Introduction

Signal-to-noise ratio (SNR) is a standard used to describe the performance of an MRI system. An MRI image is not created by pure MRI signals but from a combination of MRI signals and unavoidable background noise.

MRI image = signal + noise

Noise in an MRI image does not contribute useful information toward image formation and is produced by the static fluctuation of signal intensity, usually appearing as grains or irregular patterns. Noise in MRI is from two main sources:

1. Molecular movement - charged particles in the human body create electromagnetic noise

2. Electrical resistance - resistance from the receiver coils, data cables and the electronic components of the measurement system

Noise produced in the MRI image depends on:

1. The coil - the number of elements, type and size and of the coil e.g. 8-channel body coil, 4-channel flex coil

2. Bandwidth - differs in each pulse sequence

In MRI the SNR is mainly used for image evaluation and measurement of contrast enhancement. SNR is also used for quality assurance, pulse sequence comparison and radiofrequency (RF) coil comparison.

Several methods are available to measure SNR. The most common of these is done by measuring the signal of two separate regions within a single image and utilising a formula. Most MRI systems have a region of interest (ROI) option in their image processing section which measures signal intensities. In order to measure the SNR on an image, place the first ROI on tissues with the most homogenous area and the highest signal intensity. Then record the values of the signal intensity in that region. Subsequently, position the second ROI (in the largest possible size to include maximum noise), outside of the tissues in the noisy image background. Record the values of signal intensity in that region. Calculate the image SNR using this formula:

SNR = signal/noise

In the example below, a T1 axial brain image is used and the signal intensity of the white matter and the image background is taken. In order to get the SNR value of the image divide the signal intensity value of the white matter by the standard deviation value in the background.

Factors affecting signal-to-noise ratio (SNR)

Field strength and SNR

Field strength and SNR are directly proportional to each other. Increasing the field strength will increase the longitudinal magnetisation by aligning more protons to the axis of the main magnetic field. This results in an overall increase in the amount of signal produced which will improve SNR. MRI systems using higher field strengths produce higher SNR images in comparison to the low field strength systems. Along with higher SNR images, high field strength MRI systems will also be able to produce high spatial resolution images in a shorter amount of time. This is particularly advantageous in high resolution imaging or in performing fast scans on claustrophobic or moving patients. For example, a T1 tse sequence with matrix size of 320x320 and number of excitations (NEX) 2 with 100% SNR will take approximately 5 minutes in a 1.5T scanner. A similar amount of SNR can be achieved in a 3T system with NEX 1 in 2.5 minutes.

Radiofrequency coil and SNR

There are a wide range of RF transmitter and receiver coils available in most MRI systems. Correct selection of the appropriate radiofrequency coil is essential to achieving the optimum SNR. In order to achieve maximum SNR, the RF coils should be as close as possible to the anatomy being imaged. This the main reason that most MRI systems have dedicated coils for each body part. SNR also depends on the number of transmitter and receiver elements within the RF coils. The higher the number of transmitter and receiver elements, the better the SNR eg. a 32-channel (receiver element) body coil will produce better SNR compared to a 4-channel body coil.

MRI operators will most often notice a significant drop in SNR while scanning patients with a high BMI. The reason being the centre of the anatomy is too far from the receiver coils. The user must manipulate the scanning parameters to improve SNR while scanning patients with a high BMI.

Tissue Characteristics and SNR

SNR also depends on the magnetic characteristics of the tissue being imaged. Tissues with a higher number of protons will produce better signal intensities and higher SNR. For example, if a scan were performed using the same protocol in an infant and adult, the infant scan will see a higher SNR.

MRI operators may have noticed that adding fat saturation to sequences often results in a grainy, low SNR image. Here, nullifying the signals from the fat protons reduces the overall signal intensity from the anatomy being imaged. Any reduction in signal intensities reduces the overall SNR.

TR and SNR

Increasing the TR will increase the SNR as a high TR will allow the longitudinal magnetization to approach its maximum and produce high signal intensities. Increasing the TR beyond a certain limit will however reduce the T1 effect. If we take a T1 sequence with TR of 500 and SNR of 100%, increasing the TR to 1000 will increase the SNR but reduce the T1 effect and produce images with very poor tissue contrast. Adequate use of TR is very important in T1 weighted imaging to produce good SNR and tissue contrast.

TE and SNR

Increasing the TE will reduce the SNR. A long TE will cause the transverse magnetisation to decay to very low values and result in signal loss. Decreasing the TE beyond a certain limit will reduce the T2 effect. For example, a T2 sequence with TR of 5000, TE 110 and SNR of 100%, decreasing the TE to 50 will increase the SNR but reduce the T2 effect and produce images with very poor tissue contrast. Reduction of the TE to increase the SNR should only be used for T1 weighted sequences.

Flip angle and SNR

High transmitter bandwidth can be used effectively to reduce scan times in claustrophobic and moving patients. This option will significantly reduce the minimum TR and TE values allowing the user to reduce the TR and TE values manually, reducing scan time. The main disadvantage of this method is shortening the TEs and TRs usually results in more noise and increases the potential for peripheral stimulation. The diagrams below show how to choose these options and the results of the manipulations.

Slice thickness and SNR

Increasing the slice thickness will increase the SNR. This follows the principle that increasing the slice thickness increases the voxel size resulting in an increase in the amount of signal received by the individual voxels. Increasing the slice thickness will however reduce the spatial resolution and increase the partial volume effects. Taking a T1 sequence with slice thickness of 2 mm and relative SNR of 40%, increasing the slice thickness to 6 mm will increase the SNR to 90%.

Slice gap and SNR

Slice gap is the distance between two adjacent slices. Slice gap is usually calculated as a percentage of the slice thickness. They are necessary on order to avoid slice overlapping due to the imperfection of the RF pulse. Even choosing a rectangular slice profile does not produce perfect rectangular signals. By removing the slice gap the two adjacent slices will overlap at their edges. This will result in the RF pulse of one slice exciting a small portion of the adjacent slice. This phenomenon is also called a cross-talk. The cross-talk effect creates a saturation effect in the area of slice overlap and results in a significant reduction of SNR.

If a T2 single shot FSE (HASTE) sequence with a 4mm slice thickness and 50% (2mm) slice gap sees a reduction in the slice gap to 0%, this will result in a slice overlap and produce low SNR images. This is one of the main reasons why HASTE sequences with continuous slice acquisitions will have a high SNR in the first image followed by low SNR in subsequent images. This can be either reduced by increasing the slice gap or choosing the interleave option (which scans odd number slices together then even numbered slices together). It should be noted that increasing the slice gap beyond certain limits, usually above 50%, can cause misregistration.

Matrix size and SNR

Increasing the matrix size will reduce the SNR. Increasing the matrix reduces the voxel size, reducing the amount of signal received by the individual pixels. Smaller pixels will receive less signal and produce a low SNR image. As an example, after a T1 tse sequence with 100 FOV, 4mm slice thickness, 256x256 matrix size and a relative SNR of 100%, sees an increase in the matrix size from 256X256 to 320X320 the SNR will reduce to 74%. Conversely, decreasing the matrix size will reduce the spatial resolution and produce blurry images. Correct selection of matrix size for a particular FOV is necessary for the production of optimum quality images.





Field of view (FOV) and SNR

Increasing the FOV will increase the SNR. Increasing the FOV will increase the pixel size which will increase the amount of signals received by individual pixels. Large pixels will receive more signal and produce high SNR images. Increasing the FOV will however reduce the spatial resolution and produce blurry images. In order to achieve maximum spatial resolution, the user must increase the matrix size whenever the FOV is increased. If a T1 tse sequence with 100 FOV, 192x192 matrix and relative SNR of 100% has an increase in the FOV from 100mm to 210mm, this increases the SNR to 170%. This step produces a high SNR but low spatial resolution image. To overcome this, increasing the matrix size to 256x256 will improve the spatial resolution and give an SNR of 120%.





Receiver bandwidth and SNR

Increasing the receiver bandwidth reduces the SNR. Receiver bandwidth is the amount of frequencies or wavelengths collected during the reception phase (frequency encoding) of RF pulses. Increasing the bandwidth reduces the scan time, susceptibility artefacts and chemical shift artefacts. Decreasing the band width to half will increase the SNR by 30%. In this example, a T1 sequence with a 260 bandwidth, acquisition time of 2 minutes and relative SNR of 100% has a decrease in the bandwidth from 260 to 130. This increases the relative SNR to 130% but increases the scan time to 3 minutes. Care should be taken as decreasing the bandwidth beyond certain limits will see an increase in susceptibility and chemical shift artefacts.





Number of excitations (NEX) and SNR

Number of excitations (averages) are a measurement parameter that is used to represent the number of times data is repeatedly acquired to form the same image. Increasing the Number of excitations (averages) will increase the SNR by the square root of two (√2). Doubling the NEX will increase the SNR by 140% and double the scan time. If a T1 tse sequence with a scan time of 2 minutes, NEX 1 and SNR of 40% sees an increase in the NEX from 1 to 4, it will have its SNR increased to 100% but have a scan time of 8 minutes.

Phase oversampling and SNR

Phase oversampling is a technique used to eliminate phase wrap or wrap-around artefact. Phase oversampling is performed by increasing the sampling frequency in the phase encoding direction. Increasing the phase oversampling will increase the FOV and number of phase-encoding steps in the phase direction. Increasing the phase oversampling will also increase the SNR and acquisition time. If a T1 tse sequence with 150 FOV and relative SNR of 100% has its phase oversampling changed to 100%, the SNR will increase to 120%.

Partial K-space filling and SNR

Partial K- space filling techniques save significant amounts of scan time without making changes to any other scanning parameters. The two main types of partial k-space acquisition techniques are partial Fourier imaging and partial echo imaging. Both of these techniques are used for fast imaging to produce similar resolution images but with compromised SNR.

In partial Fourier imaging only half or just more than half of the K- space lines in the phase encoding direction are acquired to create the entire image. This will reduce the scan time but produce low SNR images.

In partial echo imaging only part of the echoes in the frequency encoding direction are sampled to create the entire image. This method will again reduce the scan time but produce low SNR scans.

Parallel imaging techniques and SNR

Using parallel imaging techniques will degrade the SNR of the reconstructed images. The noise produced in the reconstructed images are due to decreased data sampling and noise amplification caused by the parallel reconstruction algorithm. Increasing the acceleration factor in parallel imaging will reduce the SNR. As a final example, if a T1 tse sequence with a parallel imaging acceleration factor of 2 has an increase in the acceleration factor to 4, there will be a reduction in the original SNR by 40%.