Reproducibility of multiphase pseudo-continuous arterial spin labeling and the effect of post-processing analysis methods Amir Fazlollahi, Pierrick Bourgeat, Xiaoyun Liang, Fabrice Meriaudeau, Alan Connelly, Olivier Salvado, Fernando Calamante PII: DOI: Reference:
S1053-8119(15)00430-9 doi: 10.1016/j.neuroimage.2015.05.048 YNIMG 12243
To appear in:
NeuroImage
Received date: Accepted date:
30 January 2015 18 May 2015
Please cite this article as: Fazlollahi, Amir, Bourgeat, Pierrick, Liang, Xiaoyun, Meriaudeau, Fabrice, Connelly, Alan, Salvado, Olivier, Calamante, Fernando, Reproducibility of multiphase pseudo-continuous arterial spin labeling and the effect of post-processing analysis methods, NeuroImage (2015), doi: 10.1016/j.neuroimage.2015.05.048
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Reproducibility of multiphase pseudo-continuous arterial spin labeling and the effect of post-processing analysis methods
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Amir Fazlollahia,b,∗, Pierrick Bourgeata , Xiaoyun Liangc , Fabrice Meriaudeaub , Alan Connellyc,d , Olivier Salvadoa , Fernando Calamantec,d a
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CSIRO Digital Productivity Flagship, The Australian e-Health Research Centre, Herston, QLD, Australia b Le2I, University of Burgundy, Le Creusot, France c Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia d Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
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Abstract
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Arterial spin labeling (ASL) is an emerging MRI technique for non-invasive measurement of cerebral blood flow (CBF). Compared to invasive perfusion
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imaging modalities, ASL suffers from low sensitivity due to poor signal-tonoise ratio (SNR), susceptibility to motion artifacts and low spatial resolution, all of which limit its reliability. In this work, the effects of various state of the art image processing techniques for addressing these ASL limitations are investigated. A processing pipeline consisting of motion correction, ASL motion correction imprecision removal, temporal and spatial filtering, partial volume effect correction, and CBF quantification was developed and assessed. To further improve the SNR for pseudo-continuous ASL (PCASL) Corresponding author at: Australian e-Health Research Centre, Level 5 UQ Health Science Building 901/16, Royal Brisbane and Women’s Hospital, Herston QLD 4029, Tel.: +61732533618, Mobile: +61405292473 Email address:
[email protected] (Amir Fazlollahi) ∗
Preprint submitted to NeuroImage
May 22, 2015
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by accounting for errors in tagging efficiency, the data from multiphase (MP)
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acquisitions were analyzed using a novel weighted-averaging scheme. The performances of each step in terms of SNR and reproducibility were eval-
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uated using test-retest ASL data acquired from 12 young healthy subjects.
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The proposed processing pipeline was shown to improve the within-subject coefficient of variation and regional reproducibility by 17% and 16%, respec-
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tively, compared to CBF maps computed following motion correction but without the other processing steps. The CBF measurements of MP-PCASL
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compared to PCASL had on average 23% and 10% higher SNR and reproducibility, respectively.
Keywords: arterial spin labeling; multiphase pseudo-continuous arterial
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spin labeling; perfusion MRI; cerebral blood flow; test-retest;
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1. Introduction
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reproducibility;
Arterial spin labeling (ASL) (Detre et al., 1992) is emerging as a power-
ful magnetic resonance imaging (MRI) technique for non-invasive assessment of cerebral blood flow (CBF). In contrast to commonly used but invasive perfusion imaging modalities, such as positron emission tomography (PET), dynamic susceptibility contrast (DSC)-MRI and CT perfusion, ASL uses magnetically labeled arterial water protons as an endogenous tracer. Perfusion is then calculated from the difference of labeled (tag) and non-labeled (control) images. Despite early concerns due to ASL’s intrinsic low sensitivity, recent developments of labeling strategies (Wu et al., 2007; Dai et al., 2008) and acquisition methods (Fern´andez-Seara et al., 2005; Garcia et al., 2
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2005) have considerably improved its reliability, with a high degree of agree-
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ment to PET (Xu et al., 2010; Donahue et al., 2006b) and DSC-MRI (Weber et al., 2003). In addition, ASL can be used to assess functional activity and
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functional connectivity, with a number of important advantages compared
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with the commonly used blood oxygen level-dependent (BOLD) approach (Boscolo Galazzo et al., 2014; Federspiel et al., 2006; Liang et al., 2012,
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2014a). All these advances have led to ASL being increasingly used in clinical studies, and a consensus recommendation has recently been published for
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the ASL implementation for clinical applications (Alsop et al., 2015). Even though many studies have applied ASL in rest and task-related studies (Watts et al., 2013), obtaining a reliable measurement of CBF remains
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challenging. ASL suffers from a number of limitations (Petersen et al., 2006;
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Calamante et al., 1999), including partial voluming effect (PVE) due to its low spatial resolution, possible contamination from BOLD effects, low signal-
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to-noise ratio (SNR), reduced temporal sampling when compared to BOLD, tagging efficiency issues, blood equilibrium magnetization estimate ambiguity for quantification, and sensitivity to subject motion. Consequently, advanced acquisition methods and elaborate post-processing are essential to maximize the reliability and sensitivity of CBF maps prior to any statistical evaluation. Various strategies have been proposed to address these ASL limitations.
For example, correction methods for PVE have been introduced (Asllani et al., 2008; Liang et al., 2013a), as well as acquisition sequence modifications to increase the spatial resolution (Tan et al., 2011; Liang et al., 2014b). The BOLD contamination was addressed in (Hernandez-Garcia et al., 2010; Liu and Wong, 2005), and also by incorporating background signal suppression
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(Duyn et al., 2001), or by using a 3D GRASE-based acquisition (Fern´andez-
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Seara et al., 2005). Similarly, various strategies have been proposed to increase SNR (Wang et al., 2005, 2008; Wells et al., 2010; Wang, 2012; Behzadi
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et al., 2007; Lu et al., 2006). Motion related artifacts were also overcome in
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(Sidaros et al., 2005; Wang et al., 2008; Wang, 2012; Maumet et al., 2012). Moreover, the multiphase PCASL (MP-PCASL) variant (Jung et al., 2010)
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has been proposed to estimate phase-tracking errors, which can be employed for correcting ASL tagging efficiency, and subsequently improving CBF re-
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liability. In addition, a separate proton density scan with (Robson et al., 2009; Xu et al., 2010) or without (Roberts et al., 1996; C ¸ avu¸so˘glu et al., 2009) an inversion pulse is usually acquired for perfusion quantification and
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compensation for RF coil inhomogeneities (C ¸ avu¸so˘glu et al., 2009).
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Previous image analysis studies have been mainly focused on addressing the influence of the individual above-mentioned factors in ASL analysis.
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The individual improvements from PVE correction, spatial and temporal noise reduction, motion correction, image subtraction and CBF quantification techniques need to be aggregated in a proper order to maximize ASL sensitivity. To the best of our knowledge, there has been only one study that has proposed such a post-processing pipeline, with the focus on ASL fMRI in order to improve functional activation detection in CASL sequence (Wang et al., 2003). However, factors such as PVE correction, and spatial denoising which are known to have significant influence on detection sensitivity, were not investigated. In addition, reproducibility of the majority of the processing steps has not been investigated in detail yet. There is currently a great demand for a robust processing pipeline to
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improve the reliability of ASL measurements, and therefore contribute to
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widespread application of ASL in clinical and research studies. Moreover, a reliable quantification of CBF is crucial for investigating brain abnormali-
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ties in disease, and to characterize functional changes. Various studies have
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previously investigated the reliability and reproducibility of ASL (Wu et al., 2014; Kilroy et al., 2014). However, this area remains under-explored. In
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the present work, we have investigated the performance of various postprocessing techniques for improving CBF quantification in terms of test-
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retest reproducibility and SNR. State-of-the-art techniques were combined to construct a full image processing pipeline. Each technique was adapted for MP-PCASL, including a novel weighted-averaging strategy to use all the
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available MP acquisitions. An overview of the proposed pipeline is shown in
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Methods section.
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Figure 1, and the various steps are described in detail in the Materials and
2. Materials and Methods 2.1. Dataset
MRI data from 12 healthy subjects (29.2±4.6 years-old with a range of
22-35, 7 males) were included in this study, and informed written consent was obtained in accordance with ethical approval from the local Human Research Ethics Committee. All MR images were acquired on a 3T Siemens Tim Trio scanner with a 12-channel head coil. Each subject underwent two identical sessions scheduled one day apart acquiring an anatomical T1-weighted (T1w) and a multiphase pseudo-continuous ASL (MP-PCASL) with background suppression and 3D GRASE readout. 5
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For the MP-PCASL labeling, 60 pairs of conventional PCASL (with RF
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offset phases of 0◦ for tag (T0 ) and 180◦ for control (C0 )) were collected. Subsequently, 60 ASL pairs with +90◦ shift in phase offset (T90 and C90 )
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relative to conventional PCASL were also acquired; the data from this ASL
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protocol will be referred to as the ‘symmetric’ dataset. In order to reduce the acquisition time, only 20 repetitions with +90◦ shift were considered for
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7 subjects, and thereafter referred to as the ‘asymmetric’ dataset. For all datasets, the labeling plane was selected using an MR angiogram following
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the recommendations from (Aslan et al., 2010). For data acquisition, a 3D GRASE PCASL sequence with k -space sharing to improve brain coverage was used (Liang et al., 2012). The relevant imaging
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parameters were: TR/TE of 3750/56 ms (TR=4150 ms was used for both
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scans from one subject, due to power deposition issues), labeling RF pulse flip angle of 28.5◦ , RF pulse width 650 µs, with inter-pulse gap of 450 µs (spacing
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between RF pulses of 1070 µs), peak/average gradient of 8/1 mT/m, peak RF amplitude of 53 mG, labeling duration of 1584 ms, post-labeling delay of 1540 ms, and BS inversion-times (relative to imaging module) of 1800 ms and 520 ms. The images have in-plane resolution of 4×4 mm2 and slice thickness of 6 mm, with a matrix size of 64×51×20. A proton density (PD) image (often referred to as M0 or calibration image) with 8 repetitions and without labeling and background suppression was also acquired. This image was used for ASL quantification, as well as a reference for multi-modal image registration. The overall acquisition times for symmetric and asymmetric dataset were 15 and 10 minutes, respectively, and a static tissue suppression level of approximately 90% was achieved from the background suppression
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technique.
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The anatomical T1-weighted (T1w) images were acquired using a standard 3D magnetization-prepared rapid gradient echo (MPRAGE) sequence
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with 1×1×1 mm3 resolution, TR/TE/TI= 1900/2.55/900 ms, flip angle 9◦ ,
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field of view 256×256, and 160 slices.
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2.2. T1w image processing
For each subject, the T1w image was classified into gray matter (GM), white matter (WM) and cerebro-spinal fluid (CSF) maps, and brain mask
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using the expectation maximization segmentation algorithm (Van Leemput et al., 1999). T1w images were parcellated following two anatomical tem-
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plates by spatially normalizing the Montreal Neurological Institute (MNI)
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(Collins et al., 1995) atlas to each subject. The Automated Anatomical Labeling (AAL) (Tzourio-Mazoyer et al., 2002) and the Internet Brain Segmen-
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tation Repository (IBSR) [Available online: http://www.cma.mgh.harvard .edu/ibsr/] templates were used to determine various brain region masks, including cortical areas and ventricles. In particular, results for the following regions are reported here: the four cortical lobes, hippocampus, amygdala, caudate, and putamen. A rigid transformation between the anatomical T1w image and the averaged ASL calibration image was used to align the anatomical atlases to ASL space. 2.3. ASL analysis The post-processing steps considered prior to ASL quantification were: motion correction, temporal filtering, multi-phase correction, spatial filtering,
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and PVE correction. An overview of the pipeline can be found in Figure 1
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2.3.1. Motion correction
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and the steps are described in detail below.
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ASL motion correction involves (1) realigning all the ASL images to a reference image, and (2) regressing out realignment imprecision in motionfree images that are due to labeling intensity shifts between tag and control
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(Wang, 2012), (3) and intensity shifts across multiphase images. A rigid-body transformation, based on a least-square similarity measure
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as provided by SPM8 (http://www.fil.ion.ucl.ac.uk/spm/), was used for motion correction. All ASL images were aligned to the first image of the T0
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sequence (reference image). A regression model has been proposed in (Wang,
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2012) to correct the ‘zig-zag’ confounds among tag and control images. Here, the model is extended to accommodate the two extra acquisitions of MP-
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PCASL; three linear regression models were independently fitted to the estimated motion parameters of (T0 ,C0 ), (T0 ,T90 ) and (T0 ,C90 ) datasets. For the regression, the independent values were set to -1 for T0 and 1 for C0 , T90 and C90 .
ASL images were then resampled using the corrected motion vectors, and
the M0 images were realigned to the reference image by an independent rigid transformation without any further correction. 2.3.2. Temporal filtering Considering temporally adjacent images, it is possible to identify low- and high-frequency signal drift due to random thermal and physiological noise, as well as residual motion correction artifacts. Here, four different techniques 8
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for suppressing outliers in perfusion-weighted images (PWIs) were compared:
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slice rejection (SlcRej), volume rejection (VolRej), robust mean (RobMean) (Maumet et al., 2012), and component based correction (CompCor) (Behzadi
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et al., 2007).
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In SlcRej and VolRej, three empirical constraints were defined to discard an image slice or volume, based on motion parameters and intensity
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variations.
An empirical threshold of 2 mm was chosen on the total head displacement
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to discard image volumes considered to have large amount of motion. Subsequently, PWIs were obtained by a pairwise subtraction of temporally adjacent images among the remaining (T0 ,C0 ) and (T90 , C90 ) images.
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Physiological and thermal variations were then identified based on a Z-score
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analysis across each two dataset. A perfusion slice or volume is rejected if its mean (µi ) or standard deviation (σi ) satisfy one of the following constraints
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in a given mask as proposed in (Tan et al., 2009):
|µi | > avg{µ1 , ..., µN } + 3 × std{µ1 , ..., µN },
(1)
σi > avg{σ1 , ..., σN } + 2 × std{σ1 , ..., σN }
where N is the total number of PWIs (half the number of repetitions), µi and σi are the average and standard deviation of the image i within the mask, respectively. The GM mask was adopted since both physiological and thermal sources of noise may be present. In RobMean, after a similar pair-wise subtraction on the motion corrected data, a Huber’s M-estimator of location was applied to obtain a robust average perfusion image, as suggested in (Maumet et al., 2012). 9
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In CompCor, a GLM analysis was adapted for confound removal in ASL
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as proposed in (Wang, 2012). These confounds (nuisance parameters) are comprised of motion estimation errors and the subsequent interpolation im-
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perfection, global signal fluctuations, and physiological activations. There-
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fore, covariates contributing to signal changes were assumed as follows: six motion parameters, a global signal measured as mean signal within the brain
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mask (Wang, 2012) and the first eight principal components extracted from signals within ventricle in which there is no neural activity (Behzadi et al.,
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2007). The GLM analysis was performed on a voxel-by-voxel basis using SPM8 on the (T0 ,C0 ) and (T90 ,C90 ) time course. In GLM, a high-pass filter with a cut-off at 120 seconds was used to remove low frequency components
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from the data.
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2.3.3. Tagging efficiency: MP-PCASL analysis It has been shown that the tagging efficiency is dependent on off-resonance
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effects at the tagging location, and on the orientation of arteries relative to the tagging plane (Luh et al., 2013). The MP-PCASL data acquisition was specifically introduced to characterize the tagging efficiency loss due to offresonance effects and improved perfusion quantification (Jung et al., 2010). ◦
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The measured signals for voxel j with offset phases of θ = {0 , 90 , 180 , ◦
270 } can be modeled as (Jung et al., 2010):
sj,θ = pj f (θ − εj ) + bj
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where p is the perfusion signal, f (.) is the expected inversion response function (Figure 2A) that models the MP-PCASL inversion (Jung et al., 2010), ε is the phase-tracking error, and b is the baseline signal. 10
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In order to adapt the MP-PCASL analysis for both symmetric and asym-
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metric datasets, first, two perfusion images from an equal number of mulPN tiphase ASL images were computed: ∆M A = j=1 (sj,180 − sj,0 )/N and PN ∆M B = j=1 (sj,270 −sj,90 )/N , where for asymmetric data (0◦ -phase-offset=60
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pairs, 90◦ -phase-offset=20) only the first 20 pairs (N = 20) were considered,
while for symmetric datasets (0◦ -phase-offset=60 pairs, 90◦ -phase-offset=60)
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all 60 pairs were used. A non-linear optimization process was performed on
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a voxel basis to determine the two unknowns p and ε:
tagging efficiency (αj,A )
z }| { ∆M A = pj (f (180 − εj ) − f (0 − εj )),
(3)
tagging efficiency (αj,B )
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∆M B = pj (f (270 − εj ) − f (90 − εj )) | {z }
The estimated p can, however, be interpreted as a tag/control signal dif-
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ference scaled by the corresponding tagging efficiency (α) which is directly computed from the optimized ε, where −2 6 α 6 2. Both ∆M A and ∆M B , and α contain a certain level of noise, and subsequent scaling would significantly amplify the noise contribution in the resulting p. Figure 2B demonstrates the effect of tagging efficiency as a function of phase-tracking error on the ∆M signals. In order to use all the available data for computing perfusion signal, a weighted-averaging scheme among the ∆M images is proposed. To compare tagging efficiency characteristics of the ∆M A over ∆M B signal, a normalized coefficient wj is considered:
wj,A =
|αj,A | |αj,B | and wj,B = , 0 6 wj,A|B 6 1 |αj,A | + |αj,B | |αj,A | + |αj,B | 11
(4)
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The optimum p is then computed with the following weighted-average
∆M j,A ∆M j,B + wj,B . αj,A αj,B
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p′j = wj,A .
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scheme, taking into account all ASL images for computing ∆M images:
(5)
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The noise extent can be spatially suppressed by filtering the estimated ε, assuming local phase homogeneity; in particular, a median filter of size 3×3
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was applied to the final phase-tracking error maps. This weighted scheme can be easily generalized to higher order MP-PCASL schemes as described
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in Supplementary Material. 2.3.4. Spatial filtering
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To reduce spatial noise, a standard Gaussian smoothing and three adap-
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tive filters were evaluated:
• 2D Gaussian smoothing filter, with full width at half maximum of 6
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mm.
• Wiener filter with a kernel size of 7×7. • Anisotropic diffusion (AD) filter (Wells et al., 2010), with the number of iterations, conduction coefficient and the diffusion time step optimized empirically, and set to 7, 25 and 0.02, respectively.
• Non-local means combined dual-tree complex wavelet transform (DTCWT) denoising method (Liang et al., 2013b), with a kernel size of 7×7, and the background signal used to initialize the noise level. The denoising parameters for each approach were determined empirically through maximizing the reproducibility measure. 12
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2.3.5. Partial volume correction
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Given that the most commonly used ASL acquisitions have a relatively
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low spatial resolution (e.g. 4×4×6 mm3 in the current study), PVE correction is expected to improve the CBF reliability. Two recently introduced
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ASL PVE correction methods based on linear regressions (LR) (Asllani et al., 2008) and modified least trimmed squares (mLTS) (Liang et al., 2013a) were
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considered in the current analysis. These PVE correction algorithms rely on the tissue segmentation maps obtained from high resolution T1w images.
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They model the perfusion (p′ ) voxel intensity as a weighted sum of the tissue contribution on a small neighborhood of size 5×5×1 to compute WM and GM perfusion maps. The mLTS method was demonstrated to produce less
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blurring effect than the linear regression method. 2.3.6. Blood magnetization
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To obtain CBF maps in absolute units, estimation of the equilibrium magnetization of arterial blood (M0B ) plays an important role (Wong et al., 1998):
M0B = RT T . M0,T T . e(1/T 2T T −1/T 2B )×T E . (1/(1 − e−T R/T 1GM ))
(6)
where T T indicates the tissue type (GM, WM and CSF) used as the
quantification reference, T 2B is the transverse relaxation time of blood signal (which was set to 275 ms (Stanisz et al., 2005)), T E = 56 ms is the PD echo time, RT T is the ratio of tissue transverse magnetization to blood in a PD image, M0,T T is the signal intensity of the fully relaxed signal (M0 image) within the tissue of interest. The last term adjusts the underestimated PD 13
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signal due to short TR=3750 ms, considering T1GM =1331 ms (Stanisz et al.,
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2005).
To estimate M0,T T , four previously proposed methods were evaluated
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here. The first three use a global scaling value and therefore, do not ac-
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count for the RF coil inhomogeneities and are based on estimating M0B from (i) a CSF ventricular region M0B CSF, with values of 0.87 and 250 ms were
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assigned to RCSF and T 2CSF , respectively (C ¸ avu¸so˘glu et al., 2009; Stanisz et al., 2005); (ii) a WM region (M0B WM), with RW M = 1.19 (Donahue et al.,
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2006a) and T 2W M =79.6 ms (Stanisz et al., 2005); and (iii) a GM region (M0B GM), with RGM =0.98 and T 2GM =110 ms. The fourth method is based on a voxel-wise magnetization map (M0B Map) (C ¸ avu¸so˘glu et al., 2009).
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The M0 image was corrected for PVE and then every voxel value in the
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resulting WM and GM PVE corrected maps was used as the local blood magnetization reference (M0,T T ) in Eq.(6). This method has the advantage of
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simultaneously correcting for receiver coil inhomogeneities (C ¸ avu¸so˘glu et al., 2009), and signal loss caused by imperfect slab profile in 3D imaging (Liang et al., 2014a).
2.3.7. CBF quantification CBF can be calculated using the following equation (Buxton et al., 1998):
CBF =
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∆M 6000 × −τ [1 − exp( T1B )] M0B
LD T1B exp( −P ) T1B
(7)
where α is the tagging efficiency of MP-PCASL (set to 0.95 (Jung et al., 2010)), T1B is the longitudinal relaxation time of blood (set to 1.66 s (Lu et al., 2004)), P LD is the post-labeling delay (1.54 s), τ is the labeling
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duration (1.5836 s), M0B is equilibrium magnetization of blood (computed
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from Eq.(6) using the four approaches mentioned above), ∆M is the perfusion signal, and the factor of 6000 converts the units to ml/(100g)/min. CBF was
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PVE corrected maps obtained from Sec.2.3.5.
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computed separately for WM and GM tissues, where ∆M was set to the
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2.4. Evaluation
The performance of each individual processing step was evaluated through SNR and/or test-retest reproducibility via intraclass correlation coefficient
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(ICC) and within-subject coefficient of variation (wsCV). The ICC is defined
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as:
BM S − W M S BM S + W M S
(8)
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ICC =
where BMS is the between-group mean square and WMS is the within-
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group mean square. ICC measures the degree of absolute between-subject agreement, and ranges between 0-1 (>0.80 high, 0.61-0.80 good, 0.41-0.60 moderate, 0.21-0.40 fair, and
