MaTransformerPreprocessing

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Master thesis: Transformer-Based Pipeline for Pre-Processing MR Spectroscopic Imaging Data

Advisor: Prof. Bjoern Menze; PD Tobias Lasser
Supervision by: John LaMaster

Abstract

MRS data is composed of 1D spectra that can quantitatively characterize the metabolic composition of in-vivo tissue. This is especially useful for characterizing brain tumors. The primary drawback to this data type is the extensive and costly pre-processing and analysis necessary to prepare and annotate the data. Attempts to accelerate this work using deep learning is a budding, active research field. Transformers were initially developed for NLP tasks. However, recent research has shown them to be highly effective for image classification in computer vision as well. Due to the spatial nature of MRS data, CV CNN models and MLPs have been effective in this pre-processing task. Therefore, we would like to explore the use of transformers to assess their potential for automating the MRSI pre-processing pipeline. Steps to be evaluated would include things like phase and frequency correction, baseline estimation, and eddy current corrections.

Project Description

PDF: Transformer-Based Pipeline for Pre-Processing MR Spectroscopic Imaging Data

Tasks

Develop, optimize, and evaluate a pipeline based on transformer networks to automate the pre-processing of 1D MR spectroscopic imaging data.

Requirements

  • Machine Learning background
  • Proficient with PyTorch?
  • Ambitious with a strong motivation
  • Ability to work independently

Magnetic Resonance Spectropy

MRS is a quantitative MR technique used to characterize the metabolism or metabolic composition of tissue in vivo. This is done by analyzing the specific frequencies present in the FID, instead of just considering the magnitude which is used for qualitative imaging. These frequencies are metabolite dependent, making them easy to identify. While all protons resonate at the same Larmor frequency, the precessions that occur when excited by the RF pulse cause small changes in the magnetic field of the nucleus. MR-visible molecules are visible because there are many of these small perturbations that cumulatively change the local effective magnetic field of the molecule. This change in the magnetic field changes the molecule's resonant frequency. In the frequency domain, this change in resonant frequency shifts the frequency peak to the left or the right. This chemical shift is unique to each molecule and by comparing peaks of different metabolites, maps of relative metabolite concentrations can be generated. This type of information is useful for characterizing tissue, including potential tumors.

Contact

John LaMaster
Bjoern Menze

Students.ProjectForm
Title: Transformer-Based Pipeline for Pre-Processing MR Spectroscopic Imaging Data
Abstract: MRS data is composed of 1D spectra that can quantitatively characterize the metabolic composition of in-vivo tissue. This is especially useful for characterizing brain tumors. The primary drawback to this data type is the extensive and costly pre-processing and analysis necessary to prepare and annotate the data. Attempts to accelerate this work using deep learning is a budding, active research field. Transformers were initially developed for NLP tasks. However, recent research has shown them to be highly effective for image classification in computer vision as well. Due to the spatial nature of MRS data, CV CNN models and MLPs have been effective in this pre-processing task. Therefore, we would like to explore the use of transformers to assess their potential for automating the MRSI pre-processing pipeline. Steps to be evaluated would include things like phase and frequency correction, baseline estimation, and eddy current corrections.
Student:  
Director: Prof. Bjoern Menze; PD Tobias Lasser
Supervisor: John LaMaster
Type: Master Thesis
Area: Machine Learning, Medical Imaging, Computer Vision
Status: open
Start:  
Finish:  
Thesis (optional):  
Picture:  


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