Iterative RAKI with Complex-Valued Convolution for Improved Image
Reconstruction with Limited Scan-Specific Training Samples
- URL: http://arxiv.org/abs/2201.03560v1
- Date: Mon, 10 Jan 2022 16:14:27 GMT
- Title: Iterative RAKI with Complex-Valued Convolution for Improved Image
Reconstruction with Limited Scan-Specific Training Samples
- Authors: Peter Dawood, Martin Blaimer, Felix Breuer, Paul R. Burd, Istv\'an
Homolya, Peter M. Jakob, Johannes Oberberger
- Abstract summary: This study investigates the influence of training data on the reconstruction quality for standard 2D imaging.
An iterative k-space approach (iRAKI) is evaluated, which includes training data augmentation via an initial GRAPPA reconstruction, and refinement of convolution filters by iterative training.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: MRI scan time reduction is commonly achieved by Parallel Imaging methods,
typically based on uniform undersampling of the inverse image space (a.k.a.
k-space) and simultaneous signal reception with multiple receiver coils. The
GRAPPA method interpolates missing k-space signals by linear combination of
adjacent, acquired signals across all coils, and can be described by a
convolution in k-space. Recently, a more generalized method called RAKI was
introduced. RAKI is a deep-learning method that generalizes GRAPPA with
additional convolution layers, on which a non-linear activation function is
applied. This enables non-linear estimation of missing signals by convolutional
neural networks. In analogy to GRAPPA, the convolution kernels in RAKI are
trained using scan-specific training samples obtained from
auto-calibration-signals (ACS). RAKI provides superior reconstruction quality
compared to GRAPPA, however, often requires much more ACS due to its increased
number of unknown parameters. In order to overcome this limitation, this study
investigates the influence of training data on the reconstruction quality for
standard 2D imaging, with particular focus on its amount and contrast
information. Furthermore, an iterative k-space interpolation approach (iRAKI)
is evaluated, which includes training data augmentation via an initial GRAPPA
reconstruction, and refinement of convolution filters by iterative training.
Using only 18, 20 and 25 ACS lines (8%), iRAKI outperforms RAKI by suppressing
residual artefacts occurring at accelerations factors R=4 and R=5, and yields
strong noise suppression in comparison to GRAPPA, underlined by quantitative
quality metrics. Combination with a phase-constraint yields further
improvement. Additionally, iRAKI shows better performance than GRAPPA and RAKI
in case of pre-scan calibration and strongly varying contrast between training-
and undersampled data.
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