On the same page--physicist and radiologist perspectives on protocol management and review

Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA

Pooling data across diverse sources acquired by multisite consortia requires compliance with a predefined reference protocol i.e., ensuring different sites and scanners for a given project have used identical or compatible MR physics parameter values. Traditionally, this has been an arduous and manual process due to difficulties in working with the complicated DICOM standard and lack of resources allocated towards protocol compliance. Moreover, issues of protocol compliance is often overlooked for lack of realization that parameter values are routinely improvised/modified locally at various sites. The inconsistencies in acquisition protocols can reduce SNR, statistical power, and in the worst case, may invalidate the results altogether. An open-source tool, mrQA was developed to automatically assess protocol compliance on standard dataset formats such as DICOM and BIDS, and to study the patterns of non-compliance in over 20 open neuroimaging datasets, including the large ABCD study. The results demonstrate that the lack of compliance is rather pervasive. The frequent sources of non-compliance include but are not limited to deviations in Repetition Time, Echo Time, Flip Angle, and Phase Encoding Direction. It was also observed that GE and Philips scanners exhibited higher rates of non-compliance relative to the Siemens scanners in the ABCD dataset. Continuous monitoring for protocol compliance is strongly recommended before any pre/post-processing, ideally right after the acquisition, to avoid the silent propagation of severe/subtle issues. Although, this study focuses on neuroimaging datasets, the proposed tool mrQA can work with any DICOM-based datasets.

A General Framework for Monitoring Image Acquisition Workflow in the Radiology Environment: Timeliness for Acute Stroke CT Imaging

Many facets of an image acquisition workflow leave a digital footprint, making workflow analysis amenable to an informatics-based solution. This paper describes a detailed framework for analyzing workflow and uses acute stroke response timeliness in CT as a practical demonstration. We review methods for accessing the digital footprints resulting from common technologist/device interactions. This overview lays a foundation for obtaining data for workflow analysis. We demonstrate the method by analyzing CT imaging efficiency in the setting of acute stroke. We successfully used digital footprints of CT technologists to analyze their workflow. We presented an overview of other digital footprints including but not limited to contrast administration, patient positioning, billing, reformat creation, and scheduling. A framework for analyzing image acquisition workflow was presented. This framework is transferable to any modality, as the key steps of image acquisition, image reconstruction, image post processing, and image transfer to PACS are common to any imaging modality in diagnostic radiology.

CT and MR Protocol Standardization Across a Large Health System: Providing a Consistent Radiologist, Patient, and Referring Provider Experience

Building and maintaining a comprehensive yet simple set of standardized protocols for a cross-sectional image can be a daunting task. A single department may have difficulty preventing "protocol creep," which almost inevitably occurs when an organized "playbook" of protocols does not exist and individual radiologists and technologists alter protocols at will and on a case-by-case basis. When multiple departments or groups function in a large health system, the lack of uniformity of protocols can increase exponentially. In 2012, the University of Colorado Hospital formed a large health system (UCHealth) and became a 5-hospital provider network. CT and MR imaging studies are conducted at multiple locations by different radiology groups. To facilitate consistency in ordering, acquisition, and appearance of a given study, regardless of location, we minimized the number of protocols across all scanners and sites of practice with a clinical indication-driven protocol selection and standardization process. Here we review the steps utilized to perform this process improvement task and insure its stability over time. Actions included creation of a standardized protocol template, which allowed for changes in electronic storage and management of protocols, designing a change request form, and formation of a governance structure. We utilized rapid improvement events (1 day for CT, 2 days for MR) and reduced 248 CT protocols into 97 standardized protocols and 168 MR protocols to 66. Additional steps are underway to further standardize output and reporting of imaging interpretation. This will result in an improved, consistent radiologist, patient, and provider experience across the system.

Tracking Patterns of Nonadherence to Prescribed CT Protocol Parameters

PURPOSE

Quantification of the frequency, understanding the motivation, and documentation of the changes made by CT technologists at scan time are important components of monitoring a quality CT workflow.

METHODS

CT scan acquisition data were collected from one CT scanner for a period of 1 year. The data included all relevant acquisition parameters needed to define the technical side of a CT protocol. An algorithm was created to sort these data in groups of irradiation events with the same combinations of scan acquisition parameters. For scans modified at scan time, it was hypothesized that these examinations would show up only once in the organized data. A classification scheme was developed to place each "one-off" examination into a category related to what motivated the scan-time change.

RESULTS

A total of 132,707 irradiation events were organized into 434 groups of unique scan acquisition parameters. One hundred forty-four irradiation events had acquisition parameters that showed up only once in the data. These "one-offs" were classified as follows: 25% represented rarely used protocols, 17% were due to service scans, 16% were changed for unknown and therefore undesired reasons, 15% were changed by technologists trying to adapt protocol to patient size, 12% were allowable scan-time changes, 8% of scans had tube current maxed out, and 6% of scans were changed to a higher dose mode as requested by radiologists.

CONCLUSIONS

The outcome of this study suggests many areas of needed technologist training and chances for optimizing this institution's CT protocols.

Implementation and evaluation of a protocol management system for automated review of CT protocols

Protocol review is important to decrease the risk of patient injury and increase the consistency of CT image quality. A large volume of CT protocols makes manual review labor‐intensive, error‐prone, and costly. To address these challenges, we have developed a software system for automatically managing and monitoring CT protocols on a frequent basis. This article describes our experiences in the implementation and evaluation of this protocol monitoring system. In particular, we discuss various strategies for addressing each of the steps in our protocol‐monitoring workflow, which are: maintaining an accurate set of master protocols, retrieving protocols from the scanners, comparing scanner protocols to master protocols, reviewing flagged differences between the scanner and master protocols, and updating the scanner and/or master protocols. In our initial evaluation focusing only on abdomen and pelvis protocols, we detected 309 modified protocols in a 24‐week trial period. About one‐quarter of these modified protocols were determined to contain inappropriate (i.e., erroneous) protocol parameter modifications that needed to be corrected on the scanner. The most frequently affected parameter was the series description, which was inappropriately modified 47 times. Two inappropriate modifications were made to the tube current, which is particularly important to flag as this parameter impacts both radiation dose and image quality. The CT protocol changes detected in this work provide strong motivation for the use of an automated CT protocol quality control system to ensure protocol accuracy and consistency.

PACS number(s): 87.57.Q‐