Md Tahmid Yasar¹, Mahir Tazwar ¹, Ashish A. Tamhane², Arnold M. Evia², David A. Bennett², Julie A. Schneider², and Konstantinos Arfanakis^1,2
1Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States, ²Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States

Cerebral amyloid angiopathy (CAA) is characterized by deposition of amyloid-β protein in the walls of cortical and leptomemingeal small vessels. CAA is common in community-based older adults and has been associated with cognitive decline and dementia. To date, the association of CAA with the transverse relaxation rate, R₂, remains unknown. This study in a large number of autopsied brains from community-based older adults showed for the first time that CAA is associated with higher R₂, independent of other neuropathologies and demographic factors. The spatial pattern for this association derived from voxel-wise analysis included subcortical and frontal lobe structures.

Justin Remer¹, Eric Smith¹, Javier Villanueva-Meyer¹, Laura B Eisenmenger², M. Travis Caton¹, Amanda Baker¹, Vinil Shah¹, Karl Meisel³, Adelyn Tu-Chan³, and Matthew Amans^1
1Radiology, UCSF, San Francisco, CA, United States, ²Radiology, University of Wisconsin, Madison, WI, United States, ³Neurology, UCSF, San Francisco, CA, United States

Pulsatile tinnitus, a severely debilitating condition where a “whooshing” sound is heard with each heart beat, affects over 3 million Americans, leading to anxiety, depression and even suicide. While many there are many diseases associated with pulsatile tinnitus one common and serious condition is idiopathic intracranial hypertension.  Our study is the first to show that the presence of brain herniations through arachnoid granulations into dural sinuses on MRI is significantly associated with idiopathic intracranial hypertension in patient's with pulsatile tinnitus.

Chengcheng Zhu¹, Huibin Kang², Decai Tian³, Mahmud Mossa-basha¹, Michael Levitt⁴, David Hasan⁵, Xinjian Yang², and Yisen Zhang^2
1Radiology, University of Washington, Seattle, WA, United States, ²Neurosurgery, Tiantan Hospital, Beijing, China, ³Neurology, Tiantan Hospital, Beijing, China, ⁴Neurosurgery, University of Washington, Seattle, WA, United States, ⁵Neurosurgery, University of Iowa, Iowa, IA, United States

Aneurysm wall enhancement as shown on contrast-enhanced vessel wall MRI (VW-MRI) is a surrogate  biomarker of intracranial aneurysm (IA) wall inflammation, and it is associated with IA symptoms, growth, and rupture. However, it hasn't been used in clinical trails for UIA treatment. We performed the first prospective randomized controlled trial on the effect of statins for changes in aneurysm wall enhancement as shown in VW-MRI. We found that statins decreased aneurysm wall enhancement of UIAs in a short term of 6 months. Our results suggest VWI-MRI has great potential in future image-guided clinical trails to investigate novel treatment of IAs.

Kaiyu Zhang¹, Anders Gould², Li Chen¹, Zhensen Chen³, Gador Canton¹, Niranjan Balu¹, Thomas Hatsukami¹, and Chun Yuan^1
1Vascular Imaging Lab and BioMolecular Imaging Center, Department of Radiology, University of Washington, Seattle, WA, United States, ²Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, United States, ³Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

Intracranial artery feature extraction (iCafe), a custom-made semi-automatic vascular map construction software, can quantitatively measure intracranial vascular features on time of flight (TOF) MRA with good scan-rescan and inter/intra operator reproducibility. To fully extend the use of iCafe to a novel MRA technique named simultaneous non-contrast angiography and intraplaque hemorrhage (SNAP), we will first evaluate its vascular feature measurements reproducibility and then compare these measurements with those acquired with a newly developed AICafe (AI + iCafe) technique on the same dataset. Good reproducibility was obtained on SNAP MRA using iCafe, as well as the AICafe technique.

Chul Han¹, Gregory Harrison Turner², Candice Nguyen¹, Michael Lawton¹, and S. Paul Oh^1
1Barrow Aneurysm and AVM Research Center, Barrow Neurological Institute, Phoenix, AZ, United States, ²Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States

A mouse model of arteriovenous malformations was developed with a genetics-based approach that conditionally deleted the causative activin receptor-like kinase 1 (ACVRL1 or ALK1) gene. This model was characterized in vivo using time-of-flight angiography (TOF_MRA). We correlated radiographic and histopathologic findings and determined that the 55 Tagln-Cre (+);Alk12f/2f mouse was a promising experimental brain AVM model for future studies of AVM pathophysiology, growth, rupture, and therapeutic regression.

Ramy Hussein¹, David D. Shin², Moss Zhao¹, Jia Guo³, Michael Moseley¹, and Greg Zaharchuk^1
1Radiology, Stanford University, Stanford, CA, United States, ²Global MR Applications & Workflow, GE Healthcare, Menlo Park, CA, United States, ³Department of Bioengineering, University of California Riverside, Riverside, CA, United States

We present an attention-based 3D convolutional encoder-decoder network to synthetize PET Cerebral Blood Flow (CBF) maps from multi-parametric MRI images without using radioactive tracers. Inputs to the prediction model are structural MRI (T1 and T2 fluid-attenuated inversion recovery [FLAIR]), and arterial spin labeling (ASL) perfusion MRI images. Results show that encoder-decoder networks, with attention mechanisms and customized loss functions, can adequately combine multiple MRI image types and predict the gold-standard oxygen-15-water PET CBF maps. Adequate quantification of PET from MRI has a great potential for increasing the accessibility of cerebrovascular diseases assessment for underserved populations, underprivileged communities, and developing nations.

Alexander M Barclay¹, Alessandra S Caporale¹, Hengyi Rao¹, Hyunyeol Lee², Michael C Langham¹, and Felix W Wehrli^1
1University of Pennsylvania, Philadelphia, PA, United States, ²Kyungpook National University, Daegu, Korea, Republic of

OxFlow was used to track the global cerebral metabolic rate of oxygen (CMRO₂) during wakefulness and sleep, with concurrent EEG, in eleven healthy subjects scanned continuously for 80 minutes. CMRO₂ was derived from phase-contrast measurements of cerebral blood flow (CBF) in the neck with susceptometry-based oximetry measurements of venous oxygenation (SvO₂) in the superior sagittal sinus (SSS). Results reveal that there is negligible bias between total CBF (tCBF), obtained by upscaling the value measured in the SSS, and CBF measured at the neck, suggesting that single-slice OxFlow can be implemented to simplify data acquisition and processing without sacrificing accuracy. 

Zhehao Hu^1,2, Alexander Lerner¹, Roy Poblete³, and Zhaoyang Fan^1,4,5
1Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, ²Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, ⁴Department of Radiation Oncology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, ⁵Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States

3D MR vessel wall imaging (VWI) is a non-invasive imaging modality for directly assessing intracranial arterial wall diseases. A typical intracranial VWI protocol requires 6-12 minutes per scan to obtain adequate spatial coverage and resolution. Such a long scan time hinders widespread use of VWI in clinical settings. We have developed a novel intracranial vessel-dedicated CAscaded Multi-level WAvelet REfine (CAMWARE) network that enables a VWI scan within 4 minutes. The proposed network achieved significant improvement in vessel wall delination over conventional compressed sensing reconstruction, and several state-of-the-art deep neural networks, such as U-Net and multi-level wavelet U-Net.