Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Advances in the application of auxiliary imaging techniques in parathyroid diseases

Hypoparathyroidism is one of the main complications after total thyroidectomy,severely affecting patients’quality of life.How to effectively protect parathyroid function after surgery and reduce the incidence of hypoparathyroidism has always been a key research area in thyroid surgery.Therefore,precise localization of parathyroid glands during surgery,effective imaging,and accurate surgical resection have become hot topics of concern for thyroid surgeons.In response to this clinical phenomenon,this study compared several different imaging methods for parathyroid surgery,including nanocarbon,indocyanine green,near-infrared imaging techniques,and technetium-99m methoxyisobutylisonitrile combined with gamma probe imaging technology.The advantages and disadvantages of each method were analyzed,providing scientific recommendations for future parathyroid imaging.In recent years,some related basic and clinical research has also been conducted in thyroid surgery.This article reviewed relevant literature and provided an overview of the practical application progress of various imaging techniques in parathyroid surgery.

Evaluation of Medical Prescribers’ Theoretical Knowledge on Medical Imaging in the Northern Region of Burkina Faso

Introduction: Medical imaging is a medical specialty that involves producing images of the human body and interpreting them for diagnostic, therapeutic purposes, and for monitoring the progress of pathologies. We aimed to assess the theoretical knowledge of doctors and interns in medical imaging in the northern region of Burkina Faso. Methodology: This was a descriptive cross-sectional survey based on a self-administered questionnaire. Prescribers knowledge was estimated based on scores derived from questionnaire responses. Results: We collected 106 questionnaires out of 163, i.e. a participation rate of 65.03%. The average knowledge score was 81.71% for the contribution of medical imaging to patient management. It was 60.02% for the indications/counter-indications of radiological examinations and 72.56% for the risks associated with exposure to radiation during these examinations. The score was 59.83% for the methods used to select the appropriate radiological examination. As regards the completeness of the clinical and biological information on the forms requesting imaging examinations, the score was 96.65%. Specialist doctors had the highest overall level of knowledge (74.68%). Conclusion: Improved technical facilities, good initial and in-service training, and interdisciplinary collaboration will help to ensure that imaging tests are properly prescribed, leading to better patient care.

Relationship between pancreatic morphological changes and diabetes in autoimmune pancreatitis:Multimodal medical imaging assessment has important potential

Autoimmune pancreatitis(AIP)is a special type of chronic pancreatitis with cli-nical symptoms of obstructive jaundice and abdominal discomfort;this condition is caused by autoimmunity and marked by pancreatic fibrosis and dysfunction.Previous studies have revealed a close relationship between early pancreatic atrophy and the incidence rate of diabetes in type 1 AIP patients receiving steroid treatment.Shimada et al performed a long-term follow-up study and reported that the pancreatic volume(PV)of these patients initially exponentially decreased but then slowly decreased,which was considered to be an important factor related to diabetes;moreover,serum IgG4 levels were positively correlated with PV during follow-up.In this letter,regarding the original study presented by Shimada et al,we present our insights and discuss how multimodal medical imaging and arti-ficial intelligence can be used to better assess the relationship between pancreatic morphological changes and diabetes in patients with AIP.

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

The Artificial Intelligence-Enabled Medical Imaging:Today and Its Future

Medical imaging is now being reshaped by artificial intelligence (AI) and progressing rapidly toward future.In this article,we review the recent progress of AI-enabled medical imaging.Firstly,we briefly review the background about AI in its way of evolution.Then,we discuss the recent successes of AI in different medical imaging tasks,especially in image segmentation,registration,detection and recognition.Also,we illustrate several representative applications of AI-enabled medical imaging to show its advantage in real scenario,which includes lung nodule in chest CT,neuroimaging,mammography,and etc.Finally,we report the way of human-machine interaction.We believe that,in the future,AI will not only change the traditional way of medical imaging,but also improve the clinical routines of medical care and enable many aspects of the medical society.

Collaborations of Industry,Academia,Research and Application Improve the Healthy Development of Medical Imaging Artificial Intelligence Industry in China

In recent years,artificial intelligence (AI) has developed rapidly in the field of medical imaging.However,the collaborations among hospitals,research institutes and enterprises are insufficient at the present,and there are various issues in technological transformation and value landing of products in this area.To solve the core problems in the developmental path of medical imaging AI,the Chinese Innovative Alliance of Industry,Education,Research and Application of Artificial Intelligence for Medical Imaging compiled the White Paper on Medical Image AI in China.This article introduces the current status of collaboration,the clinical demands for medical imaging AI technique,and the key points in AI technology transformation:robustness,usability and security.We are facing challenges of lacking industry standards,data desensitization standard,assessment system,as well as corresponding regulations and policies to realize the application values of AI products in medical imaging.Further development of AI in medical imaging requires breakthroughs of the core algorithm,deep involvement of doctors,input from capitals,patience from societies,and most importantly,the resolutions from government for multiple difficulties in links of landing the technology.

Importance of Features Selection,Attributes Selection,Challenges and Future Directions for Medical Imaging Data:A Review

In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of data.These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality.On the other hand,this growth also made it difficult to comprehend and utilize data for various purposes.The results of imaging data can become biased because of extraneous features present in larger datasets.Feature selection gives a chance to decrease the number of components in such large datasets.Through selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization precision.The correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast control.This paper presents a review of feature selection techniques and attributes selection measures for medical imaging.This review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining algorithms.The review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced data.Moreover,this review provides the importance of feature selection for correct classification of medical infections.In the end,critical analysis and future directions are provided.

Benign gallbladder diseases:Imaging techniques and tips for differentiating with malignant gallbladder diseases

Benign gallbladder diseases usually present with intraluminal lesions and localized or diffuse wall thickening.Intraluminal lesions of the gallbladder include gallstones,cholesterol polyps,adenomas,or sludge and polypoid type of gallbladder cancer must subsequently be excluded.Polyp size,stalk width,and enhancement intensity on contrast-enhanced ultrasound and degree of diffusion restriction may help differentiate cholesterol polyps and adenomas from gallbladder cancer.Localized gallbladder wall thickening is largely due to segmental or focal gallbladder adenomyomatosis,although infiltrative cancer may present similarly.Identification of Rokitansky-Aschoff sinuses is pivotal in diagnosing adenomyomatosis.The layered pattern,degree of enhancement,and integrity of the wall are imaging clues that help discriminate innocuous thickening from gallbladder cancer.High-resolution ultrasound is especially useful for analyzing the layering of gallbladder wall.A diffusely thickened wall is frequently seen in inflammatory processes of the gallbladder.Nevertheless,it is important to check for coexistent cancer in instances of acute cholecystitis.Ultrasound used alone is limited in evaluating complicated cholecystitis and often requires complementary computed tomography.In chronic cholecystitis,preservation of a two-layered wall and weak wall enhancement are diagnostic clues for excluding malignancy.Magnetic resonance imaging in conjunction with diffusion-weighted imaging helps to differentiate xathogranulomatous cholecystitis from gallbladder cancer by identifying the presence of fat and degree of diffusion restriction.Such distinctions require a familiarity with typical imaging features of various gallbladder diseases and an understanding of the roles that assorted imaging modalities play in gallbladder evaluations.

Adaptive diagonal loaded minimum variance beamforming applied to medical ultrasound imaging

In order to enhance the robustness and contrast in the minimum variance(MV) beamformer, adaptive diagonal loading method was proposed. The conventional diagonal loading technique has already been used in the MV beamformer, but has the drawback that its level is specified by predefined parameter and without consideration of input-data. To alleviate this problem, the level of diagonal loading was computed appropriately and automatically from the given data by shrinkage method in the proposed adaptive diagonal loaded beamformer. The performance of the proposed beamformer was tested on the simulated point target and cyst phantom was obtained using Field II. In the point target simulation, it is shown that the proposed method has higher lateral resolution than the conventional delay-and-sum beamformer and could be more robust in estimating the amplitude peak than the MV beamformer when acoustic velocity error exists. In the cyst phantom simulation, the proposed beamformer has shown that it achieves an improvement in contrast ratio and without distorting the edges of cyst.

Imaging of the small bowel in Crohn's disease: A review of old and new techniques

The investigation of small bowel morphology is often mandatory in many patients with Crohn's disease. Traditional radiological techniques (small bowel enteroclysis and small bowel follow-through) have long been the only suitable methods for this purpose. In recent years, several alternative imaging techniques have been proposed. To review the most recent advances in imaging studies of the small bowel, with particular reference to their possible application in Crohn's disease, we conducted a complete review of the most important studies in which traditional and newer imaging methods were performed and compared in patients with Crohn's disease. Several radiological and endoscopic techniques are now available for the study of the small bowel; each of them is characterized by a distinct profile of favourable and unfavourable features. In some cases, they may also be used as complementary rather than alternative techniques. In everyday practice, the choice of the technique to be used stands upon its availability and a careful evaluation of diagnostic accuracy, clinical usefulness, safety and cost. The recent development ofinnovative imaging techniques has opened a new and exciting area in the exploration of the small bowel in Crohn's disease patients.

Deep Learning for Image Segmentation: A Focus on Medical Imaging

Image segmentation is crucial for various research areas. Manycomputer vision applications depend on segmenting images to understandthe scene, such as autonomous driving, surveillance systems, robotics, andmedical imaging. With the recent advances in deep learning (DL) and itsconfounding results in image segmentation, more attention has been drawnto its use in medical image segmentation. This article introduces a surveyof the state-of-the-art deep convolution neural network (CNN) models andmechanisms utilized in image segmentation. First, segmentation models arecategorized based on their model architecture and primary working principle.Then, CNN categories are described, and various models are discussed withineach category. Compared with other existing surveys, several applicationswith multiple architectural adaptations are discussed within each category.A comparative summary is included to give the reader insights into utilizedarchitectures in different applications and datasets. This study focuses onmedical image segmentation applications, where the most widely used architecturesare illustrated, and other promising models are suggested that haveproven their success in different domains. Finally, the present work discussescurrent limitations and solutions along with future trends in the field.

Non-invasive evaluation of liver steatosis with imaging modalities:New techniques and applications

In the world,nonalcoholic fatty liver disease(NAFLD)accounts for majority of diffuse hepatic diseases.Notably,substantial liver fat accumulation can trigger and accelerate hepatic fibrosis,thus contributing to disease progression.Moreover,the presence of NAFLD not only puts adverse influences for liver but is also associated with an increased risk of type 2 diabetes and cardiovascular diseases.Therefore,early detection and quantified measurement of hepatic fat content are of great importance.Liver biopsy is currently the most accurate method for the evaluation of hepatic steatosis.However,liver biopsy has several limitations,namely,its invasiveness,sampling error,high cost and moderate intraobserver and interobserver reproducibility.Recently,various quantitative imaging techniques have been developed for the diagnosis and quantified measurement of hepatic fat content,including ultrasound-or magnetic resonancebased methods.These quantitative imaging techniques can provide objective continuous metrics associated with liver fat content and be recorded for comparison when patients receive check-ups to evaluate changes in liver fat content,which is useful for longitudinal follow-up.In this review,we introduce several imaging techniques and describe their diagnostic performance for the diagnosis and quantified measurement of hepatic fat content.

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers

Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply artificial intelligence(AI)techniques to multiple clinical scenarios of ovarian cancer,especially in the field of medical imaging.AI-assisted imaging studies have involved computer tomography(CT),ultrasonography(US),and magnetic resonance imaging(MRI).In this review,we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer,and bring up the advances in terms of four clinical aspects,including medical diagnosis,pathological classification,targeted biopsy guidance,and prognosis prediction.Meanwhile,current status and existing issues of the researches on AI application in ovarian cancer are discussed.

A Triple-Channel Encrypted Hybrid Fusion Technique to Improve Security of Medical Images

Assuring medical images protection and robustness is a compulsory necessity nowadays.In this paper,a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform(DWT)with the energy compaction of the Discrete Wavelet Transform(DCT).The multi-level Encryption-based Hybrid Fusion Technique(EbhFT)aims to achieve great advances in terms of imperceptibility and security of medical images.A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT transform.Afterwards,a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the watermark.Lastly,a PN-sequence key is formed along with a supplementary key in the third layer of the EbHFT.Thus,the watermarked image is generated by enclosing both keys into DWT and DCT coefficients.The fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT methods.In order to validate the proposed technique,a standard dataset of medical images is used.Simulation results show higher performance of the visual quality(i.e.,57.65)for the watermarked forms of all types of medical images.In addition,EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation(NC).Finally,extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.

Preprocedure ultrasound imaging combined with palpation technique in epidural labor analgesia

BACKGROUND For parturients with paroxysmal uterine contraction pain,rapid analgesia is needed.We used preprocedure ultrasound imaging combined with the palpation technique in epidural analgesia for labor,and evaluated the usefulness of this technique in epidural labor analgesia.AIM To evaluate the usefulness of preprocedure ultrasound imaging in epidural analgesia for labor.METHODS In this prospective randomized observational study,72 parturients were assigned to two groups(combined or palpation group).The target interspace of all parturients was first identified by the palpation technique.Then in the combined group,preprocedure ultrasound imaging was used before epidural puncture.In the palpation group,only the traditional anatomical landmarks technique(palpation technique)was performed.The primary outcome was total duration of the epidural procedure(for the ultrasound group,the duration of the preprocedure ultrasound imaging was included).The secondary outcomes were the number of skin punctures,the success rate at first needle pass,the number of needle passes,the depth from the skin to epidural space,and the complications of the procedure.RESULTS Total duration of the epidural procedure was similar between the two groups(406.5±92.15 s in the combined group and 380.03±128.2 s in the palpation group;P=0.318).A significant improvement was demonstrated for epidural puncture and catheterization in the combined group.The number of needle passes was 1.14 in the combined group and 1.72 in the palpation group(P=0.001).The number of skin puncture sites was 1.20 in the combined group and 1.25 in the palpation group(P=0.398).The success rate at first needle pass was 88.89%in the combined group and 66.67%in the palpation group(P=0.045).CONCLUSION This study demonstrated that the total duration of epidural procedures with preprocedure ultrasound imaging combined with the palpation technique was not longer than the traditional anatomical landmarks technique,which were performed by six experienced anesthesiologists in parturients with normal weights undergoing labor analgesia.

Releasing of The White Paper on Medical Imaging Artificial Intelligence in China

IN the afternoon of March 26,2019,The White Paper on Medical Imaging Artificial Intelligence in China (hereinafter referred to as the “white paper”) was officially released in Beijing by the Chinese Innovative Alliance of Industry,Education,Research and Application of Artificial Intelligence for Medical Imaging (CAIERA)(Figure 1).The white paper was co-operatively written by the medical imaging experts from the tertiary Chinese hospitals,the scientific experts from AI research institutions and the leading AI medical enterprises in China.The contents of the white paper not only cover the uptodate application of AI in medical field,the latest advances of AI algorithms in medical image processing,the data requirement for medical AI development,and the current situation of structured data,but also expatiate the goal and challenge of clinical application for medical imaging AI development in 16 medical subject areas,which helps to identify the demands and opportunities for the AI industry.

THEORY OF THE EEG CORTICAL IMAGING TECHNIQUES

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Using mutual information to evaluate performance of medical imaging systems

Information on physical image quality of medical images is important for imaging system assessment in order to promote and stimulate the development of state-of-the-art imaging systems. In this paper, we present a method for evaluating physical performance of medical imaging systems. In this method, mutual information (MI) which is a concept from information theory was used to measure combined properties of image noise and resolution of an imaging system. In our study, the MI was used as a measure to express the amount of information that an output image contains about an input object. The more the MI value provides, the better the image quality is. To validate the proposed method, computer simulations were per- formed to investigate the effects of noise and resolution degradation on the MI, followed by measuring and comparing the performance of two imaging systems. Our simulation and experimental results confirmed that the combined effect of deteriorated blur and noise on the images can be measured and analyzed using the MI metric. The results demonstrate the potential usefulness of the proposed method for evaluating physical quality of medical imaging systems.

Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.