Spectral processing

The signal detected by an MRI scanner is actually a series of electric currents induced in the receiving coil, all of which oscillate and decay at different rates[21]. The raw electrical data collected by the MRI scanner is known as ‘free induction decay’ and is illustrated in Figure 1.3.4 below[21]. There are a number of different software packages and methods available to process the raw FID into a spectrum, and then derive clinically useful data from the spectrum itself[103, 104, 112]. Some processes are applied universally, such as Fourier transformation which separates the FID into a spectrum of frequencies like that seen in Figure 1.3.3). Others will depend on the preferences of the operator and the software they have access to. In general, besides Fourier transformation, the signal will be phase-

corrected (either manually or automatically, depending on the software) and a filter (also known as apodization) will be applied to improve the quality of the spectra, and various techniques are applied to accurately determine the frequency of each peak[112]. Some of these techniques are explored in more detail below.

MR spectroscopy frequency can be measured in either hertz (Hz) or parts-per-million (ppm). The two units are interchangeable, although ppm has the benefit of not being dependent on magnetic field strength[112]. If the operator is measuring frequency in hertz they must remember to adjust any calculations for the magnetic field strength of the scanner.

1.3.6.2 Filters and Zero-filling

A filter is applied to the signal before Fourier transformation, and is a well established method to improve the quality of the final spectrum. The two most commonly used filters are Lorentzian and Gaussian filters. The mathematical processes used for each are slightly different, but both work to reduce the amount of ‘noise’ that appears in the final spectrum, and increase the contribution of metabolite signals. Lorentzian filters produce better SNR than Gaussian filters, and are often favoured for this reason[104, 112]. However, Gaussian filters produce better spectral resolution, particularly of overlapping peaks [21], and may therefore be better suited to MR thermography, where extremely accurate frequency identification is required[104].

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Zero-filling is likewise applied almost universally[112]. This technique involves adding a number of zero-points to the end of the FID signal after the point at which the signal itself has decayed to zero. This simulates continuing to collect the signal over a longer time, but does so without collecting the noise that would be detected if the receiver were to actually continue collecting data over this time[112] (See Figure 1.3.4). Zero-filling makes little difference to the signal itself, as it does not add any new data, but it does improve the digital resolution of the spectrum. The digital resolution will determine how precisely the operator can determine the frequency of each peak, and hence determine the precision of any temperature measurements made from the spectrum [21, 111]. To minimise the time required by the computer to perform Fourier transformation, the total number of data- points collected should be a power of 2[112]. Therefore, the number of zeros added to a signal during zero-filling should ideally make the final number of data-points a power of 2[104].

Figure 1.3.4 Effects of zero-filling on the FID (Left) and the resulting spectrum after Fourier Transformation (Right). From [112], reproduced with permission.

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1.3.6.3 Line-fitting software

As mentioned previously, it can be difficult to attribute one specific frequency to a peak that appears to span a range of frequencies. To overcome this, many researchers use software which attempts to fit a line-of-best fit (typically Lorentzian-shaped[105]) to each peak, and use this line to identify the centre frequency of the peak. This same line-fitting technique is sometimes used to remove the water peak from the spectrum mathematically [110] rather than (or as well as) suppressing the water signal during the scanning

process[104]. Each MRI scanner will have its own software for performing line-fitting, and there are also several off-line software packages available. Some researchers develop proprietary software for the purposes of MR thermography [111, 113]. Thus, researchers analysing clinical MR spectra have a number of options with regard to software, a relevant selection of which will be discussed below.

Utilising the standard software supplied with the MRI scanner provides a number of benefits. This would simplify the processing as spectral data would not need to be

transferred to another computer for analysis. Any methods using scanner software will be easily generalizable to other hospitals using the same type of MRI scanner and do not add to the cost of the experiment (in contrast to purchasing third party software).

Unfortunately, not all scanner packages are suitable for this purpose. The MRI scanners used for the purposes of this thesis were both Siemens Scanners using the Siemens Syngo software. While the line-fitting algorithms included with this software are excellent, they only report the resonance frequency of individual peaks to a precision of 0.01ppm or 1Hz which is not sufficient for temperature estimation (0.01ppm being equivalent to

approximately 1°C [100]). This may be why researchers conducting MR thermography do not typically use standard scanner software despite the logistical benefits of doing so. There are a number of third-party software packages that can be used to process MR spectra. Some, such as LCModel, provide excellent processing algorithms but are prohibitively expensive. One package, the java Magnetic User Interface (jMRUI) is also well-regarded by researches and has the advantage of being free for academic use [114]. Unfortunately early experiments using jMRUI on spectra collected for this series of experiments were unsuccessful and this software package was abandoned. It was later determined that the line-fitting algorithm in jMRUI assumes that a separate unsuppressed

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water reference has been collected, and does not produce accurate results when this is not the case. To collect such a reference on the Syngo software being used at the time would have required conducting a separate scan of each voxel after conducting the initial scan. It was decided during the initial design of the study that the extra time and complexity of this deviation from the standard scanning protocol would discourage the clinical application of the protocol in an acute stroke setting. However, the lack of an unsuppressed water reference is a key weakness of the thermography paradigm tested in this thesis. At the time of submission, a new version of Siemens Syngo is in use which does collect a separate unsuppressed water reference as part of the standard single-voxel MRS protocol, which could have a positive impact on the accuracy of MR thermography based on the standard MRS protocol.

Slight differences in the line-fitting algorithms used by different software packages can affect the calibration curve of PRF against temperature, in particular the intercept of the PRF/temperature graph [105]. It is worthwhile, therefore, for each researcher to calibrate their own technique, including their method of data processing, rather than relying on calibration equations published by other researchers, unless the previous scanning and post-processing paradigm is being replicated exactly.

There is another pitfall with regards to line-fitting software. Line-fitting algorithms typically rely on ‘prior knowledge’ regarding the relative locations of different peaks. By relying on this prior knowledge, the software may fit the curve where its files state the peak should be, rather than determining where it actually is, especially if the peak is shifting as a result of temperature changes.

1.3.6.4 Temperature-insensitive reference

There are several metabolites with temperature-insensitive PRFs found in the brain. Many of these metabolites produce signals which decay quite quickly, so a short echo time (TE) is needed to detect the signals[104] (See Appendix 1 for an explanation of echo time). However, longer TEs tend to produce cleaner baselines and therefore spectra that are simpler to analyse with regard to certain metabolites, at the expense of not detecting other metabolites at all [21, 104]. Complex baselines are particularly prevalent in spectra

acquired by MRSI, but can be present in spectra acquired using SVS. Most MR

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therefore longer TEs have been favoured in order to simplify the spectral baseline for this single reference peak [110, 111, 115].

The three resonance peaks that are easiest to identify in the brain at long TE (or short TE, for that matter), are the CH3 resonance of Choline, the CH3 resonance of Creatine and the Acetyl moiety of N-Acetylaspartate (NAA) at approximately 3.2, 3.0 and 2.0ppm,

respectively[103, 104]. Each of these metabolites also produce other, smaller resonances (in particular the CH2 group on creatine, labelled ‘Cr2’ by Siemens Syngo software, is frequently discernible even at longer echo times). For the purposes of this thesis, all references to choline or ‘Cho’ creatine or ‘Cr’ and NAA refer to the prominent resonances described above, unless otherwise indicated. NAA is the most widely used as a

temperature reference[116], in part because it is present in higher concentrations in the brain than the other metabolites, and is well separated from other resonances[104]. Choline and creatine are quite close to each other and the resonance peaks may overlap if the voxel has been poorly shimmed, making them less widely used [105] although careful post-processing can mitigate this problem to a certain extent (see section 1.3.6.2). Injuries such as ischaemic stroke may alter the concentrations of certain metabolites making the choline and creatine peaks difficult to identify. The few studies that have compared temperature measurements made using different reference peaks have typically found no significant difference between them, and it has been demonstrated that more accurate results can be obtained by averaging the temperature measurements from more than one reference chemical [117]. This is a very convenient technique for increasing the accuracy of the temperature measurements made using MRS, although it remains to be fully validated. In ischaemic brain tissue, it may not be possible to identify all three reference peaks

readily. Furthermore, one study did find significantly different results by using two

chemical references (choline and NAA) in stroke-affected dogs[118], even though they had previously found that this was not the case in pigs, before, or after death[105].

Some validation studies have used fat as a reference chemical. This is relevant to MR thermography of fatty tissue such as the human breast, but is of little use in the brain [99].

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