Perfusion MRI is an important modality in many brain imaging protocols, since it probes cerebrovascular changes in aging and many diseases; however, it may not be always available. Here we introduce a method that seeks to estimate regional perfusion properties using spectral information of resting-state functional MRI (rsfMRI) via machine learning. We used pairs of rsfMRI and arterial spin labeling (ASL) images from the same elderly individuals with normal cognition (NC; n = 45) and mild cognitive impairment (MCI; n = 26), and built support vector machine models aiming to estimate regional cerebral blood flow (CBF) from the rsfMRI signal alone. This method demonstrated higher associations between the estimated CBF and actual CBF (ASL-CBF) at the total lobar gray matter (r = 0.40; FDR-p = 1.9e-03), parietal lobe (r = 0.46, FDR-p = 8e-04), and occipital lobe (r = 0.35; FDR-p = 0.01) using rsfMRI signals of frequencies [0.01-0.15] Hertz compared to frequencies [0.01-0.10] Hertz and [0.01-0.20] Hertz, respectively. We further observed significant associations between the estimated CBF and actual CBF in 24 regions of interest (p < 0.05), with the highest association observed in the superior parietal lobule (r = 0.50, FDR-p = 0.002). Moreover, the estimated CBF at superior parietal lobule showed significant correlation with the mini-mental state exam (MMSE) score (r = 0.27; FDR-p = 0.04) and decreased in MCI with lower MMSE score compared to NC group (FDR-p = 0.04). Overall, these results suggest that the proposed framework can obtain estimates of regional perfusion from rsfMRI, which can serve as surrogate perfusion measures in the absence of ASL.
Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Imperial College London（伦敦帝国学院）；Technical University of Munich（慕尼黑工业大学）；University Hospital Zurich（苏黎世大学医院）；Toronto Western Hospital（多伦多西部医院）；Krembil Research Institute（克雷比尔研究所）
Australian Institute for Machine Learning（澳大利亚机器学习研究所）；The University of Adelaide（阿德莱德大学）；University of Nottingham（诺丁汉大学）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）
Segmentation of cerebral blood vessels from Magnetic Resonance Imaging (MRI) is an open problem that could be solved with deep learning (DL). However, annotated data for training is often scarce. Due to the absence of open-source tools, we aim to develop a classical segmentation method that generates vessel ground truth from Magnetic Resonance Angiography for DL training of segmentation across a variety of modalities. The method combines size-specific Hessian filters, hysteresis thresholding and connected component correction. The optimal choice of processing steps was evaluated with a blinded…
University of Maryland School of Medicine（马里兰大学医学院）
Purpose: Arterial spin labeling (ASL) perfusion imaging indicates direct and absolute measurement of cerebral blood flow (CBF). Arterial transit time (ATT) is a related physiological parameter reflecting the duration for the labeled spins to reach the brain region of interest. Multiple post-labeling delay (PLDs) can provide robust measures of both CBF and ATT, allowing for optimization of regional CBF modeling based on ATT. The prolonged acquisition time can potentially reduce the quality and accuracy of the CBF and ATT estimation. We proposed a novel network to significantly reduce the number…
With substantial public concerns on potential cancer risks and health hazards caused by the accumulated radiation exposure in medical imaging, reducing radiation dose in X-ray based medical imaging such as Computed Tomography Perfusion (CTP) has raised significant research interests. In this paper, we embrace the deep Convolutional Neural Networks (CNN) based approaches and introduce Smoothed Dense-Convolution Neural Network (SDCNet) to recover high-dose quality CTP images from low-dose ones. SDCNet is composed of sub-network blocks cascaded by skip-connections to infer the noise (differential…
AngioMoCo: Learning-based Motion Correction in Cerebral Digital Subtraction Angiography
Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）；Maastricht University Medical Center（马斯特里赫特大学医学中心）；Delft University of Technology（代尔夫特理工大学）；Philips Healthcare（飞利浦医疗）；Massachusetts Institute of Technology（麻省理工学院）
IBM Research - Almaden（IBM阿尔马登研究中心）；MicroVention, Inc.（MicroVention公司）；AYL Consulting LLC（AYL咨询有限责任公司）
Intrasaccular flow disruptors treat cerebral aneurysms by diverting the blood flow from the aneurysm sac. Residual flow into the sac after the intervention is a failure that could be due to the use of an undersized device, or to vascular anatomy and clinical condition of the patient. We report a machine learning model based on over 100 clinical and imaging features that predict the outcome of wide-neck bifurcation aneurysm treatment with an intravascular embolization device. We combine clinical features with a diverse set of common and novel imaging measurements within a random forest model. W…
Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps
Columbia University（哥伦比亚大学）；Zuckerman Institute（祖克曼研究所）；Taub Institute for Research on Alzheimer’s Disease and the Aging Brain（陶布阿尔茨海默病与衰老大脑研究所）；Vagelos College of Physicians and Surgeons（瓦格洛斯内科与外科医学院）；New York State Psychiatric Institute（纽约州精神病学研究所）
Resting state fMRI (rsfMRI) has been shown to be a promising tool to study intrinsic functional connectivity and assess its integrity in cerebral development. In neonates, where fMRI is limited to few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults, neonates can't be processed with the existing adult pipelines. Therefore, we …
Shanghai University（上海大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）
Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Intracranial artery labels provide valuable guidance and navigation to numerous clinical applications and disease diagnoses. Various machine learning algorithms have been carried out for automation in the anatomical labeling of cerebral arteries. However, the task remains challenging because of the high complexity and variations of intracranial arteries. This study investigates a novel graph convolutional neural network with deep feature fusion for cerebral artery labeling. We introduce stacked graph …
Fondazione Ricerca Molinette Onlus（莫利内特研究基金会（非营利组织））；University of Turin（都灵大学）；University Medical Center Utrecht（乌得勒支大学医学中心）
The CT perfusion (CTP) is a medical exam for measuring the passage of a bolus of contrast solution through the brain on a pixel-by-pixel basis. The objective is to draw "perfusion maps" (namely cerebral blood volume, cerebral blood flow and time to peak) very rapidly for ischemic lesions, and to be able to distinguish between core and penumubra regions. A precise and quick diagnosis, in a context of ischemic stroke, can determine the fate of the brain tissues and guide the intervention and treatment in emergency conditions. In this work we present UniToBrain dataset, the very first open-source…
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Erasmus MC（伊拉斯姆斯医学中心）；University of Copenhagen（哥本哈根大学）；German Center for Neurodegenerative Disease（德国神经退行性疾病中心）；Center for Behavioral Brain Sciences（行为脑科学中心）
Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as Alzheimer's disease. With the advancement of 7 Tesla MRI systems, higher spatial image resolution can be achieved, enabling the depiction of very small vessels in the brain. Non-Deep Learning-based approaches for vessel segmentation, e.g., Frangi's vessel enhancement with subsequent…
Learned Local Attention Maps for Synthesising Vessel Segmentations
University of Leeds（利兹大学）；Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)（生物医学计算成像与模拟技术中心（CISTIB））；NIHR Leeds Biomedical Research Centre (BRC)（利兹国家健康研究院生物医学研究中心（BRC））；Alan Turing Institute（艾伦图灵研究所）；KU Leuven（鲁汶大学）；Medical Imaging Research Center (MIRC)（医学成像研究中心（MIRC））
Vascular Segmentation of Functional Ultrasound Images using Deep Learning
AIstroSight；Inria（法国国家信息与自动化技术研究院）；Hospices Civils de Lyon（里昂慈善医院）；University Claude Bernard Lyon 1（里昂第一大学）；Lyon Neuroscience Research Center（里昂神经科学研究中心）
Temple University（天普大学）；University of Maryland School of Medicine（马里兰大学医学院）
Arterial spin labeling (ASL) perfusion MRI provides a non-invasive way to quantify cerebral blood flow (CBF) but it still suffers from a low signal-to-noise-ratio (SNR). Using deep machine learning (DL), several groups have shown encouraging denoising results. Interestingly, the improvement was obtained when the deep neural network was trained using noise-contaminated surrogate reference because of the lack of golden standard high quality ASL CBF images. More strikingly, the output of these DL ASL networks (ASLDN) showed even higher SNR than the surrogate reference. This phenomenon indicates a…
