Introduction fMRI: BOLD Acquisition & Practical Considerations
Daniel E. Gomez1,2,3
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3Department of Biomedical Engineering, Boston University, Boston, MA, United States


This course describes the acquisition of functional MRI (fMRI) data and the practical concerns and trade-offs involved when designing imaging protocols. Focus is given to blood oxygenation level dependent (BOLD) acquisitions with a gradient-echo (GRE) 2D echo-planar-imaging (EPI) sequence. Key sequence parameters and common artifacts are discussed.

Target Audience

Researchers interested in an elementary introduction to BOLD fMRI data acquisition, and willing to learn more about protocol parameters and their impact on image quality and possible artifacts. Understanding principles of MRI such as magnetic relaxation and spatial encoding of the MRI data would be beneficial but are not necessary.


The talk will focus on practical considerations for designing BOLD fMRI protocols using a 2D GRE EPI sequence (Stehling, Turner, and Mansfield 1991). The objective of the course is to explain key parameters of the 2D GRE EPI sequence and their influence on the spatial and temporal properties of the resulting BOLD fMRI data.


BOLD fMRI measures fluctuations in image contrast driven by changes in the magnetic susceptibility of blood (Ogawa et al. 1990). The most effective way to observe these changes is through the sequences providing contrast sensitive to magnetic susceptibility, and that are capable of measuring its changes at the rate that they happen. As of 2020, the most sensitive and time-efficient sequence available for BOLD fMRI is the GRE-EPI ( see Norris 2012 for reasons why gradient-echo is generally preferred). Although multiple variants of EPI exist, the most ubiquitous variant uses a 2D cartesian readout.

Key Acquisition Parameters

The key acquisition parameters of the GRE-EPI control the size of the volume being imaged (field-of-view (FOV), number of slices, slice positioning), impact the signal-to-noise ratio (SNR), temporal SNR and sensitivity to BOLD fluctuations (TE, flip angle, TR), the spatial resolution (voxel size, slice thickness) and the sampling rate of images (TR, echo train length, acceleration factor). Because spatial encoding is a time consuming process, most trade-offs in acquisition parameters revolve around minimizing the amount of spatial encoding necessary to answer the research question at hand, sampling as efficiently as possible yet preserving sufficient image quality.

Repetition Time
The repetition time (TR) is the time taken between the acquisition of the same imaging slice. It determines the number of samples that can be acquired in a given amount time and sets an upper limit to the frequencies that can be unambiguously observed in the time-series data. Reducing the TR is possible by decreasing the spatial resolution of the data or volume coverage, or by decreasing the echo train length (time spent encoding a single slice). Usually, shorter repetition times are claimed to improve statistical analysis (Constable and Spencer 2001; Feinberg et al. 2010).

FOV and slice positioning
The FOV determines the volume being imaged. For 2D-EPI, with all other parameters held constant, the number of slices sets an upper bound on the volume acquisition speed. There is a clear trade-off between the number of slices and the volume TR.

Echo Time
The BOLD contrast is dependent on the echo time (TE) of the acquisition (Ogawa et al. 1992; Fera et al. 2004; Triantafyllou, Wald, and Hoge 2011). The sensitivity to BOLD is assumed to be maximized when imaging with a TE that matches the relaxation time (T2*) of the tissue of interest (Posse et al. 1999; Poser et al. 2006). However, for GRE-EPI, long TEs lead to increased signal dropouts due to magnetic field inhomogeneities, and in some circumstances may require increased repetition times (TR).

Flip Angle
The flip angle is directly related to the SNR of each image. To maximize SNR, the flip angle is usually set to a specific value (the Ernst angle) that depends on the TR and the relaxation constant (T1) that determines the time it takes for the tissue to recover it’s equilibrium magnetization. Nonetheless, because physiological noise (BOLD fluctuations that are not necessarily related to neuronal activity) also scales with SNR, it is possible to consider acquiring images with lower flip angles (Gonzalez-Castillo et al. 2011).

Duration of acquisition
How much data should we acquire? The answer will depend on the kind of analysis that is planned. Laumann et al. 2015 note that measures of individual functional brain organization converge with the sufficient data, and suggest that for resting-state correlation analysis: "5-10 minutes of data, as commonly collected, may not capture a precise representation of stationary functional connectivity". For task-based fMRI, the duration of the fMRI run may depend on the experimental design and/or be limited by subject comfort.

Spatial Resolution
A higher spatial resolution may resolve finer structures and be useful for improving fMRI analysis (Menon 2002), but it increases spatial encoding time and may introduce severe image artifacts and reduce SNR. Lower spatial resolutions, on the other hand, may suffer from partial volume effects.

Acceleration Methods
The acquisition of EPI can be accelerated both in-plane by reducing the amount of spatial encoding steps, or in the slice direction by exciting multiple slices simultaneously. In-plane acceleration (Griswold et al. 2002; Pruessmann et al. 1999) may also reduce artefacts, yet it imposes an SNR penalty equal to the square root of the acceleration factor. Slice acceleration (Feinberg et al. 2010; Moeller et al. 2010; Setsompop et al. 2012; Barth et al. 2016) does not impose a direct SNR penalty, yet it leads to an increase in power deposition and possibly reduced tissue contrast as a consequence of shorter repetition times (Feinberg and Setsompop 2013; Barth et al. 2016).

Common Artifacts

The most common artefacts seen in 2D GRE EPI images are dropouts , distortions (Jezzard and Balaban 1995), and FOV/2 ghosts (Buonocore and Gao 1997). Their origins will be explained and approaches to mitigate them will be touched upon.


Each fMRI experiment is unique in its own way. Making sure we can get the best possible data is the first step towards meaningful inferences about the research question at hand. Understanding of the data acquisition, its contraints and trade-offs can be extremely helpful for crafting useful, efficient and artifact-free protocols.


No acknowledgement found.


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Proc. Intl. Soc. Mag. Reson. Med. 28 (2020)