Brain Trauma

INTRODUCTION Background Trauma is the most common cause of death and a significant cause of morbidity in children. Both accidental and nonaccidental trauma are common in children. Several anatomic differences in younger children should be highlighted to understand why younger children are more susceptible to certain types of injury. First, the skull of young children is thin and pliable and thickens during the first 2 years…

Phakomatoses

INTRODUCTION Phakomatoses are a group of neurocutaneous disorders that involve structures arising from the embryologic ectoderm, resulting in abnormalities of the skin, nervous system, retina, and globe. There are approximately 30 different phakomatoses; however, the main phakomatoses include neurofibromatosis type 1 (NF-1), neurofibromatosis type 2 (NF-2), tuberous sclerosis, Sturge-Weber syndrome, and Von Hippel-Lindau disease. Table 9.1 highlights the CNS, globe and orbit, skin, and other abnormalities…

Epilepsy

INTRODUCTION Background Epilepsy is a chronic seizure condition in which abnormal excessive or uncoordinated neuronal activity leads to abnormal brain function. About 1% of children in the United States have epilepsy. Approximately 66% to 75% of children with epilepsy will become seizure free with anticonvulsant medication. Intractable epilepsy is defined as failure of two or more appropriate antiepileptic drugs and more than one seizure per month…

Brain Tumors and Treatment Complications

INTRODUCTION Background Central nervous system (CNS) tumors are the second most common pediatric cancer diagnosed each year, accounting for approximately 25% of childhood cancers. They are responsible for the second most common cause of cancer deaths in children. Survival has slowly improved over the years, and overall survival is now approximately 75%. Supratentorial tumors predominate during the first 2 years of life and late adolescence, while…

Demyelinating and Inflammatory Disorders

INTRODUCTION Background Demyelinating and inflammatory disorders are neuroimmunologic disorders in which there is an exaggerated immune response to the central nervous system (CNS, Fig. 6.1 ). Neuroimmune disorders have become increasingly recognized as more prevalent than was previously understood, and the understanding of these disorders is rapidly changing and continually evolving. Advances in autoantibodies detection, combined with imaging, clinical signs and symptoms, and cerebrospinal fluid (CSF)…

Infectious Disorders of the Brain

INTRODUCTION Background Central nervous system (CNS) infections can be due to multiple pathogens; however, viral and bacterial infections are the most common ( Table 5.1 ). The CNS injury that results from the infection depends on the timing of infection, pathogen, immune response, and medical treatment. TABLE 5.1 Etiologies of CNS Infections Developmental Stage Pathogenic Agents Congenital (Torch) Viral Cytomegalovirus (CMV) Toxoplasmosis Herpes simplex virus (HSV)…

Inherited and Acquired Metabolic Disorders

INTRODUCTION Background Inherited metabolic disorders are challenging to diagnose because they have variable clinical severity and age at presentation, are uncommonly encountered, and can have a range of imaging appearances. The variability in clinical manifestation and imaging appearance relates to the underlying genetic mutation and degree of protein function remaining. Some metabolic disorders can have infantile, juvenile, and adult forms. Many of the metabolic disorders can…

Hypoxic Ischemic Injury and Cerebrovascular Disorders

HYPOXIC ISCHEMIC INJURY Key Points Background Hypoxic ischemic injury (HII) typically refers to a combination of a hypoxic and hypoperfusion injury to the fetal or neonatal brain. Clinical manifestations of HII include low Apgar scores (0–3 at 5 minutes and ≤5 at 10 minutes), umbilical cord pH < 7.1, poor cry, weak suck, seizures, diminished movement, absent neonatal reflexes, and hypotonia. Depending on the maturity of…

Brain Malformations

INTRODUCTION Brain malformations represent a disruption of normal development and can be caused by genetic, infectious, ischemic, and hemorrhagic factors. As seen in Table 2.1 , the time at which the insult occurs during development will determine the malformation. Because the majority of brain malformations occur early in development, the majority of malformations will be present when imaging is performed. Brain malformations are often first encountered…

Normal Development

INTRODUCTION The human brain is a fascinating and essentially miraculous structure with layers upon layers of complexity in its anatomic organization, microscopic connectivity, biochemical workings, and functions. No human can grasp all of the complexity that allows the brain to function, but that doesn’t stop us from trying! Learning about the brain’s development is particularly exciting, daunting, and exhausting. Understanding even just a small fraction of…

Adoption of Lung CT AI Into Clinical Medicine

Introduction The successes in the 2000s and 2010s in developing reactive machine AI and limited-memory AI methods to detect and assess the present and severity of x-ray chest CT imaging findings associated with COVID-19 pneumonia, COPD, ILD, and lung cancer have spurred the development of multiple quantitative CT (QCT) lung AI companies, such as VIDA, that offer point-of-care lung CT AI products to assess lung diseases.…

Lung CT AI Enables Advanced Computer Modeling of Lung Physiome Structure and Function

Virtual Physiological Human and a Lung Physiome Model The International Union of Physiological Physiome project was the foundation for the Virtual Physiological Human (VPH) initiative and the human physiome. The term physiome describes the physiology of the whole organism. The concept of computational physiology and the human physiome is to have mathematicians and bioengineers, working together with physiologists and molecular biologists, link together the different scales…

Using Limited Memory Lung CT AI to Derive Advanced Quantitative CT Lung Metrics of COPD, ILD, and COVID-19 Pneumonia

Introduction In this chapter, we look at more advanced AI machine learning computer programs for the assessment of normal and diseased lungs using lung CT AI. These AI programs go beyond the reactive machine methods and use the more advanced limited memory AI methods. We have discussed in detail several reactive machine AI approaches to assessing the presence of emphysema, air trapping, and lung fibrosis in…

Using Reactive Machine AI and Dynamic Changes in Lung Structure to Derive Functional Quantitative Lung CT Metrics of COPD, ILD, and Asthma

Introduction The previous chapter reviewed the lung CT AI methods to assess normal and diseased lung structure using a single TLC chest CT scan. COPD, ILD, and asthma can all produce small and large airway disease. This chapter will look at how lung CT AI can be used to assess lung ventilation by obtaining two chest CT scans with each scan taken at a different lung…

Using Reactive Machine AI to Derive Quantitative Lung CT Metrics of COPD, ILD, and COVID-19 Pneumonia

Introduction This chapter will first review the basic structure of the human lung and use this information to explore the different QCT lung metrics that can be obtained from a single chest CT scan obtained at total lung capacity (TLC). A chest CT scan obtained at TLC and analyzed using lung CT AI can detect and assess lung density changes that occur in patients with emphysema…

Quantitative Assessment of Lung Nodule Size, Shape, and Malignant Potential Using Both Reactive and Limited-Memory Lung CT AI

CT Assessment of Lung Nodules—CT Versus Projection Radiography (PR) This chapter will describe the importance of detecting and assessing the risk of lung cancer in a lung nodule by lung CT AI. The diameter of the pulmonary nodule was the first widely used research and clinical quantitative CT (QCT) metric derived from chest CT scans and predated the use of clinical QCT metrics of diffuse lung…

X-ray CT Scanning Protocols for Lung CT AI Applications

In this chapter we will discuss the elements of chest CT scanning protocols that are optimized for lung CT AI applications. These lung CT AI applications can be divided into visual CT (VCT) applications, where the analysis of the lung CT images is done by an expert imaging physician, and quantitative CT (QCT) applications, where the image analysis is done by AI. The focus of this…

Three-Dimensional (3D) Digital Images of the Lung Using X-ray Computed Tomography

This chapter will discuss the digital lung, x-rays, and key components of the x-ray CT scanner to help better understand lung CT AI scanning protocols, and to briefly review the historical progression of advancements in x-ray computed tomography of the lungs from the 1970s through the development of multidetector spiral CT (MDCT) scanners in the early 2000s. Each CT technology advancement improved visual and quantitative assessment…

Introduction to Lung CT AI

AI: An Intelligent Agent The foundation for this book about lung CT AI is the application of what Alan Turing described in 1936 as the “universal Turing machine.” This is what is known today as the computer hardware and software that dominates so much of our lives, and is at the heart of lung CT AI. In his recent book, Stuart Russell describes succinctly what Alan…