Developing the Digital Lung: From First Lung CT to Clinical AI

Developing the Digital Lung: From First Lung CT to Clinical AI
Copyright
Contents
Dedication
Preface
Confidence is ClinicalKey
Acknowledgments
Any screen. Any time. Anywhere.
1
 Introduction to Lung CT AI
	AI: An Intelligent Agent
		AI Definitions and Levels
	Diagnosis of COPD, ILD, Lung Cancer, and Other Smoking-Related Diseases
	Information for Healthcare Providers and Administrators, Patients, and Researchers
	Describing Lung CT AI in Three Stages
	References
2
 Three-Dimensional (3D) Digital Images of the Lung Using X-ray Computed Tomography
	The Digital Lung
	X-ray Computed Tomography
		X-rays
		Important Components of an X-ray Computed Tomographic (CT) Scanner
			CT X-ray Tube
			CT X-ray Beam Shape and Energy Spectrum
			X-ray CT Detectors
			CT Gantry
			CT Table, Isocenter, Scan Pitch, and Scanning Modes
			Scanning Modes
			Collection of a Scanned Object’s Projection Data
			Image Reconstruction
				FBP Versus Iterative Reconstruction Methods.
			Scan Field of View (SFOV), Display Field of View (DFOV), and Reconstruction Matrix Size
			Hounsfield Units and the CT Voxel
			Visually Display of Lung Images
			Quantitative CT Metrics
	CT Scanning Protocols
	X-ray CT Radiation Dose
	Brief History of X-ray CT
		The First Head and Body CT Scanners – 1971 to 1975 (EMI, ACTA, Ohio Nuclear)
		Rapid Evolution of CT Scanner Designs
		Moderate-Resolution Whole Lung Acquisition in a Single Breath—Spiral CT Scanner
		High-Resolution Lung CT in a Single Breath for All Patients: Multidetector Spiral CT Scanners
	References
3
 X-ray CT Scanning Protocols for Lung CT AI Applications
	Early Work in the Development of QCT Scanning Protocols
		Workshop: Quantitative Computed Tomography Scanning in Longitudinal Studies of Emphysema
			Can X-ray CT Detect and Quantify Pulmonary Emphysema?
			Single Versus Multiple Detector Row CT Scanners
			Constant and Optimal X-ray Tube Peak Kilovoltage, mAs, and Radiation Dose
			Scan Mode and Pitch
			Detector Width and Recommended Axial Image Thickness and Spacing
			Image Reconstruction
			Optimal Lung Volume—Total Lung Capacity (TLC)
			Administration of Intravenous Iodinated X-ray CT Contrast Media
			X-ray CT Phantoms for Image Quality Assessments
			CT Image Analysis
			Image Data Transfer, Analysis, and Storage
			Summary of the Recommended Quantitative Lung CT Scanning Protocol
	Current Recommended Quantitative CT Scanning Protocol
		Radiation Dose
		MDCT Scanner Models, Scan Mode, Z-Axis Detector Size, Rotation Time, Pitch
		DFOV, Isocenter, Scanning at TLC and RV
		CT Image Reconstruction
		Quality Control
			Personnel Training and Certification
			CT Scanner Calibration and Certification
			CT Scan Acquisition
			CT Image Data Transfer
	CT Scanner Quality Control
		CT Scanner Quality Control Measures, ACR CT Phantom
		COPDGene CT Phantom
	Current QIBA Lung Density CT Profile
	Summary
	References
4
 Quantitative Assessment of Lung Nodule Size, Shape, and Malignant Potential Using Both Reactive and Limited-Memory Lung  ...
	CT Assessment of Lung Nodules—CT Versus Projection Radiography (PR)
		CT Protocol to Assess Lung Nodules
	CT Determination of Lung Nodule Size
	CT Determination of Nodule Growth
	CT Determination of Nodule Density
	CT Determined Nodule Mass, Location, Morphology, Shape, Contour
	CT Determined Nodule Texture—Limited-Memory AI
	CT Assessment of Lung Tissue Adjacent to the Lung Nodule—Limited-Memory AI
	References
5
 Using Reactive Machine AI to Derive Quantitative Lung CT Metrics of COPD, ILD, and COVID-19 Pneumonia
	Introduction
	Normal Lung Structure
	QCT Scanning Protocol and Lung Segmentation
	Chronic Obstructive Pulmonary Disease (COPD) Induced Changes in Lung Structure
		Quantitative CT Metrics of Lung Density in COPD
			Mean Lung Density (MLD) for the Detection and Assessment of Emphysema
			Low Attenuating Area (LAA) for the Detection and Assessment of Emphysema
			15th Percentile Method for the Detection and Assessment of Emphysema
	Clinical Value of Using Lung CT AI in Patients with Environmental Exposure to Cigarette Smoke
		Clinical Benefit of LAA−950
	Interstitial Lung Disease (ILD) Induced Changes in Lung Structure
		Lung Density, Volumes, Specific Air and Tissue Volumes in IPF
		Histogram Measures of ILD—MLD, Skewness, Kurtosis
		Percent High Attenuating Areas (%HAA) in ILD
	QCT of COVID-19 Acute Viral Pneumonia
	Summary
	References
6
 Using Reactive Machine AI and Dynamic Changes in Lung Structure to Derive Functional Quantitative Lung CT Metrics of COP ...
	Introduction
	Expiratory QCT Assessment of Air Trapping Due to Small Airway Disease in the Lung
		Whole Lung Assessment of Air Trapping Using LAA in Severe Asthma Patients
		Whole Lung Assessment of Air Trapping Using LAA in COPD Patients
		Whole Lung Assessment of Air Trapping in the COPDGene 2019 Classes of COPD
		Whole Lung Assessment of Air Trapping Using MLD and CT Determined Lung Volumes
		Whole Lung Assessment of Air Trapping in Bronchiolitis Obliterans
	Assessment of Air Trapping at the Voxel Level Using Image Registration
		Parametric Response Map
		Disease Probability Map
	Assessment of Biomechanics and Tissue Stiffness Using Image Registration
	Direct Measurements of Large Airway Geometry Using Lung CT AI
		Segmentation of the Airways of the Lungs
		QCT Metrics of Airway Geometry
		COPDGene Airway Geometry Features and Spirometric Measures of Airflow
		Pi10 and COPDGene 2019 Classes of COPD
	Summary
	References
7
 Using Limited Memory Lung CT AI to Derive Advanced Quantitative CT Lung Metrics of COPD, ILD, and COVID-19 Pneumonia
	Introduction
	Limited Memory Lung CT AI and the Assessment of Emphysema
		Adaptive Multiple Feature Method (AMFM) AI Agent (Supervised, Bayesian Classifier)
		Deep Learning Enables Automatic Classification of Emphysema Pattern at CT
	Limited Memory Lung CT AI and the Assessment of Interstitial Lung Disease (ILD)
		AMFM AI Method for Assessing Interstitial Lung Disease
		CALIPER (Computer-Aided Lung Informatics for Pathology Evaluation and Rating)
		DTA (Data-Driven Textural Analysis for Assessment of Fibrotic Lung Disease)
	CNN for COVID-19 Pneumonia
	Summary
	References
8
 Lung CT AI Enables Advanced Computer Modeling of Lung Physiome Structure and Function
	Virtual Physiological Human and a Lung Physiome Model
	Finite Element Model of Lung Structure and Function
		Generating the 3D Finite Element Mesh of the Lung
		Generating the Airway Tree Within the 3D Mesh of the Lung
		Generating the Pulmonary Vascular Tree
			Modeling the Extra-Acinar Pulmonary Vessels
			Modeling the Intra-Acinar Pulmonary Vessels
	Lung Physiome (LP) Model Applied to the Assessment of Acute Pulmonary Embolism
		Results of Lung Physiome Model in Predicting Hypoxemic Risk in APE
		Extending the Lung Physiome Model Approach to Using Generic Vascular Anatomy
	Summary of Important Concepts of the Lung Physiome Model
	References
9
 Adoption of Lung CT AI Into Clinical Medicine
	Introduction
	Healthcare Imaging IT
	Electronic Medical Record (EMR)
		Picture Archiving and Communication System (PACS)
		Radiology Information Software (RIS)
		Medical Imaging Reporting and Voice Recognition Software (VR)
	Clinical Lung CT AI Software
		VIDA Insights–Clinical Lung CT AI Software
			VIDA Insights Density/tMPR Reactive Machine AI Tool for Assessing Volumes, LAA, and HAA
			VIDA Discovery Limited-Memory AI Texture Tool
			Enhanced Visualization of Airways and Subpleural Lung Tissue
			VIDA Lung Nodule Tool
			VIDA Discovery Lung Ventilation Tool
	Responsible AI
	References
Index