Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image reconstruction by domain transform manifold learning   Image reconstruction plays a critical role in the implementation of all
contemporary imaging modalities across the physical and life sciences including
optical, MRI, CT, PET, and radio astronomy. During an image acquisition, the
sensor encodes an intermediate representation of an object in the sensor
domain, which is subsequently reconstructed into an image by an inversion of
the encoding function. Image reconstruction is challenging because analytic
knowledge of the inverse transform may not exist a priori, especially in the
presence of sensor non-idealities and noise. Thus, the standard reconstruction
approach involves approximating the inverse function with multiple ad hoc
stages in a signal processing chain whose composition depends on the details of
each acquisition strategy, and often requires expert parameter tuning to
optimize reconstruction performance. We present here a unified framework for
image reconstruction, AUtomated TransfOrm by Manifold APproximation (AUTOMAP),
which recasts image reconstruction as a data-driven, supervised learning task
that allows a mapping between sensor and image domain to emerge from an
appropriate corpus of training data. We implement AUTOMAP with a deep neural
network and exhibit its flexibility in learning reconstruction transforms for a
variety of MRI acquisition strategies, using the same network architecture and
hyperparameters. We further demonstrate its efficiency in sparsely representing
transforms along low-dimensional manifolds, resulting in superior immunity to
noise and reconstruction artifacts compared with conventional handcrafted
reconstruction methods. In addition to improving the reconstruction performance
of existing acquisition methodologies, we anticipate accelerating the discovery
of new acquisition strategies across modalities as the burden of reconstruction
becomes lifted by AUTOMAP and learned-reconstruction approaches.

17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th