CT diagnosis of early stroke : the initial approach to the new CAD tool based on multiscale estimation of ischemia

Background: Computer aided diagnosis (CAD) becomes one of the most important diagnostic tools for urgent states in cerebral stroke and other life-threatening conditions where time plays a crucial role. Routine CT is still diagnostically insufficient in hyperacute stage of stroke that is in the therapeutic window for thrombolytic therapy. Authors present computer assistant of early ischemic stroke diagnosis that supports the radiologic interpretations. A new semantic-visualization system of ischemic symptoms applied to noncontrast, routine CT examination was based on multiscale image processing and diagnostic content estimation. Material/Methods: Evaluation of 95 sets of examinations in patients admitted to a hospital with symptoms suggesting stroke was undertaken by four radiologists from two medical centers unaware of the final clinical findings. All of the consecutive cases were considered as having no CT direct signs of hyperacute ischemia. At the first test stage only the CTs performed at the admission were evaluated independently by radiologists. Next, the same early scans were evaluated again with additional use of multiscale computer-assistant of stroke (MulCAS). Computerized suggestion with increased sensitivity to the subtle image manifestations of cerebral ischemia was constructed as additional view representing estimated diagnostic content with enhanced stroke symptoms synchronized to routine CT data preview. Follow-up CT examinations and clinical features confirmed or excluded the diagnosis of stroke constituting 'gold standard' to verify stroke detection performance. Results: Higher AUC (area under curve) values were found for MulCAS -aided radiological diagnosis for all readers and the differences were statistically significant for random readers-random cases parametric and non-parametric DBM MRMC analysis. Sensitivity and specificity of acute stroke detection for the readers was increased by 30% and 4%, respectively. Conclusions: Routine CT completed with proposed method of computer assisted diagnosis provided noticeable better diagnosis efficiency of acute stroke according to the rates and opinions of all test readers. Further research includes fully automatic detection of hypodense regions to complete assisted indications and formulate the suggestions of stroke cases more objectively. Planned prospective studies will let evaluate more accurately the impact of this CAD tool on diagnosis and further treatment in patients suffered from stroke. It is necessary to determine whether this method is possible to be applied widely

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas

CHARACTERIZATION OF STROKE LESION USING FRACTAL ANALYSIS

Objective: The characterization of stroke lesions is a challenging research issue due to the wide variability in the structure of lesion patterns. The objective of this research work is to characterize the stroke lesion structures using fractal analysis.Methods: To characterize the complex nature of the lesion structures, fractal box counting analysis is presented in this work. Three parameters from fractal dimension (FD) are considered to characterize the nature of the normal and abnormal brain tissues.Results: The experimental results are presented for 15 different datasets. Three different parameters namely FD average, FD deviation, and FDlacunarity are extracted to quantify the properties of the stroke lesion. The observations indicate that there is a significant proportion of separationof feature values between the normal and abnormal brain tissues.Conclusion: This work presents an efficient scheme for characterizing the stroke lesions using fractal parameters. It could be further enhanced by incorporating features extracted from other non-linear techniques.Â

Ischemic Stroke Detection System with a Computer-Aided Diagnostic Ability Using an Unsupervised Feature Perception Enhancement Method

We propose an ischemic stroke detection system with a computer-aided diagnostic ability using a four-step unsupervised feature perception enhancement method. In the first step, known as preprocessing, we use a cubic curve contrast enhancement method to enhance image contrast. In the second step, we use a series of methods to extract the brain tissue image area identified during preprocessing. To detect abnormal regions in the brain images, we propose using an unsupervised region growing algorithm to segment the brain tissue area. The brain is centered on a horizontal line and the white matter of the brain’s inner ring is split into eight regions. In the third step, we use a coinciding regional location method to find the hybrid area of locations where a stroke may have occurred in each cerebral hemisphere. Finally, we make corrections and mark the stroke area with red color. In the experiment, we tested the system on 90 computed tomography (CT) images from 26 patients, and, with the assistance of two radiologists, we proved that our proposed system has computer-aided diagnostic capabilities. Our results show an increased stroke diagnosis sensitivity of 83% in comparison to 31% when radiologists use conventional diagnostic images

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Polymer Micro- and Nanofluidic Systems for In Vitro Diagnostics: Analyzing Single Cells and Molecules

Polymer micro- and nanofluidic systems, with their critical dimensions, offer a potential to outperform conventional analysis techniques and diagnostic methods by enhancing speed, accuracy, sensitivity and specificity. In this work, applications of microfluidics have been demonstrated to address the existing challenges in stroke diagnosis, by mRNA expression profiling from whole blood within \u3c20 min. A brief overview of various biomarkers for stroke diagnosis is given in chapter 1 followed by design and testing of individual microfluidic modules (chapter 2 and 3) required for the development of POC diagnostic strategy for stroke. We have designed and evaluated the performance of polymer microfluidic devices for the isolation of leukocyte subsets, known for their differential gene expression in the event of stroke. Target cells (T-cells and neutrophils) were selected from with greater purities, from 50 µl whole human blood by using affinity based capture in COC devices within a 6.6 min processing time. In addition, we have also demonstrated the ability to isolate and purify total RNA by using UV activated polycarbonate solid phase extraction platform. Polymer-based nanofluidic devices were used to study the effects of surface charge on the electrodynamic transport dynamics of target molecules. In this work, we report the fabrication of mixed-scale micro- and nanofluidic networks in poly(methylmethacrylate), PMMA, using thermal nanoimprint lithography using a resin stamp and surface modification of polymer nanoslits and nanochannels for the assessment of the associated electrokinetic parameters – surface charge density, zeta potential and electroosmotic flow. This study provided information on possible routes that can be adopted to engineer proper wall chemistry of polymer nanochannels for the enhancement or reduction of solute/wall interactions in a variety of relevant single-molecule studies