Ultra-high field quantitative susceptibility mapping of the neonatal brain
Poster No:
1300
Submission Type:
Abstract Submission
Authors:
Chiara Casella1,2, Katy Vecchiato1,2, Ayse Sila Dokumaci3, Philippa Bridgen4,5, Beya Bonse1,5, Pierluigi Di Cio4,5, Joseph Hajnal1,3,4, Sharon Giles3,4, Jucha Willers Moore1,6, Jan Sedlacik4,7,8, Wilkinson Thomas3, Raphael Tomi-Tricot3,9, David Carmichael3, Jonathan O'Muircheartaigh1,2,6, Tomoki Arichi1,5,6, Shaihan Malik1,3,4
Institutions:
1Centre for the Developing Brain, King's College London, London, United Kingdom, 2Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom, 3Biomedical Engineering Department, King's College London, London, United Kingdom, 4London Collaborative Ultra high field System (LoCUS), London, United Kingdom, 5Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom, 6MRC Centre for Neurodevelopmental Disorders, London, United Kingdom, 7Mansfield Centre for Innovation, Imaging Sciences, Institute of Clinical Sciences, Imperial College, London, United Kingdom, 8Robert Steiner MR Unit, MRC Laboratory of Medical Sciences, Hammersmith Hospital, London, United Kingdom, 9MR Research Collaborations, Siemens Healthcare Limited, London, United Kingdom
First Author:
Chiara Casella
Centre for the Developing Brain, King's College London|Institute of Psychiatry, Psychology and Neuroscience, King’s College London
London, United Kingdom|London, United Kingdom
Centre for the Developing Brain, King's College London|Institute of Psychiatry, Psychology and Neuroscience, King’s College London
London, United Kingdom|London, United Kingdom
Co-Author(s):
Katy Vecchiato
Centre for the Developing Brain, King's College London|Institute of Psychiatry, Psychology and Neuroscience, King’s College London
London, United Kingdom|London, United Kingdom
Centre for the Developing Brain, King's College London|Institute of Psychiatry, Psychology and Neuroscience, King’s College London
London, United Kingdom|London, United Kingdom
Philippa Bridgen
London Collaborative Ultra high field System (LoCUS)|Guys and St Thomas’ NHS Foundation Trust
London, United Kingdom|London, United Kingdom
London Collaborative Ultra high field System (LoCUS)|Guys and St Thomas’ NHS Foundation Trust
London, United Kingdom|London, United Kingdom
Beya Bonse
Centre for the Developing Brain, King's College London|Guys and St Thomas’ NHS Foundation Trust
London, United Kingdom|London, United Kingdom
Centre for the Developing Brain, King's College London|Guys and St Thomas’ NHS Foundation Trust
London, United Kingdom|London, United Kingdom
Pierluigi Di Cio
London Collaborative Ultra high field System (LoCUS)|Guys and St Thomas’ NHS Foundation Trust
London, United Kingdom|London, United Kingdom
London Collaborative Ultra high field System (LoCUS)|Guys and St Thomas’ NHS Foundation Trust
London, United Kingdom|London, United Kingdom
Joseph Hajnal
Centre for the Developing Brain, King's College London|Biomedical Engineering Department, King's College London|London Collaborative Ultra high field System (LoCUS)
London, United Kingdom|London, United Kingdom|London, United Kingdom
Centre for the Developing Brain, King's College London|Biomedical Engineering Department, King's College London|London Collaborative Ultra high field System (LoCUS)
London, United Kingdom|London, United Kingdom|London, United Kingdom
Sharon Giles
Biomedical Engineering Department, King's College London|London Collaborative Ultra high field System (LoCUS)
London, United Kingdom|London, United Kingdom
Biomedical Engineering Department, King's College London|London Collaborative Ultra high field System (LoCUS)
London, United Kingdom|London, United Kingdom
Jucha Willers Moore
Centre for the Developing Brain, King's College London|MRC Centre for Neurodevelopmental Disorders
London, United Kingdom|London, United Kingdom
Centre for the Developing Brain, King's College London|MRC Centre for Neurodevelopmental Disorders
London, United Kingdom|London, United Kingdom
Jan Sedlacik
London Collaborative Ultra high field System (LoCUS)|Mansfield Centre for Innovation, Imaging Sciences, Institute of Clinical Sciences, Imperial College|Robert Steiner MR Unit, MRC Laboratory of Medical Sciences, Hammersmith Hospital
London, United Kingdom|London, United Kingdom|London, United Kingdom
London Collaborative Ultra high field System (LoCUS)|Mansfield Centre for Innovation, Imaging Sciences, Institute of Clinical Sciences, Imperial College|Robert Steiner MR Unit, MRC Laboratory of Medical Sciences, Hammersmith Hospital
London, United Kingdom|London, United Kingdom|London, United Kingdom
Raphael Tomi-Tricot
Biomedical Engineering Department, King's College London|MR Research Collaborations, Siemens Healthcare Limited
London, United Kingdom|London, United Kingdom
Biomedical Engineering Department, King's College London|MR Research Collaborations, Siemens Healthcare Limited
London, United Kingdom|London, United Kingdom
Jonathan O'Muircheartaigh
Centre for the Developing Brain, King's College London|Institute of Psychiatry, Psychology and Neuroscience, King’s College London|MRC Centre for Neurodevelopmental Disorders
London, United Kingdom|London, United Kingdom|London, United Kingdom
Centre for the Developing Brain, King's College London|Institute of Psychiatry, Psychology and Neuroscience, King’s College London|MRC Centre for Neurodevelopmental Disorders
London, United Kingdom|London, United Kingdom|London, United Kingdom
Tomoki Arichi
Centre for the Developing Brain, King's College London|Guys and St Thomas’ NHS Foundation Trust|MRC Centre for Neurodevelopmental Disorders
London, United Kingdom|London, United Kingdom|London, United Kingdom
Centre for the Developing Brain, King's College London|Guys and St Thomas’ NHS Foundation Trust|MRC Centre for Neurodevelopmental Disorders
London, United Kingdom|London, United Kingdom|London, United Kingdom
Shaihan Malik
Centre for the Developing Brain, King's College London|Biomedical Engineering Department, King's College London|London Collaborative Ultra high field System (LoCUS)
London, United Kingdom|London, United Kingdom|London, United Kingdom
Centre for the Developing Brain, King's College London|Biomedical Engineering Department, King's College London|London Collaborative Ultra high field System (LoCUS)
London, United Kingdom|London, United Kingdom|London, United Kingdom
Introduction:
Iron is essential for healthy brain development 1, but its non-invasive quantification in early life is extremely challenging. 7T MRI provides enhanced SNR, resolution, and susceptibility effects, which can greatly enhance the ability to image brain iron. Specifically, 7T quantitative susceptibility mapping (QSM) 2 affords uniquely high sensitivity for detecting subtle variations in brain iron deposition, but has never been used to study the newborn brain. Here, we explore the feasibility and sensitivity of 7T QSM for assessing brain iron in the first weeks after birth.
Methods:
Subjects and data acquisition:
5 neonates (median age: 39.7 weeks postmenstrual age (PMA), range: 37-41.28) were imaged in natural sleep at 7T (MAGNETOM Terra, Siemens Healthineers) with 2D T2w acquisitions for anatomical information (axial, sagittal, coronal: 0.6x0.6x1.2mm resolution, TR=8640ms, TE=156ms, flip angle=120°) and a 3D T2*w GRE sequence for QSM (0.7mm isotropic, TR=32.2ms, flip angle= 15°, 6 TEs, TE1=2.92ms, echo spacing=5.19ms).
For comparison, 11 children (mean age: 11.9 years, range: 8-14) were imaged on the same scanner with 3D MP2RAGE 3 (0.65mm isotropic, TE/TR=3.15/4000ms,TIs=650/2280ms), 3D FLAIR (0.8mm isotropic, TE/TR=240/9000ms,TI=2600ms), and 3D T2*w GRE sequences (0.7mm isotropic, TR=29ms, flip angle=15.5°, 6 TEs, TE1=2.68ms, echo spacing=4.69ms).
QSM reconstruction:
Combination of complex data and QSM computation were carried out using the approaches outlined in 4 .
Image registration and tissues segmentation:
Neonates: T2w images were non-linearly registered to a 37 week PMA template 5 . Magnitude images were rigidly co-registered to the corresponding T2w volume. QSM normalization to the template was achieved through composition of the above transformations. Tissue segmentations and surfaces were generated in template space using the developing Human Connectome Project (HCP) pipeline 6 .
Children: Magnitude images were rigidly co-registered to the corresponding MP2RAGE volume, and QSM normalization was achieved through the composition of the above transformations. FLAIR and T1w images were analyzed with the HCP pipeline 7 to perform tissue segmentation and surface reconstruction in native space.
Analysis:
Susceptibility (χ) was examined in caudate, lentiform nucleus, corpus callosum (CC) and lateral ventricles. Additionally, χ was sampled along the grey/white matter (GM/WM) boundary.
5 neonates (median age: 39.7 weeks postmenstrual age (PMA), range: 37-41.28) were imaged in natural sleep at 7T (MAGNETOM Terra, Siemens Healthineers) with 2D T2w acquisitions for anatomical information (axial, sagittal, coronal: 0.6x0.6x1.2mm resolution, TR=8640ms, TE=156ms, flip angle=120°) and a 3D T2*w GRE sequence for QSM (0.7mm isotropic, TR=32.2ms, flip angle= 15°, 6 TEs, TE1=2.92ms, echo spacing=5.19ms).
For comparison, 11 children (mean age: 11.9 years, range: 8-14) were imaged on the same scanner with 3D MP2RAGE 3 (0.65mm isotropic, TE/TR=3.15/4000ms,TIs=650/2280ms), 3D FLAIR (0.8mm isotropic, TE/TR=240/9000ms,TI=2600ms), and 3D T2*w GRE sequences (0.7mm isotropic, TR=29ms, flip angle=15.5°, 6 TEs, TE1=2.68ms, echo spacing=4.69ms).
QSM reconstruction:
Combination of complex data and QSM computation were carried out using the approaches outlined in 4 .
Image registration and tissues segmentation:
Neonates: T2w images were non-linearly registered to a 37 week PMA template 5 . Magnitude images were rigidly co-registered to the corresponding T2w volume. QSM normalization to the template was achieved through composition of the above transformations. Tissue segmentations and surfaces were generated in template space using the developing Human Connectome Project (HCP) pipeline 6 .
Children: Magnitude images were rigidly co-registered to the corresponding MP2RAGE volume, and QSM normalization was achieved through the composition of the above transformations. FLAIR and T1w images were analyzed with the HCP pipeline 7 to perform tissue segmentation and surface reconstruction in native space.
Analysis:
Susceptibility (χ) was examined in caudate, lentiform nucleus, corpus callosum (CC) and lateral ventricles. Additionally, χ was sampled along the grey/white matter (GM/WM) boundary.
Results:
The susceptibility contrast between GM and WM in neonates is low. χ in GM nuclei is negative in neonates and positive in children, where clearer structural boundaries are observed, reflecting greater iron deposition. χ is negative in the CC of neonates, underscoring a significant effect of diamagnetic myelin on WM susceptibility even in the initial weeks after birth. In children, χ in the CC is more negative, reflecting increased myelination. χ in ventricles is higher in the neonatal brain. This finding is consistent with previous 3T evidence 8 and is possibly due to the higher content of iron-containing neutrophils in the newborn brain 9. Positive χ values are observed in posterior auditory and visual cortical areas in neonates, while in children positive χ values are more widespread, consistent with a high iron layer in the deeper cortex corresponding to underlying myeloarchitecture 10, and with a posterior to anterior pattern of myelination in neurodevelopment.
Conclusions:
We demonstrate the feasibility of QSM of the neonatal brain at 7T and show that it can detect regional variations in tissue composition through different stages of brain development.
These findings implicate the huge potential of this approach for providing novel insights into neurodevelopment and for improving understanding of iron-related tissue damage in the initial months after birth, as well as its association with prognosis.
These findings implicate the huge potential of this approach for providing novel insights into neurodevelopment and for improving understanding of iron-related tissue damage in the initial months after birth, as well as its association with prognosis.
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 1
Modeling and Analysis Methods:
Methods Development 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development
Keywords:
Development
HIGH FIELD MR
MRI
PEDIATRIC
1|2Indicates the priority used for review
Provide references using author date format
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