Diagnostic Potential of Pulsed Arterial Spin Labeling in Alzheimer's Disease

Stefano Trebeschi, Isabelle Riederer, Christine Preibisch, Karl P Bohn, Stefan Förster, Panagiotis Alexopoulos, Claus Zimmer, Jan S Kirschke, Alexander Valentinitsch, Stefano Trebeschi, Isabelle Riederer, Christine Preibisch, Karl P Bohn, Stefan Förster, Panagiotis Alexopoulos, Claus Zimmer, Jan S Kirschke, Alexander Valentinitsch

Abstract

Alzheimers disease (AD) is the most common cause of dementia. Although the underlying pathology is still not completely understood, several diagnostic methods are available. Frequently, the most accurate methods are also the most invasive. The present work investigates the diagnostic potential of Pulsed Arterial Spin Labeling (PASL) for AD: a non-invasive, MRI-based technique for the quantification of regional cerebral blood flow (rCBF). In particular, we propose a pilot computer aided diagnostic (CAD) procedure able to discriminate between healthy and diseased subjects, and at the same time, providing visual informative results. This method encompasses the creation of a healthy model, the computation of a voxel-wise likelihood function as comparison between the healthy model and the subject under examination, and the correction of the likelihood function via prior distributions. The discriminant analysis is carried out to maximize the accuracy of the classification. The algorithm has been trained on a dataset of 81 subjects and achieved a sensitivity of 0.750 and a specificity of 0.875. Moreover, in accordance with the current pathological knowledge, the parietal lobe, and limbic system are shown to be the main discriminant factors.

Keywords: Alzheimer's disease; MRI; PASL; diagnosis; neuroimaging.

Figures

Figure 1
Figure 1
Pulsed Arterial Spin-Labeling (PASL) scan region. (A) represents the bounding plane of the PASL signal coverage. (B) shows the coverage of the PASL signal throughout the cohort. The intensity values represent the percentage of patients, who showed a non-zero pASL signal at that voxel location. All voxel locations whose coverage was equal or less than 0.95 were discarded.
Figure 2
Figure 2
Comparison of one 75 years old patient with Alzheimers disease (AD) and one 68 years old healthy control (HC). (A) shows the normalized CBF distribution, (B) shows the Tscore-map computed voxel-wise. A substantial difference could be already spotted in the parietal lobe. (C) shows the likelihood of hypo-perfusion L. In the AD patient severely hypo-perfused areas in the parietal lobe and neighboring regions are visible, whereas the HC subject shows regions with artificially low perfusion in the frontotemporal lobes and in the volume boundaries. These high values are the effect of uncertainty resulting from image noise, and/or patient-specific conditions. (D) shows the posterior probability (p), which represents the disease related likelihood of hypo-perfusion resulting from the application of the prior distribution. Areas with artificially low perfusion on volume boundaries have been suppressed, while the the sensitivity to interesting hypo-perfused region in the parietal lobe is still preserved. Upper labels Rh, Cp, and Lh indicates right hemisphere, central posterior, and left hemisphere, respectively. Lateral labels Ex and In indicates external view and internal view, respectively.
Figure 3
Figure 3
Model of the cerebral blood perfusion based on HC subjects: (A) shows the mean and (B) the standard deviation of the healthy group. High blood perfusion is visible in the parietal lobe and in the cuneus. We included for visual comparison (C), which shows the mean perfusion of the AD cohort. (D) visualization of the prior distribution using principal component analysis to depict the predictive power of certain cerebral regions, where the parietal lobe showed the highest predictive power.
Figure 4
Figure 4
Sensitivity, specificity, and misclassification rate functions. Discrete representation over varying threshold values. The x-axis (ranging from 0 to 5000) is associated with the Between Subject Threshold. The y-axes (ranging from 1 to 0.5) is associated with the Within Subject Threshold.
Figure 5
Figure 5
Region Analysis. Group distributions of hypo-perfused voxels in the parietal (blue), temporal (orange), and limbic system (green). Lobes which were not fully covered by the pASL signal have been excluded. Talairach brain atlas was used to perform the analysis.

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