Quality control (QC) of X-ray equipment is an important task for patient safety. Periodic QC should not take long to perform, especially in a stressful clinical environment where downtime should be minimised. DOSESTAT-QC® is a new quick QC software with automatic image analysis that has been developed into a quality-assured and user-friendly tool for daily use. Trained X-ray personnel can easily perform the QC with selected image phantoms and immediately approve the results onsite before the equipment is used clinically. Image analysis includes visibility of contrast detail groups, homogeneity, signal-to-noise ratio and contrast-to-noise ratio. In the event of unapproved QC, a message is automatically sent to medical physicists and/or medical engineers. The results are stored over time and are available for trend analysis. The present paper describes the DOSESTAT-QC® software and its application in QC of interventional X-ray systems and mammography systems.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1093/rpd/ncab064 | DOI Listing |
JAMA Netw Open
January 2025
Medical Oncology, The Ottawa Hospital Cancer Centre, University of Ottawa Faculty of Medicine, Ottawa, Ontario, Canada.
Importance: Evolving breast cancer treatments have led to improved outcomes but carry a substantial financial burden. The association of treatment costs with the cost-effectiveness of screening mammography is unknown.
Objective: To determine the cost-effectiveness of population-based breast cancer screening in the context of current treatment standards.
J Imaging
December 2024
Computer Science and Engineering Department, College of Engineering, University of Nevada, Reno, Main Campus, Reno, NV 89557, USA.
Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in images for the mediolateral oblique view makes designing a robust automated breast cancer detection system more challenging. Most of the current methods for removing the pectoral muscle are based on traditional machine learning approaches.
View Article and Find Full Text PDFPLOS Digit Health
December 2024
Heart and Vascular Institute, Stamford Hospital, Stamford, Connecticut, United States of America.
Breast artery calcification (BAC) obtained from standard mammographic images is currently under evaluation to stratify risk of major adverse cardiovascular events in women. Measuring BAC using artificial intelligence (AI) technology, we aimed to determine the relationship between BAC and coronary artery calcification (CAC) severity with Major Adverse Cardiac Events (MACE). This retrospective study included women who underwent chest computed tomography (CT) within one year of mammography.
View Article and Find Full Text PDFCan Assoc Radiol J
December 2024
Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
Breast Imaging-Reporting and Data System (BI-RADS) density scores have been included in screening mammography reports in BC since 2018. Despite these density scores being present in screening mammography reports for numerous years, there remains insufficient evidence to guide supplemental testing for patients with dense breasts. The primary objective of this study was to evaluate how primary care providers in Canada utilize BI-RADS density scores reported on normal screening mammograms of average risk, asymptomatic patients in their clinical practice.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!