ZEMEDI-10545; No. of Pages 9

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ORIGINALARBEIT

Improving perfusion quantification in Arterial Spin Labeling for delayed arrival times by using optimized acquisition schemes Johanna Kramme a,b,∗ , Johannes Gregori c , Volker Diehl a,d , Vince I. Madai e,f , Federico C. von Samson-Himmelstjerna a,e,f , Markus Lentschig d , Jan Sobesky e,f , Matthias Günther a,b,c a

Fraunhofer MEVIS-Institute for Medical Image Computing, Bremen, Germany Faculty of Physics and Electronics, University of Bremen, Germany c mediri GmbH, Heidelberg, Germany d ZEMODI (Zentrum für moderne Diagnostik), Bremen, Germany e Center for Stroke Research Berlin (CSB) Charité-Universitätsmedizin Berlin, Germany f Department of Neurology, Charité-Universitätsmedizin Berlin, Germany b

Received 18 March 2014; accepted 10 July 2014

Abstract Objective: The improvement in Arterial Spin Labeling (ASL) perfusion quantification, especially for delayed bolus arrival times (BAT), with an acquisition redistribution scheme mitigating the T1 decay of the label in multi-TI ASL measurements is investigated. A multi inflow time (TI) 3D-GRASE sequence is presented which adapts the distribution of acquisitions accordingly, by keeping the scan time constant. Material and Methods: The MR sequence increases the number of averages at long TIs and decreases their number at short TIs and thus compensating the T1 decay of the label. The improvement of perfusion quantification is evaluated in simulations as well as in-vivo in healthy volunteers and patients with prolonged BATs due to age or steno-occlusive disease. Results: The improvement in perfusion quantification depends on BAT. At healthy BATs the differences are small, but become larger for longer BATs typically found in certain diseases. The relative error of perfusion is improved up to 30% at BATs > 1500 ms in comparison to the standard acquisition scheme.

∗ Corresponding

Verbesserung der quantitativen Perfusionsbildgebung bei verspäteter Blutankunftszeit durch Verwendung optimierter Aufnahmeschemata bei Arterial Spin Labeling Zusammenfassung Ziel: Bei Arterial-Spin-Labeling-(ASL)-Experimenten zerfällt die Markierung des Blutes mit T1. Durch Umverteilung der sonst gleich verteilten Mittelungen bei Experimenten mit verschiedenen Einströmzeiten (multi-TI) kann dem T1-induzierten Signalzerfall entgegengewirkt werden. Die Verbesserung der Perfusionsbestimmung gerade bei später Ankunft des markierten Blutes (bolus arrival time, BAT) wird untersucht. Material und Methoden: Die multi-TI-3D-GRASESequenz ist so angepasst, dass kurze Einströmzeiten weniger oft gemittelt werden als lange. Die gesamte Anzahl der Akquisitionen bleibt unverändert und damit auch die Messzeit konstant. Die hierdurch verbesserte Perfusionsbestimmung wird anhand von

author: Johanna Kramme, Universitätsallee 29, 28359 Bremen, Germany. Tel.: +49 421 218 59252. E-mail: [email protected] (J. Kramme).

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Conclusion: This adapted acquisition scheme improves the perfusion measurement in comparison to standard multi-TI ASL implementations. It provides relevant benefit in clinical conditions that cause prolonged BATs and is therefore of high clinical relevance for neuroimaging of steno-occlusive diseases.

Simulationen, Messung gesunder Probanden und Patienten mit altersbedingt verlängerter BAT oder stenosierenden Verschlusserkrankungen evaluiert. Ergebnisse: Mit unserem angepassten Akquisitionsschema zeigen sich für gesunde BATs keine Unterschiede in der Perfusionsbestimmung im Vergleich zum Standardverfahren mit gleich verteilten Mittelungen. Für BATs > 1500ms hingegen kann der Fehler in der Perfusionsbestimmung um 30% verringert werden. Schlussfolgerung: Unser angepasstes Aufnahmeverfahren verbessert die Perfusionsbestimmung gerade bei langen BATs. Im klinischen Alltag ist nur eine multiTI-Messung notwendig, da Ergebnisse im krankhaften BAT-Bereich verbessert werden, ohne diese im gesunden zu verschlechtern. Dies unterstreicht die hohe klinische Relevanz für die neurologische Bildgebung, insbesondere bei stenosierenden Verschlusserkrankungen.

Keywords: Magnetic Resonance Imaging, Arterial Spin Labeling, ASL, optimized acquisition, perfusion, bolus arrival time, signal to noise

Schlüsselwörter: Magnetresonanz Bildgebung, Arterial Spin Labeling, ASL, Perfusion, optimierte Bildaufnahme, Bolus Ankunftszeit, Signal zu Rauschen

Introduction Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is a noninvasive technique for quantitative cerebral blood flow (CBF) measurements. The water molecules in the blood are used as an endogenous perfusion contrast agent [1,2] by selectively inverting the hydrogen spins of the water molecules in inflowing blood. The perfusion weighted image is the subtraction of a paired label and control image, where the static tissue cancels out. As blood has only a small fraction of the overall MR signal within a typical voxel in human brain, usually up to 5% in gray matter, the signal of the perfusionweighted image is usually limited by low signal to noise ratio (SNR). Typically, labeled blood water spins need 1-2 seconds from the site of labeling to the capillary exchange site. With increasing age and in certain pathologies such as steno-occlusive disease, this time can be prolonged which requires data acquisition at long inflow times (TI) to accurately estimate brain perfusion [3]. However, this requirement is contradicted by the fact that the signal decays with T1 of blood after labeling and during inflow into the readout region. This leads to low signal intensity especially at long TIs. Over the last few years, ASL imaging was constantly improved to increase the SNR: background suppression techniques [4,5] were introduced to globally reduce the tissue signal, while preserving the ASL signal; image acquisition [6] and data analysis strategies [7] have been improved; physiological noise has been studied and minimized in ASL acquisition [8]; further approaches use optimal sampling schedule design techniques [9,10]

to optimize the sampling scheme of time series, which were tested in young and healthy subjects. As for clinical use, BATs can be extensively prolonged, e.g. with increasing age, or in steno-occlusive disease of the cerebral vessels and may require measurements at TIs far beyond 2000 ms. In these circumstances it is even more challenging to compensate for the SNR loss due to T1 decay. Since SNR improves with the square root of the number of readouts, further called averages, an increased number of averages at longer TIs can compensate the T1 decay and might yield comparable SNR at all TIs in a multi-TI pulsed-ASL experiment. In standard imaging, the low SNR for long TIs is compensated by acquiring a high number of around 10 averages for all TIs, thereby also increasing the SNR for short TIs that already exhibit sufficient SNR. This causes a relevant increase of the total number of readouts at all TIs, further called acquisitions. Thus, a multi TI experiment with a very high number of acquisitions is not suitable for clinical purposes where scanning times for CBF measurement should not exceed 5 minutes. In this work, we present a 3D-GRASE sequence with a linear, quadratic and exponential redistribution scheme of the acquisitions. This means that the number of averages is adapted for long TIs (where many averages are necessary to provide sufficient SNR) as well as for short TIs (where usually few averages are sufficient). In the presented approach the total number of acquisitions is kept constant and only the average distribution over the TIs differs. Compared to standard imaging, where all TIs are imaged with an equal number of averages and therefore the total number of acquisitions and the scan time has to be increased to gain SNR, the presented

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redistribution scheme keeps the scan time constant and ensures sufficient SNR even at TIs above 2500 ms. Preliminary results of the idea were presented recently [11,12].

Methods In the presented method, the total number of image acquisitions obtained by the scanner can be distributed in four different modes - equal, linear, quadratic, and exponential. Other distribution schemes are possible, but were not investigated in this work. The equal mode corresponds to the classical mode in a multi TI experiment. Here every TI is repeated an equal number of times and the number of averages can be chosen in the user interface of the scanner software. In the linear, quadratic and exponential mode the number of averages increases with increasing TI, but, depending on the mode, at a different rate. The number of averages for the i-th TI (N(i)) in the corresponding mode is calculated with the following formula:

N(i) =

(Ntot − NTI ) ∗ f (i) +1 NTI i=1 f (i)

Simulated Images (1)

Ntot is the total number of acquisitions, NTI is the number of TIs, and NfTI(i) a weighting depending on the distribution i=1

Figure 1. Signal decay due to T1 relaxation (red curve) is schematically compared with a quadratic redistribution scheme (blue histogram). Note, the signal decay curve only represents the signal decay due to T1 relaxation and not the signal curve from the General Kinetic Model [13]. As can be seen, TIs with low signal intensity due to T1 decay are acquired more often than those with higher signal intensity.

f (i)

mode. f(i) for the different distribution modes is defined as follows: flinear (i) = i

(2)

fquadratic (i) = i ∗ i

(3)

fexponential (i) = ei

(4)

To ensure that every TI is imaged at least once, and not zero times due to rounding, only the number Ntot − NTI is weighted. Afterwards, it is made sure  that the sum of N(i) at the difTI ferent TIs is equal to Ntot . If ( N / Ntot ) averages at i=1 N(i) = the last TI are added or subtracted till both are equal. The advantage of the linear, quadratic, or exponential distribution is that the scan time is kept constant while improving the SNR at higher TIs. In Fig. 1 the idea of redistribution of the acquisitions is displayed by schematically comparing the signal decay due to T1 relaxation to a quadratic redistribution scheme. The signal decay curve only represents the signal decay due to T1 relaxation and not the signal curve from the General Kinetic Model [13]. As can be seen, TIs with low signal intensity due to T1 decay are acquired more often than those with higher signal intensity.

The simulated images were generated in MeVisLab [14], where they were calculated according to the General Kinetic Model [13]. The perfusion (f) was varied from 20100 ml/100 g/min in steps of 10 ml/100 g/min and BAT from 100-1500 ms in steps of 100 ms. This was done for 50 different TIs ranging from 50-2500 ms in steps of 50 ms. Random Gaussian noise of sigma 17 was applied to all images, which is a typical value of our volunteer images. All other parameters used to generate the simulated images can be found in Table 1. The value for M0 was chosen similar to the calculated values for M0 of the volunteer study. For each combination of perfusion, BAT, and TI 28 images are simulated to compare the redistribution schemes. The four previously described distribution schemes have in common that SNR loss due to T1 decay is partly compensated. The distribution schemes are not trying to equalize the SNR of the signal curve from the complete General Kinetic Model [13], because therefore more parameters like for example BAT needed to be known, which also change from voxel to voxel in an in-vivo study. Table 1 Parameters for generating the simulated images. T1arterial T1tissue M0 perfusion BAT Bolus length TI T2 gray matter Gaussian noise

1600 ms [21] 1200 ms [22,23] 0.0017 20-100 ml/100g/min 100-1500 ms 900 ms 250-2500 ms 70 ms [24] 17

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In the simulated images all parameters of the General Kinetic Model are known and for comparison equal SNR was simulated for a perfusion value of 40 ml/100 g/min in combination with a BAT of 400 ms, all other parameters can be found in Table 1. To gain equal SNR for this parameter combination a maximum of 28 averages was needed. For later analysis of the simulated images, averaging was done in five different ways. First, SNR was equalized for all 50 TIs not considering the inflow of the tagged bolus. The other four variations follow the scanner modes of the volunteer study (TI 2502500 ms, 250 ms spacing, NTI = 10, distribution of number of acquisitions: equal mode (3,3,3,3,3,3,3,3,3,3), linear mode (1,2,2,2,3,3,4,4,4,5), quadratic mode (1,1,1,2,2,3,4,4,5,7), and exponential mode (1,1,1,1,1,1,2,3,6,13)). For the variations according to the scanner modes, always a total of thirty acquisitions (Ntot = 30) are distributed over the different TIs, while for equal SNR considering the complete signal curve of the General Kinetic Model with our example parameters 28 averages are needed. To have a statistical relevant sample size the whole procedure was performed 100 times on independently calculated data sets. Fitting of perfusion and BAT was done with the fitting routines of MPFIT [15]. The fixed parameters were kept the same as given in Table 1. Sigma for every TI changed due to the different number of averages for every TI and is included in the calculation of the fitting results. Fitting errors for the single parameters were also obtained from the fitting routine. The results were analyzed by comparing the mean fitted perfusion and BAT values of the 100 independent data sets to their nominal values and by evaluating the mean relative fitting error of the perfusion values at the different BATs. In-vivo study Five healthy volunteers (1 female, age: 27-39 years) as well as five patients participated in the study (1 female, age 74 years, no vascular pathology findings; 4 males, age 30-82 years, occlusion or high grade stenosis of the internal carotid artery). Since the study was carried out on two different sites, the study was approved by all local ethical boards and written informed consent was obtained from all volunteers and patients. The volunteers and the female patient were scanned at one site, while the male patients were scanned on a second site. All scans were performed on a 3T scanner (Siemens Magnetom Verio or Trio) with a 20 channel head coil and in the cases of the patients with ICA-occlusion or stenosis with a 12 channel head coil. An optimized FAIR PASL pulse scheme including post labeling saturation, background suppression and Q2TIPS was used in combination with a 3D-GRASE readout [6] with a spatial resolution of 3.5 mm × 3.5 mm × 3.5 mm. Eight partitions were obtained, aligned with the corpus callosum. Slice Partial Fourier of 6/8 and a turbo factor of 6 were used. TE was 42.14 ms and TR 2800 ms. The maximal bolus length was set to 1400 ms, being truncated earlier by

the Q2TIPS for TIs shorter than 1400 ms. A time series of 10 TIs starting at 250 ms to 2500 ms with 250 ms spacing was acquired fourteen times for the volunteers - measurements 17 (equal, linear, quadratic mode, test dataset), 8-14 (equal, linear, quadratic mode, retest dataset), and 1-13 (exponential mode, no retest)- and seven times for the patients. Obtaining more than seven measurements from the patients was not reasonable due to the long scan time. Therefore neither an exponential distribution nor a retest could be calculated for them. By obtaining independent time series the four distribution modes could be calculated out of the same data sets for a reliable comparison. A registration of the fourteen or in the case of the patients seven measurements was not necessary. The number of averages for the four distribution modes was the same as for the simulated images (equal mode (3,3,3,3,3,3,3,3,3,3), linear mode (1,2,2,2,3,3,4,4,4,5), quadratic mode (1,1,1,2,2,3,4,4,5,7), and exponential mode (1,1,1,1,1,1,2,3,6,13)). T1 weighted images were obtained to generate masks for gray and white matter. These masks are also used to calculate the SNR of gray as well as white matter with the two region method [16]. SNR √ for every TI is compared to its theoretical improvement of n, with n being the number of averages. To determine the perfusion value and BAT, the same fitting routine as for the simulated images was applied on a voxel wise basis. The fixed parameters of the fit were chosen alike to the simulated image study (compare with Table 1). To quantify perfusion, the value for the longitudinal equilibrium blood magnetization (M0) needs to be known. Under the assumption that the sample contains at least one voxel completely filled with artery blood, M0 can be extracted from the measured data. Sigma for every TI changed due to the different number of averages for every TI and is included in the calculation of the fitting results. To exclude the background, a gray and white matter mask was applied on the fitted data, additionally a signal threshold of 10 was used for the fitting. The results were analyzed by comparing the relative fitting error of the perfusion values for different BATs. The allowed range for the BAT fit was 1-1500 ms. The perfusion values and their fitting errors (from 20-160 ml/100 g/min in bin steps of 10 ml/100 g/min for BAT values from 1-1500 ms in 100 ms bin steps) were determined for the four distribution modes. The results were compared with the simulated images. Additionally the number of voxels with a certain BAT value were counted and compared among volunteers and patients.

Results Simulated Images BAT: Table 2 gives an overview of the mean fitted BAT values and their standard deviations averaged over all perfusion

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Table 2 BAT reproducibility of the simulated data: Fitted mean BAT values with their standard deviation averaged over all perfusion values. For a statistical difference compared to equal mode the f-test value needs to be >1.12 (p = 0.95, f-test value>1.12, n1 = 900, n2 = 900). Nominal BAT value [ms]

50 TI fitted equal SNR at all TIs [ms]

200 500 800 1100 1500

198 500 798 1086 1448

± ± ± ± ±

3 2 5 25 100

10 TI linear mode [ms] | f-test value compared to equal mode

10 TI equal mode [ms] 206 498 783 1070 1324

± ± ± ± ±

5 7 26 49 216

204 503 787 1074 1392

± ± ± ± ±

10 7 22 45 160

10 TI quadratic mode [ms] | f-test value compared to equal mode

4.00 1.00 1.40 1.19 1.82

values depending on the distribution mode. The fitted BAT values are lower than the initial BAT values. For BAT values under 1000 ms the underestimation is small, independent of the acquisition distribution mode. For higher BAT values the degree of underestimation depends on the distribution mode. Best reproducibility with a small standard deviation is retrieved by fitting 50 TIs and fully equalizing SNR. However, even in this case BATs above 1000 ms can be underestimated by over 50 ms with a standard deviation of 100 ms compared to the initial value. Comparing the distribution modes also tested in-vivo, best reproducibility is retrieved for the quadratic mode. In general, the standard deviation of the fitted BAT values is increasing with increasing BAT. Nevertheless, for quadratic mode this increase is clearly smaller than for equal mode. Statistical significance was tested with an f-test. The values are shown in Table 2 as well. The high underestimation independent of the distribution mode is smaller if only perfusion values above 30 ml/100 g/min are consider. For quadratic mode and BAT = 1500 ms the mean value would be 1492 +/- 13ms. Comparing the distribution modes for only ten TIs, the standard deviation becomes equal for all three modes at BATs of 600-800 ms, depending slightly on the perfusion value. Below BATs of 600 ms the equal mode has the smallest standard deviation. It is up to 5 ms smaller as for the other two modes. Above 800 ms the standard deviation of the quadratic

209 503 786 1080 1417

± ± ± ± ±

8 10 21 30 126

2.56 2.04 1.53 2.67 2.94

10 TI exponential mode [ms] | f-test value compared to equal mode 208 506 782 1055 1416

± ± ± ± ±

11 12 15 54 128

4.84 2.94 3.00 1.21 2.85

mode is the smallest. The difference of the standard deviation between equal and quadratic mode can become up to 100 ms for long BATs. The exponential mode does not perform better than the quadratic mode. Perfusion: Table 3 and Table 4 summarize the results of the fitted perfusion values. The initial perfusion value is in almost all cases accurately fitted, independent of the distribution mode. The standard deviation of the fitted values is under 2 ml/100 g/min over all perfusion values. Nevertheless, the relative fitting error (in the following only called relative error) can be higher than 2 ml/100 g/min, as shown in Tables 3 and 4. Independent of the distribution mode, the relative error of the perfusion is rising with increasing BAT (Table 4) and decreasing with higher perfusion values (Table 3). By fitting all 50 TIs and equalizing SNR at all TIs the increase of the relative error with rising BAT is small. Comparing equal, linear, quadratic, and exponential mode the relative error of the perfusion is minimally smaller for the equal mode for BATs up to 200 ms, but already from 300 ms onward linear and quadratic mode present a smaller relative error. Between the latter two, a difference is seen for BATs higher than 800 ms. For the linear mode the relative error of the perfusion is always two to four percent higher than for the quadratic mode. The relative error of

Table 3 Perfusion reproducibility of the simulated data. Fitted mean perfusion values with their relative fitting errors averaged over all BAT values. The relative fitting error decreases with higher perfusion value. The change of the relative error becomes statistically significant (p = 0.95, f-test value >1.09, n1 = 1400, n2 = 1400) for all shown perfusion values within one distribution mode. Nominal f value [ml/100g/min]

50 TI fitted equal SNR at all TIs [ml/100g/min]

20 30 40 50 60 70 80 90 100

20 30 40 50 60 70 80 90 100

± ± ± ± ± ± ± ± ±

17 11 8 7 6 5 4 4 3

10 TI equal mode [ml/100g/min] 20 30 40 50 60 70 81 91 100

± ± ± ± ± ± ± ± ±

39 24 18 14 12 10 9 8 7

10 TI linear mode [ml/100g/min] 20 30 40 50 60 70 80 91 100

± ± ± ± ± ± ± ± ±

33 21 15 12 10 9 8 7 6

10 TI quadratic mode [ml/100g/min] 21 30 41 50 60 70 80 91 100

± ± ± ± ± ± ± ± ±

30 20 15 11 10 8 7 6 6

10 TI exponential mode [ml/100g/min] 21 + /- 29 30 ± 20 41 ± 15 50 ± 11 60 ± 10 70 ± 8 80 ± 7 91 ± 6 100 ± 6

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Table 4 Simulated data: Relative fitting error of the perfusion at a certain BAT value, averaged over all perfusion values (20-100 ml/100 g/min) The relative fitting error increases with higher BAT value. The change of the relative error becomes statistically significant (p = 0.95, f-test value >1.12, n1 = 900, n2 = 900) for a BAT difference >200 ms, except for exponential mode where the relative error values lie closer together. Relative fitting error of f at a certain BAT value averaged over all f values Nominal BAT value [ms]

50 TI fitted equal SNR at all TIs [%]

10 TI equal mode [%]

10 TI linear mode [%]

10 TI quadratic mode [%]

10 TI exponential mode [%]

100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500

4.7 5.1 5.3 5.4 5.8 6.2 6.5 6.8 7.0 7.3 7.8 8.3 9.3 10.5 12.8

7.9 8.6 9.1 9.9 10.5 11.4 12.1 13.2 14.8 16.2 18.0 19.5 21.9 26.1 33.6

8.1 8.5 8.9 9.4 9.8 10.3 10.9 11.6 12.8 13.8 14.8 17.0 18.4 20.9 25.1

8.5 8.8 9.2 9.5 9.8 10.1 10.6 11.2 12.0 13.1 13.5 14.9 16.3 18.5 22.5

10.2 10.6 10.9 11.2 11.4 11.5 11.5 11.9 12.1 12.5 12.6 13.5 11.5 15.7 15.1

the exponential mode is smaller from BATs of 700ms onward compared to equal mode and compared to the quadratic mode for BATs higher than 1000ms. In detail, for perfusion values of 40 ml/100 g/min, the relative error in quadratic mode is below 25% decreasing to below 10% for perfusion values of 100 ml/100 g/min even for a fix BAT of 1500 ms. For BATs up to 1000 ms the relative error for quadratic mode is below 10% already for perfusion values above 50 ml/100 g/min. In-vivo study SNR: For all four distribution modes the SNR was calculated for gray and white matter. The results for each volunteer in test and retest study were normalized to the SNR of the equal mode. As can be seen in Fig. √ 2 the SNR increase follows the theoretical improvement of n for n being the number of averages. The mean value of SNR for grey and white matter is plotted. Perfusion: For the volunteers the percentual difference of the relative perfusion errors from test and retest measurement is 2.1% ± 4.5% (mean ± std) in equal mode, 1.5% ± 3.7% in linear mode and 1.6% ± 3.5% in quadratic mode averaged over all perfusion values (20-160 ml/100 g/min). Due to this small difference no further distinction between test and retest measurement is made. For reasons explained above, for the exponential distribution a retest was not performed.

Figure 2. (In-vivo data) The results for each volunteer in test and retest study were normalized to the SNR of the equal mode. The √ SNR increase follows the theoretical improvement of n for n being the number of averages. The mean value of SNR for grey and white matter is plotted.

Alike to the simulated images the relative error of the perfusion decreases with higher perfusion values (Fig. 3), and rises with increasing BAT (Fig. 4). For BATs smaller than 300 ms, equal averaging has the smallest relative error. For BATs of 300-500 ms, the relative error does not differ much between equal, linear and quadratic distribution. For BATs longer than 500 ms, the relative error first for linear and then for quadratic distribution becomes clearly smaller than for equal averaging. Statistical significance (p = 0.95) was tested with an f-test. The relative error of the exponential

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Figure 3. (In-vivo data) The relative error of the perfusion is decreasing with higher perfusion value. Data in each mode is averaged over all volunteers, patients and BATs. For exponential mode only the volunteer data is considered. The statistical significance (p = 0.95) was tested with an f-test from one perfusion range to the next.

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Figure 5. (In-vivo data) Relative error improvement in relation to equal mode. The relative error of the perfusion of the volunteer and patient data for linear, quadratic, and exponential mode is normalized with the value for equal mode. For BATs < 300 ms equal mode has a smaller relative error, while for BATs > 300 ms linear and quadratic mode already have a smaller relative fitting error of perfusion. The exponential mode preforms better as the equal mode for BATs > 900ms.

Figure 4. (In-vivo data) The relative error of the perfusion increases with increasing BAT. Data in each mode is averaged over all volunteers, patients and perfusion values (20-150 ml/100 g/min) at different BATs in the gray matter for the three distribution modes. For exponential mode only the volunteer data is considered. All differences in relation to equal mode were calculated to be statistically significant (p = 0.95).

distribution (only calculated for the volunteers) is almost constant over the BAT values. It only becomes smaller as the quadratic mode for BAT values above 1200 ms. For BATs below 1000 ms the relative error of perfusion is under 20% for perfusion of 30 ml/100 g/min and above, falling to under 10% for perfusion values above 60 ml/100 g/min with quadratic averaging. The difference of the relative error of perfusion between equal and quadratic or exponential mode can be up to 7%, while the relative error improvement in relation to equal mode is over 30%, see Fig. 5. For white matter similar values were found for the relative perfusion error at the different BATs and perfusion values.

Figure 6. (In-vivo data) Percentage of the number of voxels within grey matter at the different BATs for the volunteers and the patients.

Only the number of voxels with certain perfusion values was different, which is expected since the average perfusion is different in gray and white matter. Comparing the relative error of the different perfusion values at the varying BATs in the volunteer study with the relative errors in the simulated images yields almost equal values. Only for BATs up to 1000 ms the simulated images present slightly smaller relative errors. Volunteers and patients showed similar results for the relative perfusion error and the relative BAT error. However the

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Figure 7. Perfusion map of a representative volunteer (left) and a patient (right). Perfusion values exceeding 450 ml/100 g/min are marked in red.

number of voxels with long BATs is higher for the patients than for the volunteers. Fig. 6 shows the percentile voxel distribution at the different BATs for the volunteers and patients. Fig. 7 shows a perfusion map from a volunteer and a patient.

Discussion and Conclusion Our data shows that the relative error of the perfusion is significantly reduced with the quadratic redistribution scheme compared to equal mode – especially at long BATs. Acquisition redistribution clearly improves SNR at long TIs to an extent expected by theory. At shorter TIs some SNR is lost in linear and quadratic mode, but owing to a high signal at these TIs this loss is negligible for the quality of perfusion fitting compared to equal mode. For exponential mode the improvement of SNR at long TIs is a little bit higher but results in increases fitting errors compared to quadratic mode at smaller TIs. In general, the relative error of the perfusion measurement decreases with higher perfusion values but also increases for longer BATs. Nevertheless, for perfusion values above 40 ml/100 g/min and quadratic distribution the relative error is under 20% for all BATs. The simulated images showed that the perfusion values influence the quality of the BAT fit result. For small perfusion values BAT is most likely underestimated. For quadratic mode this underestimation is less severe than for equal mode. This underlines the necessity for a redistribution scheme especially for diseases with prolonged BAT and low perfusion values (e.g. steno-occlusive disease).

The advantages of a quadratic distribution are of greater importance when multi TI experiments with TIs above 3000 ms are performed. This can be beneficial in patients with prolonged BATs. In these patients, standard perfusion imaging (i.e. equal mode) leads to signal loss with “hypointense” areas that may be misinterpreted as “hypoperfusion”. This may be correct in patients with prolonged BAT and subsequent hypoperfusion but may be misleading in patients with prolonged BAT and normal perfusion which is often seen in chronic steno-occlusive disease. A recent study imaging at inflow times above 3000 ms showed that the perfusion values may in fact be normal in these areas and that only the BATs are prolonged, as evidenced by a high number of occurrences of the arterial transit delay artefact [17]. Thus, imaging at these long TIs might be necessary to avoid erroneous conclusions, which is especially important for a clinical use of ASL. Qiu et al. [18] increased the whole amount of signal averaging in their study to overcome the problem of low SNR at long BATs but stated that their scan time might not be feasible in clinical practice. Our acquisition redistribution approach ensures sufficient SNR when imaging at these long BATs and does not increase scan time compared to standard equal mode, allowing an easy implementation into clinical protocols. For a sequence protocol as described in this study, which directly uses quadratic or linear mode, the scan time is below 3 minutes when obtaining 8 partitions and below 6 minutes when obtaining 24 partitions. To further reduce noise, a major issue in ASL imaging, the SNR of single TI points should be integrated into a real time adaption, as already shown for the sampling [10]. Finally,

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a real time acquisition scheme should be targeted, where a certain time point is averaged as often as necessary to reach a certain SNR. Another area where quadratic acquisition distribution might prove beneficial is two compartment fitting. Perfusion experiments were extended to include T2 measurements of the difference signal [19,20], where the acquisition of data at different echo times is needed to sample the signal decay and different TIs are of interest. Usually, this leads to rather long scan times to gain sufficient SNR. Applying our quadratic distribution scheme to these measurements would also improve the SNR of the different echoes and the accuracy of the T2 measurement, while again reducing scanning time. In summary, we present a new redistribution scheme for ASL based CBF measurement. This sequence improves the CBF measurement in comparison to standard multi-TI ASL implementations done in equal mode, especially when the BAT range of the patient is unknown before the measurement. It provides relevant benefit in clinical conditions that cause a prolonged BAT and is therefore suitable for imaging of steno-occlusive diseases of the brain. Our method is straightforward to implement, does not increase the total scan time and should be further evaluated in clinical imaging protocols.

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Improving perfusion quantification in arterial spin labeling for delayed arrival times by using optimized acquisition schemes.

The improvement in Arterial Spin Labeling (ASL) perfusion quantification, especially for delayed bolus arrival times (BAT), with an acquisition redist...
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