Neele S Dellschaft¹, Rachel Allcock¹, Jana Hutter², Lopa Leach³, Nia Jones⁴, and Penny Gowland^1
1Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom, ²Department of Perinatal Imaging and Health, King's College London, London, United Kingdom, ³Life Sciences, University of Nottingham, Nottingham, United Kingdom, ⁴Division of Child Health, Obstetrics and Gynaecology, University of Nottingham, Nottingham, United Kingdom

Subclinical uterine contractions in the third trimester have been detected with MRI in recent years and we regularly observe these contractions in 10 minute longitudinal scans. We have recently described how placental pump contractions differ from Braxton Hicks contractions, as defined by a reduction in placental volume. In this study, we have observed in detail the effects of 18 contractions on longitudinal single echo-planar imaging T2* weighted scans.

Datta Singh Goolaub^1,2, Jiawei Xu³, Eric Schrauben⁴, Davide Marini⁵, Mike Seed^5,6, and Christopher Macgowan^1,2
1Medical Biophysics, University of Toronto, Toronto, ON, Canada, ²Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada, ³Labatt Family Heart Centre, The Hospital for Sick Children, Toronto, ON, Canada, ⁴Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, Netherlands, ⁵Pediatric Cardiology, The Hospital for Sick Children, Toronto, ON, Canada, ⁶Department of Pediatrics, University of Toronto, Toronto, ON, Canada

In this study, we demonstrate multidimensional fetal blood flow visualization and quantification, using highly accelerated multislice radial phase contrast MRI with slice-to-volume reconstruction. This acquisition and analysis pipeline provides real-time reconstructions for in-plane motion correction and cardiac gating for subsequent CINE reconstruction. CINEs are combined into a dynamic flow sensitive volume using slice-to-volume reconstruction with interslice motion correction. Experimental validation is presented in two adult volunteers. Feasibility is demonstrated in four human fetuses capturing complex hemodynamics in the fetal circulation.

Ruiming Chen¹, Sydney Nguyen^2,3,4, Megan E. Murphy^2,3,4, Kathleen M. Anthony^2,3,4, Terry K. Morgan⁵, Philip Corrado¹, Sean B. Fain^1,6, Dinesh M. Shah⁷, Ronald R. Magness⁸, Thaddeus Golos^2,3,4, Oliver Wieben^1,6,9, and Kevin M. Johnson^1,9
1Medical Physics, University of Wisconsin - Madison, Madison, WI, United States, ²Wisconsin National Primate Research Center, University of Wisconsin - Madison, Madison, WI, United States, ³Comparative Biosciences, University of Wisconsin - Madison, Madison, WI, United States, ⁴Obstetrics & Gynecology, University of Wisconsin - Madison, Madison, WI, United States, ⁵Pathology, Oregon Health & Science University, Portland, OR, United States, ⁶Biomedical Engineering, University of Wisconsin - Madison, Madison, WI, United States, ⁷Obstetrics and Gynecology, University of Wisconsin - Madison, Madison, WI, United States, ⁸Obstetrics and Gynecology, University of South Florida, Tampa, FL, United States, ⁹Radiology, University of Wisconsin - Madison, Madison, WI, United States

Adequate maternal blood supply is an important factor to maintain placental health, and placenta vascular markers may be predictive of pregnancy outcomes. Here, we report longitudinal quantitative results of maternal fractional, regional, and total blood volume measurements in rhesus macaque placenta across gestation ages using Ferumoxytol-enhanced variable flip angle (VFA)-T1 mapping. We observe regional heterogeneity in fractional blood volume and increased maternal placental blood volume throughout pregnancy.

Daniel Seiter¹, Ruiming Chen¹, Kai Ludwig¹, Ante Zhu², Dinesh Shah³, Oliver Wieben^1,4, and Kevin Johnson^1,4,5
1Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ²GE Global Research, Niskayuna, NY, United States, ³Obstetrics and Gynecology, University of Wisconsin-Madison, Madison, WI, United States, ⁴Radiology, University of Wisconsin-Madison, Madison, WI, United States, ⁵Biomedical Engineering, University of Madison-Wisconsin, Madison, WI, United States

Proper placental development is crucial to fetal health. To the best of our knowledge, we report the earliest measurement of perfusion in the human placenta using velocity-selective arterial spin labeling (VS-ASL) MRI. Patients were scanned at 14- and 20-weeks gestation (term is 40 weeks) and clinical data was collected. Regression analysis shows no evidence for an increase in placental perfusion with gestational age, but strong evidence of an increase in perfusion with patient body mass index (BMI).

Yuhao Liao¹, Taotao Sun^2,3, Ling Jiang^2,3, Zhiyong Zhao¹, Tingting Liu¹, Zhaoxia Qian^2,3, Yi Sun⁴, Yi Zhang¹, and Dan Wu^1
1Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China, ²Department of Radiology, International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China, ³Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China, ⁴MR Collaboration, Siemens Healthcare China, Shanghai, China

Intravoxel Incoherent Motion (IVIM) imaging has been used to assess placental microcirculatory flow for prenatal examination. Here we proposed a joint analysis of flow-compensated (FC) and non-compensated (NC) diffusion MRI to estimate the fraction and velocity of ballistic microcirculatory flow, and evaluated the diagnostic performance of the new IVIM markers in maternal and fetal disorders. We found the flow velocity measurement from FC-NC joint model could differentiate patients with maternal hyperglycemia and fetal growth restriction compared to the controls, while the conventional IVIM parameters based on bi-exponential model using FC-only or NC-only data could not show the group difference.

Bryan Quah¹, Anna Dong¹, Neil Rao¹, Patrick Hoang¹, Michael Hirano¹, Manjiri K. Dighe¹, and Colin Studholme^1
1University of Washington, Seattle, WA, United States

We present fully automated deep learning approaches to placental tissue segmentation on our dataset of 68 3D R2* images. Using this dataset, we employ different data schemes to get 4 new datasets consisting of full 3D images, full 2D slices, 3D patches and 2D patches. An unmodified U-Net architecture is trained and tested on these datasets to evaluate the robustness of the model when presented with different data. We find that by artificially increasing the size of the dataset, the model is able to perform better at the segmentation task.

Maximilian Pietsch¹, Alison Ho², Alessia Bardanzellu¹, Aya Zeydan¹, Joseph V Hajnal³, Lucy Chappell², Mary A Rutherford³, and Jana Hutter^1,4
1Centre for Medical Engineering, King's College London, London, United Kingdom, ²Women's Health, King's College London, London, United Kingdom, ³King's College London, London, United Kingdom, ⁴Centre for the Developing Brain, King's College London, London, United Kingdom

The placenta is key for any successful pregnancy. Deviations from the normal dynamic maturation throughout gestation are closely linked to major pregnancy complications. Automatic segmentation and age prediction based on a 30sec MRI T2* scan is enabled and evaluated in >100 pregnancies. High abnormality scores correlate with low birth weight, premature birth and histopathological findings. Retrospective application on a different cohort imaged at 1.5T illustrates the ability for direct clinical translation. The proposed machine-learning pipeline runs in close to real-time and, deployed in clinical settings, has the potential to become a cornerstone of diagnosis and intervention of placental insufficiency.

Paddy J. Slator¹, Jana Hutter^2,3, Razvan V. Marinescu¹, Marco Palombo¹, Laurence Jackson^2,3, Alison Ho⁴, Lucy C Chappell⁴, Mary Rutherford², Joseph V Hajnal^2,3, and Daniel Alexander^1
1Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, ²Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ³Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ⁴Women’s Health Department, King's College London, London, United Kingdom

We quantify placental structure and function across gestation using a combined T2*-diffusion protocol and InSpect, a data-driven approach for quantitative MRI analysis. We identify and map seven distinct placental tissue environments, and show that these environments are related to dysfunction. Our approach shows promise for diagnosis and monitoring of pregnancy complications.

Alena Uus¹, Irina Grigorescu¹, Aditi Shetty¹, Alexia Egloff Collado², Joseph Davidson^3,4, Milou van Poppel^1,5, Johannes Steinweg², Lisa Story², Michael Aertsen^6,7, Jan Deprest⁸, Jim Carmichael⁹, Joseph V Hajnal^1,2, Mary Rutherford², and Maria Deprez^1
1Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ²Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ³Prenatal Cell and Gene Therapy, Elizabeth Garrett Anderson Institute of Women’s Health, University College London, London, United Kingdom, ⁴Stem Cells and Regenerative Medicine, GOS-UCL Institute of Child Health, London, United Kingdom, ⁵Department of Congenital Heart Disease, Evelina Children’s Hospital, London, United Kingdom, ⁶Department of Radiology, University Hospitals Leuven, Leuven, Belgium, ⁷Department of Imaging and Pathology, Biomedical Sciences, KU Leuven, Leuven, Belgium, ⁸Department of Obstetrics, University Hospitals KU Leuven, Leuven, Belgium, ⁹Paediatric Radiology, Evelina London Children’s Hospital, London, United Kingdom

This work presents a continuous 4D atlas of fetal lung development within 22-32 weeks gestational age (GA) generated from ~130 motion-corrected fetal body MRI datasets. The corresponding growth charts for fetal MRI lung indices are used for definition of the of normal ranges. In addition, we implemented and evaluated an automated method for fetal lung volumetry based on 3D UNet segmentations. 

Netanell Avisdris^1,2, Daphna Link-Sourani², Liat Ben-Sira^3,4,5, Leo Joskowicz¹, Elka Miller⁶, and Dafna Ben-Bashat^2,3,5
1School of computer science and engineering, Hebrew University of Jerusalem, Jerusalem, Israel, ²Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, ³Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, ⁴Division of Pediatric Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, ⁵Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel, ⁶Medical Imaging, Children's Hospital of Eastern Ontario, University of Ottawa, Ottawa, ON, Canada

The aim of this study was to establish a fully automatic method for ocular measurements from in-vivo fetal brain MRI. Axial brain MRI of 47 fetuses (29-38 weeks’ gestational age) were included. The method includes fetal brain ROI computation and fetal eye segmentation using deep learning, followed by geometric algorithms for 2D and 3D measurements of the binocular (BOD), interocular (IOD), and ocular (OD) diameters. The performance of the 2D measurements was found to be preferable over 3D, with <1mm deviation from manual expert neuro-radiologist annotations. This is the first fully automatic method for fetal ocular biometric measurements in MRI.