Arterial Spin Labeling Images Synthesis From sMRI Using Unbalanced Deep Discriminant Learning

A scoping review of magnetic resonance angiography and perfusion image synthesis

Introduction

Deregulation of the cerebrovascular system has been linked to neurodegeneration, part of a putative causal pathway into etiologies such as Alzheimer's disease (AD). In medical imaging, time-of-flight magnetic resonance angiography (TOF-MRA) and perfusion MRI are the most common modalities used to study this system. However, due to lack of resources, many large-scale studies of AD are not acquiring these images; this creates a conundrum, as the lack of evidence limits our knowledge of the interaction between the cerebrovascular system and AD. Deep learning approaches have been used in recent developments to generate synthetic medical images from existing contrasts. In this review, we study the use of artificial intelligence in the generation of synthetic TOF-MRA and perfusion-related images from existing neuroanatomical and neurovascular acquisitions for the study of the cerebrovascular system.

Method

Following the PRISMA reporting guidelines we conducted a scoping review of 729 studies relating to image synthesis of TOF-MRA or perfusion imaging, from which 13 met our criteria.

Results

Studies showed that T1-w, T2-w, and FLAIR can be used to synthesize perfusion map and TOF-MRA. Other studies demonstrated that synthetic images could have a greater signal-to-noise ratio compared to real images and that some models trained on healthy subjects could generalize their outputs to an unseen population, such as stroke patients.

Discussion

These findings suggest that generating TOF-MRA and perfusion MRI images holds significant potential for enhancing neurovascular studies, particularly in cases where direct acquisition is not feasible. This approach could provide valuable insights for retrospective studies of several cerebrovascular related diseases such as stroke and AD. While promising, further research is needed to assess their sensitivity and specificity, and ensure their applicability across diverse populations. The use of models to generate TOF-MRA and perfusion MRI using commonly acquired data could be the key for the retrospective study of the cerebrovascular system and elucidate its role in the development of dementia.

Auto Diagnosis of Parkinson's Disease Via a Deep Learning Model Based on Mixed Emotional Facial Expressions

Parkinson's disease (PD) is a common degenerative disease of the nervous system in the elderly. The early diagnosis of PD is very important for potential patients to receive prompt treatment and avoid the aggravation of the disease. Recent studies have found that PD patients always suffer from emotional expression disorder, thus forming the characteristics of "masked faces". Based on this, we thus propose an auto PD diagnosis method based on mixed emotional facial expressions in the paper. Specifically, the proposed method is cast into four steps: Firstly, we synthesize virtual face images containing six basic expressions (i.e., anger, disgust, fear, happiness, sadness, and surprise) via generative adversarial learning, in order to approximate the premorbid expressions of PD patients; Secondly, we design an effective screening scheme to assess the quality of the above synthesized facial expression images and then shortlist the high-quality ones; Thirdly, we train a deep feature extractor accompanied with a facial expression classifier based on the mixture of the original facial expression images of the PD patients, the high-quality synthesized facial expression images of PD patients, and the normal facial expression images from other public face datasets; Finally, with the well-trained deep feature extractor, we thus adopt it to extract the latent expression features for six facial expression images of a potential PD patient to conduct PD/non-PD prediction. To show real-world impacts, we also collected a new facial expression dataset of PD patients in collaboration with a hospital. Extensive experiments are conducted to validate the effectiveness of the proposed method for PD diagnosis and facial expression recognition.

Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey

Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities.

A review on medical imaging synthesis using deep learning and its clinical applications

This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.

Deep learning for brain disorders: from data processing to disease treatment

In order to reach precision medicine and improve patients' quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.

Arterial Spin Labeling Applications in Pediatric and Adult Neurologic Disorders

Arterial spin labeling (ASL) is a powerful noncontrast magnetic resonance imaging (MRI) technique that enables quantitative evaluation of brain perfusion. To optimize the clinical and research utilization of ASL, radiologists and physicists must understand the technical considerations and age-related variations in normal and disease states. We discuss advanced applications of ASL across the lifespan, with example cases from children and adults covering a wide variety of pathologies. Through literature review and illustrated clinical cases, we highlight the subtleties as well as pitfalls of ASL interpretation. First, we review basic physical principles, techniques, and artifacts. This is followed by a discussion of normal perfusion variants based on age and physiology. The three major categories of perfusion abnormalities-hypoperfusion, hyperperfusion, and mixed patterns-are covered with an emphasis on clinical interpretation and relationship to the disease process. Major etiologies of hypoperfusion include large artery, small artery, and venous disease; other vascular conditions; global hypoxic-ischemic injury; and neurodegeneration. Hyperperfusion is characteristic of vascular malformations and tumors. Mixed perfusion patterns can be seen with epilepsy, migraine, trauma, infection/inflammation, and toxic-metabolic encephalopathy. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 3.

A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease

Introduction: The goal of this study was to investigate and compare the classification performance of machine learning with behavioral data from standard neuropsychological tests, a cognitive task, or both.

Methods: A neuropsychological battery and a simple 5-min cognitive task were administered to eight individuals with mild cognitive impairment (MCI), eight individuals with mild Alzheimer's disease (AD), and 41 demographically match controls (CN). A fully connected multilayer perceptron (MLP) network and four supervised traditional machine learning algorithms were used.

Results: Traditional machine learning algorithms achieved similar classification performances with neuropsychological or cognitive data. MLP outperformed traditional algorithms with the cognitive data (either alone or together with neuropsychological data), but not neuropsychological data. In particularly, MLP with a combination of summarized scores from neuropsychological tests and the cognitive task achieved ~90% sensitivity and ~90% specificity. Applying the models to an independent dataset, in which the participants were demographically different from the ones in the main dataset, a high specificity was maintained (100%), but the sensitivity was dropped to 66.67%.

Discussion: Deep learning with data from specific cognitive task(s) holds promise for assisting in the early diagnosis of Alzheimer's disease, but future work with a large and diverse sample is necessary to validate and to improve this approach.