Implementation of an Institution-Wide Rules-Based Automated CT Protocoling System.

BACKGROUND. Examination protocoling is a noninterpretive task that increases radiologists' workload and can cause workflow inefficiencies. OBJECTIVE. The purpose of this study was to evaluate effects of an automated CT protocoling system on examination process times and protocol error rates. METHODS. This retrospective study included 317,597 CT examinations (mean age, 61.8 ± 18.1 [SD] years; male, 161,125; female, 156,447; unspecified sex, 25) from July 2020 to June 2022. A rules-based automated protocoling system was implemented institution-wide; the system evaluated all CT orders in the EHR and assigned a protocol or directed the order for manual radiologist protocoling. The study period comprised pilot (July 2020 to December 2020), implementation (January 2021 to December 2021), and postimplementation (January 2022 to June 2022) phases. Proportions of automatically protocoled examinations were summarized. Process times were recorded. Protocol error rates were assessed by counts of quality improvement (QI) reports and examination recalls and comparison with retrospectively assigned protocols in 450 randomly selected examinations. RESULTS. Frequency of automatic protocoling was 19,366/70,780 (27.4%), 68,875/163,068 (42.2%), and 54,045/83,749 (64.5%) in pilot, implementation, and postimplementation phases, respectively (p < .001). Mean (± SD) times from order entry to protocol assignment for automatically and manually protocoled examinations for emergency department examinations were 0.2 ± 18.2 and 2.1 ± 69.7 hours, respectively; mean inpatient examination times were 0.5 ± 50.0 and 3.5 ± 105.5 hours; and mean outpatient examination times were 361.7 ± 1165.5 and 1289.9 ± 2050.9 hours (all p < .001). Mean (± SD) times from order entry to examination completion for automatically and manually protocoled examinations for emergency department examinations were 2.6 ± 38.6 and 4.2 ± 73.0 hours, respectively (p < .001); for inpatient examinations were 6.3 ± 74.6 and 8.7 ± 109.3 hours (p = .001); and for outpatient examinations were 1367.2 ± 1795.8 and 1471.8 ± 2118.3 hours (p < .001). In the three phases, there were three, 19, and 25 QI reports and zero, one, and three recalls, respectively, for automatically protocoled examinations, versus nine, 19, and five QI reports and one, seven, and zero recalls for manually protocoled examinations. Retrospectively assigned protocols were concordant with 212/214 (99.1%) of automatically protocoled versus 233/236 (98.7%) of manually protocoled examinations. CONCLUSION. The automated protocoling system substantially reduced radiologists' protocoling workload and decreased times from order entry to protocol assignment and examination completion; protocol errors and recalls were infrequent. CLINICAL IMPACT. The system represents a solution for reducing radiologists' time spent performing noninterpretive tasks and improving care efficiency.

Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations.

OBJECTIVES: To investigate the image quality and perception of a sinogram-based deep learning image reconstruction (DLIR) algorithm for single-energy abdominal CT compared to standard-of-care strength of ASIR-V. METHODS: In this retrospective study, 50 patients (62% F; 56.74 ± 17.05 years) underwent portal venous phase. Four reconstructions (ASIR-V at 40%, and DLIR at three strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H)) were generated. Qualitative and quantitative image quality analysis was performed on the 200 image datasets. Qualitative scores were obtained for image noise, contrast, small structure visibility, sharpness, and artifact by three blinded radiologists on a 5-point scale (1, excellent; 5, very poor). Radiologists also indicated image preference on a 3-point scale (1, most preferred; 3, least preferred). Quantitative assessment was performed by measuring image noise and contrast-to-noise ratio (CNR). RESULTS: DLIR had better image quality scores compared to ASIR-V. Scores on DLIR-H for noise (1.40 ± 0.53), contrast (1.41 ± 0.55), small structure visibility (1.51 ± 0.61), and sharpness (1.60 ± 0.54) were the best (p < 0.05) followed by DLIR-M (1.85 ± 0.52, 1.66 ± 0.57, 1.69 ± 0.59, 1.68 ± 0.46), DLIR-L (2.29 ± 0.58, 1.96 ± 0.61, 1.90 ± 0.65, 1.86 ± 0.46), and ASIR-V (2.86 ± 0.67, 2.55 ± 0.58, 2.34 ± 0.66, 2.01 ± 0.36). Ratings for artifacts were similar for all reconstructions (p > 0.05). DLIRs did not influence subjective textural perceptions and were preferred over ASIR-V from the beginning. All DLIRs had a higher CNR (26.38-102.30%) and lower noise (20.64-48.77%) than ASIR-V. DLIR-H had the best objective scores. CONCLUSION: Sinogram-based deep learning image reconstructions were preferred over iterative reconstruction subjectively and objectively due to improved image quality and lower noise, even in large patients. Use in clinical routine may allow for radiation dose reduction. KEY POINTS: • Deep learning image reconstructions (DLIRs) have a higher contrast-to-noise ratio compared to medium-strength hybrid iterative reconstruction techniques. • DLIR may be advantageous in patients with large body habitus due to a lower image noise. • DLIR can enable further optimization of radiation doses used in abdominal CT.

Deploying Clinical Process Improvement Strategies to Reduce Motion Artifacts and Expiratory Phase Scanning in Chest CT.

We hypothesized that clinical process improvement strategies can reduce frequency of motion artifacts and expiratory phase scanning in chest CT. We reviewed 826 chest CT to establish the baseline frequency. Per clinical process improvement guidelines, we brainstormed corrective measures and priority-pay-off matrix. The first intervention involved education of CT technologists, following which 795 chest CT were reviewed. For the second intervention, instructional videos on optimal breath-hold were shown to 245 adult patients just before their chest CT. Presence of motion artifacts and expiratory phase scanning was assessed. We also reviewed 311 chest CT scans belonging to a control group of patients who did not see the instructional videos. Pareto and percentage run charts were created for baseline and post-intervention data. Baseline incidence of motion artifacts and expiratory phase scanning in chest CT was 35% (292/826). There was no change in the corresponding incidence following the first intervention (36%; 283/795). Respiratory motion and expiratory phase chest CT with the second intervention decreased (8%, 20/245 patients). Instructional videos for patients (and not education and training of CT technologists) reduce the frequency of motion artifacts and expiratory phase scanning in chest CT.

Data-driven CT protocol review and management­experience from a large academic hospital.

PURPOSE: Protocol review plays a critical role in CT quality assurance, but large numbers of protocols and inconsistent protocol names on scanners and in exam records make thorough protocol review formidable. In this investigation, we report on a data-driven cataloging process that can be used to assist in the reviewing and management of CT protocols. METHODS: We collected lists of scanner protocols, as well as 18 months of recent exam records, for 10 clinical scanners. We developed computer algorithms to automatically deconstruct the protocol names on the scanner and in the exam records into core names and descriptive components. Based on the core names, we were able to group the scanner protocols into a much smaller set of "core protocols," and to easily link exam records with the scanner protocols. We calculated the percentage of usage for each core protocol, from which the most heavily used protocols were identified. RESULTS: From the percentage-of-usage data, we found that, on average, 18, 33, and 49 core protocols per scanner covered 80%, 90%, and 95%, respectively, of all exams. These numbers are one order of magnitude smaller than the typical numbers of protocols that are loaded on a scanner (200-300, as reported in the literature). Duplicated, outdated, and rarely used protocols on the scanners were easily pinpointed in the cataloging process. CONCLUSIONS: The data-driven cataloging process can facilitate the task of protocol review.