MIT Develops Personalized Heart Models for Surgical Planning

The work was funded by Boston Children’s Hospital and Harvard Catalyst, a consortium aimed at rapidly moving scientific innovation into the clinic.

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Medical research scientists at the Massachusetts Institute of Technology (MIT) and Boston Children’s Hospital have developed a system that can take MRI scans of a patient’s heart and, in a matter of hours, convert them into a tangible, physical model that surgeons can use to plan surgery.

This fall, seven cardiac surgeons at Boston Children’s Hospital will participate in a study intended to evaluate the models’ usefulness.

The work was funded by Boston Children’s Hospital and Harvard Catalyst, a consortium aimed at rapidly moving scientific innovation into the clinic.

Medhi Moghari, a physicist at Boston Children’s Hospital, developed new procedures that increase the precision of MRI scans tenfold, and Andrew Powell, a cardiologist at the hospital, leads the project’s clinical work.

MRI data consist of a series of cross sections of a three-dimensional object. Like a black-and-white photograph, each cross section has regions of dark and light, and the boundaries between those regions may indicate the edges of anatomical structures, but that is not certain.

Typically, the way to make an image-segmentation algorithm more precise is to augment it with a generic model of the object to be segmented.

Human hearts, for instance, have chambers and blood vessels that are usually in roughly the same places relative to each other. That anatomical consistency could give a segmentation algorithm a way to weed out improbable conclusions about object boundaries.

The problem with that approach is that many of the cardiac patients at Boston Children’s Hospital require surgery precisely because the anatomy of their hearts is irregular.

Inferences from a generic model could obscure the very features that matter most to the surgeon.

In the past, researchers have produced printable models of the heart by manually indicating boundaries in MRI scans. But with the 200 or so cross sections in one of Moghari’s high-precision scans, that process can take eight to 10 hours.

Pace and Golland opted to ask a human expert to identify boundaries in a few of the cross sections and allow algorithms to take over from there.

Their strongest results came when they asked the expert to segment only a small patch —one-ninth of the total area — of each cross section.

The clinical study this fall will involve MRIs from 10 patients who have already received treatment at Boston Children’s Hospital.

Each of seven surgeons will be given data on all 10 patients — some, probably, more than once. That data will include the raw MRI scans and, on a randomized basis, either a physical model or a computerized 3-D model, based, again at random, on either human segmentations or algorithmic segmentations.

Using that data, the surgeons will draw up surgical plans, which will be compared with documentation of the interventions that were performed on each of the patients.

The hope is that the study will shed light on whether 3-D-printed physical models can actually improve surgical outcomes.