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Source: IEE Transactions on Medical Imaging
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Total 19 results found since Jan 2013.
Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets
Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and reduce the risk of disability or even death. Understanding the location and size of infarcts plays a critical role in the diagnosis decision. However, manual localization and quantification of stroke lesions are laborious and time-consuming. In this paper, we propose a novel automatic method to segment acute ischemic stroke from diffusion weighted images (DWIs) using deep 3-D convolutional neural networks (CNNs). Our method can e...
Source: IEE Transactions on Medical Imaging - August 31, 2018 Category: Biomedical Engineering Source Type: research
Monitoring Acute Stroke Progression: Multi-Parametric OCT Imaging of Cortical Perfusion, Flow, and Tissue Scattering in a Mouse Model of Permanent Focal Ischemia
Cerebral ischemic stroke causes injury to brain tissue characterized by a complex cascade of neuronal and vascular events. Imaging during the early stages of its development allows prediction of tissue infarction and penumbra so that optimal intervention can be determined in order to salvage brain function impairment. Therefore, there is a critical need for novel imaging techniques that can characterize brain injury in the earliest phases of the ischemic stroke. This paper examined optical coherence tomography (OCT) for imaging acute injury in experimental ischemic stroke in vivo. Based on endogenous optical scattering sig...
Source: IEE Transactions on Medical Imaging - May 31, 2019 Category: Biomedical Engineering Source Type: research
A Machine Learning Approach for Classifying Ischemic Stroke Onset Time From Imaging
Current clinical practice relies on clinical history to determine the time since stroke (TSS) onset. Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options, such as thrombolysis. The patients with unknown or unwitnessed TSS are usually excluded from thrombolysis, even if their symptoms began within the therapeutic window. In this paper, we demonstrate a machine learning approach for TSS classification using routinely acquired imaging sequences. We develop imaging features from the magnetic resonance (MR) images and train machine learning ...
Source: IEE Transactions on Medical Imaging - June 30, 2019 Category: Biomedical Engineering Source Type: research
Multi-Frequency Electromagnetic Tomography for Acute Stroke Detection Using Frequency-Constrained Sparse Bayesian Learning
Imaging the bio-impedance distribution of the brain can provide initial diagnosis of acute stroke. This paper presents a compact and non-radiative tomographic modality, i.e. multi-frequency Electromagnetic Tomography (mfEMT), for the initial diagnosis of acute stroke. The mfEMT system consists of 12 channels of gradiometer coils with adjustable sensitivity and excitation frequency. To solve the image reconstruction problem of mfEMT, we propose an enhanced Frequency-Constrained Sparse Bayesian Learning (FC-SBL) to simultaneously reconstruct the conductivity distribution at all frequencies. Based on the Multiple Measurement ...
Source: IEE Transactions on Medical Imaging - December 1, 2020 Category: Biomedical Engineering Source Type: research
Closed-Loop Construction and Analysis of Cortico-Muscular-Cortical Functional Network After Stroke
In this study, we integrate corticomuscular and intermuscular interactions to cortico-cortical network and propose a novel closed-loop construction of cortico-muscular-cortical functional network, named closed-loop network (CLN). Directional characteristic in terms of differentiating causal interactions is endowed on basis of the CLN framework, further expanding the definition of functional connectivity (FC) and effective connectivity (EC) dedicated to CLN. Next, CLN is applied to stroke patients to reveal the underlying after-effects mechanism of low frequency repetitive transcranial magnetic stimulation (rTMS) induced al...
Source: IEE Transactions on Medical Imaging - June 1, 2022 Category: Biomedical Engineering Source Type: research
A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation— With Application to Tumor and Stroke
We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It ...
Source: IEE Transactions on Medical Imaging - March 31, 2016 Category: Biomedical Engineering Source Type: research
autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients
The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter- and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural...
Source: IEE Transactions on Medical Imaging - September 1, 2021 Category: Biomedical Engineering Source Type: research
Capacitively Coupled Electrical Impedance Tomography for Brain Imaging
Electrical impedance tomography (EIT) is considered as a potential candidate for brain stroke imaging due to its compactness and potential use in bedside and emergency settings. The electrode–skin contact impedance and low conductivity of skull pose some practical challenges to the EIT head imaging. This paper studies the application of capacitively coupled electrical impedance tomography (CCEIT) in brain imaging for the first time. CCEIT is a new contactless EIT technique which uses voltage excitation without direct contact with the skin, as oppose to directly injecting the current to the skin in EIT. Because the s...
Source: IEE Transactions on Medical Imaging - August 31, 2019 Category: Biomedical Engineering Source Type: research
Dynamic Iterative Reconstruction for Interventional 4-D C-Arm CT Perfusion Imaging
We present a dynamic, iterative reconstruction (DIR) approach to reconstruct TACs described by a weighted sum of basis functions. To reduce noise, a regularization technique based on joint bilateral filtering (JBF) is introduced. We evaluated the algorithm with a digital dynamic brain phantom and with data from six canine stroke models. With our dynamic approach, we achieve an average Pearson correlation (PC) of the PCCT canine blood flow maps to co-registered perfusion CT maps of 0.73. This PC is just as high as the PC achieved in a recent PCCT study, which required repeated injections and acquisitions.
Source: IEE Transactions on Medical Imaging - June 28, 2013 Category: Biomedical Engineering Source Type: research
An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial–Temporal Total Variation Regularization
Cerebrovascular diseases, i.e., acute stroke, are a common cause of serious long-term disability. Cerebral perfusion computed tomography (CPCT) can provide rapid, high-resolution, quantitative hemodynamic maps to assess and stratify perfusion in patients with acute stroke symptoms. However, CPCT imaging typically involves a substantial radiation dose due to its repeated scanning protocol. Therefore, in this paper, we present a low-dose CPCT image reconstruction method to yield high-quality CPCT images and high-precision hemodynamic maps by utilizing the great similarity information among the repeated scanned CPCT images. S...
Source: IEE Transactions on Medical Imaging - February 1, 2019 Category: Biomedical Engineering Source Type: research
Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks
Fully convolutional networks have been achieving remarkable results in image semantic segmentation, while being efficient. Such efficiency results from the capability of segmenting several voxels in a single forward pass. So, there is a direct spatial correspondence between a unit in a feature map and the voxel in the same location. In a convolutional layer, the kernel spans over all channels and extracts information from them. We observe that linear recombination of feature maps by increasing the number of channels followed by compression may enhance their discriminative power. Moreover, not all feature maps have the same...
Source: IEE Transactions on Medical Imaging - November 29, 2019 Category: Biomedical Engineering Source Type: research
Perfusion Imaging: An Advection Diffusion Approach
Perfusion imaging is of great clinical importance and is used to assess a wide range of diseases including strokes and brain tumors. Commonly used approaches for the quantitative analysis of perfusion images are based on measuring the effect of a contrast agent moving through blood vessels and into tissue. Contrast-agent free approaches, for example, based on intravoxel incoherent motion and arterial spin labeling, also exist, but are so far not routinely used clinically. Existing contrast-agent-dependent methods typically rely on the estimation of the arterial input function (AIF) to approximately model tissue perfusion. ...
Source: IEE Transactions on Medical Imaging - December 1, 2021 Category: Biomedical Engineering Source Type: research
Multifrequency Electrical Impedance Tomography Using Spectral Constraints
We present a method for performing MFEIT using spectral constraints. Boundary voltage data is employed directly to reconstruct the volume fraction distribution of component tissues using a nonlinear method. Given that the reconstructed parameter is frequency independent, this approach allows for the simultaneous use of all multifrequency data, thus reducing the degrees of freedom of the reconstruction problem. Furthermore, this method allows for the use of frequency difference data in a nonlinear reconstruction algorithm. Results from empirical phantom measurements suggest that our fraction reconstruction method points to ...
Source: IEE Transactions on Medical Imaging - February 1, 2014 Category: Biomedical Engineering Source Type: research
Stacked Bidirectional Convolutional LSTMs for Deriving 3D Non-Contrast CT From Spatiotemporal 4D CT
The imaging workup in acute stroke can be simplified by deriving non-contrast CT (NCCT) from CT perfusion (CTP) images. This results in reduced workup time and radiation dose. To achieve this, we present a stacked bidirectional convolutional LSTM (C-LSTM) network to predict 3D volumes from 4D spatiotemporal data. Several parameterizations of the C-LSTM network were trained on a set of 17 CTP-NCCT pairs to learn to derive a NCCT from CTP and were subsequently quantitatively evaluated on a separate cohort of 16 cases. The results show that the C-LSTM network clearly outperforms the baseline and competitive convolutional neur...
Source: IEE Transactions on Medical Imaging - March 31, 2020 Category: Biomedical Engineering Source Type: research
Automatic Collateral Scoring From 3D CTA Images
The collateral score is an important biomarker in decision making for endovascular treatment (EVT) of patients with ischemic stroke. The existing collateral grading systems are based on visual inspection and prone to subjective interpretation and interobserver variation. The purpose of our work is the development of an automatic collateral scoring method. In this work, we present a method that is inspired by human collateral scoring. Firstly, we define an anatomical region by atlas-based registration and extract vessel structures using a deep convolutional neural network. From this, high-level features based on the ratios ...
Source: IEE Transactions on Medical Imaging - May 31, 2020 Category: Biomedical Engineering Source Type: research
