Radiation Dose for Multiregion CT Protocols: Challenges and Limitations.

OBJECTIVE. The purpose of this study was to devise a method for classification of individual chest and abdomen-pelvis CT doses for multiregion CT. MATERIALS AND METHODS. A retrospective analysis of volume CT dose index (CTDIvol) and dose-length product (DLP) associated with chest (150 adult patients), abdomen-pelvis (150 patients), and multiregion combined chest-abdomen-pelvis CT (210 patients; 60 single-run chest-abdomen-pelvis CT; 150 split-run with separate chest and abdomen-pelvis CT). All 510 CT examinations were performed with one of four MDCT scanners (64-, 64-, 128-, 256-MDCT). CTDIvol, DLP, and scan length were recorded. Scan lengths were obtained for these 510 CT examinations and for an additional 7745 examinations of patients at another institution. Data were analyzed by ANOVA and ROC analysis. RESULTS. The respective DLPs (chest, 258-381 mGy · cm; abdomen-pelvis, 360-433 mGy · cm; single-run chest-abdomen-pelvis, 595-636 mGy · cm) and scan lengths (chest, 31-33 cm; abdomen-pelvis, 45-46 cm; single-run chest-abdomen-pelvis, 63-65 cm) for chest, abdomen-pelvis, and multiregion combined chest-abdomen-pelvis CT were significantly different (p < 0.0001). For split-run, chest-abdomen-pelvis CT, scan lengths and dose indexes for individual body regions were not different from those of single-body-region CT (p > 0.05). ROC analysis of chest and abdomen examinations showed an ideal scan length threshold of 38 cm to differentiate abdomen-pelvis CT from chest CT with accuracy of 97.39% and an AUC of 0.9764. CONCLUSION. Despite interscanner variabilities in CT radiation doses, shorter scan length for chest than for abdomen-pelvis CT enables accurate binning of radiation doses for split-run combined chest-abdomen-pelvis CT.

Process improvement for reducing side discrepancies in radiology reports.

BACKGROUND: Laterality errors in radiology reports can lead to serious errors in management. PURPOSE: To reduce errors related to side discrepancies in radiology reports from thoracic imaging by 50% over a six-month period with education and voice recognition software tools. MATERIAL AND METHODS: All radiology reports at the Thoracic Imaging Division from the fourth quarter of 2016 were reviewed manually for presence of side discrepancies (baseline data). Side discrepancies were defined as a lack of consistency in side labeling of any abnormality in the "Findings" to "Impression" sections of the reports. Process map and Ishikawa fishbone diagram (Microsoft Visio) were created. All thoracic radiologists were educated on side-related errors in radiology reports for plan-design-study-act cycle 1 (PDSA #1). Two weeks later, voice recognition software was configured to capitalize sides (RIGHT and LEFT) in the reports during dictated (PDSA# 2). Radiology reports were analyzed to determine side-discrepancy errors following each PDSA cycle (post-interventional data). Statistical run charts were created using QI Macros statistical software. RESULTS: Baseline data revealed 33 side-discrepancy errors in 47,876 reports with an average of 2.5 errors per week (range = 1-8 errors). Following PDSA #1, there were seven errors pertaining to side discrepancies over a two-week period. Errors declined following implementation of PDSA #2 to meet the target of 0.85 side-discrepancy error per week over seven weeks. CONCLUSION: Automated processes (such as capitalization of sides) help reduce left/right errors substantially without affecting reporting turnaround time.