Imaging systems in radiology

Bradley J. Erickson

doi:10.1007/978-3-030-58721-5_22

Imaging systems in radiology

Bradley J. Erickson

Radiology

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Imaging has provided the ability of physicians to 'see inside' the body, enabling significantly more accurate diagnosis than was possible by physical examination. Radiological imaging is now widely used for diagnosis, and increasingly for therapeutic guidance. Its naturally digital form has also enabled artificial intelligence, and specifically deep learning to improve diagnostic accuracy and also increase the quantitative content in imaging reports. This chapter will describe the history of these systems initially replacing film images, but now producing lifelike 3D visualizations, and applying deep learning to extract information that can rival human performance.

Original language	English (US)
Title of host publication	Biomedical Informatics
Subtitle of host publication	Computer Applications in Health Care and Biomedicine: Fifth Edition
Publisher	Springer International Publishing
Pages	733-753
Number of pages	21
ISBN (Electronic)	9783030587215
ISBN (Print)	9783030587208
DOIs	https://doi.org/10.1007/978-3-030-58721-5_22
State	Published - Jul 2 2021

Keywords

Advanced Visualization System (AVS)
DICOM
Deep Learning
Picture Archiving and Communications System (PACS)
Radiology Information System (RIS)
Speech Recognition
Teleradiology
Vendor Neutral Archive (VNA)
Workflow Orchestration

ASJC Scopus subject areas

General Medicine
General Computer Science

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10.1007/978-3-030-58721-5_22

Cite this

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abstract = "Imaging has provided the ability of physicians to 'see inside' the body, enabling significantly more accurate diagnosis than was possible by physical examination. Radiological imaging is now widely used for diagnosis, and increasingly for therapeutic guidance. Its naturally digital form has also enabled artificial intelligence, and specifically deep learning to improve diagnostic accuracy and also increase the quantitative content in imaging reports. This chapter will describe the history of these systems initially replacing film images, but now producing lifelike 3D visualizations, and applying deep learning to extract information that can rival human performance.",

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KW - Deep Learning

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KW - Speech Recognition

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KW - Vendor Neutral Archive (VNA)

KW - Workflow Orchestration

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ER -

Imaging systems in radiology

Abstract

Keywords

ASJC Scopus subject areas

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