Skip to content

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

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

Status
UNKNOWN
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT03033615
Enrollment
20
Registered
2017-01-27
Start date
2017-08-31
Completion date
2017-12-31
Last updated
2017-06-28

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Cancer Liver, Cancer, Lung, X-rays; Effects

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.

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.

Interventions

DEVICEPixelShine

Machine learning algorithm

Iterative reconstruction software

Sponsors

Cedars-Sinai Medical Center
CollaboratorOTHER
AlgoMedica, Inc.
Lead SponsorINDUSTRY

Study design

Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
SINGLE (Subject)

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes

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

Design outcomes

Primary

MeasureTime frameDescription
Visual Image Quality as Assessed by Image Noise ReductionThrough study completion, an average of 1 monthComparison of image noise

Secondary

MeasureTime frameDescription
Image Resolution as Assessed by Size of Detected LesionsThrough study completion, an average of 1 monthDetermine smallest size of detectable objects

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026