133 research outputs found
Generalising Deep Learning MRI Reconstruction across Different Domains
We look into robustness of deep learning based MRI reconstruction when tested
on unseen contrasts and organs. We then propose to generalise the network by
training with large publicly-available natural image datasets with synthesised
phase information to achieve high cross-domain reconstruction performance which
is competitive with domain-specific training. To explain its generalisation
mechanism, we have also analysed patch sets for different training datasets.Comment: Accepted for ISBI2019 as a 1-page abstrac
Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction
We present simple reconstruction networks for multi-coil data by extending
deep cascade of CNN's and exploiting the data consistency layer. In particular,
we propose two variants, where one is inspired by POCSENSE and the other is
calibration-less. We show that the proposed approaches are competitive relative
to the state of the art both quantitatively and qualitatively.Comment: Presented at ISMRM 27th Annual Meeting & Exhibition (Abstract #4663
dAUTOMAP:decomposing AUTOMAP to achieve scalability and enhance performance
AUTOMAP is a promising generalized reconstruction approach, however, it is
not scalable and hence the practicality is limited. We present dAUTOMAP, a
novel way for decomposing the domain transformation of AUTOMAP, making the
model scale linearly. We show dAUTOMAP outperforms AUTOMAP with significantly
fewer parameters.Comment: Presented at ISMRM 27th Annual Meeting & Exhibition (Abstract #658
Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
Purpose: To introduce a novel deep learning based approach for fast and
high-quality dynamic multi-coil MR reconstruction by learning a complementary
time-frequency domain network that exploits spatio-temporal correlations
simultaneously from complementary domains.
Theory and Methods: Dynamic parallel MR image reconstruction is formulated as
a multi-variable minimisation problem, where the data is regularised in
combined temporal Fourier and spatial (x-f) domain as well as in
spatio-temporal image (x-t) domain. An iterative algorithm based on variable
splitting technique is derived, which alternates among signal de-aliasing steps
in x-f and x-t spaces, a closed-form point-wise data consistency step and a
weighted coupling step. The iterative model is embedded into a deep recurrent
neural network which learns to recover the image via exploiting spatio-temporal
redundancies in complementary domains.
Results: Experiments were performed on two datasets of highly undersampled
multi-coil short-axis cardiac cine MRI scans. Results demonstrate that our
proposed method outperforms the current state-of-the-art approaches both
quantitatively and qualitatively. The proposed model can also generalise well
to data acquired from a different scanner and data with pathologies that were
not seen in the training set.
Conclusion: The work shows the benefit of reconstructing dynamic parallel MRI
in complementary time-frequency domains with deep neural networks. The method
can effectively and robustly reconstruct high-quality images from highly
undersampled dynamic multi-coil data ( and yielding 15s
and 10s scan times respectively) with fast reconstruction speed (2.8s). This
could potentially facilitate achieving fast single-breath-hold clinical 2D
cardiac cine imaging.Comment: Accepted by Magnetic Resonance in Medicin
Onset of magnetism in B2 transition metals aluminides
Ab initio calculation results for the electronic structure of disordered bcc
Fe(x)Al(1-x) (0.4<x<0.75), Co(x)Al(1-x) and Ni(x)Al(1-x) (x=0.4; 0.5; 0.6)
alloys near the 1:1 stoichiometry, as well as of the ordered B2 (FeAl, CoAl,
NiAl) phases with point defects are presented. The calculations were performed
using the coherent potential approximation within the Korringa-Kohn-Rostoker
method (KKR-CPA) for the disordered case and the tight-binding linear
muffin-tin orbital (TB-LMTO) method for the intermetallic compounds. We studied
in particular the onset of magnetism in Fe-Al and Co-Al systems as a function
of the defect structure. We found the appearance of large local magnetic
moments associated with the transition metal (TM) antisite defect in FeAl and
CoAl compounds, in agreement with the experimental findings. Moreover, we found
that any vacancies on both sublattices enhance the magnetic moments via
reducing the charge transfer to a TM atom. Disordered Fe-Al alloys are
ferromagnetically ordered for the whole range of composition studied, whereas
Co-Al becomes magnetic only for Co concentration >0.5.Comment: 11 pages with 9 embedded postscript figures, to be published in
Phys.Rev.
Telomerase activity of the Lugol-stained and -unstained squamous epithelia in the process of oesophageal carcinogenesis
Up-regulation of telomerase has been reported in many cancers. Our aim was to characterize telomerase activity in various states of the oesophagus to facilitate better understanding of carcinogenesis of oesophageal squamous cell carcinoma. During endoscopic examinations, we obtained 45 Lugol-stained normal epithelia, 31 Lugol-unstained epithelia (14 oesophagitis, 7 mild dysplasia, 5 severe dysplasia and 5 intramucosal cancer) and 9 advanced cancer. Telomerase activity was semi-quantified by a telomeric repeat amplification protocol using enzyme-linked immunosorbent assay, and expression of human telomerase reverse transcriptase mRNA was examined by in situ hybridization. In the Lugol-stained normal epithelia, telomerase activity increased in proportion to the increase of severity of the accompanying lesions, with a rank order of advanced cancer, intramucosal cancer, mild dysplasia and oesophagitis. In the Lugol-unstained lesions and advanced cancer, telomerase activity was highest in advanced cancer. Up-regulation of telomerase in normal squamous epithelium may be a marker of progression of oesophageal squamous cell carcinoma. Copyright 2001 Cancer Research Campaign © 2001 Cancer Research Campaignhttp://www.bjcancer.co
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