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Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging.

ABSTRACT: Functional magnetic resonance imaging (fMRI) has become an indispensable tool for investigating the human brain. However, the inherently poor signal-to-noise-ratio (SNR) of the fMRI measurement represents a major barrier to expanding its spatiotemporal scale as well as its utility and ultimate impact. Here we introduce a denoising technique that selectively suppresses the thermal noise contribution to the fMRI experiment. Using 7-Tesla, high-resolution human brain data, we demonstrate improvements in key metrics of functional mapping (temporal-SNR, the detection and reproducibility of stimulus-induced signal changes, and accuracy of functional maps) while leaving the amplitude of the stimulus-induced signal changes, spatial precision, and functional point-spread-function unaltered. We demonstrate that the method enables the acquisition of ultrahigh resolution (0.5 mm isotropic) functional maps but is also equally beneficial for a large variety of fMRI applications, including supra-millimeter resolution 3- and 7-Tesla data obtained over different cortical regions with different stimulation/task paradigms and acquisition strategies.

SUBMITTER: Vizioli L

PROVIDER: S-EPMC8405721 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging.

Vizioli Luca L Moeller Steen S Dowdle Logan L Akçakaya Mehmet M De Martino Federico F Yacoub Essa E Uğurbil Kamil K

Nature communications 20210830 1

Functional magnetic resonance imaging (fMRI) has become an indispensable tool for investigating the human brain. However, the inherently poor signal-to-noise-ratio (SNR) of the fMRI measurement represents a major barrier to expanding its spatiotemporal scale as well as its utility and ultimate impact. Here we introduce a denoising technique that selectively suppresses the thermal noise contribution to the fMRI experiment. Using 7-Tesla, high-resolution human brain data, we demonstrate improvemen ...[more]

PMID: 34462435

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Project description:Intraoperative magnetic resonance imaging (iMRI) is a tool now commonly used in neurosurgery. Safe and reliable patient care in this (or any other) operating room setting depends on an environment, where electrical noise (EN) does not interfere with the operation of the electronic monitoring or imaging equipment. In this investigation, we evaluated the EN generated by the iMRI system and the anesthesia patient monitor used at this institution that impacts the performance of these two devices.We measured the EN generated by our iMRI-compatible anesthesia patient monitor as detected by the EN analysis algorithm in our iMRI system. We measured the EN generated by our iMRI system during scanning as detected in the electrocardiogram (ECG) waveform of our patient monitor. We analyzed the effects on EN reduction and signal quality of the ECG noise filters provided in our iMRI-compatible anesthesia patient monitor.Our patient monitor generated EN that was detectable by the iMRI EN analysis algorithm; however, this interference was within the iMRI manufacturer's acceptable limits for an iMRI scan (<10% more than background system-level noise). In the clinical case analyzed, the iMRI generated a narrow-band low-frequency (20 Hz) relatively high-energy EN that interfered with the ECG signal of our patient monitor during an iMRI scan. This EN was correlated with the acoustic noise from the iMRI system during the scan and was associated with the radio frequency (RF) and magnetic gradient pulsations of the iMRI system. The integrity of the ECG waveform was nearly entirely lost during a scan. The filters of the ECG monitor diminished but did not entirely eliminate this 20 Hz interference. We found that the filters alter the morphology of the ECG signal, which may make it difficult to identify clinically relevant ECG changes.The EN generated by our anesthesia patient monitor is within acceptable limits for the iMRI system. The iMRI generates EN which renders the ECG unreadable in the most commonly used filter mode. The monitor's filters diminish this noise but also alter the morphology of the ECG waveform. The anesthesiologist must be cognizant of these technical compromises and recognize that adjusting the ECG filters on the monitor is required to obtain a useful ECG signal for patient monitoring during the iMRI scan but that the diagnostic value of the ECG will be reduced.

Project description:Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify the blood-brain barrier (BBB) permeability-surface area product. Serial measurements can indicate changes in BBB health, of interest to the study of normal physiology, neurological disease, and the effect of therapeutics. We performed a scan-rescan study to inform both sample size calculation for future studies and an appropriate reference change value for patient care. The final dataset included 28 healthy individuals (mean age 53.0 years, 82% female) scanned twice with mean interval 9.9 weeks. DCE-MRI was performed at 3T using a 3D gradient echo sequence with whole brain coverage, T1 mapping using variable flip angles, and a 16-min dynamic sequence with a 3.2-s time resolution. Segmentation of white and grey matter (WM/GM) was performed using a 3D magnetization-prepared gradient echo image. The influx constant Ki was calculated using the Patlak method. The primary outcome was the within-subject coefficient of variation (CV) of Ki in both WM and GM. Ki values followed biological expectations in relation to known GM/WM differences in cerebral blood volume (CBV) and consequently vascular surface area. Subject-derived arterial input functions showed marked within-subject variability which were significantly reduced by using a venous input function (CV of area under the curve 46 vs. 12%, p < 0.001). Use of the venous input function significantly improved the CV of Ki in both WM (30 vs. 59%, p < 0.001) and GM (21 vs. 53%, p < 0.001). Further improvement was obtained using motion correction, scaling the venous input function by the artery, and using the median rather than the mean of individual voxel data. The final method gave CV of 27% and 17% in WM and GM, respectively. No further improvement was obtained by replacing the subject-derived input function by one standard population input function. CV of Ki was shown to be highly sensitive to dynamic sequence duration, with shorter measurement periods giving marked deterioration especially in WM. In conclusion, measurement variability of 3D brain DCE-MRI is sensitive to analysis method and a large precision improvement is obtained using a venous input function.

Dataset Information

Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging.

Publications

Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging.

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