Quantitative assessment of the lower limb digital subtraction angiography post successful revascularization using optical flow method (syngo iFlow)

Background: Digital subtraction angiography (DSA) remains the gold standard in diagnosing complicated peripheral vascular disease and is the only imaging modality capable of visualizing endovascular treatments in all contexts. The purpose of the study is determine the means different of arterial flow time (FT) of DSA on the lower limb circulation pre and post successful revascularization using time density curve (TDC) on syngo iFlow application. Methodology: A total of 45 patients, retrospectively review the pre and post successful revascularization blood flow time using syngo iFlow application. The blood flow time (TTP) of the successful arterial flow is determined by using the application. Mean and SDs were obtained for each measurement, and level of significance for pre and post treatment was studied (P < 0.05). Result: In total of 45 subject was studied (26 male, 19 female, means age 59.3). Stenoses were graded as follows based on the angiographic finding: less than 50% stenosis 2 patients; 50-75% stenosis 37patients; 76-99%, 6 patients. There was a significant difference in means between proximal and distal stenosis for pre treatment (p-value<0.001). The mean difference of pre treatment was -0.84 with mean(SD) for pre and post stenotic segment were 3.94s(0.43) and 4.78s(0.80) respectively. There was also significant mean difference between proximal and distal stenosis for post treatment ( p-value<0.001). The mean difference of post treatment was -0.34 with mean(SD) for pre and post stenotic segment were 3.90s(0.39) and 4.16s(0.52) respectively. Before treatment, means TTP time (time to peak time) between contrast time curve proximal and distal to the treated vessels segment was 3.94s and 4.78s respectively, after treatment was 3.90s and 4.156s respectively. Gain percentage of the improvement time was highest at 62% (n=10), second most 83% (n=6) and 78% (n=6). Using pair t- test analysis, decrease in TTP time was stastistically significant pre and post treatment (p < 0.01). Conclusion: With the significant changes in blood flow parameter pre and post treatment as well as improvement of the gain percentage blood flow time, syngo iFlow application can be the first marker to determine the successful rate of the revascularization technique objectively

Generalized div-curl based regularization for physically constrained deformable image registration

Variational image registration methods commonly employ a similarity metric and a regularization term that renders the minimization problem well-posed. However, many frequently used regularizations such as smoothness or curvature do not necessarily reflect the underlying physics that apply to anatomical deformations. This, in turn, can make the accurate estimation of complex deformations particularly challenging. Here, we present a new highly flexible regularization inspired from the physics of fluid dynamics which allows applying independent penalties on the divergence and curl of the deformations and/or their nth order derivative. The complexity of the proposed generalized div-curl regularization renders the problem particularly challenging using conventional optimization techniques. To this end, we develop a transformation model and an optimization scheme that uses the divergence and curl components of the deformation as control parameters for the registration. We demonstrate that the original unconstrained minimization problem reduces to a constrained problem for which we propose the use of the augmented Lagrangian method. Doing this, the equations of motion greatly simplify and become managable. Our experiments indicate that the proposed framework can be applied on a variety of different registration problems and produce highly accurate deformations with the desired physical properties

BASIL: A Toolbox for Perfusion Quantification using Arterial Spin Labelling

Arterial Spin Labelling (ASL) MRI is now an established non-invasive method to quantify cerebral blood flow and is increasingly being used in a variety of neuroimaging applications. With standard ASL acquisition protocols widely available, there is a growing interest in advanced options that offer added quantitative precision and information about haemodynamics beyond perfusion. In this article we introduce the BASIL toolbox, a research tool for the analysis of ASL data included within the FMRIB Software Library (FSL) and explain its operation in a variety of typical use cases. BASIL is not offered as a clinical tool, and nor is this work intended to guide the clinical application of ASL. Built around a Bayesian model-based inference algorithm, the toolbox is designed to quantify perfusion and other haemodynamic measures, such as arterial transit times, from a variety of possible ASL input data, particularly exploiting the information available in more advanced multi-delay acquisitions. At its simplest, the BASIL toolbox offers a graphical user interface that provides the analysis options needed by most users; through command line tools, it offers more bespoke options for users needing customised analyses. As part of FSL, the toolbox exploits a range of complementary neuroimaging analysis tools so that ASL data can be easily integrated into neuroimaging studies and used alongside other modalities

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

Background and motivation: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. Methods: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. Findings and conclusions: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach

Automatic whole heart segmentation based on image registration

Whole heart segmentation can provide important morphological information of the heart, potentially enabling the development of new clinical applications and the planning and guidance of cardiac interventional procedures. This information can be extracted from medical images, such as these of magnetic resonance imaging (MRI), which is becoming a routine modality for the determination of cardiac morphology. Since manual delineation is labour intensive and subject to observer variation, it is highly desirable to develop an automatic method. However, automating the process is complicated by the large shape variation of the heart and limited quality of the data. The aim of this work is to develop an automatic and robust segmentation framework from cardiac MRI while overcoming these difficulties. The main challenge of this segmentation is initialisation of the substructures and inclusion of shape constraints. We propose the locally affine registration method (LARM) and the freeform deformations with adaptive control point status to tackle the challenge. They are applied to the atlas propagation based segmentation framework, where the multi-stage scheme is used to hierarchically increase the degree of freedom. In this segmentation framework, it is also needed to compute the inverse transformation for the LARM registration. Therefore, we propose a generic method, using Dynamic Resampling And distance Weighted interpolation (DRAW), for inverting dense displacements. The segmentation framework is validated on a clinical dataset which includes nine pathologies. To further improve the nonrigid registration against local intensity distortions in the images, we propose a generalised spatial information encoding scheme and the spatial information encoded mutual information (SIEMI) registration. SIEMI registration is applied to the segmentation framework to improve the accuracy. Furthermore, to demonstrate the general applicability of SIEMI registration, we apply it to the registration of cardiac MRI, brain MRI, and the contrast enhanced MRI of the liver. SIEMI registration is shown to perform well and achieve significantly better accuracy compared to the registration using normalised mutual information