Search Results for author: Joseph V. Hajnal

Found 18 papers, 11 papers with code

CINA: Conditional Implicit Neural Atlas for Spatio-Temporal Representation of Fetal Brains

no code implementations13 Mar 2024 Maik Dannecker, Vanessa Kyriakopoulou, Lucilio Cordero-Grande, Anthony N. Price, Joseph V. Hajnal, Daniel Rueckert

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.

An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field

no code implementations9 Aug 2023 Kelly Payette, Alena Uus, Jordina Aviles Verdera, Carla Avena Zampieri, Megan Hall, Lisa Story, Maria Deprez, Mary A. Rutherford, Joseph V. Hajnal, Sebastien Ourselin, Raphael Tomi-Tricot, Jana Hutter

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.

Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View

1 code implementation29 Jun 2022 Veronika A. Zimmer, Alberto Gomez, Emily Skelton, Robert Wright, Gavin Wheeler, Shujie Deng, Nooshin Ghavami, Karen Lloyd, Jacqueline Matthew, Bernhard Kainz, Daniel Rueckert, Joseph V. Hajnal, Julia A. Schnabel

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.

Image Segmentation Multi-Task Learning +3

Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction

1 code implementation22 Dec 2020 Chen Qin, Jinming Duan, Kerstin Hammernik, Jo Schlemper, Thomas Küstner, René Botnar, Claudia Prieto, Anthony N. Price, Joseph V. Hajnal, Daniel Rueckert

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.

De-aliasing Image Reconstruction

Impact of aperture, depth, and acoustic clutter on performance of Coherent Multi-Transducer Ultrasound imaging

no code implementations23 Aug 2020 Laura Peralta, Alessandro Ramalli, Michael Reinwald, Robert J. Eckersley, Joseph V. Hajnal

Transducers with larger aperture size are desirable in ultrasound imaging to improve resolution and image quality.

Medical Physics

Efficiency analysis for quantitative MRI of T1 and T2 relaxometry methods

1 code implementation6 May 2020 David Leitão, Rui Pedro A. G. Teixeira, Anthony Price, Alena Uus, Joseph V. Hajnal, Shaihan J. Malik

This work presents and validates an efficiency metric to optimize and compare the performance of qMRI methods.

Medical Physics Image and Video Processing

dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

1 code implementation24 Sep 2019 Jo Schlemper, Ilkay Oksuz, James R. Clough, Jinming Duan, Andrew P. King, Julia A. Schnabel, Joseph V. Hajnal, Daniel Rueckert

AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited.

Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging

no code implementations28 Aug 2019 Tong Zhang, Laurence H. Jackson, Alena Uus, James R. Clough, Lisa Story, Mary A. Rutherford, Joseph V. Hajnal, Maria Deprez

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.

Image Reconstruction Motion Estimation +1

Fetal whole-heart 4D imaging using motion-corrected multi-planar real-time MRI

3 code implementations5 Dec 2018 Joshua FP van Amerom, David FA Lloyd, Maria Deprez, Anthony N. Price, Shaihan J. Malik, Kuberan Pushparajah, Milou PM van Poppel, Mary A. Rutherford, Reza Razavi, Joseph V. Hajnal

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

Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction

4 code implementations5 Dec 2017 Chen Qin, Jo Schlemper, Jose Caballero, Anthony Price, Joseph V. Hajnal, Daniel Rueckert

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.

Image Reconstruction Temporal Sequences

A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

4 code implementations8 Apr 2017 Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony Price, Daniel Rueckert

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.

Dictionary Learning Image Reconstruction

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