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Purpose: To improve the image reconstruction for prospective motion correction (PMC) of simultaneous multislice (SMS) EPI of the brain, an update of receiver phase and resampling of coil sensitivities are proposed and evaluated. Methods: A camera-based system was used to track head motion (3 translations and 3 rotations) and dynamically update the scan position and orientation. We derived the change in receiver phase associated with a shifted field of view (FOV) and applied it in real-time to each k-space line of the EPI readout trains. Second, for the SMS reconstruction, we adapted resampled coil sensitivity profiles reflecting the movement of slices. Single-shot gradient-echo SMS-EPI scans were performed in phantoms and human subjects for validation. Results: Brain SMS-EPI scans in the presence of motion withPMCand no phase correction for scan plane shift showed noticeable artifacts. These artifacts were visually and quantitatively attenuated when corrections were enabled. Correcting misaligned coil sensitivity maps improved the temporal SNR (tSNR) of time series by 24% (p=0.0007) for scans with large movements (up to ∼35mm and 30◦). Correcting the receiver phase improved the tSNR of a scan with minimal head movement by 50% from 50 to 75 for a United Kingdom biobank protocol. Conclusion: Reconstruction-induced motion artifacts in single-shot SMS-EPI scans acquired with PMC can be removed by dynamically adjusting the receiver phase of each line across EPI readout trains and updating coil sensitivity profiles during reconstruction. The method may be a valuable tool for SMS-EPI scans in the presence of subject motion.
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Orientation estimation for instrumented helmet using neural networks
This work presents an integrated solution for head orientation estimation, which is a critical component for applications of virtual and augmented reality systems. The proposed solution builds upon the measurements from the inertial sensors and magnetometer added to an instrumented helmet, and an orientation estimation algorithm is developed to mitigate the effect of bias introduced by noise in the gyroscope signal. Convolutional Neural Network (CNN) techniques are introduced to develop a dynamic orientation estimation algorithm with a structure motivated by complementary filters and trained on data collected to represent a wide range of head motion profiles. The proposed orientation estimation method is evaluated experimentally and compared to both learning and non-learning-based orientation estimation algorithms found in the literature for comparable applications. Test results support the advantage of the proposed CNN-based solution, particularly for motion profiles with high acceleration disturbance that are characteristic of head motion.
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- Award ID(s):
- 2218063
- PAR ID:
- 10515031
- Publisher / Repository:
- SAGE Journals
- Date Published:
- Journal Name:
- Measurement and Control
- Volume:
- 56
- Issue:
- 7-8
- ISSN:
- 0020-2940
- Page Range / eLocation ID:
- 1156 to 1167
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/00202940221149062
