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Dayna Leigh Richter^1,2 and Samuel Colles Grant^1,2
1National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, United States, ²Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL, United States

Keywords: Neurodegeneration, Arterial spin labelling, Migraine, FAIR-EPI

Migraine is a neurological disorder with neurovascular implications. Blood flow changes based on brain's activity and needs, so excessive neural activation during migraine is likely to make significant alterations to cortical blood perfusion.  Therefore, cortical blood perfusion was measured using FAIR-PASL to monitor the progression in preclinical migraine in female Sprague-Dawley rats. Parameters for inversion RF pulse shape and slice thickness were optimized to achieve more robust fits for monitoring cortical blood perfusion during preclinical migraine.

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Jinyuan Zhang^1,2, Yishuang Yang^1,2, Rong Xue^1,2, Yan Zhuo^1,2, and Zihao Zhang^1,3
1State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, ²University of Chinese Academy of Sciences, Beijing, China, ³Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China

Keywords: Software Tools, Software Tools

We developed a python-based post-processing toolkit for rodent perfusion MRI with an easy-to-use graphical user interface (GUI). Until now, this toolkit provides interfaces for the post-processing of dynamic susceptibility contrast (DSC) MRI and flow-sensitive alternating inversion recovery (FAIR) pulsed arterial spin labeling (pASL). For each modality, the toolkit has function modules including image viewer, display of time series data, ROI tools, and visualization of quantitative parameter maps. This toolkit is open source and welcomes added features. It will benefit preclinical studies using perfusion MRI.

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Buse Buz-Yalug¹, Ayca Ersen Danyeli^2,3, M. Cengiz Yakicier^3,4, M. Necmettin Pamir^3,5, Koray Ozduman^3,5, Alp Dincer^3,6, and Esin Ozturk-Isik^1,3
1Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey, ²Department of Medical Pathology, Acibadem University, Istanbul, Turkey, ³Brain Tumor Research Group, Acibadem University, Istanbul, Turkey, ⁴Department of Molecular Biology and Genetics, Acibadem University, Istanbul, Turkey, ⁵Department of Neurosurgery, Acibadem University, Istanbul, Turkey, ⁶Department of Radiology, Acibadem University, Istanbul, Turkey

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence

The main purpose of this study is to analyze the relative cerebral blood volume (rCBV) differences of adult diffuse glioma subgroups defined in the updated WHO 2021 brain tumor classification. The IDH wildtype group had statistically significantly higher rCBV values, and IDH mutational subgroups were classified with 82.5% accuracy (precision = 82.1%, recall = 82.9%), while the accuracies of glioblastoma and oligodendroglioma classification was 81.9%, glioblastoma and astrocytoma classification was 83.3%, and astrocytoma and oligodendroglioma classification was 77.5%.

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Safa Sanami^1,2, Brittany N Intzandt³, Julia Huck¹, PREVENT-AD Research Group⁴, and Claudine J Gauthier^2,5,6
1Physics, Concordia University, Montreal, QC, Canada, ²Centre de Recherche de l’Institut de Cardiologie de Montr´eal, Montreal, QC, Canada, ³Sunnybrook Research Institute, Toronto, ON, Canada, ⁴Douglas Mental Health Institute, Montreal, QC, Canada, ⁵Concordia University, Montreal, QC, Canada, ⁶PERFORM Centre, Concordia University, Montreal, QC, Canada

Keywords: Data Processing, Alzheimer's Disease

Alzheimer’s Disease (AD) is the most common form of dementia and displays a long preclinical phase. The use of non-steroidal anti-inflammatory drugs (NSAIDs) in this phase has a protective effect and decreases the risk of developing AD cross-sectionally. However, the effects of NSAIDs therapy long-term on cerebral hemodynamics in this early phase is unclear. This is the first study to assess whether longitudinal use of NSAIDs (i.e., naproxen) has an impact on cerebral blood flow, and whether this is associated with a change in cognition and/or cerebrospinal fluid markers. 

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James Duffin^1,2, Ece Su Sayin^1,3, Olivia Sobczyk^2,3, Julien Poublanc⁴, Harrison T. Levine^1,3, David J. Mikulis⁴, and Joseph A. Fisher^1,5
1Physiology, University of Toronto, Toronto, ON, Canada, ²Department of Anaesthesiology and Pain Management, University of Toronto, Toronto, ON, Canada, ³Joint Department of Medical Imaging and the Functional Neuroimaging Lab,, University Health Network, Toronto, ON, Canada, ⁴Joint Department of Medical Imaging and the Functional Neuroimaging Lab, University Health Network, Toronto, ON, Canada, ⁵Department of Anaesthesiology and Pain Management, University Health Network, Toronto, ON, Canada

Keywords: Data Analysis, Brain

We used hypoxia-induced changes in deoxyhemoglobin concentration as a susceptibility contrast agent. Transfer function analysis of the resulting changes in the BOLD signal for each voxel provided cerebral perfusion measures that indicate the distribution of the strength of the signal response to the contrast agent (gain) and the lag of the response (phase or time). We relate the gain and lag to conventional resting hemodynamic measures cerebral blood volume and mean transit time, respectively.

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Robert Grimm¹, Malte Müller², Cornelius Jacob¹, Sabine Mollus¹, Christian Tietjen³, Moon Hyung Choi⁴, Kazuki Oyama⁵, Thomas Weikert⁶, Andrew D Hardie⁷, Jeong Hee Yoon⁸, Heinrich von Busch³, Gregor Thoermer¹, and Volker Daum^2
1MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, ²Chimaera GmbH, Erlangen, Germany, ³Digital and Automation, Siemens Healthcare GmbH, Erlangen, Germany, ⁴Eunpyeong St. Mary’s Hospital, Catholic University of Korea, Seoul, Korea, Republic of, ⁵Department of Radiology, Shinshu University Hospital, Nagano, Japan, ⁶Department of Radiology, Universitätsspital Basel, Basel, Switzerland, ⁷Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, United States, ⁸Seoul National University Hospital and College of Medicine, Seoul, Korea, Republic of

Keywords: Data Analysis, Machine Learning/Artificial Intelligence

A deep learning-based approach for automatic identification of the perfusion phases in dynamic T1-weighted liver MRI is presented. First, an encoder model combined with two dense layers was trained to classify each image into pre-contrast, arterial, portal-venous, late, or hepatobiliary phase. In a second pass, classification errors are detected and adjusted, based on the expected occurrence order and relative timing to the arterial phase. The AI model reached sensitivities of 67% to 99%. Most common mis-classifications were confusions of the portal-venous or late phase with the adjacent phases. By the rule-based adjustments, the classification performance was raised to >95% accuracy.

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Eric Schrauben^1
1Radiology and Nuclear Medicine, Academic Medical Centre, Amsterdam, Netherlands

Keywords: Software Tools, DSC & DCE Perfusion

An open-source MATLAB-based software tool was developed for digital simulation (using the MRXCAT framework) of DCE MRI acquisition and reconstruction strategies, termed the ERIC phantom: extra-dimensional respiration and inflow of contrast. The ERIC phantom is a user-friendly graphical user interface, allowing for variable respiratory motion, acquisition parameters and trajectories, motion correction strategies, and compressed sensing reconstructions. This realistic abdominal DCE phantom can be used to investigate varying strategies for producing high quality DCE reconstructions in the presence of respiratory motion and contrast inflow.

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Fraser Maurice Callaghan^1,2, Barbara Burkhardt^2,3, Julia Geiger^2,4, Emanuela Valsangiacomo Buechel^2,3, and Christian Kellenberger^2,4
1University Children's Hospital Zurich, Zurich, Switzerland, ²Children’s Research Center, University Children's Hospital Zurich, Zurich, Switzerland, ³Division of Pediatric Cardiology, University Children's Hospital Zurich, Zurich, Switzerland, ⁴Department of Diagnostic Imaging, University Children's Hospital Zurich, Zurich, Switzerland

Keywords: Data Analysis, Cardiovascular, 4D flow

4D flow MRI provides a rich dataset that can make analysis challenging. A new technique of left ventricle (LV) registration is presented and compared with established techniques for analysis of flow dynamics in a normal paediatric population. Averaging of group flow dynamics in a common space permits additional analysis, complementary to established techniques.

Our technique demonstrated flow dynamics on a common LV space and identified subtle differences in LV velocities between normal male and female subjects of a paediatric population. 

This represents a new tool in 4D flow data analysis for potential biomarker development. 

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Ziming Xu¹, Linggen Dong², Longhui Zhang², Yajie Wang¹, Jiaqi Dou¹, Peng Liu², Ming Lv², and Huijun Chen^1
1Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing, China, ²Department of Neurosurgery, Beijing Tiantan Hospital, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China

Keywords: Data Analysis, DSC & DCE Perfusion

Size of intracranial aneurysm (IA) is considered the most important clinical factor to determine the risk of IA rupture. Accurate prediction of aneurysm growth is crucial for preventive management but remains challenging. Herein, we studied the potential predictive roles of demography and imaging characteristics in a radiological follow-up study. And wall permeability calculated by DCE-MRI was firstly observed to accurately identify the IAs with a relatively high risk of aneurysm growth (AUC = 0.875). Our study demonstrated that higher wall permeability was associated with aneurysm growth and the regions with higher wall permeability co-localized with the direction of aneurysm growth.
 

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Jiachen Liu¹, Rui Shen¹, Dandan Yang², Miaoxin Yu³, and Xihai Zhao^1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China, ²Department of Radiology, Beijing Geriatric Hospital, Beijing, China, ³Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

Keywords: Data Analysis, Atherosclerosis

Geometry of VBA has been proved to be associated with the presence of BA plaques, yet relative studies are lack of 3D information due to methodological difficulties. We proposed a semi-automated algorithm for quantitative measurement, which can evaluate local diameter, local curvature and angles between vessels directly in 3D space. We used the proposed algorithm to measure the 3D geometric metrics and found significant association between VBA geometry and the presence of BA plaques. Normalized diameter variation of BA and BA & PCA-R angle projected to lateral view were also associated with the presence of BA plaques independently.

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Lixin Liu¹, Yi Lv², Peirong Jiang³, He Wang^1,4,5, and Zhensen Chen^1,5
1Institute of Science and Technology for Brain-Inspired Intelligence,Fudan University, Shanghai, China, ²School of Compute Science and Technology, Beijing Institute of Technology, Beijing, China, ³Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China, ⁴Human Phenome Institute, Fudan University, Shanghai, China, ⁵Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China

Keywords: Data Processing, Blood vessels

 Labeling of intracranial arteries is important for computer-aided diagnosis of cerebrovascular diseases and quantitative analysis of intracranial vasculature. Performance of the previously proposed automated intracranial artery labeling method based on Graph Neural Network (GNN) is limited in datasets with overt cerebrovascular steno-occlusive diseases. In this study, we improved the generalizability of the GNN-based method by using dedicated data augmentation and spatial normalization strategy. The results show that our method is more robust than the previous method in ischemic stroke patients with overt intracranial stenosis or occlusion.

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SeyyedKazem HashemizadehKolowri¹, Nadin Zanaty^2,3, Gador Canton², Niranjan Balu², Thomas S. Hatsukami², and Chun Yuan^1,2
1Radiology and Imaging Sciences, University of Utah, SALT LAKE CITY, UT, United States, ²Department of Radiology, University of Washington, Seattle, WA, United States, ³Radiology, Zagazig University, Zagazig, Egypt

Keywords: Machine Learning/Artificial Intelligence, Cardiovascular, Vessel Wall Imaging

In this work, a deep learning approach for automated localization of carotid arteries in black blood contrast MR data is proposed. This is the first step in automated analysis of vessel wall imaging data. Carotid arteries supply oxygenated blood to the brain and are susceptible to atherosclerosis, so their vessel wall imaging is of significant importance in clinical evaluations. However, currently only qualitative assessment of VW imaging data relying on visual inspection is implemented in clinics that are not scaleable.  Therefore, developing automated image processing tools to quantitatively analyze vessel wall imaging data can have a major clinical impact.

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Zhixin Li^1,2,3, Jingyuan Zhang^1,2,3, Li Liang ⁴, Jing An⁵, Hairong Qian⁴, Rong Xue^1,2,3, Yan Zhuo^1,2,3, and Zihao Zhang^1,2,6
1State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, Beijing, China, ²University of Chinese Academy of Sciences, Beijing, China, Beijing, China, ³The Innovation Center of Excellence on Brain Science, Chinese Academy of Sciences, Beijing, China, Beijing, China, ⁴Department of Neurology, the Sixth Medical Center, Chinese PLA General Hospital, Beijing, China, ⁵Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China, Shenzhen, China, ⁶Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China, Hefei, China

Keywords: Machine Learning/Artificial Intelligence, Modelling

Deep medullary veins (DMVs) support cerebral venous drainage. They may display abnormal changes in patients with cognitive impairment. They can be visualized by multi-echo gradient echo imaging at 7T. This study proposed a segmentation and tracking method based on deep learning and shortest-path optimization. It automatically quantified the morphologic parameters of DMVs from the vascular model. These characteristics of DMVs correlated with the patients’ cognitive scores, and might reflect the pathology of vascular lesions in cognitive impairment.

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Yining He¹, Jianing Tang^1,2, Tianrui Zhao^1,2, and Lirong Yan^1,3
1Radiology, Northwestern University, Chicago, IL, United States, ²Biomedical Engineering, Northwestern University, Evanston, IL, United States, ³Neurology, University of Southern California, Los Angeles, CA, United States

Keywords: Blood vessels, Aging

Arterial stiffness is an important risk marker for poor brain aging, vascular disease, and dementia. Greater arterial stiffness leads to the transmission of excessive pulsations from the greater vessels into the downstream capillary and tissue causing microvascular dysfunction. However, previous studies have mainly focused on central or peripheral pulse wave velocity  assessment. The present study has demonstrated a significant association of the PWV of the feeding arteries to the brain and its downstream territorial perfusion assessed by using two new MRI techniques, which could provide valuable insight into the neurovascular pathology of aging and brain dysfunction.

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Chaowei Wu^1,2, Lixia Wang¹, Nan Wang^1,3, Stephen Pandol⁴, Anthony G Christodoulou^1,2, Yibin Xie¹, and Debiao Li^1,2
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Radiology Department, Stanford University, Stanford, CA, United States, ⁴Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, United States

Keywords: Machine Learning/Artificial Intelligence, DSC & DCE Perfusion

Standard-of-care DCE-MRI suffers from a limited number of contrast phases and low temporal resolution, preventing the quantification of pharmacokinetic parameters. Quantitative DCE-MRI techniques have not yet been widely applied in the clinic due to the limited availability of specialized sequences and image reconstruction. To tackle this problem, we proposed to improve the temporal resolution of multi-phasic DCE-MRI by deep learning post-processing and demonstrated promising results in tumor delineation in the Duke-Breast-Cancer-MRI dataset.

Mi Zhou¹, Meining Chen², and Yuting Wang^1
1Department of Radiology, Sichuan Provincial People's Hospital, chengdu, China, ²Department of MR Scientific Marketing, Siemens Healthcare, Shanghai, China

Keywords: Data Analysis, Perfusion, rectum

Colorectal cancer’s pathological prognostic factors directly affect the patient's prognosis. Golden-angle RAdial Sparse Parallel (GRASP) imaging was invented to calculate the accurate perfusion parameters including influx forward volume transfer constant (Ktrans), rate constant (Kep), and plasma volume fraction (Ve). We found GRASP parameters showed significant differences in many prognostic factors, including histology type, extramural venous invasion (EMVI), lymphovascular invasion (LVI), tumor deposit (TD) and lymph node metastasis (LNM), and were independent factors for histology type, LNM, LVI and EMVI. GRASP parameters were strongly associated with preoperative prognostic factors in rectal cancer, providing useful information for treatment and follow-up protocol.

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Krishnapriya Venugopal¹, Esther A.H Warnert¹, Daniëlle van Dorth², Marion Smits¹, Matthias J.P van Osch², Dirk H.J Poot¹, and Juan Antonio Hernandez-Tamames^1,3
1Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands, ²Radiology, Leiden University Medical Center, Leiden, Netherlands, ³Medical Imaging, TU Delft, Delft, Netherlands

Keywords: Data Acquisition, Brain, Oxygenation

The reversible component of transverse relaxation time, R2’, enables the measurement of blood oxygenation, an important biomarker in several diseases. We propose a multi-echo HEPI technique to estimate R2’. HEPI combines GRE and SE and hence provides R2* and R2, when acquired at different echo-times. The accuracy of ME-HEPI in measuring R2’ is evaluated in a phantom in this work. The sensitivity of HEPI to different oxygenation levels, attained using respiratory challenge MRI, is studied in a healthy subject. This is also investigated using a simulation tool that simulates microvasculature and MR signal for varying oxygenation in the vessels.

 

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Yolanda Ohene^1,2, Elizabeth Powell³, Samo Lasič^4,5, Geoff J. M. Parker^3,6, Laura M. Parkes^1,2, and Ben R. Dickie^2,7
1Division of Psychology, Communication and Human Neuroscience, University of Manchester, Manchester, United Kingdom, ²Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, United Kingdom, ³UCL, London, United Kingdom, ⁴Danish Research Centre for Magnetic Resonance, Copenhagen, Denmark, ⁵Random Walk Imaging, Åkarp, Sweden, ⁶Bioxydyn Limited, Manchester, United Kingdom, ⁷Division of Informatics, University of Manchester, Manchester, United Kingdom

Keywords: Data Processing, Permeability

Filter exchange imaging (FEXI) is a promising technique for measuring water exchange across the blood-brain barrier (BBB). However, the application of FEXI for the rodent brain requires thinner slices and therefore higher crusher gradients which lead to a progressive underestimation of the apparent exchange rate (AXR). Here, we implement a crusher-compensated exchange rate (CCXR) model which reduces the bias induced by the crusher gradients and allows more accurate estimates of BBB water exchange in the rat brain.

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Bikash Panthi¹, Sanaz Pashapoor¹, Beatriz E. Adrada¹, Rosalind P. Candelaria¹, Mary S. Guirguis¹, Miral M. Patel¹, Rania M. Mohamed¹, Medine Boge¹, Zijian Zhou¹, Jong Bum Son¹, Ken-Pin Hwang¹, Huong T.C. Le-Petross¹, Jessica W.T. Leung¹, Marion E. Scoggins¹, Gary J. Whitman¹, Zhan Xu¹, Deanna L. Lane¹, Tanya Moseley¹, Frances Perez¹, Jason White¹, Elizabeth Ravenberg¹, Alyson Clayborn¹, Huiqin Chen¹, Jia Sun¹, Peng Wei¹, Alastair Thompson², Stacy Moulder¹, Anil Korkut¹, Lei Huo¹, Kelly K. Hunt¹, Jennifer K. Litton¹, Vicente Valero¹, Debu Tripathy¹, Wei Yang¹, Clinton Yam¹, Gaiane M Rauch¹, and Jingfei Ma^1
1MD Anderson Cancer Center, Houston, TX, United States, ²Baylor College of Medicine, Houston, TX, United States

Keywords: Radiomics, fMRI, Treatment response

We developed models based on radiomic features from dynamic contrast enhanced (DCE) MR images and demonstrated that these models have potential to serve as non-invasive biomarkers for early prediction of pathologic complete response (pCR) in triple negative breast cancer (TNBC) patients undergoing neoadjuvant systemic therapy (NAST).