Disambiguating the role of blood flow and global signal with partial information decomposition

Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas

Intima-Media Thickness: Setting a Standard for a Completely Automated Method of Ultrasound Measurement

The intima - media thickness (IMT) of the common carotid artery is a widely used clinical marker of severe cardiovascular diseases. IMT is usually manually measured on longitudinal B-Mode ultrasound images. Many computer-based techniques for IMT measurement have been proposed to overcome the limits of manual segmentation. Most of these, however, require a certain degree of user interaction. In this paper we describe a new completely automated layers extraction (CALEXia) technique for the segmentation and IMT measurement of carotid wall in ultrasound images. CALEXia is based on an integrated approach consisting of feature extraction, line fitting, and classification that enables the automated tracing of the carotid adventitial walls. IMT is then measured by relying on a fuzzy K-means classifier. We tested CALEXia on a database of 200 images. We compared CALEXia performances to those of a previously developed methodology that was based on signal analysis (CULEXsa). Three trained operators manually segmented the images and the average profiles were considered as the ground truth. The average error from CALEXia for lumen - intima (LI) and media - adventitia (MA) interface tracings were 1.46 ± 1.51 pixel (0.091 ± 0.093 mm) and 0.40 ± 0.87 pixel (0.025 ± 0.055 mm), respectively. The corresponding errors for CULEXsa were 0.55 ± 0.51 pixels (0.035 ± 0.032 mm) and 0.59 ± 0.46 pixels (0.037 ± 0.029 mm). The IMT measurement error was equal to 0.87 ± 0.56 pixel (0.054 ± 0.035 mm) for CALEXia and 0.12 ± 0.14 pixel (0.01 ± 0.01 mm) for CULEXsa. Thus, CALEXia showed limited performance in segmenting the LI interface, but outperformed CULEXsa in the MA interface and in the number of images correctly processed (10 for CALEXia and 16 for CULEXsa). Based on two complementary strategies, we anticipate fusing them for further IMT improvement

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

Biometric Person Identification Using Near-infrared Hand-dorsa Vein Images

Biometric recognition is becoming more and more important with the increasing demand for security, and more usable with the improvement of computer vision as well as pattern recognition technologies. Hand vein patterns have been recognised as a good biometric measure for personal identification due to many excellent characteristics, such as uniqueness and stability, as well as difficulty to copy or forge. This thesis covers all the research and development aspects of a biometric person identification system based on near-infrared hand-dorsa vein images. Firstly, the design and realisation of an optimised vein image capture device is presented. In order to maximise the quality of the captured images with relatively low cost, the infrared illumination and imaging theory are discussed. Then a database containing 2040 images from 102 individuals, which were captured by this device, is introduced. Secondly, image analysis and the customised image pre-processing methods are discussed. The consistency of the database images is evaluated using mean squared error (MSE) and peak signal-to-noise ratio (PSNR). Geometrical pre-processing, including shearing correction and region of interest (ROI) extraction, is introduced to improve image consistency. Image noise is evaluated using total variance (TV) values. Grey-level pre-processing, including grey-level normalisation, filtering and adaptive histogram equalisation are applied to enhance vein patterns. Thirdly, a gradient-based image segmentation algorithm is compared with popular algorithms in references like Niblack and Threshold Image algorithm to demonstrate its effectiveness in vein pattern extraction. Post-processing methods including morphological filtering and thinning are also presented. Fourthly, feature extraction and recognition methods are investigated, with several new approaches based on keypoints and local binary patterns (LBP) proposed. Through comprehensive comparison with other approaches based on structure and texture features as well as performance evaluation using the database created with 2040 images, the proposed approach based on multi-scale partition LBP is shown to provide the best recognition performance with an identification rate of nearly 99%. Finally, the whole hand-dorsa vein identification system is presented with a user interface for administration of user information and for person identification

DiffVein: A Unified Diffusion Network for Finger Vein Segmentation and Authentication

Finger vein authentication, recognized for its high security and specificity, has become a focal point in biometric research. Traditional methods predominantly concentrate on vein feature extraction for discriminative modeling, with a limited exploration of generative approaches. Suffering from verification failure, existing methods often fail to obtain authentic vein patterns by segmentation. To fill this gap, we introduce DiffVein, a unified diffusion model-based framework which simultaneously addresses vein segmentation and authentication tasks. DiffVein is composed of two dedicated branches: one for segmentation and the other for denoising. For better feature interaction between these two branches, we introduce two specialized modules to improve their collective performance. The first, a mask condition module, incorporates the semantic information of vein patterns from the segmentation branch into the denoising process. Additionally, we also propose a Semantic Difference Transformer (SD-Former), which employs Fourier-space self-attention and cross-attention modules to extract category embedding before feeding it to the segmentation task. In this way, our framework allows for a dynamic interplay between diffusion and segmentation embeddings, thus vein segmentation and authentication tasks can inform and enhance each other in the joint training. To further optimize our model, we introduce a Fourier-space Structural Similarity (FourierSIM) loss function, which is tailored to improve the denoising network's learning efficacy. Extensive experiments on the USM and THU-MVFV3V datasets substantiates DiffVein's superior performance, setting new benchmarks in both vein segmentation and authentication tasks

Human Identification Using Finger Vein and Texture Matching

No Abstrac

MRI-­based quantification of cerebral oxygen extraction and oxygen metabolism using the relationship between phase shift and magnetic susceptibility

Introduction: The main purpose of this study was to extract global values of oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2) in healthy volunteers, using magnetic resonance imaging (MRI) phase maps. The repeatability of the method was evaluated, and the age dependences of OEF and CMRO2 were analyzed. Material and methods: Phase data were acquired using a 3T MRI scanner with an 8- channel head coil, and a total of 20 volunteers (10 male, 10 female, age 25-84 years) participated. Magnitude and phase data were acquired from each subject, on two different occasions separated by 7-20 days, using a 3D double gradient echo pulse sequence. The difference in magnetic susceptibility between venous blood and surrounding tissue was obtained for the superior sagittal sinus (SSS) and the vein of Galen, using MRI phase data, and estimates of OEF and CMRO2 were subsequently calculated. Results: OEF estimates were 0.40 0.11 for vein of Galen and 0.31 0.08 for the SSS, and CMRO2 was 159.8 and 116.7 27.5 for the vein of Galen and the SSS, respectively. The method showed promising repeatability, with intraclass correlation coefficients (ICCs) of 0.95 and 0.82 for OEF measured in the vein of Galen and the SSS, respectively, and similar repeatability for CMRO2. The estimates showed, however, relatively large spread between volunteers, with coefficients of variation (CoVs) of 0.25 and 0.26 for OEF measured in the vein of Galen and the SSS, respectively, and similar CoVs for CMRO2. Finally, CMRO2 showed the anticipated relationship with age. Conclusion: Population mean values of OEF and CMRO2 were in good agreement with literature values, and the method delivered high repeatability, indicating stable measurements. The spread between different volunteers, however, was somewhat larger than expected. This may suggest that the method is sensitive towards measuring in different anatomical locations between volunteers.Magnetkameran används vanligen för att generera anatomiska bilder som används vid diagnostik av olika sjukdomstillstånd. Normala anatomiska bilder avspeglar magnetresonanssignalens (MR-signalens) magnitud, vilket är signalvektorns längd. Denna är proportionell mot bl.a. antalet vätekärnor i varje volymselement. Ett annat sätt att använda MR-signalen är att skapa en bild av signalvektorns fasvinkel, vilken är relaterad till hur snabbt vätekärnornas magnetiska moment roterar (precesserar) kring det externa magnetfältet i magnetkameran. Precessionsfrekvensen är proportionell mot det lokala magnetfältet i objektet och fasbilder kan därmed användas som kartor över hur magnetfältet varierar över objektet. Den magnetiska susceptibiliteten är ett mått på ett materials förmåga att bli magnetisterat av ett yttre magnetiskt fält, och denna egenskap varierar mellan syrerikt och syrefattigt blod. Följaktligen kommer arteriellt och venöst blod att ge upphov till olika lokala magnetfält och därmed ge olika fasskift. Syrefattigt blod innehåller deoxyhemoglobin, vilket är en molekyl som innehåller oparade elektroner. Ämnen med oparade elektroner kallas paramagnetiska och förstärker ett eventuellt externt magnetfält eftersom de oparade elektronerna beter sig som små magneter. Detta innebär att det lokala magnetfältet inuti en ven blir något högre än det externa magnetfältet. Syrerikt blod är, i likhet med normal vävnad, däremot svagt diamagnetiskt, vilket innebär att det skapas ett svagt motriktat magnetfält i dessa miljöer, som gör att det lokala magnetfältet blir lägre än det externa magnetfältet. Skillnaden i magnetfält mellan venöst blod och kringliggande vävnad kommer att bero på skillnaden i magnetisk susceptibilitet, vilket i sin tur beror på hur mycket syre det finns i det venösa blodet. Sjunker syrehalten i det venösa blodet så ökar det lokala magnetfältet. Detta gör att protonernas precessionsfrekvens ökar, vilket resulterar i en större fasvinkel under en given mättid. Genom att jämföra fasen i venöst blod med fasen i omkringliggande vävnad är det möjligt att beräkna motsvarande skillnad i magnetisk susceptibilitet. Från skillnaden i magnetisk susceptibilitet mellan venöst blod och omkringliggande vävnad kan sedan det venösa blodets syresättning beräknas. Om genomblödningen (perfusionen) i hjärnan är känd så är det möjligt att gå vidare med att, utifrån den venösa syresättningen, beräkna hjärnans syreförbrukning (s.k. syremetabolism). Om den venösa syrehalten mäts i ett kärl som dränerar hela hjärnan erhålls den globala metabolismen, d.v.s ett mått på hur mycket syre hela hjärnan förbrukar. Detta kan ge viktig klinisk information, eftersom metabolismen av syre är kopplad till vissa sjukdomstillstånd (t.ex. stroke och metaboliska sjukdomar) och hjärnans allmänna hälsa. Det finns andra metoder för att ta reda på den venösa syresättningen, men de är ofta invasiva eller innebär exponering för joniserande strålning, och det är därför önskvärt att kunna bedöma den venösa syresättningen med hjälp av MRavbildning. Den fasbaserade MR-metod som beskrivits ovan har, i denna studie, utvärderats med avseende på hur väl mätningarna kan upprepas, och absolutvärdena har jämförts med värden från andra studier. Metoden visade att mätningarna gav liknande resultat vid upprepade mätningar och absolutvärdena stämde väl överens med andra undersökningsmetoder, men relativt stor spridning observerades

Finger Vein Recognition Based on a Personalized Best Bit Map

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition