Discussion

Figure 7.8: Joint histograms between resulting maps of AURA (US), fast (US), slow (US) and the ground truth PK maps for the parameters , τ, α, tmax and Γmax.

7.4 Discussion

In summary, an ‘intelligent’ sequence is developed for DCE MRI, which automatically adapts the resolution to signal changes without user-interaction and is capable of recon-structing multiple resolutions within a single dynamic scan. For a perfusion phantom, robust adaption criteria based on the k-space center intensity are found. For the per-fusion phantom, the AURA sequence yields comparable fitting performance to a high temporal/low spatial resolution sequence, whilst the spatial resolution of the PK maps is higher. Additionally, the AURA sequence reliably provides high spatial resolution images near the CA peak concentration, which can be used for mophological analysis of CA distribution.

In all previous studies using adaptive sequences, the resolution changed at fixed time points. However, contrast kinetics are dependent on the tumor, the physiological con-ditions of the patient and the injection timing. Therefore, rigid switching times bear the potential of information loss, for example by missing the onset time or the CA peak, leading to fitting inaccuracies as shown in the simulations in chapter 5. The newly developed AURA prevents this loss by flexibly reacting to the acquired data in real-time.

Figure 7.9: Morphological images close thek-space center signal peak.

The employed perfusion phantom, which is described in detail in section 6, is built in-house and is not perfectly reproducible in terms of fitting results. This is especially true for the parametersτ and α, which are very sensitive to even slight curve changes.

Furthermore, for some pixels within the phantom gamma-variate fitting fails, mainly due to motion artifacts during contrast agent administration. When comparing the joint histograms of the AURA sequence with those from the phantom reproducibility investigation in chapter 6, it can be seen that the errors introduced by the AURA (US) and fast (US) sequence are in the order of the reproducibility errors of the phantom.

However, for the slow (US) sequence the effects of fitting errors due to under-sampling still prevail phantom inaccuracies. The large fitting errors of the slow (US) data arise from the low temporal resolution which causes a false estimate of the onset time. Con-sequently, systematic errors, mainly inτ and α are introduced.

For the employed phantom the fitting function describing the data best is a gamma-variate function. However, tissue signal of clinical DCE MRI data is usually described by other models than the gamma-variate function. Instead, multi-compartment models such as [Tofts1991] are used, which exhibit a slower wash-out. In this case, the wash-out could be exploited further to acquire high spatial resolution images. However, for this work neither clinical data nor a phantom describing multi-compartmental model curves were available. For the scope of this thesis however, which was to implement a prototype of the AURA sequence and to show that it outperforms equidistant schemes, the used

7.4 Discussion 119

phantom is sufficient.

A problem which is not solved in this thesis, but is required to be fixed before clinical applications, is the varying scaling factors of different resolutions. Even if the raw data are taken from the MR scanner and are off-line reconstructed, an internal data scaling by the scanner is already imposed. In this work, the problem is avoided by assuming the high spatial resolution scaling as ground truth and close time points to have similar intensity values. By that, partial volume effects due to blurring at low spatial resolu-tions are as well omitted. In this work, this approach yields a good approximation, since only one slice is acquired and time points are relatively close together for all schemes.

However, this is not possible anymore for multi-slice or 3D acquisitions with longer scan times per dynamic frame. Yet, in other adaptive imaging studies scanner scaling fac-tors are even more problematic since different resolutions are acquired using different sequences. By that, even more internal scaling operations are introduced compared to the ones of changing resolutions within a single sequence.

The chosen switch criteria work for the perfusion phantom and the k-space center signal provides a robust measure to adapt to global signal changes. For the onset de-tection criterion, a relatively high percentage of 5% is chosen since motion artifacts and overflow during CA injection cause irregularities in the signal time curve at baseline.

For wash-out the curve is smooth and a percentage of 1% is sufficient.

In general, the measure of the k-space center signal is relatively crude, containing no spatial information. For clinical applications this might not be sufficient and more refined adaption criteria would be required, such that for example enhancement outside the organ of interest can be taken into account. In that case it might be better to use bolus tracking methods such as for example Kalman filters [Kalman1960] applied to re-constructed, potentially post-processed, images.

For the comparison of the high spatial resolution images close to the peak, the under-sampling of data from the slow sequence is performed in such a way that the peak is missed. How close the high spatial resolution images are to the peak depends on the timing of the sampling relative the CA kinetics. It could as well be the case that the peak signal is perfectly acquired. However, this timing is not controlled and therefore unreliable. Using the AURA sequence, the timing is performed in a controlled way and images close to the peak are reliably provided.

A limitations of the AURA sequence is that the adaption reacts only to global signal changes. In reality, contrast curves of individual voxels within a region of interest can vary largely, especially the onset time. The AURA sequence is not optimized for each individual voxel, only for mean signal changes. This may cause missing time points during the onset and upslope and consequently lead to fitting errors as was shown in the simulations in chapter 5. It has to be investigated on in vivodata how large deviations from the mean signal time are typically.

Another disadvantage of the AURA sequence is that in case of automatic adaption failure, which can for example occur due to motion artifacts, information, for example from high spatial resolution images, is lost and cannot be recovered. This could be prevented by implementing the additional backup option to manually switch resolutions.

8 Retrospective Resolution Adaption for