Search Results for author:
Found 18 papers, 11 papers with code
Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modelling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations.
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed.
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI
In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus.
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.
Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
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.
Fetal whole-heart 4D imaging using motion-corrected multi-planar real-time MRI
Expert evaluation suggested the reconstructed volumes can be used for comprehensive assessment of the fetal heart, either as an adjunct to ultrasound or in combination with other MRI techniques.
Medical Physics
Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View
Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation.
Efficiency analysis for quantitative MRI of T1 and T2 relaxometry methods
This work presents and validates an efficiency metric to optimize and compare the performance of qMRI methods.
Medical Physics Image and Video Processing
A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling
We developed an automated system based on deep neural networks for fast and sensitive 3D image segmentation of cortical gray matter from fetal brain MRI.
Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction
Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb.
DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled with bounding box annotations.
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.
Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging
The results show that the proposed pipeline can accurately estimate the respiratory state and reconstruct 4D SR volumes with better or similar performance to the 3D SVR pipeline with less than 20\% sparsely selected slices.
Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction
We show that the proposed approaches are competitive relative to the state of the art both quantitatively and qualitatively.
Impact of aperture, depth, and acoustic clutter on performance of Coherent Multi-Transducer Ultrasound imaging
Transducers with larger aperture size are desirable in ultrasound imaging to improve resolution and image quality.
Medical Physics
An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field
In this study, we introduce a semi-automatic pipeline using quantitative MRI for the fetal body at low field strength resulting in fast and detailed quantitative T2* relaxometry analysis of all major fetal body organs.
CINA: Conditional Implicit Neural Atlas for Spatio-Temporal Representation of Fetal Brains
We demonstrate CINA's capability to represent a fetal brain atlas that can be flexibly conditioned on GA and on anatomical variations like ventricular volume or degree of cortical folding, making it a suitable tool for modeling both neurotypical and pathological brains.
