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

Trebeschi S, Riederer I, Preibisch C, Bohn KP, Förster S, Alexopoulos P, Zimmer C, Kirschke JS, Valentinitsch A - Front Neurosci (2016)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München Munich, Germany.

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.

No MeSH data available.


Related in: MedlinePlus

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.
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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.

Mentions: Image acquisition was performed at 3T either on a Philips Achieva System (Philips Healthcare, Hamburg, Germany) (n = 24) or on a Siemens mMR Biograph (Siemens Healthcare, Erlangen, Germany) (n = 57). On both scanners, a similar sequence and comparable imaging parameters were used. At the Philips Achieva system, we used the pulsed star labeling of arterial regions (PULSAR) technique (Golay et al., 2005), while the proximal inversion with a control for off-resonance effects (PICORE) labeling technique (Wong et al., 1999) was used at the Siemens mMR Biograph. In both cases single shot EPI was used for image readout with TR = 2500 ms, α = 90° and minimum TE [TE = 17 ms (Philips); TE = 13 ms (Siemens)]. In both cases thin slice periodic saturation pulses were used to obtain a defined bolus (Q2TIPS) (Luh et al., 1999) using TI1, TI1S, TI2 = (700, 1200, 1500 ms). A set of eleven slices [matrix size 64 × 63, voxel size of 3.75 × 3.75 × 6 mm (Philips) or 4 × 4 × 6 mm (Siemens), 0.6 mm gap] aligned to the hippocampus and containing the parietal lobe were acquired in ascending order (see Figure 1). Each measurement comprised eighty pairs of label-control acquisitions. The total scan time was ~440 s. In order to perform a proper alignment between perfusion and structural images, we acquired an EPI volume covering the whole brain with the same voxel sizes in 40 slices, and a T1-weighted Turbo Field Echo sequence with voxel size 1 × 1 × 1 mm in 170 sagittal slices.


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

Trebeschi S, Riederer I, Preibisch C, Bohn KP, Förster S, Alexopoulos P, Zimmer C, Kirschke JS, Valentinitsch A - Front Neurosci (2016)

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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835490&req=5

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.
Mentions: Image acquisition was performed at 3T either on a Philips Achieva System (Philips Healthcare, Hamburg, Germany) (n = 24) or on a Siemens mMR Biograph (Siemens Healthcare, Erlangen, Germany) (n = 57). On both scanners, a similar sequence and comparable imaging parameters were used. At the Philips Achieva system, we used the pulsed star labeling of arterial regions (PULSAR) technique (Golay et al., 2005), while the proximal inversion with a control for off-resonance effects (PICORE) labeling technique (Wong et al., 1999) was used at the Siemens mMR Biograph. In both cases single shot EPI was used for image readout with TR = 2500 ms, α = 90° and minimum TE [TE = 17 ms (Philips); TE = 13 ms (Siemens)]. In both cases thin slice periodic saturation pulses were used to obtain a defined bolus (Q2TIPS) (Luh et al., 1999) using TI1, TI1S, TI2 = (700, 1200, 1500 ms). A set of eleven slices [matrix size 64 × 63, voxel size of 3.75 × 3.75 × 6 mm (Philips) or 4 × 4 × 6 mm (Siemens), 0.6 mm gap] aligned to the hippocampus and containing the parietal lobe were acquired in ascending order (see Figure 1). Each measurement comprised eighty pairs of label-control acquisitions. The total scan time was ~440 s. In order to perform a proper alignment between perfusion and structural images, we acquired an EPI volume covering the whole brain with the same voxel sizes in 40 slices, and a T1-weighted Turbo Field Echo sequence with voxel size 1 × 1 × 1 mm in 170 sagittal slices.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München Munich, Germany.

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.

No MeSH data available.


Related in: MedlinePlus