Model-Based Image Reconstruction for X-Ray CT in Lung Imaging

NCT ID: NCT01979991

Last Updated: 2016-02-05

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

PHASE1

Total Enrollment

184 participants

Study Classification

INTERVENTIONAL

Study Start Date

2010-08-31

Study Completion Date

2015-12-31

Brief Summary

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To develop a computer program that will improve CT image quality and decrease the amount of x-ray radiation that future patients may be exposed to when they have a CT examination.

Detailed Description

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We will be asking patients for their permission to save and use the sinogram from their CT scan. The sinogram will be de-identified and sent to an archive system for storage. It will be exported to a computer for processing using MBIR (model based iterative reconstruction). MBIR (model based iterative reconstruction) is a new method being developed to process CT sinograms. The newly reconstructed images will be reviewed by experts to determine if they are as readable and accurate as CT images created with the software that is currently being used. Sinogram data and the reconstructed images will be shared with collaborating researchers at General Electric Global Research.

Conditions

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Lung Diseases

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

BASIC_SCIENCE

Blinding Strategy

SINGLE

Investigators

Study Groups

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CT Imaging and Reconstruction

To develop a MBIR (model-based image reconstruction) method that will improve X-ray CT lung imaging by improving image quality and reducing dose.

Group Type EXPERIMENTAL

CT Imaging and Reconstruction

Intervention Type PROCEDURE

Patients will be consented for their permission to save and use the sinogram from their CT chest/lung scan. The sinogram will be de-identified and sent to an archive system for storage.

Later it will be exported to a computer for processing using MBIR (model based image reconstruction). The newly processed images will then be read by blinded readers. The quality of the images will be reviewed to determine if they are as readable and accurate as CT images created with the software that is currently being used.

Interventions

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CT Imaging and Reconstruction

Patients will be consented for their permission to save and use the sinogram from their CT chest/lung scan. The sinogram will be de-identified and sent to an archive system for storage.

Later it will be exported to a computer for processing using MBIR (model based image reconstruction). The newly processed images will then be read by blinded readers. The quality of the images will be reviewed to determine if they are as readable and accurate as CT images created with the software that is currently being used.

Intervention Type PROCEDURE

Eligibility Criteria

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Inclusion Criteria

1\. 18 years of age and older 2. No medical or psychiatric condition precluding informed consent

\-

Exclusion Criteria

1. Inability to lie flat on the back with arms raised over the head for 30min.
2. Metallic implants or metallic devices in the chest or back.
3. Participation in other research trials involving ionizing radiation
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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National Institutes of Health (NIH)

NIH

Sponsor Role collaborator

General Electric

INDUSTRY

Sponsor Role collaborator

University of Michigan

OTHER

Sponsor Role lead

Responsible Party

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Jeffrey Fessler PhD.

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Jeffrey Fessler, PhD

Role: PRINCIPAL_INVESTIGATOR

University of Michigan Hospital

Jeffrey Fessler, PhD

Role: PRINCIPAL_INVESTIGATOR

University of Michigan

Locations

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University of Michigan Hospital

Ann Arbor, Michigan, United States

Site Status

Countries

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United States

Other Identifiers

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HUM00038447

Identifier Type: -

Identifier Source: org_study_id

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