The health of the placenta is vitally important during a pregnancy, but assessing it can be a challenge.
While ultrasounds are one easy-to-use option, they often cannot provide enough fine detail to clearly show the small structures that help exchange nutrients between the mother and fetus. Being able to do so earlier in development could help physicians treat potential disorders.
MRI scans can provide the high resolution necessary—but the complex, 3D structure of the placenta, as it wraps around the inside of the womb, brings other challenges by making the scans difficult to interpret.
To make that task easier, researchers at the Massachusetts Institute of Technology’s (MIT's) Computer Science and Artificial Intelligence Laboratory are working on algorithms that can flatten out an MRI’s 3D image into a format that doctors can easily recognize.
“The idea is to unfold the image of the placenta while it’s in the body, so that it looks similar to how doctors are used to seeing it after delivery,” said Ph.D. student and lead author Mazdak Abulnaga, working with MIT professors Justin Solomon and Polina Golland.
Additionally, the algorithm could be used to check for biomarkers of poor health, according to Golland. This could accelerate turnover by locating potential problem areas without having radiologists examine multiple slices of the image.
“While this is just a first step, we think an approach like this has the potential to become a standard imaging method for radiologists,” Abulnaga told MIT News.
The algorithm takes a model of the placenta’s shape and breaks it down into a mesh of thousands of tiny pyramids. This allows the computer to manipulate the curved image into something flatter, while still maintaining its 3D volume.
The researchers’ paper (PDF) will be presented this month in Shenzhen, China, at the International Conference on Medical Image Computing and Computer-Assisted Intervention. Going forward, the team hopes to compare in utero scans with images of the same placentas following birth.