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Time-Series Comparison for MRI
An MR sequence consists of multiple continuous time-series and discrete events that define the measurement process. The goal is to employ longest sub-sequence techniques (e.g. as used in textual diff programs) to find common parts between MR sequences and to graphically visualize their differences.
Keywords: Magnetic Resonance Imaging, MRI sequence programming, MATLAB.
Magnetic Resonance Imaging (MRI) employs concurrent gradient and radio frequency (RF) fields to perform complex magnetization manipulation and acquisition. As such, exact timing is of utmost importance, which can be hard to achieve and even harder to debug. In this project, we want to develop an analysis tool that allows to compare two MRI sequences and highlights their differences – similar to diff for text / code files.
To this end, longest common subsequence (LCS) algorithms should be employed. Classical LCS finds the longest subsequence of characters in two input strings. It is the basis for diff utilities and has also been extended for continuous, non-discretized sequences.
In addition to time-series, MRI also features discrete “events” such as time points of signal acquisition, the effective time-point of RF pulses or trigger signals for external devices. These events can help “guide” a diff tool to find meaningful blocks to compare.
Magnetic Resonance Imaging (MRI) employs concurrent gradient and radio frequency (RF) fields to perform complex magnetization manipulation and acquisition. As such, exact timing is of utmost importance, which can be hard to achieve and even harder to debug. In this project, we want to develop an analysis tool that allows to compare two MRI sequences and highlights their differences – similar to diff for text / code files.
To this end, longest common subsequence (LCS) algorithms should be employed. Classical LCS finds the longest subsequence of characters in two input strings. It is the basis for diff utilities and has also been extended for continuous, non-discretized sequences.
In addition to time-series, MRI also features discrete “events” such as time points of signal acquisition, the effective time-point of RF pulses or trigger signals for external devices. These events can help “guide” a diff tool to find meaningful blocks to compare.
This project aims at implementing a diff-viewer for MR sequences by
- defining how MR sequences can be compared
- what operations are permitted on a sequence to identify common sub-sequences (e.g. time-shifting, scaling, etc.)
- adjustment / implementation of a LCS-based diff utility with graphical output.
For more information, please contact Christian Guenthner (guenthner@biomed.ee.ethz.ch)
This project aims at implementing a diff-viewer for MR sequences by - defining how MR sequences can be compared - what operations are permitted on a sequence to identify common sub-sequences (e.g. time-shifting, scaling, etc.) - adjustment / implementation of a LCS-based diff utility with graphical output. For more information, please contact Christian Guenthner (guenthner@biomed.ee.ethz.ch)
Please directly contact Christian Guenthner (guenthner@biomed.ee.ethz.ch) with your CV and a recent transcript of records.
Supervising Professor: Prof. Sebastian Kozerke (kozerke@biomed.ee.ethz.ch)
Please directly contact Christian Guenthner (guenthner@biomed.ee.ethz.ch) with your CV and a recent transcript of records. Supervising Professor: Prof. Sebastian Kozerke (kozerke@biomed.ee.ethz.ch)