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842 results on '"Hajnal, Joseph V."'

8. Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment

Author

França, Lucas G. S., Ciarrusta, Judit, Gale-Grant, Oliver, Fenn-Moltu, Sunniva, Fitzgibbon, Sean, Chew, Andrew, Falconer, Shona, Dimitrova, Ralica, Cordero-Grande, Lucilio, Price, Anthony N., Hughes, Emer, O’Muircheartaigh, Jonathan, Duff, Eugene, Tuulari, Jetro J., Deco, Gustavo, Counsell, Serena J., Hajnal, Joseph V., Nosarti, Chiara, Arichi, Tomoki, Edwards, A. David, McAlonan, Grainne, and Batalle, Dafnis

Published
2024
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12. Structural and functional asymmetry of the neonatal cerebral cortex

Author

Williams, Logan Z. J., Fitzgibbon, Sean P., Bozek, Jelena, Winkler, Anderson M., Dimitrova, Ralica, Poppe, Tanya, Schuh, Andreas, Makropoulos, Antonios, Cupitt, John, O’Muircheartaigh, Jonathan, Duff, Eugene P., Cordero-Grande, Lucilio, Price, Anthony N., Hajnal, Joseph V., Rueckert, Daniel, Smith, Stephen M., Edwards, A. David, and Robinson, Emma C.

Published
2023
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13. Maturational networks of human fetal brain activity reveal emerging connectivity patterns prior to ex-utero exposure

Author

Karolis, Vyacheslav R., Fitzgibbon, Sean P., Cordero-Grande, Lucilio, Farahibozorg, Seyedeh-Rezvan, Price, Anthony N., Hughes, Emer J., Fetit, Ahmed E., Kyriakopoulou, Vanessa, Pietsch, Maximilian, Rutherford, Mary A., Rueckert, Daniel, Hajnal, Joseph V., Edwards, A. David, O’Muircheartaigh, Jonathan, Duff, Eugene P., and Arichi, Tomoki

Published
2023
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15. Development of neonatal-specific sequences for portable ultralow field magnetic resonance brain imaging: a prospective, single-centre, cohort study

Author

Deoni, Sean C., Ljungberg, Emil, Bennallick, Carly, Kolind, Shannon, Dean, Doug, III, Pepper, Michael S., Sekoli, Lydia, De Canha, Alexica, Van Rensburg, Jeanne, Jones, Derek K., Bourke, Niall, Sabir, Hemmen, Lecurieux Lafayette, Samson, Cawley, Paul, Padormo, Francesco, Cromb, Daniel, Almalbis, Jennifer, Marenzana, Massimo, Teixeira, Rui, Uus, Alena, O’Muircheartaigh, Jonathan, Williams, Steven C.R., Counsell, Serena J., Arichi, Tomoki, Rutherford, Mary A., Hajnal, Joseph V., and Edwards, A. David

Published
2023
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21. Predicting age and clinical risk from the neonatal connectome

Author

Taoudi-Benchekroun, Yassine, Christiaens, Daan, Grigorescu, Irina, Gale-Grant, Oliver, Schuh, Andreas, Pietsch, Maximilian, Chew, Andrew, Harper, Nicholas, Falconer, Shona, Poppe, Tanya, Hughes, Emer, Hutter, Jana, Price, Anthony N, Tournier, J-Donald, Cordero-Grande, Lucilio, Counsell, Serena J, Rueckert, Daniel, Arichi, Tomoki, Hajnal, Joseph V, Edwards, A David, Deprez, Maria, and Batalle, Dafnis

Published
2022
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26. Neonatal multi-modal cortical profiles predict 18-month developmental outcomes

Author

Fenchel, Daphna, Dimitrova, Ralica, Robinson, Emma C., Batalle, Dafnis, Chew, Andrew, Falconer, Shona, Kyriakopoulou, Vanessa, Nosarti, Chiara, Hutter, Jana, Christiaens, Daan, Pietsch, Maximilian, Brandon, Jakki, Hughes, Emer J., Allsop, Joanna, O’Keeffe, Camilla, Price, Anthony N., Cordero-Grande, Lucilio, Schuh, Andreas, Makropoulos, Antonios, Passerat-Palmbach, Jonathan, Bozek, Jelena, Rueckert, Daniel, Hajnal, Joseph V., McAlonan, Grainne, Edwards, A. David, and O’Muircheartaigh, Jonathan

Published
2022
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28. Structural Covariance Networks in the Fetal Brain Reveal Altered Neurodevelopment for Specific Subtypes of Congenital Heart Disease.

Author

Wilson, Siân, Cromb, Daniel, Bonthrone, Alexandra F., Uus, Alena, Price, Anthony, Egloff, Alexia, Van Poppel, Milou P. M., Steinweg, Johannes K., Pushparajah, Kuberan, Simpson, John, Lloyd, David F. A., Razavi, Reza, O'Muircheartaigh, Jonathan, Edwards, A. David, Hajnal, Joseph V., Rutherford, Mary, and Counsell, Serena J.

Published
2024
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29. Characterizing T1 in the fetal brain and placenta over gestational age at 0.55T.

Author

Aviles Verdera, Jordina, Tomi‐Tricot, Raphael, Story, Lisa, Rutherford, Mary A., Ourselin, Sebastien, Hajnal, Joseph V., Malik, Shaihan J., and Hutter, Jana

Subjects
FETAL brain, FETAL MRI, GRAY matter (Nerve tissue), WHITE matter (Nerve tissue), BRAIN mapping
Abstract

Purpose: T1 mapping and T1‐weighted contrasts have a complimentary but currently under utilized role in fetal MRI. Emerging clinical low field scanners are ideally suited for fetal T1 mapping. The advantages are lower T1 values which results in higher efficiency and reduced field inhomogeneities resulting in a decreased requirement for specialist tools. In addition the increased bore size associated with low field scanners provides improved patient comfort and accessibility. This study aims to demonstrate the feasibility of fetal brain T1 mapping at 0.55T. Methods: An efficient slice‐shuffling inversion‐recovery echo‐planar imaging (EPI)‐based T1‐mapping and postprocessing was demonstrated for the fetal brain at 0.55T in a cohort of 38 fetal MRI scans. Robustness analysis was performed and placental measurements were taken for validation. Results: High‐quality T1 maps allowing the investigation of subregions in the brain were obtained and significant correlation with gestational age was demonstrated for fetal brain T1 maps (p<0.05$$ p<0.05 $$) as well as regions‐of‐interest in the deep gray matter and white matter. Conclusions: Efficient, quantitative T1 mapping in the fetal brain was demonstrated on a clinical 0.55T MRI scanner, providing foundations for both future research and clinical applications including low‐field specific T1‐weighted acquisitions. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Sequence‐agnostic motion‐correction leveraging efficiently calibrated Pilot Tone signals.

Author

Brackenier, Yannick, Cordero‐Grande, Lucilio, McElroy, Sarah, Tomi‐Tricot, Raphael, Barbaroux, Hugo, Bridgen, Philippa, Malik, Shaihan J., and Hajnal, Joseph V.

Subjects
MOTION detectors, SEQUENCE spaces, IMAGE reconstruction, RANGE of motion of joints, REFERENCE values
Abstract

Purpose: This study leverages externally generated Pilot Tone (PT) signals to perform motion‐corrected brain MRI for sequences with arbitrary k‐space sampling and image contrast. Theory and Methods: PT signals are promising external motion sensors due to their cost‐effectiveness, easy workflow, and consistent performance across contrasts and sampling patterns. However, they lack robust calibration pipelines. This work calibrates PT signal to rigid motion parameters acquired during short blocks (˜4 s) of motion calibration (MC) acquisitions, which are short enough to unobstructively fit between acquisitions. MC acquisitions leverage self‐navigated trajectories that enable state‐of‐the‐art motion estimation methods for efficient calibration. To capture the range of patient motion occurring throughout the examination, distributed motion calibration (DMC) uses data acquired from MC scans distributed across the entire examination. After calibration, PT is used to retrospectively motion‐correct sequences with arbitrary k‐space sampling and image contrast. Additionally, a data‐driven calibration refinement is proposed to tailor calibration models to individual acquisitions. In vivo experiments involving 12 healthy volunteers tested the DMC protocol's ability to robustly correct subject motion. Results: The proposed calibration pipeline produces pose parameters consistent with reference values, even when distributing only six of these approximately 4‐s MC blocks, resulting in a total acquisition time of 22 s. In vivo motion experiments reveal significant (p<0.05$$ p<0.05 $$) improved motion correction with increased signal to residual ratio for both MPRAGE and SPACE sequences with standard k‐space acquisition, especially when motion is large. Additionally, results highlight the benefits of using a distributed calibration approach. Conclusions: This study presents a framework for performing motion‐corrected brain MRI in sequences with arbitrary k‐space encoding and contrast, using externally generated PT signals. The DMC protocol is introduced, promoting observation of patient motion occurring throughout the examination and providing a calibration pipeline suitable for clinical deployment. The method's application is demonstrated in standard volumetric MPRAGE and SPACE sequences. [ABSTRACT FROM AUTHOR]

Published
2024
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32. A flexible generative algorithm for growing in silico placentas.

Author

de Oliveira, Diana C., Cheikh Sleiman, Hani, Payette, Kelly, Hutter, Jana, Story, Lisa, Hajnal, Joseph V., Alexander, Daniel C., Shipley, Rebecca J., and Slator, Paddy J.

Subjects
FETAL growth retardation, PREGNANCY complications, BLOOD flow, PLACENTA, PREECLAMPSIA
Abstract

The placenta is crucial for a successful pregnancy, facilitating oxygen exchange and nutrient transport between mother and fetus. Complications like fetal growth restriction and pre-eclampsia are linked to placental vascular structure abnormalities, highlighting the need for early detection of placental health issues. Computational modelling offers insights into how vascular architecture correlates with flow and oxygenation in both healthy and dysfunctional placentas. These models use synthetic networks to represent the multiscale feto-placental vasculature, but current methods lack direct control over key morphological parameters like branching angles, essential for predicting placental dysfunction. We introduce a novel generative algorithm for creating in silico placentas, allowing user-controlled customisation of feto-placental vasculatures, both as individual components (placental shape, chorionic vessels, placentone) and as a complete structure. The algorithm is physiologically underpinned, following branching laws (i.e. Murray's Law), and is defined by four key morphometric statistics: vessel diameter, vessel length, branching angle and asymmetry. Our algorithm produces structures consistent with in vivo measurements and ex vivo observations. Our sensitivity analysis highlights how vessel length variations and branching angles play a pivotal role in defining the architecture of the placental vascular network. Moreover, our approach is stochastic in nature, yielding vascular structures with different topological metrics when imposing the same input settings. Unlike previous volume-filling algorithms, our approach allows direct control over key morphological parameters, generating vascular structures that closely resemble real vascular densities and allowing for the investigation of the impact of morphological parameters on placental function in upcoming studies. Author summary: The placenta is important in ensuring a healthy pregnancy by facilitating the exchange of oxygen and nutrients between the mother and the fetus. Disturbances of placental function are often associated with abnormalities in the placental vascular structure, and detecting these issues early on is crucial. To understand the connection between placental vascular architecture, blood flow, and oxygenation, computational models have been used. These use synthetic networks which lack precise control over crucial morphological parameters, such as branching angles, essential for predicting placental dysfunction. Our contribution is a new approach that allows for the creation of virtual placentas that closely resemble real vascular characteristics. It enables users to customize the feto-placental vascular architecture at various levels, including individual components like placental shape, chorionic vessels, and placentone, as well as the complete structure. The flexibility of this pipeline opens the door for investigating the direct impact of morphological parameters on placental function. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Neonatal frontal-limbic connectivity is associated with externalizing behaviours in toddlers with Congenital Heart Disease

Author

Bonthrone, Alexandra F., Chew, Andrew, Bhroin, Megan Ní, Rech, Francesca Morassutti, Kelly, Christopher J., Christiaens, Daan, Pietsch, Maximilian, Tournier, J-Donald, Cordero-Grande, Lucilio, Price, Anthony, Egloff, Alexia, Hajnal, Joseph V., Pushparajah, Kuberan, Simpson, John, David Edwards, A., Rutherford, Mary A., Nosarti, Chiara, Batalle, Dafnis, and Counsell, Serena J.

Published
2022
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39. Preterm birth alters the development of cortical microstructure and morphology at term-equivalent age

Author

Dimitrova, Ralica, Pietsch, Maximilian, Ciarrusta, Judit, Fitzgibbon, Sean P., Williams, Logan Z.J., Christiaens, Daan, Cordero-Grande, Lucilio, Batalle, Dafnis, Makropoulos, Antonios, Schuh, Andreas, Price, Anthony N., Hutter, Jana, Teixeira, Rui PAG, Hughes, Emer, Chew, Andrew, Falconer, Shona, Carney, Olivia, Egloff, Alexia, Tournier, J-Donald, McAlonan, Grainne, Rutherford, Mary A., Counsell, Serena J., Robinson, Emma C., Hajnal, Joseph V., Rueckert, Daniel, Edwards, A. David, and O'Muircheartaigh, Jonathan

Published
2021
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45. Fully automated planning for anatomical fetal brain MRI on 0.55T.

Author

Neves Silva, Sara, McElroy, Sarah, Aviles Verdera, Jordina, Colford, Kathleen, St Clair, Kamilah, Tomi‐Tricot, Raphael, Uus, Alena, Ozenne, Valéry, Hall, Megan, Story, Lisa, Pushparajah, Kuberan, Rutherford, Mary A., Hajnal, Joseph V., and Hutter, Jana

Subjects
FETAL MRI, FETAL brain, DIAGNOSTIC imaging, MAGNETIC resonance imaging, CEREBELLUM
Abstract

Purpose: Widening the availability of fetal MRI with fully automatic real‐time planning of radiological brain planes on 0.55T MRI. Methods: Deep learning‐based detection of key brain landmarks on a whole‐uterus echo planar imaging scan enables the subsequent fully automatic planning of the radiological single‐shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times, and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. A comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality, and the resulting image quality was conducted. Results: Prospective automatic planning was performed in real‐time without latency in all subjects. The landmark detection accuracy was 4.2 ±$$ \pm $$ 2.6 mm for the fetal eyes and 6.5 ±$$ \pm $$ 3.2 for the cerebellum, planning quality was 2.4/3 (compared to 2.6/3 for manual planning) and diagnostic image quality was 2.2 compared to 2.1 for manual planning. Conclusions: Real‐time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers. [ABSTRACT FROM AUTHOR]

Published
2024
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46. The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants

Author

Fitzgibbon, Sean P., Harrison, Samuel J., Jenkinson, Mark, Baxter, Luke, Robinson, Emma C., Bastiani, Matteo, Bozek, Jelena, Karolis, Vyacheslav, Cordero Grande, Lucilio, Price, Anthony N., Hughes, Emer, Makropoulos, Antonios, Passerat-Palmbach, Jonathan, Schuh, Andreas, Gao, Jianliang, Farahibozorg, Seyedeh-Rezvan, O'Muircheartaigh, Jonathan, Ciarrusta, Judit, O'Keeffe, Camilla, Brandon, Jakki, Arichi, Tomoki, Rueckert, Daniel, Hajnal, Joseph V., Edwards, A. David, Smith, Stephen M., Duff, Eugene, and Andersson, Jesper

Published
2020
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48. Reduced structural connectivity in cortico-striatal-thalamic network in neonates with congenital heart disease

Author

Ní Bhroin, Megan, Abo Seada, Samy, Bonthrone, Alexandra F., Kelly, Christopher J., Christiaens, Daan, Schuh, Andreas, Pietsch, Maximilian, Hutter, Jana, Tournier, J-Donald, Cordero-Grande, Lucillio, Rueckert, Daniel, Hajnal, Joseph V., Pushparajah, Kuberan, Simpson, John, Edwards, A. David, Rutherford, Mary A., Counsell, Serena J., and Batalle, Dafnis

Published
2020
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49. Parental age effects on neonatal white matter development

Author

Gale-Grant, Oliver, Christiaens, Daan, Cordero-Grande, Lucilio, Chew, Andrew, Falconer, Shona, Makropoulos, Antonios, Harper, Nicholas, Price, Anthony N, Hutter, Jana, Hughes, Emer, Victor, Suresh, Counsell, Serena J, Rueckert, Daniel, Hajnal, Joseph V, Edwards, A David, O'Muircheartaigh, Jonathan, and Batalle, Dafnis

Published
2020
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