FLASH MRI

While early applications were based on echo planar imaging, which found an important application in real-time functional MRI (rt-fMRI),^[16] recent progress is based on iterative reconstruction and FLASH MRI.^[17][18] The real-time imaging method proposed by Uecker and colleagues^[2] combines radial FLASH MRI,^[19] which offers rapid and continuous data acquisition, motion robustness, and tolerance to undersampling, with an iterative image reconstruction method based on the formulation of image reconstruction as a nonlinear inverse problem.^[20][21] By integrating the data from multiple receive coils (i.e. parallel MRI) and exploiting the redundancy in the time series of images with the use of regularization and filtering, this approach enhances the possible degree of data undersampling by one order of magnitude, so that high-quality images may be obtained out of as little as 5 to 10% of the data required for a normal image reconstruction.

Because of the very short echo times (e.g., 1 to 2 milliseconds), the method does not suffer from off-resonance effects, so that the images neither exhibit susceptibility artifacts nor rely on fat suppression. While spoiled FLASH sequences offer spin density or T1 contrast, versions with refocused or fully balanced gradients provide access to T2/T1 contrast. The choice of the gradient-echo time (e.g., in-phase vs opposed-phase conditions) further alters the representation of water and fat signals in the images and will allow for separate water/fat movies.

Balanced steady state free precession

Another GRE sequence commonly used in RT-MRI is balanced steady state free precession (bSSFP), as mentioned above with balanced gradients.^[10] Steady state free precession involves a repetition time (TR) that is shorter than T2. This prevents the magnetic signal from decaying completely before the next RF pulse is applied, which then establishes a steady state signal over time.^[22] The short TR also makes bSSFP ideal for RT-MRI.

The equation for peak MR signal in bSSFP is given as:

${\displaystyle M_{ss,xy}\vert _{\alpha =\alpha _{opt}}={\frac {1}{2}}M_{0}{\sqrt {\frac {1-E_{1}^{2}}{1-E_{2}^{2}}}}\approx {\frac {1}{2}}M_{0}{\sqrt {\frac {T_{2}}{T_{1}}}}}$

Where ${\displaystyle M_{0}}$ is the initial magnetization, ${\displaystyle E_{1}=e^{-T_{1}/T_{R}}}$ and ${\displaystyle E_{2}=e^{-T_{2}/T_{R}}}$.

Thus, the MR signal is proportional to T2/T1. Materials with similar T1 and T2, such as fluids and fat, present high T2/T1 contrast and can have signal intensity up to ${\displaystyle 0.5M_{0}}$.

The bSSFP signal is also greater than the FLASH signal by a factor of

${\displaystyle {\frac {1}{\sqrt {1-E_{2}^{2}}}}}$.^[22]

Due to this strong fluid/tissue contrast, RT-MRI with bSSFP lends itself to cardiac imaging and visualizing blood flow.^[22]

SENSitivity Encoding (SENSE)

Certain image reconstruction algorithms used alongside parallel imaging address the potential issues that can arise from undersampling the k-space. SENSitivity Encoding (SENSE) is a method that reconstructs the partial k-space data from each coil and combines the partial images into the final scan in the spatial domain.^[23] Coil sensitivities must first be acquired either before the actual imaging or during the imaging process. During the rest of imaging, the k-space is undersampled to skip every other line, resulting in a ½ FOV.

As a two-point example, pixels on the original aliased images can be “unfolded” through the following equations to give the final scan:

${\displaystyle P_{1}=A\cdot S_{1A}+B\cdot S_{1B}}$

${\displaystyle P_{2}=A\cdot S_{2A}+B\cdot S_{2B}}$

for two points, ${\displaystyle A}$ and ${\displaystyle B}$, in the final image. ${\displaystyle P_{1}}$ and ${\displaystyle P_{2}}$ denote the image signal for the aliased image. ${\displaystyle S_{1A}}$ and ${\displaystyle S_{1B}}$ are the sensitivity values for coil 1 at points ${\displaystyle A}$ and ${\displaystyle B}$, respectively, and ${\displaystyle S_{2A}}$and ${\displaystyle S_{2B}}$are the sensitivity values for coil 2 at points ${\displaystyle A}$ and ${\displaystyle B}$, respectively.^[23]

GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA)

Another reconstruction algorithm used is GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA). GRAPPA fills in the undersampled k-space data in the k-space domain before reconstructing the final image.^[24] Lines through the center of the k-space are fully sampled, typically alongside the actual image, to give the autocalibration signal (ACS) region. Weighing factors are calculated using the ACS, and these factors reflect the coil-specific distortions that each coil applies on the full field-of-view frequency domain. Then, the filled-in k-space data undergoes the inverse Fourier transform to construct the partial, non-aliased images. These images are then simply combined directly in the spatial domain.^[24]

If the k-space data is non-Cartesian, reconstruction is computationally more difficult, since the fast Fourier transform (FFT) requires Cartesian values. Typically, k-space data must be resampled into Cartesian coordinates before applying the FFT. GRAPPA can address these issues by obtaining large quantities of calibration data; however, the fastest reconstructions will generally require Cartesian data.^[10]