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PixelShine vs. Iterative Reconstruction (IR) Processing of CT Images

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ClinicalTrials.gov Identifier: NCT03033615
Recruitment Status : Unknown
Verified June 2017 by AlgoMedica, Inc..
Recruitment status was:  Not yet recruiting
First Posted : January 27, 2017
Last Update Posted : June 28, 2017
Sponsor:
Collaborator:
Cedars-Sinai Medical Center
Information provided by (Responsible Party):
AlgoMedica, Inc.

Brief Summary:
This study will compare the quality of CT images acquired with very low-dose radiation and processed with commercially available software vs. PixelShine processed images. It would potentially allow imaging facilities to acquire CT scans using lower doses of radiation without sacrificing clarity of CT images. Acquiring high quality CT images with low-dose radiation has the potential to enhance patient safety and has significant implications in imaging practices.

Condition or disease Intervention/treatment Phase
Cancer, Lung Cancer Liver X-rays; Effects Device: PixelShine Device: Conventional processing Not Applicable

Detailed Description:

Patients receiving CT scans as part of their standard treatment will be asked to consent to an additional 5 minutes of imaging using very low-dose radiation prior to the conventional-dose CT scan. The prospective review will be performed in two cohorts: Chest CT scans and abdominal CT scans.

Anonymized images will be processed by conventional CT software and compared to the same images processed with machine-learning-based PixelShine. A board-certified radiologist will assess the noise and visual quality of the imaging data.

Study patients will receive approximately 10% more dose than a standard CT scan by participating in the study. There are no known short-term safety issues associated with this study. The study-related very low dose radiation is at a level far below that used for conventional x-ray imaging. The study has been approved by the Radiation Safety Committee as part of the review process.

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 20 participants
Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Masking: Single (Participant)
Primary Purpose: Diagnostic
Official Title: Prospective Review of CT Imaging Data Comparing Quality of Low-Radiation-Dose Images Post-Processed With Iterative Reconstruction Software vs. Machine Learning (AlgoMedica PixelShine) Software
Estimated Study Start Date : August 2017
Estimated Primary Completion Date : October 2017
Estimated Study Completion Date : December 2017

Resource links provided by the National Library of Medicine


Arm Intervention/treatment
Active Comparator: Chest CT
Conventional processing vs. PixelShine processing
Device: PixelShine
Machine learning algorithm

Device: Conventional processing
Iterative reconstruction software

Active Comparator: Abdominal CT
Conventional processing vs. PixelShine processing
Device: PixelShine
Machine learning algorithm

Device: Conventional processing
Iterative reconstruction software




Primary Outcome Measures :
  1. Visual Image Quality as Assessed by Image Noise Reduction [ Time Frame: Through study completion, an average of 1 month ]
    Comparison of image noise


Secondary Outcome Measures :
  1. Image Resolution as Assessed by Size of Detected Lesions [ Time Frame: Through study completion, an average of 1 month ]
    Determine smallest size of detectable objects



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Criteria

Inclusion Criteria:

  • Patients must be 18 years of age or older
  • Patients must be able and willing to consent to participate in this project
  • Patients will be scheduled for a standard of care CT scan

Exclusion Criteria:

  • This study does not investigate either a specific disease or a specific patient population-it only examines and compares images obtained at low radiation exposure post-processed with Algomedica's PixelShine software with conventionally processed images
  • All other patients will be excluded
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Responsible Party: AlgoMedica, Inc.
ClinicalTrials.gov Identifier: NCT03033615    
Other Study ID Numbers: ALG-002
First Posted: January 27, 2017    Key Record Dates
Last Update Posted: June 28, 2017
Last Verified: June 2017
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: Yes
Product Manufactured in and Exported from the U.S.: Yes
Additional relevant MeSH terms:
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Lung Neoplasms
Respiratory Tract Neoplasms
Thoracic Neoplasms
Neoplasms by Site
Neoplasms
Lung Diseases
Respiratory Tract Diseases