Spatio-temporal data fusion in cerebral angiography

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 153-167).This thesis provides a framework for generating the previously unobtained high resolution time sequences of 3D images that show the dynamics of cerebral blood flow. These sequences allow image feedback during medical procedures that can facilitate the detection and observation of stenosis, aneurysms, and clots. The 3D time series is constructed by fusing together a single static 3D image with one or more time sequence of 2D projections. The fusion process utilizes a variational approach that constrains the volumes to have both smoothly varying regions separated by edges and sparse regions of non-zero support. Results are presented on both clinical and simulated phantom data sets. The 3D time series results are visualized using the following tools: time series of intensity slices, synthetic X-rays from an arbitrary view, time series of isosurfaces, and 3D surfaces that show arrival times of contrast using color. This thesis also details the different steps needed to prepare the two classes of data. In addition to the spatio-temporal data fusion algorithm, three new algorithms are presented: a single pass groupwise registration algorithm for registering the time series, a 2D-3D registration algorithm for registering the time series with respect to the 3D volume, and a modified adaptive version of the Cusum algorithm used for determining arrival times of contrast within the 2D time sequences.by Andrew David Copeland.Ph.D

Computer aided diagnosis of cerebrovascular disease based on DSA image

In recent years, the incidence of cerebrovascular diseases in China has shown a significant upward trend, and it has become a common disease threatening people's lives. Digital Subtraction Angiography (DSA) is the gold standard for the diagnosis of clinical cerebrovascular disease, and it is the most direct method to check the brain lesion. At present, there are the following two problems in the clinical research of DSA images: DSA is a real-time image with numerous frames, containing much useless information in frames; thus, human interpretation and annotation are time-consuming and labor-intensive. The blood vessel structure in DSA images is so complicated that high practical skills are required for clinicians. In the computer-aided diagnosis of DSA sequence images, there is currently a lack of automatic and effective computer-aided diagnosis algorithms for cerebrovascular diseases. Based on the above issues, the main work of this paper is as follows: 1.A multi-target detection algorithm based on Faster-RCNN is designed and applied to the analysis of brain DSA images. The algorithm divides DSA images into arterial phase, capillary phase, pre-venous phase and sinus phase by identifying the main blood vessel structure in each frame. And on this basis, we analyze the time relationship between the time phases. 2.On the basis of DSA phase detection, a key frame location algorithm based on single blood vessel structure detection is designed for moyamoya disease. First, the target detection model is applied to locate the internal carotid artery and the Willis circle. Then, five frames of images are extracted from the arterial period as keyframes. Finally, the nidus' ROI is determined according to the position of the internal carotid artery. 3.A diagnostic method for cerebral arteriovenous malformation (AVM) is designed, which combines temporal features and radiomics features. First, on the basis of DSA time phase detection, we propose a deep learning network to extract vascular time features from the DSA video; then, the time feature is combined with the radiomics features of the static keyframe to establish an AVM diagnosis model. While assisting diagnosis, this method does not require any human intervention, and reduces the workload of clinicians. The diagnostic model that combines time features and radiomics features is applied to the study of AVM staging. The experimental results prove that the classification model trained by fusion features has better diagnostic performance than the model trained by either time features or radiomics features. Based on the above three parts, this paper establishes a cerebrovascular disease analysis framework based on radiomics method and deep learning. We introduce corresponding solutions for DSA automatic image reading, rapid diagnosis of moyamoya disease, and precise diagnosis of AVM. The method proposed in this paper has practical significance for assisting the diagnosis of cerebrovascular disease and reducing the burden of medical staff.Digital Subtraction Angiography(DSA), Radiomics analysis, Arteriovenous malformations, Moyamoya, Faster-RCNN, Temporal features, Fusion feature

Guest Editorial Special Issue on Medical Imaging and Image Computing in Computational Physiology

International audienceThe January 2013 Special Issue of IEEE transactions on medical imaging discusses papers on medical imaging and image computing in computational physiology. Aslanid and co-researchers present an experimental technique based on stained micro computed tomography (CT) images to construct very detailed atrial models of the canine heart. The paper by Sebastian proposes a model of the cardiac conduction system (CCS) based on structural information derived from stained calf tissue. Ho, Mithraratne and Hunter present a numerical simulation of detailed cerebral venous flow. The third category of papers deals with computational methods for simulating medical imagery and incorporate knowledge of imaging physics and physiology/biophysics. The work by Morales showed how the combination of device modeling and virtual deployment, in addition to patient-specific image-based anatomical modeling, can help to carry out patient-specific treatment plans and assess alternative therapeutic strategies

Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. Time is brain is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions

CT Perfusion is All We Need: 4D CNN Segmentation of Penumbra and Core in Patient With Suspected Ischemic Stroke

Precise and fast prediction methods for ischemic areas comprised of dead tissue, core, and salvageable tissue, penumbra, in acute ischemic stroke (AIS) patients are of significant clinical interest. They play an essential role in improving diagnosis and treatment planning. Computed Tomography (CT) scan is one of the primary modalities for early assessment in patients with suspected AIS. CT Perfusion (CTP) is often used as a primary assessment to determine stroke location, severity, and volume of ischemic lesions. Current automatic segmentation methods for CTP mostly use already processed 3D parametric maps conventionally used for clinical interpretation by radiologists as input. Alternatively, the raw CTP data is used on a slice-by-slice basis as 2D+time input, where the spatial information over the volume is ignored. In addition, these methods are only interested in segmenting core regions, while predicting penumbra can be essential for treatment planning. This paper investigates different methods to utilize the entire 4D CTP as input to fully exploit the spatio-temporal information, leading us to propose a novel 4D convolution layer. Our comprehensive experiments on a local dataset of 152 patients divided into three groups show that our proposed models generate more precise results than other methods explored. Adopting the proposed 4D mJ-Net, a Dice Coefficient of 0.53 and 0.23 is achieved for segmenting penumbra and core areas, respectively. The code is available on https://github.com/Biomedical-Data-Analysis-Laboratory/4D-mJ-Net.git

Optogenetic Interrogation and Manipulation of Vascular Blood Flow in Cortex

Understanding blood flow regulatory mechanisms that correlate the regional blood flow with the level of local neuronal activity in brain is an ongoing research. Discerning different aspects of this coupling is of substantial importance in interpretation of functional imaging results, such as functional magnetic resonance imaging (fMRI), that rely on hemodynamic recordings to detect and image brain neuronal activity. Moreover, this understanding can provide insight into blood flow disorders under different pathophysiological conditions and possible treatments for such disorders. The blood regulatory mechanisms can be studied at two different; however, complementary levels: at the cellular level or at the vascular level. To fully understand the regulatory mechanisms in brain, it is essential to discern details of the coupling mechanism in each level. While, the cellular pathways of the coupling mechanism has been studied extensively in the past few decades, our understanding of the vascular response to brain activity is fairly basic. The main objective of this dissertation is to develop proper methods and instrumentation to interrogate regional cortical vasodynamics in response to local brain stimulation. For this purpose we offer the design of a custom-made OCT scanner and the necessary lens mechanisms to integrate the OCT system, fluorescence imaging, and optogenetic stimulation technologies in a single system. The design uses off-the-shelf components for a cost-effective design. The modular design of the device allows scientists to modify it in accordance with their research needs. With this multi-modal system we are able to monitor blood flow, blood velocity, and lumen diameter of pial vessels, simultaneously. Additionally, the system design provides the possibility of generating arbitrary spatial stimulation light pattern on brain. These abilities enables researchers to capture more diverse datasets and, eventually, obtain a more comprehensive picture of the vasodynamics in the brain. Along with the device we also proposed new biological experiments that are tailored to investigate the spatio-temporal properties of the vascular response to optical neurostimulation of the excitatory neurons. We demonstrate the ability of the proposed methods to investigate the effect of length and amplitude of stimulation on the temporal pattern of response in the blood flow, blood velocity, and diameter of the pial vessels. Moreover, we offer systemic approaches to investigate the spatial characteristics of the response in a vascular network. In these methods we apply arbitrary spatial patterns of optical stimulation to the cortex of transgenic mice and monitor the attributes of surrounding vessels. With this flexibility we were able to image the brain region that is influenced by a pial artery. After characterizing the spatio-temporal properties of the vascular blood flow response to optical neuro-modulation, we demonstrate the design and application of an optogenetic-based closed-loop controller mechanism in the brain. This controller, uses a proportional–integral–derivative (PID) compensator to engineer temporal optogenetic stimulation light pulses and maintain the flow of blood at various user defined levels in a set of selected arteries. Upon tuning the gain values of the PID controller we obtained a near to critically-damped response in the blood flow of selected arterial vessels

A compactness based saliency approach for leakages detection in fluorescein angiogram

This study has developed a novel saliency detection method based on compactness feature for detecting three common types of leakage in retinal fluorescein angiogram: large focal, punctate focal, and vessel segment leakage. Leakage from retinal vessels occurs in a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. The proposed framework consists of three major steps: saliency detection, saliency refinement and leakage detection. First, the Retinex theory is adapted to address the illumination inhomogeneity problem. Then two saliency cues, intensity and compactness, are proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. Finally, the leaking sites can be detected by masking the vessel and optic disc regions. The effectiveness of this framework has been evaluated by applying it to different types of leakage images with cerebral malaria. The sensitivity in detecting large focal, punctate focal and vessel segment leakage is 98.1, 88.2 and 82.7 %, respectively, when compared to a reference standard of manual annotations by expert human observers. The developed framework will become a new powerful tool for studying retinal conditions involving retinal leakage