Priors-guided adaptive outlier cleaning for arterial spin labeling perfusion MRI

Ze Wang^{1,2}

Repeat ASL data acquired with a pseudo continuous ASL sequence were identified from previous study [4] and local database. Data acquisitions were approved by IRB with signed consent form from all subjects. 15 old subjects were included. ASL images were preprocessed using ASLtbx with state-of-art denoising procedures [5, 6]. After CBF calculations, both the original AOC and the priors-guided AOC (PAOC) were used to remove outliers before calculating the final mean CBF map. Mean CBF images were normalized into the MNI space using SPM12. The test-retest stability of CBF maps was then calculated using the intraclass correlation coefficient (ICC).

PAOC was implemented as shown in Fig. 1. The pseudo CBF map was created to have higher value in GM and lower value in WM and can be replaced with the M0 map after excluding CSF. The threshold for labeling timepoints as possible outliers can be adjusted as well. In practice, we found that one standard deviation is enough to label all suspicious points. Since CBF value doesn’t vary significantly within each brain tissue, similar to [7], we remove the suspicious timepoint if that results in reduced variations. Meanwhile, since CBF varies more in GM (for example, CBF is higher in the default mode network at rest), the outlier definition is further conditioned at whether a removal will result in increased GM/WM variation difference.

1. Wong, E.C., Potential and pitfalls of arterial spin labeling based perfusion imaging techniques for MRI, in Functional MRI, C.T.W.M.a.P.A. Bandettini, Editor. 1999: New York. p. 63-69.

2. Wang, Z., et al., Arterial Spin Labeled MRI in Prodromal Alzheimer's Disease: A Multi-Site Study. Neuroimage: clinical, 2013. 2: p. 630-636.

3. Ze Wang, S.D., David Wolk, John A Detre, Assessing Arterial Spin Labeled Perfusion MRI as an Early-Alzheimer's Disease Marker using the ADNI 2 data, in the 21th Annual Meeting of the International Society of Magnetic Resonance in Medicine. 2013: Salt Lake City, USA. p. 1500.

4. Zhang, Q., et al., Microvascular Perfusion Based on Arterial Spin Labeled Perfusion MRI as a Measure of Vascular Risk in Alzheimer's Disease. Journal of Alzheimers Disease, 2012. 32(3): p. 677-687.

5. Wang, Z., et al., Empirical optimization of ASL data analysis using an ASL data processing toolbox: ASLtbx. Magnetic Resonance Imaging, 2008. 26(2): p. 261-269, PMC2268990.

6. Wang, Z., Improving Cerebral Blood Flow Quantification for Arterial Spin Labeled Perfusion MRI by Removing Residual Motion Artifacts and Global Signal Fluctuations. Magnetic Resonance Imaging, 2012. 30(10): p. 1409-15.

7. Sudipto Dolui, Z.W., David A. Wolk, John A. Detre An Outlier Rejection Algorithm for ASL Time Series : Validation with ADNI Control Data, in the 23th Annual Meeting of the International Society of Magnetic Resonance in Medicine. 2015: Toronto, Canada.

Fig. 1. The pipeline of the prios-guided adaptive outlier cleaning algorithm. CC means correlation coefficient. std means standard deviation.

Fig. 2. Outlier cleaning results for a representative subject's scan 1 data. The display window is 0-60 ml/100g/min.

Fig. 3. Results of PAOC with different punishment criteria. The bottom right panel shows that punishing the sum of GM/WM standard deviations may not be sufficient for a good quality AOC. The display window is 0-90 ml/100g/min.

Fig. 4. ICC results. The display window is 0-1. All ICC maps were thresholded at ICC>0.5, indicating high test-retest stability.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)

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