193 research outputs found
Review of data types and model dimensionality for cardiac DTI SMS-related artefact removal
As diffusion tensor imaging (DTI) gains popularity in cardiac imaging due to
its unique ability to non-invasively assess the cardiac microstructure, deep
learning-based Artificial Intelligence is becoming a crucial tool in mitigating
some of its drawbacks, such as the long scan times. As it often happens in
fast-paced research environments, a lot of emphasis has been put on showing the
capability of deep learning while often not enough time has been spent
investigating what input and architectural properties would benefit cardiac DTI
acceleration the most. In this work, we compare the effect of several input
types (magnitude images vs complex images), multiple dimensionalities (2D vs 3D
operations), and multiple input types (single slice vs multi-slice) on the
performance of a model trained to remove artefacts caused by a simultaneous
multi-slice (SMS) acquisition. Despite our initial intuition, our experiments
show that, for a fixed number of parameters, simpler 2D real-valued models
outperform their more advanced 3D or complex counterparts. The best performance
is although obtained by a real-valued model trained using both the magnitude
and phase components of the acquired data. We believe this behaviour to be due
to real-valued models making better use of the lower number of parameters, and
to 3D models not being able to exploit the spatial information because of the
low SMS acceleration factor used in our experiments.Comment: 11 pages, 3 tables, 1 figure. To be published at the STACOM workshop,
MICCAI 202
MRI reconstruction using Markov random field and total variation as composite prior
Reconstruction of magnetic resonance images (MRI) benefits from incorporating a priori knowledge about statistical dependencies among the representation coefficients. Recent results demonstrate that modeling intraband dependencies with Markov Random Field (MRF) models enable superior reconstructions compared to inter-scale models. In this paper, we develop a novel reconstruction method, which includes a composite prior based on an MRF model and Total Variation (TV). We use an anisotropic MRF model and propose an original data-driven method for the adaptive estimation of its parameters. From a Bayesian perspective, we define a new position-dependent type of regularization and derive a compact reconstruction algorithm with a novel soft-thresholding rule. Experimental results show the effectiveness of this method compared to the state of the art in the field
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
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