SLIMM: Slice localization integrated MRI monitoring

Abstract

Functional MRI (fMRI) is extremely challenging to perform in subjects who move because subject motion disrupts blood oxygenation level dependent (BOLD) signal measurement. It has become common to use retrospective framewise motion detection and censoring in fMRI studies to eliminate artifacts arising from motion. Data censoring results in significant loss of data and statistical power unless the data acquisition is extended to acquire more data not corrupted by motion. Acquiring more data than is necessary leads to longer than necessary scan duration, which is more expensive and may lead to additional subject non-compliance. Therefore, it is well established that real-time prospective motion monitoring is crucial to ensure data quality and reduce imaging costs. In addition, real-time monitoring of motion allows for feedback to the operator and the subject during the acquisition, to enable intervention to reduce the subject motion. The most widely used form of motion monitoring for fMRI is based on volume-to-volume registration (VVR), which quantifies motion as the misalignment between subsequent volumes. However, motion is not constrained to occur only at the boundaries of volume acquisition, but instead may occur at any time. Consequently, each slice of an fMRI acquisition may be displaced by motion, and assessment of whole volume to volume motion may be insensitive to both intra-volume and inter-volume motion that is revealed by displacement of the slices. We developed the first slice-by-slice self-navigated motion monitoring system for fMRI by developing a real-time slice-to-volume registration (SVR) algorithm. Our real-time SVR algorithm, which is the core of the system, uses a local image patch-based matching criterion along with a Levenberg-Marquardt optimizer, all accelerated via symmetric multi-processing, with interleaved and simultaneous multi-slice acquisition schemes. Extensive experimental results on real motion data demonstrated that our fast motion monitoring system, named S lice L ocalization I ntegrated M RI M onitoring (SLIMM), provides more accurate motion measurements than a VVR based approach. Therefore, SLIMM offers improved online motion monitoring which is particularly important in fMRI for challenging patient populations. Real-time motion monitoring is crucial for online data quality control and assurance, for enabling feedback to the subject and the operator to act to mitigate motion, and in adaptive acquisition strategies that aim to ensure enough data of sufficient quality is acquired without acquiring excess data.

MRI Super-Resolution Through Generative Degradation Learning

“…High spatial resolution in MRI allows for the precise delineation of the imaged tissues. However, the high spatial resolution requires a long scan time, and in turn unfortunately increases the potential of subject motion [1,22] and reduces the signal-to-noise ratio [2,16]. Super-resolution reconstruction (SRR) has recently emerged as a technique of post-acquisition processing, which allows for obtaining MRI images at a high spatial resolution, high SNR, and short scan time [7-9, 19, 26].…”

Working memory deficit in schizophrenia: a systematic review and meta-analysis of fMRI studies examining frontal and parietal brain activity

“…In these investigations, the acquisition, processing, and first-level modelling of fMRI data involved the initial acquisition of pictures using multiple scanners. The acquired images were then subjected to processing steps, including accurate slice-timing, motion alignment, normalization based on a standard template, and finally, smoothing [8,29,35,36]. A comparative analysis was conducted on the fMRI scan findings obtained from individuals diagnosed with schizophrenia and a control group of healthy individuals.…”

Matched neurofeedback during fMRI differentially activates reward‐related circuits in active and sham groups

“…Another limitation that could be improved is the prospective motion correction, which is expected to have a direct impact on the calculated feedback signal. We used volume‐to‐volume motion correction implemented as part of used software; however, slice‐to‐slice motion correction and monitoring might improve the consistency of the selected ROI voxels across the run and consequently the accuracy of the feedback signal (in terms of the region it is derived from) 32 . The final and perhaps the most important limitation of this study is the small sample size, with independent replication of the findings presented here being a requisite immediate next step.…”