Computer Number: 129

A. Luis, A. Uus, J. Matthew, S. Arulkumaran, A. Egloff Collado, V. Kyriakopoulou, S. Neves Silva, J. Aviles Verdera, M. Hall, S. McElroy, K. Colford, J. Hajnal, J. Hutter, L. Story, M. Rutherford
King's College London, London, United Kingdom

Impact: The benefits of automating the time-consuming manual biometry method include improved diagnostic accuracy, confidence and reliability of derived measurements, enabling precise quantification of fetal brain development, as well as improved workflow efficiency and turnaround time for radiology reports.  

Computer Number: 130

K. Payette, R. Kottke, P. Grehten, L. Mazzone, M. Meuli, B. Latal, N. Ochsenbein-Kölble, B. Padden, U. Moehrlen, A. Jakab
University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland

Impact: Longitudinal fetal brain MRI of spina bifida patients has the potential to expand our knowledge of fetal brain development in neural tube disorders.

Computer Number: 131

S. Huang, K. Zhang, Z. Lian, G. Chen, D. Shen
School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China

Impact: Our end-to-end fetal brain super-resolution approach bypasses traditional two-step iterative optimization paradigm, and substantially reduces reconstruction time. It eliminates the need for slice-to-volume registration, and shifts the focus of implicit neural representation from addressing appearance estimation issues to motion estimation.

Computer Number: 132

Y. Li, M. Liu, J. Zhu, H. Yang, J. Zheng, Z. Li, Y. Liao, H. Qu, Q. Tian
Beihang University, Beijing, China

Impact: Source-free unsupervised domain adaptation addresses the problem that the pre-trained fetal brain extraction model is inaccurate for data acquired with different scanning hardware and parameters. Moreover, our work supports cross-center fetal studies and promotes practical clinical diagnostic applications.

Computer Number: 133

L. Ji, B. Chen, I. Menu, C. Trentacosta, M. Thomason
New York University School of Medicine, New York, United States

Impact: These findings provide the first insights into R2* development across birth longitudinally, laying the foundation for future research into the neural mechanisms underlying iron deficiency-related developmental disturbances.

Computer Number: 134

M. Liu, Y. Liao, J. Zhu, H. Li, H. Yang, J. Zheng, Z. Li, Z. Li, H. Qu, Q. Tian
Tsinghua University, Beijing, China

Impact: FreeHemoSeg provides accurate, automated segmentation and diagnosis of GMH-IVH without hemorrhage data and labels for training, substantially simplifying clinical workflows, aiding early diagnosis and prognosis, enabling hemorrhage volume measurement, supporting large-scale neuroscience research, and enhancing prenatal care and management strategies.

Computer Number: 135

S. Huang, L. Kong, S. Bai, Z. Lian, Q. Xu, G. Chen, M. Zhao, D. Shen
School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China

Impact: This study analyzes fetal growth restriction (FGR) impacts on cortical development, identifying altered brain regions like the insula, cuneus, and rostral anterior cingulate, affecting sensory integration, cognition, and emotional regulation, and also highlighting areas of both vulnerability and resilience.

Computer Number: 136

H. Li, M. Liu, Y. Liao, J. Zhu, J. Zheng, H. Yang, Z. Li, H. Qu, Q. Tian
Tsinghua University, Beijing, China

Impact: CortexKAN model achieves superior performance in fetal cortical surface reconstruction, outperforming existing methods. Additionally, a training strategy without cortical labels was demonstrated effective, enabling accurate reconstruction when surface labels are unavailable.

Computer Number: 137

S. Ahmad, P-T Yap
The University of North Carolina at Chapel Hill, Chapel Hill, United States

Impact: In-utero characterization of cortical microstructure offers key insights into the development of brain connections and functions, aiding early detection of abnormalities linked to malformation and dysfunction.

Computer Number: 138

T. Ciceri, A. Righini, L. Squarcina, A. Ferro, F. Arrigoni, C. Parazzini, N. Persico, S. Boito, I. Cetin, G. Conte, F. Triulzi, A. Bertoldo, P. Brambilla, D. Peruzzo
IRCCS Eugenio Medea, Bosisio Parini (LC), Italy

Impact: The spatio-temporal fetal MRI atlas of the GE allows researchers to study potential future clinical conditions attributable to GE alterations in pregnancy. The GE reached its maximum expansion at 21 weeks, followed by a pronounced reduction throughout the pregnancy.

Computer Number: 139

M. Lowe, K. Colford, K. Clair, C. Fauni, W. Norman, J-D Tournier, A. Price
Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom

Impact: Performing T1 mapping on a cohort of fetal participants will allow a more comprehensive dataset to be collected, enabling T1 contrast to be better optimised in fetal imaging and expanding on the T1 characterisation available in the literature. 

Computer Number: 140

C. Bradshaw, J. Aviles Verdera, M. Rutherford, M. Hall, A. Uus, A. Arechvo, M. Moser, K. Nicolaides, J. Hutter, L. Story
King's College London, London, United Kingdom

Impact: This study has demonstrated that alterations in brain development have antenatal antecedents in fetuses with CDH. Further work is required to correlate these findings with longer term neurodevelopmental outcomes to aid prognostication and further management.

Computer Number: 141

J. Aviles Verdera, K. Payette, S. Neves Silva, D. Cromb, R. Tomi-Tricot, M. Hall, S. Bansal, K. St Clair, S. Counsell, L. Story, M. Rutherford, J. Hajnal, J. Hutter
King's College London, London, United Kingdom

Impact: First emerging signatures of human neurological development can be systematically and automatically assessed retro- and prospectively on fetal MRI. Signatures of fetal activity and their correlation to fetal health can offer new opportunities for research and future clinical application.

Computer Number: 142

H. Li, Y. Liao, J. Zhu, M. Liu, J. Zheng, H. Yang, Z. Li, Z. Li, Q. Tian, H. Qu
School of Biomedical Engineering, Tsinghua University, Beijing, China

Impact: This study used a large dataset to clarify the negative consequence of GMH-IVH on prenatal and postnatal brain development, which can lead to improved diagnosis, management strategies, and potentially better outcomes for affected fetuses and infants.

Computer Number: 143

S. You, C. S. Amador Izaguirre, G. T. Milo, S. Jeong, H. J. Yun, P. E. Grant, K. Im
Boston Children's Hospital, Harvard Medical School, Boston, United States

Impact: The proposed anomaly detection framework offers a new tool for clinicians to identify fetal brain anomalies with high accuracy, enhancing the precision of prenatal diagnostics. This can lead to earlier and more targeted interventions for neurodevelopmental abnormalities, potentially improving outcomes.