The heart is a dynamic, beating organ, and until now it has been challenging to fully capture its com-
plexity by magnetic resonance imaging (MRI). In an ideal world, doctors could create a 3-dimensional
(3D) visual representation of each patient’s unique heart and watch as it pumps, moving through each
phase of the cardiac cycle. [2]
The standard cardiac MRI includes multiple 2D image slices stacked next to each other that must
be carefully positioned by the MRI technologist based on a patient’s anatomy. Planning the location
and angle for the slices requires a highly-knowledgeable operator and takes time. [2]
Recently, a new MRI-based technology, referred to as “3D cine”, has been developed that can
produce moving 3D images of the heart. It allows cardiologists and cardiac surgeons to see a patient’s
heart from any angle and observe its movement throughout the entire cardiac cycle [2], as well as the
assessment of cardiac morphology and function [4].
Fully automatic methods for analysis of 3D cine cardiovascular MRI would improve the clinical
utility of this promising technique. At the moment, there is no automatic segmentation algorithm
available for 3D cine images of the heart. Furthermore, manual segmentation of 3D cine images is
time-consuming and impractical. Therefore, in this master thesis, di erent deep learning techniques
(DL) based on 3D MRI data will be investigated in order to automate the segmentation process. In
particular, two time frames of every 3D image might be rst semi-automatically segmented [3]. The
segmentation of these two time frames will be used to train a deep neural network for automatic
segmentation of the other time frames.
The datasets are acquired from 125 di erent patients at the Boston Children’s Hospital1. In
contrast to the standard cardiac MRI that patients must hold their breath while the picture is being
taken, these datasets are obtained by tracking the patient’s breathing motion and only collecting data
during expiration, when the patient is breathing out [1].
The segmentation results will be quantitatively validated using Dice score and qualitatively eval-
uated by clinicians.
The thesis has to comprise the following work items:
Data processing and manual annotation of the available datasets in order to utilize them for the
DL methods.
Development and implementation of 3D cine segmentation models based on DL techniques.
Quantitative evaluation of the segmentation results with respect to Dice score.
The thesis will be carried out at the Department of Pediatrics at Harvard University Medical School
and the Department of Cardiology at Boston Children’s Hospital, in cooperation with the Pattern
Recognition Lab at FAU Erlangen-Nuremberg and the Computer Science and Arti cial Intelligence
Lab of MIT. Furthermore, the results of the study are expected to be published as an abstract and
article at the International Society for Cardiovascular Magnetic Resonance in Medicine2.
1Department of Cardiology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
2https://scmr.org/
References
[1] Mehdi Hedjazi Moghari, Ashita Barthur, Maria Amaral, Tal Geva, and Andrew Powell. Free-
breathing whole-heart 3d cine magnetic resonance imaging with prospective respiratory motion
compensation: Whole-heart 3d cine mri. Magnetic Resonance in Medicine, 80, 2017.
[2] Erin Horan. The future of cardiac mri: 3-d cine. Boston Children’s Hospital’s science and clinical
innovation blog, 2016. [Online]. Available: https://vector.childrenshospital.org/2016/12/
the-future-of-cardiac-mri-3-d-cine.
[3] Danielle F. Pace. Image segmentation for highly variable anatomy: Applications to congenital heart
disease. Doctoral dissertation, Massachusetts Institute of Technology, Cambridge, MA, USA, 2020.
[4] Jens Wetzl, Michaela Schmidt, Francois Pontana, Benjamin Longere, Felix Lugauer, Andreas
Maier, Joachim Hornegger, and Christoph Forman. Single-breath-hold 3-d cine imaging of the
left ventricle using cartesian sampling. Magnetic Resonance Materials in Physics, Biology and
Medicine, 31:1{13, 2017.