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Abstract #5140

Frequency and Phase Correction of MRS Data by Leveraging Sequential Patterns along the Transient Dimension

Christopher Wu1, Kay Igwe1, and Jia Guo1
1Columbia University, New York, NY, United States

Synopsis

Keywords: Analysis/Processing, Analysis/Processing, frequency and phase correction

Motivation: Freqeuncy and phase correction of MRS data typically works on one transient at a time. However, by processing all transients simultaneously, the FPC algorithm can account for systematic variations in the transients that occurs due to certain factors like scanner B0 drift.

Goal(s): The goal is to propose a deep learning method can directly on in vivo data without the need for pre-training on simulated data.

Approach: 2D-FPC is unsupervised learning approach based on a CNN-LSTM that takes in all transients at once.

Results: 2D-FPC showed strong performance on simulated and in vivo data. The method is robust against addtional noise and offsets.

Impact: The 2D-FPC method proposed follows an unsupervised learning approach that allows it to be directly applied in vivo dataset without the need for pre-training. The simultaneous processing of all transients allows the algorithm to capture long-range patterns in the data.

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Keywords