Thesis Proposal: Blake Dewey
Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.
Title: Harmonization of Structural MRI for Consistent Image Analysis
Abstract: Magnetic resonance imaging (MRI) is a flexible, non-invasive medical imaging modality that uses strong magnetic fields and radio-frequency pulses to produce images with excellent contrast in the soft tissues of the body. MRI is commonly used in diagnosis and monitoring of many conditions, but is especially useful in disorders of the central nervous system, such as multiple sclerosis (MS), where the brain and spinal cord are heavily involved. An MRI scan normally contains a number of imaging volumes, where different pulse sequence parameters are selected to highlight different tissue properties. These volumes can then be used together to provide complimentary information about the imaged area. Flexible design of the imaging system allows for a variety of questions to be answered during a single scanning session, but also comes with a cost. As there are many parameters to define when designing an imaging sequence, there is no common standard that is widely used. These differences lead to variability in image appearance between manufacturers, imaging centers, and even individual scanners. As an example, a commonly acquired MR volume is a T1-weighted image, where differences in a specific magnetic property (longitudinal relaxation time or T1) is highlighted. However, this general effect can be achieved with a myriad of different pulse sequences even before the individual parameters are considered. This is perhaps most apparent in the difference between T1-weighted images with and without a preparatory inversion pulse, where images with an inversion pulse tend to have a much clearer contrast between grey and white matter in the brain. With the advent of advanced machine learning methods, variations such as the example above create a large problem, as accurate methods become closely tied to the data used to train them and any variation in inputs can have unknown effects on output quality. This problem sets the stage for image harmonization, where synthetic “harmonized” images are produced after acquisition to provide consistent inputs to image analysis routines.
This thesis aims to develop harmonization strategies for structural brain MR images that will allow for the synthesis of harmonized images from differing inputs. These images can then be used downstream in automated analysis pipelines, most commonly whole-brain segmentation for volumetric analysis. Recently, deep learning-based techniques have been shown to be excellent candidates in the realm of image synthesis and can be readily incorporated in harmonization tasks. However, this is complicated, as training data (especially in multi-site settings) is rarely available. This work will approach these problems by covering three main topics:
- Development of a supervised harmonization technique for structural MRI that utilizes overlapping subjects scanned using multiple protocols.
- Development of a semi-supervised learning strategy that exploits existing multi-contrast MRI information within a single scan session to perform the harmonization task without overlapping subjects.
- Demonstration of the feasibility of MRI harmonization from the viewpoint of clinical research through validation and investigation in real-world data samples.
- Jerry L. Prince, Department of Electrical and Computer Engineering
- Vishal M. Patel, Department of Electrical and Computer Engineering
- Muyinatu A. Lediju Bell, Department of Electrical and Computer Engineering
- Peter C.M. van Zijl, Department of Radiology and Radiological Sciences
- Peter A. Calabresi, Department of Neurology