Placental vascular tree characterization based on ex-vivo MRI with a potential application for placental insufficiency assessment.

INTRODUCTION: Understanding regarding the whole placental vascular network structure is limited. Our aim was to quantitatively characterize the human placental vascular tree ex-vivo using high-resolution MRI. METHODS: 34 normal placentas were rinsed and injected with a solution of gelatin and contrast agent through the umbilical vessels. A sample of six placentas taken from pregnancies with intrauterine-growth-restriction (IUGR) was used to demonstrate the potential application to cases with placental insufficiency. Structural ex-vivo MR scans of the placenta were performed using high resolution T1 weighted images. A semi-automatic method was developed to segment and characterize the placental vascular architecture: placental volume and cord insertion location; number of bifurcations, generations and vessels diameters. RESULTS: Different vascular patterns were found in placentas with central versus marginal cord-insertion. Based on the placental volume and number of bifurcations we were able to predict birth weight. Furthermore, preliminary results on IUGR sample demonstrated the potential of this method to differentiate between small newborns with suspected IUGR from small normal newborns who reached their full growth potential. Results obtained using the automatic method were validated against manual values demonstrating no significant differences or bias. Histopathology supported the imaging findings. DISCUSSION: This is the first study to quantitatively characterize the human placental vascular architecture using high resolution ex-vivo MRI. Different patterns of vascular architecture may be related to different functioning of the placenta and affect fetal development. This method is simple, relatively fast, provides detailed information of the placental vascular architecture, and may have important clinical applications.

Placental vascular tree characterization based on ex-vivo MRI with a potential application for placental insufficiency assessment.

INTRODUCTION: Understanding regarding the whole placental vascular network structure is limited. Our aim was to quantitatively characterize the human placental vascular tree ex-vivo using high-resolution MRI. METHODS: 34 normal placentas were rinsed and injected with a solution of gelatin and contrast agent through the umbilical vessels. A sample of six placentas taken from pregnancies with intrauterine-growth-restriction (IUGR) was used to demonstrate the potential application to cases with placental insufficiency. Structural ex-vivo MR scans of the placenta were performed using high resolution T1 weighted images. A semi-automatic method was developed to segment and characterize the placental vascular architecture: placental volume and cord insertion location, number of bifurcations, generations and vessels diameters. RESULTS: Different vascular patterns were found in placentas with central versus marginal cord-insertion. Based on the placental volume and number of bifurcations we were able to predict birth weight. Furthermore, preliminary results on IUGR sample demonstrated the potential of this method to differentiate between small newborns with suspected IUGR from small normal newborns who reached their full growth potential. Results obtained using the automatic method were validated against manual values demonstrating no significant differences or bias. Histopathology supported the imaging findings. DISCUSSION: This is the first study to quantitatively characterize the human placental vascular architecture using high resolution ex-vivo MRI. Different patterns of vascular architecture may be related to different functioning of the placenta and affect fetal development. This method is simple, relatively fast, provides detailed information of the placental vascular architecture, and may have important clinical applications.

Automatic Measurement of Fetal Brain Development from Magnetic Resonance Imaging: New Reference Data.

BACKGROUND: Accurate fetal brain volume estimation is of paramount importance in evaluating fetal development. The aim of this study was to develop an automatic method for fetal brain segmentation from magnetic resonance imaging (MRI) data, and to create for the first time a normal volumetric growth chart based on a large cohort. SUBJECTS AND METHODS: A semi-automatic segmentation method based on Seeded Region Growing algorithm was developed and applied to MRI data of 199 typically developed fetuses between 18 and 37 weeks' gestation. The accuracy of the algorithm was tested against a sub-cohort of ground truth manual segmentations. A quadratic regression analysis was used to create normal growth charts. The sensitivity of the method to identify developmental disorders was demonstrated on 9 fetuses with intrauterine growth restriction (IUGR). RESULTS: The developed method showed high correlation with manual segmentation (r2 = 0.9183, p < 0.001) as well as mean volume and volume overlap differences of 4.77 and 18.13%, respectively. New reference data on 199 normal fetuses were created, and all 9 IUGR fetuses were at or below the third percentile of the normal growth chart. DISCUSSION: The proposed method is fast, accurate, reproducible, user independent, applicable with retrospective data, and is suggested for use in routine clinical practice.