Northwestern University Feinberg School of Medicine
EP-36
AW Korutz, D Ban, F Syed, A Honarmand, MC Hurley, TJ Carroll and SA Ansari
Departments of Radiology and Biomedical Engineering, Northwestern University,
Feinberg School of Medicine, Chicago, IL

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Disclosures
.

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Background
• Stroke is the 3 rd leading cause of death in the US 1
• Intracranial atherosclerotic disease (ICAD) is responsible for
7-10% of all strokes, with up to a 23% recurrence rate in 1 year despite optimal medical management. 2-4
• MRI Perfusion - Traditionally a measure of relative CBV by
Dynamic Susceptibility Contrast MRI (i.e rCBV
DSC
) 5
• Tissue-concentration curve generated with GRE-EPI
• Arterial Input Function (AIF) generated by the concentration curve in region of major intracranial vessel (i.e. MCA)
• rCBV related to the ratio: area under tissue-concentration curve / area under AIF curve

Background
• Initial attempts at quantitation used population-based scaling factors 6,7
• i.e. scaling “presumed normal” white matter to a constant value or calculation of venous output function.
• Does not account for patient-specific physiology/pathology.
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• Newer attempts at quantification of perfusion 8-11
• Employ patient specific calibration factors based on T1 changes in white matter relative to the blood pool (CBVss)
• Additional modeling of rate of intra-extravascular water exchange
• Performed using a “Bookend” technique with T1 measurement before and after perfusion imaging
• Reproducible, reliable and accurate 12

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Background
• Despite the attempts at quantification in perfusion MRI, perfusion imaging continues to underestimate the effects of bolus delay and dispersion (DnD).
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• Broadening of the contrast bolus during injection
• Broadening of the bolus during the circulation through the heart and lungs
• Broadening of the bolus due to proximal arterial stenoocclusive disease.
• Recently published research has proposed an algorithm to correct for DnD effects as seen in MR perfusion imaging performed with Bookend Dynamic Susceptibility
Contrast MRI (DSC-MR PWI).
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Background
• Our goal
• To compare conventional mean transit time (MTT) to voxel transit time (VTT) derived from a locally determined AIF in patients with vessels supplied by vessels with severe
(>50%) intracranial atherosclerotic disease (ICAD).
• Our hypothesis
• VTT is a more sensitive metric of hypoperfusion than MTT in the setting of severe ICAD.

Materials and Methods
• Using our institution ’s PACS and medical record databases we retrospectively identified and analyzed 7 patients
• Patients were excluded with occlusion related to presumed central emboli or those with a history of prior endovascular/surgical intervention.
• Territories were excluded with encephalomalacia involving >50% of the territory volume or intermediate atherosclerotic narrowing (0-50%)
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• Included patients had moderate to severe narrowing
(>50%) of one or more of the major intracranial vessels
• Supraclinoid ICA, A2 ACA, M1/M2 MCA or P2 PCA

Materials and Methods
• Analysis was performed on the major intracranial vascular territories (ACA, MCA and PCA)
• Separated into those with no vascular narrowing
(Normal), intermediate vascular narrowing (0-50% stenosis) and advanced vascular narrowing (> 50%).
• Territories with intermediate narrowing were excluded from analysis
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• For supraclinoid ICAD  Imaging was reviewed to evaluate the ipsilateral A1 ACA and the PCOM.
• If the A1 was hypoplastic/aplastic, the ACA territory was included in the
“Normal” group.
• If a fetal type PCA was present, the PCA was included in the “Advanced ICAD” group.

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Materials and Methods
• Voxel transit time was calculated by forward convolving the global AIF to remove the effects on the shape of the contrast bolus as it flows through the cerebral vasculature, including the pial collateral arteries.
• The final deconvolution analysis yields a much shorter mean time for the bolus to traverse the image voxel.

Materials and Methods
• To only include brain parenchyma in the perfusion quantification, regions of encephalomalacia were excluded manually by a neuroradiologist on T2 images.
These images were then used to create subtraction masks.
• Each subject’s T1 images were warped to MNI space using the SPM software package in MATLAB.
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• Perfusion and T2 images were then coregistered to the warped T1 images as well as vascular territory maps
• Automated segmentation of vascular territories could be performed for quantification of territory-specific MTT/VTT.

Materials and Methods
• Vascular Territory Maps
• Maps were created when a neurointerventional radiologist manually outlined the vascular territories on a high resolution T1 sequence from a single subject in MNI space
• These images were coregistered to an average of 128 normal subject T1 images
SPM ’s T1.nii template
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Three select T1 images with vascular maps applied. These images show the ACA, PCA and all three divisions of the
MCA (superior, inferior and perforators)

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Materials and Methods
• Using the vascular maps, co-registered perfusion images were segmented into separate vascular territories in
MATLAB.
• T2 masks were applied to the vascular territories
• Final VTT and MTT values were generated for each vascular territory

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Materials and Methods
• Statistics
• Perfusion values were compared using a two-tailed
Student ’s t-test
• Patient age in the two experimental groups was compared using a two-tailed Student ’s t-test
• Patient sex in the two experimental groups was compared using a Chi Square test.

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Results
• Demographics
• 3 male, 4 female patients (mean 66 +/- 15 years)
• 17 vascular territories with severe ICAD were included
2 ACA, 7 MCA and 8 PCA
• 16 vascular territories without ICAD were included
8 ACA, 4 MCA and 4 PCA

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Results
• Demographics
• Age (Not significant between normal and severe ICAD)
65 +/- 17 years in vascular territories with normal vessels
66 +/- 15 years in vascular territories with severe ICAD
• Sex (Significant difference p = 0.02)
Territories with normal vessels - 11 men and 5 women
Territories with severe ICAD - 5 men and 12 women

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Results
MTT VTT
Normal Vessel 4.0 +/- 0.26 sec 0.36 +/-0.04 sec
Severe ICAD 3.8 +/- 0.21 sec 1.0 +/- 0.19 sec
MTT difference between groups was not significant: p=0.46
VTT difference between groups was significant: p< 0.01

Conclusions
• ICAD alters the the shape of the contrast bolus as it flows towards the parenchyma that it supplies
• Mean transit time derived from typical deconvolution analysis does not include DnD effects from the supplying arterial network
• Voxel transit time may better represent transit time in flowlimiting pathologies such as ICAD
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• DnD correction may unveil territories which are hemodynamically compromised, but would otherwise appear similar to territories supplied by normal vessels.

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References
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References
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References
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Delay and Dispersion in Absolute Quantitative Cerebral Perfusion DSC MR
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