20,578 research outputs found
Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting
Magnetic Resonance Fingerprinting (MRF) enables the simultaneous
quantification of multiple properties of biological tissues. It relies on a
pseudo-random acquisition and the matching of acquired signal evolutions to a
precomputed dictionary. However, the dictionary is not scalable to
higher-parametric spaces, limiting MRF to the simultaneous mapping of only a
small number of parameters (proton density, T1 and T2 in general). Inspired by
diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF
sequence with embedded diffusion-encoding gradients along all three axes to
efficiently encode orientational diffusion and T1 and T2 relaxation. We take
advantage of a convolutional neural network (CNN) to reconstruct multiple
quantitative maps from this single, highly undersampled acquisition. We bypass
expensive dictionary matching by learning the implicit physical relationships
between the spatiotemporal MRF data and the T1, T2 and diffusion tensor
parameters. The predicted parameter maps and the derived scalar diffusion
metrics agree well with state-of-the-art reference protocols. Orientational
diffusion information is captured as seen from the estimated primary diffusion
directions. In addition to this, the joint acquisition and reconstruction
framework proves capable of preserving tissue abnormalities in multiple
sclerosis lesions
Linear-time Online Action Detection From 3D Skeletal Data Using Bags of Gesturelets
Sliding window is one direct way to extend a successful recognition system to
handle the more challenging detection problem. While action recognition decides
only whether or not an action is present in a pre-segmented video sequence,
action detection identifies the time interval where the action occurred in an
unsegmented video stream. Sliding window approaches for action detection can
however be slow as they maximize a classifier score over all possible
sub-intervals. Even though new schemes utilize dynamic programming to speed up
the search for the optimal sub-interval, they require offline processing on the
whole video sequence. In this paper, we propose a novel approach for online
action detection based on 3D skeleton sequences extracted from depth data. It
identifies the sub-interval with the maximum classifier score in linear time.
Furthermore, it is invariant to temporal scale variations and is suitable for
real-time applications with low latency
Fast Algorithms for Parameterized Problems with Relaxed Disjointness Constraints
In parameterized complexity, it is a natural idea to consider different
generalizations of classic problems. Usually, such generalization are obtained
by introducing a "relaxation" variable, where the original problem corresponds
to setting this variable to a constant value. For instance, the problem of
packing sets of size at most into a given universe generalizes the Maximum
Matching problem, which is recovered by taking . Most often, the
complexity of the problem increases with the relaxation variable, but very
recently Abasi et al. have given a surprising example of a problem ---
-Simple -Path --- that can be solved by a randomized algorithm with
running time . That is, the complexity of the
problem decreases with . In this paper we pursue further the direction
sketched by Abasi et al. Our main contribution is a derandomization tool that
provides a deterministic counterpart of the main technical result of Abasi et
al.: the algorithm for -Monomial
Detection, which is the problem of finding a monomial of total degree and
individual degrees at most in a polynomial given as an arithmetic circuit.
Our technique works for a large class of circuits, and in particular it can be
used to derandomize the result of Abasi et al. for -Simple -Path. On our
way to this result we introduce the notion of representative sets for
multisets, which may be of independent interest. Finally, we give two more
examples of problems that were already studied in the literature, where the
same relaxation phenomenon happens. The first one is a natural relaxation of
the Set Packing problem, where we allow the packed sets to overlap at each
element at most times. The second one is Degree Bounded Spanning Tree,
where we seek for a spanning tree of the graph with a small maximum degree
- …