Real-time MRI

Real-time magnetic resonance imaging (RT-MRI) refers to the continuous monitoring ("filming") of moving objects in real time. Because

History

1977/1978 - Raymond Damadian built the first MRI scanner and achieved the first MRI scan of a healthy human body (1977) with the intent of diagnosing cancer.^[3] Additionally, Peter Mansfield develops the echo-planar technique, producing images in seconds and becoming the basis for fast MRIs.^[4]

1983 - Introduction of the k-space by D B Twieg^[5]

1987 - First real-time MRI of the heart is developed^[6]

1997 - Parallel imaging with an RF coil array is introduced by D K Sodickson^[7]

1999 - SENSE image reconstruction is introduced by K P Pruessmann^[8]

2002 - GRAPPA image reconstruction is introduced by Mark Griswold^[9]

Physical basis

Overview

In general, real time MRI relies on

k-space sampling

Efficient k-space sampling also decreases data collection time. Rectilinear scanning has become the standard k-space sampling method for MRI.^[13] However, the process takes a relatively long time as it samples the entire k-space equally. Because of this delay, other sampling methods are used to capture real-time motion. Single shot echo planar imaging is one extremely fast sampling method in which all of the data for the MR image is collected from one RF pulse.^[14] However, it is important to note that the EPI method is still a Cartesian sampling method, like the rectilinear scan, equally sampling the entire k-space. Spiral sampling, like EPI, only requires a single RF pulse to sample the entire k-space. Radial and spiral sampling are also used as methods to efficiently sample the k-space, with spiral also only requiring a single RF pulse to sample the k-space. Both radial and spiral sampling are more efficient than the Cartesian methods because they oversample low frequencies, which allows for general motion capture and better real-time image reconstruction.^[10] Thus, radial or spiral sampling of the k-space are now the preferred methods for real-time MRI reconstruction.

Parallel imaging

Parallel imaging involves the addition of multiple coils surrounding the target with each coil acquiring a fraction of the total image. Because modern GPUs have parallel processing capabilities, they can reconstruct each portion of the image simultaneously. Therefore, the more coils used, the faster the acquisition of the MR images.^[15]

Gradient-echo sequences

FLASH MRI

While early applications were based on echo planar imaging, which found an important application in real-time

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]

Image reconstruction

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.

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]

Signal-to-noise ratio

Lastly, within parallel image reconstruction there is another factor to consider, which is the signal to noise ratio (SNR). The SNR for parallel imaging can be calculated using the following equation:

${\displaystyle SNR_{parallel}={\frac {SNR_{MRI}}{g{\sqrt {R}}}}}$^[25]

Where ${\displaystyle R}$ is the acceleration factor and ${\displaystyle g}$ is the spatially dependent geometry factor (proportional to the number of coils used or the interactions between coils). Therefore, the more coils used, the faster the imaging process and the more inter-coil interactions; hence, the lower the SNR.^[25]

Applications

Cardiac MRI

Although applications of real-time MRI cover a broad spectrum ranging from non-medical studies of

Parallel imaging coils are available for torso and cardiac imaging, but they are not yet standardized for other body parts. Dynamic coil setups for speech and musculoskeletal imaging are key areas for current research.[10]

Machine learning

Image reconstruction in RT-MRI benefits from machine learning (ML) or deep learning (DL). A nonlinear kernel, or mapping function, can be developed from the ACS to fill in k-space data and generate the final image.^[35] This process as a whole significantly accelerates the MRI process. Image segmentation or identification of lesions can be achieved through machine learning. In deep learning, with a convolutional neural network, the mapping function can be specified by the network. ML and DL improve image resolution as well as imaging speed.^[35]

High-performance, low-field scanners

High-performance, low-field MRI scanners are also an area of development.^[10] These scanners operate at relatively low magnetic field strengths, such as 0.35 T or 0.55 T. Many RT-MRI acquisition sequences, such as bSSFP, experience significant off-resonance effects. Off-resonance effects increase linearly with B0 field strength, so minimizing B0 also minimizes these effects that can lead to artifacts and image distortion.^[36] This allows for longer TRs, which then opens the door for a wider range of k-space sampling methods and sequence designs.^[10] Finally, lower strength MRI scanners will reduce the dangers associated with heating of metallic implants and decrease the cost of MRI.^[37]

References