction Using Pre-Trained Deep Denoisers
Current spatiotemporal deep learning approaches to Magnetic Resonance
Fingerprinting (MRF) build artefact-removal models customised to a particular
k-space subsampling pattern which is used for fast (compressed) acquisition.
This may not be useful when the acquisition process is unknown during training
of the deep learning model and/or changes during testing time. This paper
proposes an iterative deep learning plug-and-play reconstruction approach to
MRF which is adaptive to the forward acquisition process. Spatiotemporal image
priors are learned by an image denoiser i.e. a Convolutional Neural Network
(CNN), trained to remove generic white gaussian noise (not a particular
subsampling artefact) from data. This CNN denoiser is then used as a
data-driven shrinkage operator within the iterative reconstruction algorithm.
This algorithm with the same denoiser model is then tested on two simulated
acquisition processes with distinct subsampling patterns. The results show
consistent de-aliasing performance against both acquisition schemes and
accurate mapping of tissues' quantitative bio-properties. Software available:
https://github.com/ketanfatania/QMRI-PnP-Recon-PO
Geometry of Deep Learning for Magnetic Resonance Fingerprinting
Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are
bottlenecked by the heavy storage and computation requirements of a
dictionary-matching (DM) step due to the growing size and complexity of the
fingerprint dictionaries in multi-parametric quantitative MRI applications. In
this paper we study a deep learning approach to address these shortcomings.
Coupled with a dimensionality reduction first layer, the proposed MRF-Net is
able to reconstruct quantitative maps by saving more than 60 times in memory
and computations required for a DM baseline. Fine-grid manifold enumeration
i.e. the MRF dictionary is only used for training the network and not during
image reconstruction. We show that the MRF-Net provides a piece-wise affine
approximation to the Bloch response manifold projection and that rather than
memorizing the dictionary, the network efficiently clusters this manifold and
learns a set of hierarchical matched-filters for affine regression of the NMR
characteristics in each segment
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