Variable Resolution Sampling and Deep Learning-Based Image Recovery for Faster Multi-Spectral Imaging Near Metal Implants

Nikolai J. Mickevicius; Azadeh Sharafi; Andrew S. Nencka; Kevin M. Koch

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arXiv · 2306.01933

Variable Resolution Sampling and Deep Learning-Based Image Recovery for Faster Multi-Spectral Imaging Near Metal Implants

Nikolai J. Mickevicius·Azadeh Sharafi·Andrew S. Nencka·Kevin M. Koch

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Abstract

Purpose: In multi-spectral imaging (MSI), several fast spin echo volumes with discrete Larmor frequency offsets are acquired in an interleaved fashion with multiple concatenations. Here, a variable resolution (VR) method to nearly halve scan time is proposed by only acquiring low resolution autocalibrating signal in half of the concatenations. Methods: Knee MSI datasets were retrospectively undersampled with the proposed variable resolution sampling scheme. A U-Net model was trained to predict the full-resolution images from the VR input. Image quality was assessed in 10 test subjects. Results: Spectral bin-combined images produced with the proposed variable resolution sampling with deep learning reconstruction appear to be of high quality and exhibited a median structural image similarity of 0.984 across test subjects and slices. Conclusion: The proposed variable resolution sampling method shows promise for drastically reducing the time it takes to collect multi-spectral imaging data near metallic implants. Further studies will rigorously examine its clinical utility across multiple implant scenarios.

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Nikolai J. Mickevicius, Azadeh Sharafi, Andrew S. Nencka, Kevin M. Koch. 2023-06-02. Variable Resolution Sampling and Deep Learning-Based Image Recovery for Faster Multi-Spectral Imaging Near Metal Implants. https://arxiv.org/abs/2306.01933

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