Acceleration of cerebral blood flow and arterial transit time maps estimation from multiple post-labeling delay arterial spin-labeled MRI via deep learning
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 of PLDs with higher signal-to-noise ratio (SNR). Method: CBF and ATT estimations were performed for one PLD and two PLDs sepa-rately. Each model was trained independently to learn the nonlinear transformation from perfusion weighted image (PWI) to CBF and ATT images. Results: Both one-PLD and two-PLD models outperformed the conventional method visually on CBF and two-PLD model showed more accurate structure on ATT estima-tion. The proposed method significantly reduces the number of PLDs from 6 to 2 on ATT and even to single PLD on CBF without sacrificing the SNR. Conclusion: It is feasible to generate CBF and ATT maps with reduced PLDs using deep learning with high quality.
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…
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）
A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal $\left[^{18}\text{F}\right]$FDG PET imaging
Department of Physics and Technology, UiT The Arctic University of Norway（物理与技术系，UiT 北极大学）
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…
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 cer…
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）
Zhejiang University（浙江大学）；Massachusetts General Hospital/Harvard Medical School（麻省总医院/哈佛医学院）
Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is a powerful imaging technology that can measure cerebral blood flow (CBF) quantitatively. However, since only a small portion of blood is labeled compared to the whole tissue volume, conventional ASL suffers from low signal-to-noise ratio (SNR), poor spatial resolution, and long acquisition time. In this paper, we proposed a super-resolution method based on a multi-scale generative adversarial network (GAN) through unsupervised training. The network only needs the low-resolution (LR) ASL image itself for training and the T1-weight…
Babol Noshirvani University of Technology（巴博勒努希尔瓦尼技术大学）；Italian Institute of Technology（意大利技术研究院）；Royan Institute for Stem Cell Biology and Technology（罗扬干细胞生物学与技术研究所）；Sharif University of Technology（谢里夫理工大学）
Prediction of the blood flow characteristics is of utmost importance for understanding the behavior of the blood arterial network, especially in the presence of vascular diseases such as stenosis. Computational fluid dynamics (CFD) has provided a powerful and efficient tool to determine these characteristics including the pressure and velocity fields within the network. Despite numerous studies in the field, the extremely high computational cost of CFD has led the researchers to develop new platforms including Machine Learning approaches that instead provide faster analyses at a much lower cos…
Multi-Delay single-shot arterial spin labeling (ASL) imaging provides accurate cerebral blood flow (CBF) and, in addition, arterial transit time (ATT) maps but the inherent low SNR can be challenging. Especially standard fitting using non-linear least squares often fails in regions with poor SNR, resulting in noisy estimates of the quantitative maps. State-of-the-art fitting techniques improve the SNR by incorporating prior knowledge in the estimation process which typically leads to spatial blurring. To this end, we propose a new estimation method with a joint spatial total generalized variat…
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）
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 …
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…
Christian Medical College Ludhiana（卢迪亚纳基督教医学院）；Pacific Neuroscience Institute（太平洋神经科学研究所）
A stroke occurs when an artery in the brain ruptures and bleeds or when the blood supply to the brain is cut off. Blood and oxygen cannot reach the brain's tissues due to the rupture or obstruction resulting in tissue death. The Middle cerebral artery (MCA) is the largest cerebral artery and the most commonly damaged vessel in stroke. The quick onset of a focused neurological deficit caused by interruption of blood flow in the territory supplied by the MCA is known as an MCA stroke. Alberta stroke programme early CT score (ASPECTS) is used to estimate the extent of early ischemic changes in pa…
