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 ($16 \times$ and $24 \times$ 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