Functional magnetic resonance imaging reference phantom

Renvall, Ville

doi:10.1016/j.mri.2008.11.007

Cited by 9 publications

(14 citation statements)

References 11 publications

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“…We studied fMRI signals in a well-controlled setting, devoid of confounding factors such as movement, using two fMRI phantoms modified from our previous design (10). The experimental setup is explained in Methods and illustrated in Fig.…”

Section: Resultsmentioning

confidence: 99%

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Transients may occur in functional magnetic resonance imaging without physiological basis

Renvall¹,

Hari²

2009

Proc. Natl. Acad. Sci. U.S.A.

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Functional magnetic resonance imaging (fMRI) has revolutionized the study of human brain activity, in both basic and clinical research. The commonly used blood oxygen level dependent (BOLD) signal in fMRI derives from changes in oxygen saturation of cerebral blood flow as a result of brain activity. Beyond the traditional spatial mapping of stimulus-activation correspondences, the detailed waveforms of BOLD responses are of high interest. Especially intriguing are the transient overshoots and undershoots, often, although inconclusively, attributed to the interplay between changes in cerebral blood flow and volume after neuronal activation. While physically simulating the BOLD response in fMRI phantoms, we encountered prominent transient deflections, although the magnetic field inside the phantom varied in a square-wave manner. Detailed analysis and modeling indicated that the transients arise from activation-related partial misalignment of the imaging slices and depend heavily on measurement parameters, such as the time between successive excitations. The results suggest that some transients encountered in normal fMRI recordings may be spurious, potentially compromising the physiological interpretation of BOLD signal overshoots and undershoots.fMRI phantom ͉ overshoot ͉ undershoot ͉ BOLD ͉ fMRI S ince the advent of the blood oxygen level dependent (BOLD) imaging method (1-4), functional magnetic resonance imaging (fMRI) of human brain function has spread so rapidly that currently as many as eight new fMRI papers appear each day. The proper analysis of fMRI data (5) and the relationship between the fMRI signal and the underlying brain activity (6) continues to receive much attention, whereas the basic physics of the signal acquisition is considered to be sufficiently understood. Accordingly, computer simulations are used to successfully model the effects of different imaging parameters on the measured signals (7-9).Such computational methods can, however, oversimplify factors affecting the signal acquisition and the MR signal. To scrutinize the behavior of the BOLD response in more detail, we recently introduced physical ''fMRI phantoms'' (10, 11) in which the BOLD signals can be physically simulated by accurately controlling current flow within a conducting medium.The MRI signals of the brain arise from protons that are excited with radio frequency pulses applied at the protons' Larmor frequency in a static magnetic field (B 0 ). With brief gradient fields, a slice of protons (e.g., perpendicular to the magnet's main axis z) can be selected. In a typical fMRI experiment, several (up to 50) slices are repeatedly excited while a subject performs a task or perceives stimuli. Soon after an excitation, the protons start to precess at slightly different frequencies because of local magnetic field inhomogeneities that arise from tissue properties, especially from a blood oxygenation level that varies according to neuronal activity. The field inhomogeneities result in dephasing, or transverse relaxation, of the original...

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Section: Resultsmentioning

confidence: 99%

“…To scrutinize the behavior of the BOLD response in more detail, we recently introduced physical ''fMRI phantoms'' (10,11) in which the BOLD signals can be physically simulated by accurately controlling current flow within a conducting medium.…”

mentioning

confidence: 99%

Transients may occur in functional magnetic resonance imaging without physiological basis

Renvall¹,

Hari²

2009

Proc. Natl. Acad. Sci. U.S.A.

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“…In 1999, Simmons et al applied the Shewhart charting approach with QA phantom to check SNR, signal-to-ghost ratio, and signal drift for MRI system (28). Over time, several groups have developed fMRI reference phantoms to simulate BOLD signals and measure several metrics such as SNR and contrastto-noise ratio (CNR) (4)(5)(6)(7)(8)(9). Leading study projects in neuroscience communities such as Human Connectome Project (HCP) (11) and Function Biomedical Informatics Research Network (FBIRN) (42) have developed complete QA processes including QA phantoms, QA protocols, and software to calculate QA metrics.…”

Section: Other Factorsmentioning

confidence: 99%

“…To track the brain cognitive process, MR scanners need excellent temporal stability to acquire the BOLD signal over time (28). Additionally, fMRI usually applies fast imaging sequences such as echo planar imaging (EPI), which require excellent time resolutions and temporal stability during fMRI (4)(5)(6)(7)(8)(9). EPI is sensitive to susceptibility effects (7).…”

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confidence: 99%

Quality assurance of human functional magnetic resonance imaging: a literature review

Dong

Cui

et al. 2019

Quant. Imaging Med. Surg

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Background Functional magnetic resonance imaging (fMRI) is one of the most popular methods to probe and understand the human brain, offering a noninvasive way for the in vivo investigation of brain function (1). fMRI accounts for the growth of neuroscience research with some 40,000 peerreviewed publications in the last two decades (2,3). The results of fMRI studies are largely determined by fMRI systems and informatics tools which process complex data generated from fMRI scan. fMRI quality assurance (QA) plays a critical role to guarantee high reliability of fMRI studies (4-11), since fMRI QA programs and methods can be used for calibrating fMRI scanner (10-13), testing the stability of fMRI system (14-17), assessing fMRI data quality (15,16,18-22) and evaluating informatics tools (4-9). Moreover, many QA-related metrics such as test-retest reliability and family-wise error rate (FWER) are applied to evaluate the reliability of fMRI studies (3,23,24). Many QA programs can test basic MRI system performances, such as resolution, signal contrast, geometric distortion, intensity uniformity, and ghosting artifacts (25-27). These programs are helpful but inadequate for fMRI study. The reasons are as follows: Scanner temporal stability The blood oxygenation level dependent (BOLD) signal

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“…While recent CT-based studies on COPD [ 13 ] or CF [ 1 ] applied commercial reference phantoms, appropriate phantoms for lung MRI are not yet commercially available. Reference phantoms designed for the quality assurance of MRI studies have, until now, mainly been developed with focus on cerebral imaging, along with software which provides fully automated image processing [ 15 – 19 ], especially for functional MRI (fMRI) [ 20 ]. However, none have been designed with the challenges of proton lung MRI studies in mind, such as low proton density and local magnetic field inhomogeneities, due to the rarity of lung imaging in MRI.…”

Section: Introductionmentioning

confidence: 99%

Design and application of an MR reference phantom for multicentre lung imaging trials

et al. 2018

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IntroductionAs there is an increasing number of multicentre lung imaging studies with MRI in patients, dedicated reference phantoms are required to allow for the assessment and comparison of image quality in multi-vendor and multi-centre environments. However, appropriate phantoms for this purpose are so far not available commercially. It was therefore the purpose of this project to design and apply a cost-effective and simple to use reference phantom which addresses the specific requirements for imaging the lungs with MRI.MethodsThe phantom was designed to simulate 4 compartments (lung, blood, muscle and fat) which reflect the specific conditions in proton-MRI of the chest. Multiple phantom instances were produced and measured at 15 sites using a contemporary proton-MRI protocol designed for an in vivo COPD study at intervals over the course of the study. Measures of signal- and contrast-to-noise ratio, as well as structure and edge depiction were extracted from conventionally acquired images using software written for this purpose.ResultsFor the signal to noise ratio, low intra-scanner variability was found with 4.5% in the lung compartment, 4.0% for blood, 3.3% for muscle and 3.7% for fat. The inter-scanner variability was substantially higher, with 41%, 32%, 27% and 32% for the same order of compartments. In addition, measures of structure and edge depiction were found to both vary significantly among several scanner types and among scanners of the same model which were equipped with different gradient systems.ConclusionThe described reference phantom reproducibly quantified image quality aspects and detected substantial inter-scanner variability in a typical pulmonary multicentre proton MRI study, while variability was greater in lung tissue compared to other tissue types. Accordingly, appropriate reference phantoms can help to detect bias in multicentre in vivo study results and could also be used to harmonize equipment or data.

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Functional magnetic resonance imaging reference phantom

Cited by 9 publications

References 11 publications

Transients may occur in functional magnetic resonance imaging without physiological basis

Transients may occur in functional magnetic resonance imaging without physiological basis

Quality assurance of human functional magnetic resonance imaging: a literature review

Design and application of an MR reference phantom for multicentre lung imaging trials

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