4.3 Results
4.3.2 Spatio-temporal correction
In order to evaluate the effectiveness of spatio-temporal correction, which takes into ac- count both spatial and temporal variations of the signal, four different regions of white matter as defined in figure 4.1 were studied. The mean pixel values from b0-images in these regions were used to visualize the signal drift in uncorrected and corrected data. As before, the signal drift is defined as the change in the mean pixel value of the b0-images over time, compared to the initial image in the sequence i.e. the first b0-image in run 1 for each scans sequence. To compare the performance of spatio-temporal correction with the temporal correction, both results are displayed on the same graph. The data in this section were mainly collected from volunteers A and B which were acquired on different days. Additionally, the mean and standard deviation of the relative signal drift was cal- culated for the whole group in order to give a comprehensive overview of the temporal and spatio-temporal correction methods.
To evaluate the mean signal drift of a given region, the mean pixel values of consecutive b0-images were normalized to the first b0-image obtained in the first run (run 1) of each volunteer and scan sequence. The group average of the signal drift and its standard deviation for each scan sequence were then calculated using the mean signal drift obtained from the two runs (run 1 and run 2) of the four ROIs in the brain (A-L, A-R, P-L and P-R ) from all six volunteers, see table 4.1.
Table 4.1: The group average signal drift, ( ¯S), from both runs (run 1 and run 2) of the combined regions (A-L, A-R, P-L and P-R ) and their standard deviation for the six volunteers.
The signal drift is calculated as the ratio between the group average mean pixel value of the consecutive b0-images and the group average mean pixel value of the initial b0-image (run 1) for each scan sequence and.
S¯Uncorrected ± std S¯Temporal correction± std S¯Spatio-temporal correction ± std sIVIM 1.000 ± 0.007 1.001 ± 0.006 1.000 ± 0.010
IVIM-10b 0.989 ± 0.014 0.998 ± 0.010 1.007 ± 0.029 IVIM-FC 0.998 ± 0.007 1.002 ± 0.007 0.998 ± 0.009 IVIM-NC 1.000 ± 0.006 1.002 ± 0.006 0.999 ± 0.008
Table 4.1 shows the average signal drift observed in the different protocols for all six vol- unteers. The mean value of the signal in uncorrected data for scan sIVIM and IVIM-NC indicate minor observable signal drift present for the whole population, while a decrease in the mean signal is observed for IVIM-10b and IVIM-FC. The maximal mean value of the signal was observed for, IVIM-10b, at 1.1% for the group.
For the sIVIM protocol, see figure 4.10, the maximal signal drift in the uncorrected data is 2.78% in region A-R (run 2). The maximal drift in spatio-temporal corrected data is observed in region P-L (run 1) at 2.27%, where the maximal signal drift in uncorrected data was 1.97%. In comparison, the maximal signal drift within this region and run, was 1.27% for the temporal corrected data.
Figure 4.10: Uncorrected, temporal corrected and spatio-temporal corrected data from sIVIM, volunteer A, for the repeated scans run 1 (left) and run 2 (right). The data for each row is the mean pixel values from each of the four respective regions: A-L, A-R, P-L, and P-R. Note all uncorrected data are normalized to the first b0 from run 1.
Figure 4.11: Uncorrected, temporal corrected and spatio-temporal corrected data from IVIM- 10b, volunteer A, for the repeated scans run 1 (left) and run 2 (right). It should be noted that the temporal correction performs better at correcting for signal drift in this scan. The uncorrected signal was observed to decrease for both runs for all regions. The data for each row is the mean pixel value from each of the four distinct regions: A-L, A-R, P-L, and P-R. Note all uncorrected data are normalized to the first b0 from run 1.
For IVIM-10b in figure 4.11, a signal drift can be observed.IVIM-10b has a larger signal drift compared to the other scan protocols. The maximal signal drift in the uncorrected data was observed at 7.55% for A-R (run 1). The maximal signal drift observed in spatio- temporal corrected data was 5.96% in P-L (run 1), the signal drift in the uncorrected data in this scan and region was 3.96%. In comparison, the maximal signal drift measured in P-L (run 1) was 3.63% for temporal correction. It is worth noting that the regions of interest vary in shape, but are roughly equivalent in size.
Figure 4.12: Uncorrected, temporal corrected and spatio-temporal corrected data from IVIM- FC, volunteer A, for the repeated scans run 1 (left) and run 2 (right). The data for each row is the mean pixel value from the four respective regions: A-L, A-R, P-L, and P-R. Note all uncorrected data are normalized to the first b0 from run 1.
For IVIM-FC (figure 4.12 ), the maximal signal drift in both uncorrected and spatio- temporal corrected data was observed in A-R (run 1), 3.66% and 3.83%, respectively.
Note that the signal drift in the spatio-temporal corrected data is greater. On the other hand, the maximal signal drift observed in this region and run for temporally corrected data was 1.18%.
Figure 4.13: Uncorrected, temporal corrected and spatio-temporally corrected data from IVIM-NC, volunteer A, for the repeated scans run 1 (left) and run 2 (right). The data for each row is the mean pixel value from the four respective regions: A-L, A-R, P-L, and P-R. Note all uncorrected data are normalized to the first b0 from run 1.
Figure 4.13 shows that the uncorrected and corrected data points for region A-L nearly overlap. In contrast, the correction for region P-R (run 2) overestimates the signal drift, resulting in amplifying the drift rather than correct for it. The maximum signal drift in the uncorrected data was observed in region A-R (run 1) at 1.40%, while the maximum drift in the spatio-temporal corrected data within this region was 2.40%. However, for the temporal corrected data it was found to be 1.27%.
Figure 4.14: Uncorrected, temporal corrected and spatio-temporally corrected data from sIVIM, volunteer B, for the repeated scans run 1 (left) and run 2 (right). The data for each row is the mean pixel value from the four respective regions: A-L, A-R, P-L, and P-R. Note all uncorrected data are normalized to the first b0 from run 1.
For volunteer B in figure 4.14, the maximal signal drift was observed at 3.19% for region P-L (run 2). The signal drift in this region for the spatio-temporal corrected data was 2.60% and 1.44% for the temporal correction.
Figure 4.15: Uncorrected, temporal corrected and spatio-temporally corrected data from IVIM-10b, volunteer B, for the repeated scans run 1 (left) and run 2 (right). The data for each row is the mean pixel value from the four respective regions: A-L, A-R, P-L, and P-R. Note all uncorrected data are normalized to the first b0 from run 1.
The maximal signal drift observed for IVIM-10b, as shown in figure 4.15, was in region A- L (run 2) at 3.03% in the uncorrected data. By comparison, the maximal signal drift was 1.67% with spatio-temporal correction and 3.40% with temporal correction. It should be noted that spatio-temporal correction outperformed temporal correction in this instance.
Figure 4.16: Uncorrected, temporal corrected and spatio-temporally corrected data from IVIM-FC, volunteer B, for the repeated scans run 1 (left) and run 2 (right). The data for each row is the mean pixel value from the four respective regions: A-L, A-R, P-L, and P-R. Note all uncorrected data are normalized to the first b0 from run 1.
For IVIM-FC in figure 4.16, the maximum signal drift in uncorrected data was 1.65%
for region P-R (run 2). The maximum signal drift in spatio-temporal corrected data for this region and run was 0.44%, while the maximal signal drift in temporal correction was 0.86%. Note that the spatio-temporal correction yielded better results in this case.
Figure 4.17: Uncorrected, temporal corrected and spatio-temporally corrected data from IVIM-NC, volunteer B, for the repeated scans run 1 (left) and run 2 (right). The data for each row is the mean valuefrom the four respective regions: A-L, A-R, P-L, and P-R. Note all uncorrected data are normalized to the first b0 from run 1.
For IVIM-FC in figure 4.17, the maximum signal drift in uncorrected data was observed in region A-R for run 1, at 2.24%. In contrast, the maximum signal drift in spatio- temporal corrected data was 3.36%. On the other hand, temporal correction resulted in a maximum signal drift of 2.08%, which was the best performance among the three methods for this protocol.
4.3.3 Perfusion fraction
This section examines the various results for the estimation of the perfusion fraction for the white matter in region A-L, using all six volunteers and protocols.
In table 4.2, the values of the uncorrected and corrected perfusion fraction for sIVIM are shown for all volunteers. For both temporal and spatio temporal correction, the maximum difference between corrected and uncorrected was observed for volunteer D at 0.43% and 0.98%, respectively. Overall, the differences between the uncorrected and corrected perfusion fractions were less than 1% for all volunteers.
Table 4.2: The mean value of the absolute perfusion fraction from sIVIM was calculated for uncorrected and corrected data, using the mean of perfusion fraction values from runs 1 and 2. The corrected value was normalized to the uncorrected value and presented as a percentage.
This analysis was performed on all volunteers in region A-L
Volunteer f
Uncorrectedf
Temporal correction(%) f
Spatio-temporal correction(%) A 3.330 3.336
(100.18)3.316
(99.58)B 3.134 3.124
(99.68)3.107
(99,14)C 3.201 3.209
(100.25)3.222
(100,66)D 3.051 3.064
(100.43)3.081
(100.98)E 3.371 3.375
(100.12)3.371
(100.00)F 3.243 3.237
(99.81)3.256
(100,40)The values of the uncorrected and corrected perfusion fraction for IVIM-10b are shown for all volunteers in table 4.3. For temporal correction, the maximum difference between the corrected and uncorrected perfusion fraction was observed for volunteer A at 11.57%.
For spatio-temporal correction, the maximum difference was observed for volunteer C at 10.44%.
Table 4.3: The mean value of the absolute perfusion fraction from IVIM-10b was calculated for uncorrected and corrected data, using the mean of perfusion fraction values from runs 1 and 2. The corrected value was normalized to the uncorrected value and presented as a percentage.
This analysis was performed on all volunteers in region A-L.
Volunteer f
Uncorrectedf
Temporal correction(%) f
Spatio-temporal correction(%)
A 3.406 3.012
(88.43)3.240
(95.13)B 3.228 3.206
(99.32)3.127
(96.87)C 3.466 3.172
(91.52)3.104
(89.56)D 3.412 3.126
(91.62)3.125
(91.59)E 3.908 3.699
(94.65)4.20
(107.47)F 3.763 3.597
(95.59)3.723
(98.94)Table 4.4 presents the values of the uncorrected and corrected perfusion fraction for IVIM- FC/NC for all volunteers. In this protocol, the corrected perfusion fraction deviates more from the uncorrected value compared to sIVIM and IVIM-10b. For temporal correction, the maximum difference between the corrected and uncorrected perfusion fraction was observed for volunteer A at 112.82%. For spatio-temporal correction, the maximum difference was observed for volunteer D at 46.80%.
Table 4.4: The mean value of the absolute perfusion fraction from IVIM-FC/NC was calculated for uncorrected and corrected data, using the mean of perfusion fraction values from runs 1 and 2. The corrected value was normalized to the uncorrected value and presented as a percentage.
This analysis was performed on all volunteers in region A-L.
Volunteer f
Uncorrectedf
Temporal correction(%) f
Spatio-temporal correction(%) A 2.199 4.680
(212.82)3.205
(145.75)B 3.691 3.025
(81.96)2.458
(66.59)C 3.243 2.852
(77.30)2.875
(88.65)D 3.216 2.486
(77.30)1.711
(53.20)E 3.202 3.172
(99.06)3.026
(94.50)F 3.788 1.351
(35.67)2.395
(63.23)The repeatability of the perfusion fraction was evaluated by calculating the ratio be- tween the perfusion fractions obtained in two subsequent runs (run 1 and run 2) for all six volunteers. The results show that the two runs are not equally affected by signal drift, resulting in different perfusion fractions. On average for sIVIM, as shown in table 4.5, the perfusion fraction for both uncorrected and corrected data in run 1 is 8.8-9.93%
higher than run 2. In a similar manner, the perfusion fraction in run 1 for IVIM-10b (as shown in table 4.6) is found to be between 14.8-17.9% higher than that in run 2. The same trend follows for IVIM-FC/NC, as shown in table 4.7, the perfusion fraction from run 1 is found to be 0.57-11.40% higher than run 2.
Table 4.5: The mean value of the absolute perfusion fraction ratio (R = ffrun1
run2) and the mean difference (1−R) from sIVIM, for the uncorrected and corrected data respectively. This analysis¯ was performed on all volunteers in region A-L.
Volunteer RUncorrected (%) RTemporal correction (%) RSpatio-temporal correction (%)
A 113.00 110.90 112.20
B 114.50 114.70 114.10
C 111.10 109.70 111.20
D 107.50 106.30 107.10
E 99.00 97.90 99.60
F 114.50 113.80 114.10
Mean difference -9.93 -8.88 -9.72
Table 4.6: The mean value of the absolute perfusion fraction ratio (R = ffrun1
run2) and mean difference (1−R) from IVIM-10b, for the uncorrected and corrected data respectively. This¯ analysis was performed on all volunteers in region A-L.
Volunteer RUncorrected (%) RTemporal correction (%) RSpatio-temporal correction (%)
A 104.10 109.70 108.60
B 85.20 88.30 78.70
C 100.10 102.10 101.30
D 94.50 98.70 98.40
E 108.00 112.10 112.40
F 117.90 117.30 114.10
Mean difference - 17.90 -17.30 -14.80
Table 4.7: The mean value of the absolute perfusion fraction ratio (R = ffrun1
run2) and the mean difference (1−R) from IVIM-FC/NC, for the uncorrected and corrected data respectively. This¯ analysis was performed on all volunteers in region A-L.
Volunteer RUncorrected (%) RTemporal correction (%) RSpatio-temporal correction (%)
A 80.10 146.10 102.90
B 94.20 75.60 41.90
C 103.80 116.50 132.40
D 132.80 106.20 160.30
E 79.00 168.90 116.40
F 106.70 55.10 91.00
Mean difference -0.57 -11.40 -7.48
5
Discussion
5.1 Preprocessing
Diffusion MRI (dMRI) is a complex and sensitive imaging technique, and various factors can affect the accuracy of the results. Preprocessing helps correct confounding factors and improve the data quality, making it easier to analyze and interpret. Some com- mon preprocessing steps in dMRI include motion and distortion correction, denoising, and eddy current correction. These steps help to improve the signal-to-noise ratio and reduce artifacts in the data, which can improve the accuracy and reliability of the results.
Mapping out the appropriate preprocessing steps for intravoxel incoherent motion (IVIM) analysis can be difficult due to the lack of consensus on the optimal preprocessing pipeline for dMRI. The literature review of existing preprocessing tools for dMRI data revealed that some of these tools are designed for protocols with high b-values. FSL includes tools for the correction of motion artifacts, eddy current-induced distortions, outlier detection, susceptibility distortion, B1 bias field, spatial normalization and brain extraction. While all of these tools can be useful in dMRI data preprocessing, only the brain extraction tool (BET), susceptibility correction, B1 bias field correction and spatial normalization tools are directly applicable for IVIM. There are other tools that could be suited for IVIM, such as denosing methods, however, they require evaluation.
Some of the steps in the preprocessing pipeline for dMRI, such as, noise distribution bias, B1 bias field correction and B-Matrix incompatibility might not be relevant for IVIM imaging. Generally, B-matrix incompatibility is not an issue for IVIM. However, in the studies of signal drift, it was found that this preprocessing step can be useful in situations where incompatibility does occur. The reason for this is that the chronological order of the b0-images is necessary for studying the signal drift over time. If the b-values and diffusion gradients direction are rearranged in a different order than the one specified, they lose their chronological order and can no longer be used for this purpose.
Preprocessing is a crucial step in IVIM analysis that can greatly impact the quality of the resulting parameter estimations. The specific preprocessing steps needed may vary depending on the data and the circumstances. In general, however, some key prepro- cessing correction steps that are often essential for obtaining good parameter estimations for IVIM include denoising, motion, susceptibility, Gibbs ringing, eddy current, Nyquist ghost and signal drift correction. The presence of these disturbances can make it difficult to accurately estimate IVIM parameters, as the oscillations in the signal can interfere with
the measurements. This can lead to underestimation or overestimation of parameters, such as the perfusion fraction. Appropriate correction methods to reduce or eliminate these artifacts are recommended.
5.2 Signal drift correction
Temporal signal drift was observed for all studied protocols: sIVIM, IVIM-10b, IVIM-FC and IVIM-NC. A signal decrease over time was the most common trend seen in the data that were acquired for all subjects. However, an increasing signal trend was also observed but with smaller magnitude. The repeated scans did not show the same trend in signal drift and varied in magnitude.
The maximum magnitude of signal drift for all volunteers except one was observed for the IVIM-10b protocol. The pulse sequence for sIVIM and IVIM-10b is the same, the only difference is the number of b-values used. IVIM-10b utilized 10 b-values as opposed to the three in sIVIM. The results could imply that the use of more b-values result in increased signal drift. The sequence for IVIM-FC/IVIM-NC differs from that of sIVIM/IVIM-10b, making comparison more difficult. It is possible that the differences observed between sIVIM and IVIM-10b may be attributed to the order in which the scans were performed, with sIVIM being scanned prior to IVIM-10b. It is worth noting, however, that this hypothesis has not been tested and it is unclear whether the opposite would have been observed if the order were reversed.
The temporal correction improved signal stabilization and reduced signal drift for the majority of scans. In one subject, two cases with a wider dispersion of signal drift were observed, which resulted in an insufficient temporal correction with similar dispersion as the uncorrected data. The reason for this dispersion in signal was not investigated further and is unclear, although, motion or heating have been suggested as potential contributing factors.
As shown in Chapter 4.3.2, the signal in white matter was found to vary both temporally and spatially. This suggests the need for correction for both effects. In general, the max- imum signal drift was observed for IVIM-10b, while the least signal drift was observed for sIVIM.
The spatio-temporal correction analysis revealed that there was no spatial bias of the correction method and the signal drift effect between the regions of white matter that were studied. However, it was found that the signal drift did exhibit spatial variability.
While the temporal correction did not amplify the signal drift to the same extent as the spatio-temporal correction, it is not necessarily a superior correction method because it does not address the spatial variations of the signal drift. It is worth noting that the ef- fectiveness of a correction method should be evaluated in the context of the specific data and research goals being considered. However, there were cases in which the temporal correction outperformed the spatio-temporal correction without taking spatial variations
into account. On the other hand, there were also cases where the spatio-temporal cor- rection performed better.
It is important to acknowledge that the analysis in this study did not correct for noise or other sources of interference. There are also other potential sources of error, such as the manually drawn areas of white matter analyzed, which may not have been homoge- neous and could have introduced bias into the analysis. Additionally, these masks were created from the first volume of each protocol. Manual observation was used to adjust the areas to fit last and first volume within a series of protocols, without considering the intermediate volumes. Furthermore, since Dynamic Stabilization was not performed for the acquired volumes, the potential benefits of updating the center of frequency were not explored in this study. It is possible that Dynamic Stabilization could have improved the results, but this was not investigated.
The perfusion fraction is a measure that can provide insight into the microcirculatory functioning of a tissue or organ. It is related to the proportion of blood flow and oxy- genation within a given region. It is important to note that the accuracy of the perfusion fraction can be affected by factors such as signal drift, which can cause the estimated perfusion fraction to be too high or too low. As there was no reference for the true per- fusion fraction, it is difficult to determine which correction approach was superior. The perfusion fractions obtained during the different runs (run 1 and run 2) are not identical.
On average, for all scans, the perfusion fraction obtained during run 1 was found to be higher than in run 2. This may suggest that the true perfusion fraction may be closer to the mean value of multiple consecutive runs.
While it is difficult to determine the relative effectiveness of different correction meth- ods without a reference standard, the results of this study did show a bigger difference between the corrected and uncorrected perfusion fraction for the IVIM-FC/NC protocol compared to the sIVIM and IVIM-10b. These differences suggest that the effectiveness of correction methods may vary depending on the protocol used, and further research is needed to understand the underlying factors that contribute to these variations.
5.3 Future perspectives
Several factors can impact the magnitude of the observed signal drift and its effect on IVIM parameter estimates, including the protocol and signal model used for parameter estimation. Our results indicate that even with the use of temporal and spatio-temporal corrections, residual signal drift may still be present. While complete elimination of signal drift may not be feasible, it is important to minimize it as much as possible in order to enhance the reliability of the data. To this end, we recommend that correction methods be optimized for a given protocol. It would be ideal to propose a general signal drift correction method. However, in order to accomplish this, it is necessary to establish a standardized protocol for IVIM imaging. At present, there is no widely accepted stan- dardized protocol for IVIM imaging or preprocessing pipeline.