
Medical physics 3.0 versus 1.0: A case study in digital radiography quality control
Author(s) -
Carver Diana E.,
Willis Charles E.,
Stauduhar Paul J.,
Nishino Thomas K.,
Wells Jered R.,
Samei Ehsan
Publication year - 2018
Publication title -
journal of applied clinical medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 48
ISSN - 1526-9914
DOI - 10.1002/acm2.12425
Subject(s) - quality assurance , medical physics , nuclear medicine , detector , contrast to noise ratio , medicine , digital radiography , image quality , medical imaging , radiography , radiology , computer science , physics , artificial intelligence , optics , pathology , external quality assessment , image (mathematics)
Purpose The study illustrates how a renewed approach to medical physics, Medical Physics 3.0 ( MP 3.0), can identify performance decrement of digital radiography ( DR ) systems when conventional Medical Physics 1.0 ( MP 1.0) methods fail. Methods MP 1.0 tests included traditional annual tests plus the manufacturer's automated Quality Assurance Procedures ( QAP ) of a DR system before and after a radiologist's image quality ( IQ ) complaint repeated after service intervention. Further analysis was conducted using nontraditional MP 3.0 tests including longitudinal review of QAP results from a 15‐yr database, exposure‐dependent signal‐to‐noise ( SNR 2 ), clinical IQ , and correlation with the institutional service database. Clinical images were analyzed in terms of IQ metrics by the Duke University Clinical Imaging Physics Group using previously validated software. Results Traditional metrics did not indicate discrepant system performance at any time. QAP reported a decrease in contrast‐to‐noise ratio ( CNR ) after detector replacement, but remained above the manufacturer's action limit. Clinical images showed increased lung noise (Ln), mediastinum noise (Mn), and subdiaphragm‐lung contrast ( SL c), and decreased lung gray level (Lgl) following detector replacement. After detector recalibration, QAP CNR improved, but did not return to previous levels. Lgl and SL c no longer significantly differed from before detector recalibration; however, Ln and Mn remained significantly different. Exposure‐dependent SNR 2 documented the detector operating within acceptable limits 9 yr previously but subsequently becoming miscalibrated sometime before four prior annual tests. Service records revealed catastrophic failure of the computer containing the original detector calibration from 11 yr prior. It is likely that the incorrect calibration backup file was uploaded at that time. Conclusions MP 1.0 tests failed to detect substandard system performance, but MP 3.0 methods determined the root cause of the problem. MP 3.0 exploits the wealth of data with more sensitive performance indicators. Data analytics are powerful tools whose proper application could facilitate early intervention in degraded system performance.