Quantification and Optimisation of Lung Ventilation SPECT Images.

Format:

Book

Author/Creator:

Norberg, Pernilla.

Series:

Linköping University Medical Dissertations

Linköping University Medical Dissertations ; v.1403

Language:

English

Subjects (All):

Diagnostic imaging.

Three-dimensional imaging in medicine.

Physical Description:

1 online resource (86 pages)

Edition:

1st ed.

Other Title:

Linköping University Medical Dissertations

Place of Publication:

Linköping : Linköping University Electronic Press, 2014.

Summary:

This dissertation by Pernilla Norberg focuses on the development and optimization of lung ventilation imaging techniques, specifically using Single Photon Emission Computed Tomography (SPECT). The work aims to enhance early detection of lung abnormalities, particularly in patients with conditions like COPD, asthma, and allergies. Through innovative methods such as the CV T-method, which employs the coefficient of variation to measure heterogeneity in lung function, the research demonstrates improved identification of early-stage lung diseases. The study compares reconstruction algorithms, with findings indicating the superiority of Ordered Subset Expectation Maximisation (OSEM) in achieving higher spatial resolution and lower noise levels. This research is valuable for medical professionals and researchers in the field of medical imaging and respiratory diagnostics, offering insights into advanced techniques for more accurate and early diagnosis of lung conditions. Generated by AI.

Contents:

Intro

CONTENTS

ABSTRACT

SAMMANFATTNING

LIST OF PAPERS

ABBREVIATIONS

INTRODUCTION

AIM OF THE THESIS

BACKGROUND

IMAGE AND COMPUTER PROCESSING

EVALUATION OF RECONSTRUCTION ALGORITHMS

DEVELOPMENT AND OPTIMISATION OF THE CVT-METHOD

EVALUATION OF LUNG FUNCTION ON HUMAN SUBJECTS

REVIEW OF PUBLICATIONS

SUMMARY AND CONCLUSIONS

ACKNOWLEDGEMENTS

REFERENCES.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

9789175193595

9175193590

OCLC:

927227515