Improving MRI Scanner Utilization Using Modality Log Files

Predicting slot lengths of MRI exams to decrease observed discrepancies between planning and execution

This retrospective study aimed to reveal discrepancies between planned (Tplan) and actual (Tact) slot lengths of abdomen MRI exams, and to improve Tplan by predicting slot lengths via a machine learning algorithm. Tplan and Tact were retrieved from RIS and modality logfiles, respectively, covering 3038 MRI exams of 17 protocols performed at an abdomen department. Comparisons showed that 30% of exams exceeded planned slot lengths. On the other hand, exams completed within planning failed to manifest good adherence to schedule, as many of them were assigned with an unnecessarily long slot. While adjusting the planned exam duration by a fixed amount of time for each protocol could move Tplan closer to the mean or median Tact, the large spread of Tact would still be unaffected. This is why this study goes one step further, introducing a method to predict the required slot length not only per protocol, but for each individual exam. A Random Forest Regression model was trained on historic data to predict individual slot lengths (Tpred) based on patient and exam context. The correlation between Tpred and Tact was found to be better than that of Tplan and Tact, with Pearson correlation factors of 0.66 and 0.50, respectively. The overall adherence to schedule was also improved by the prediction, as seen by a reduction of both the root mean squared error (-28%) and the standard deviation (-16%) of the differences between planned/predicted slot times and Tact. To provide further insights into the discrepancies between planning and execution of MRI exams, nineteen exams from the Liver protocol with verified clinical information were selected. This case study showed that patient conditions, diagnostic purposes and the selection of sequences during exams could explain some variations of exam durations, but the potential for improving the exam time prediction by including this additional context is limited.

Analysis and predictability of technologists' perception of MR exam complexity

INTRODUCTION

As MRI becomes a routine clinical diagnostic method, its complexity of techniques, protocols and scanning is growing. On the other hand, aggravated by the ubiquitous shortage of workforce, technologists' stress level and burnout rates are increasing. In this context, our study aims to shed light on technologists' perceived complexity of MR exams, by analyzing a multidimensional dataset composed of workflow, patient, and operational details, and further predicting perceived exam complexity.

METHODS

In this IRB-approved study, data about imaging workflow, exam context, and patient characteristics were collected over one year from MR modality logfiles and from technologist questionnaires, including the perceived exam complexity. The association of individual factors with complexity was analyzed via Fisher's exact tests and Cramér's V values. Predictability of exam complexity was further evaluated via ROC analysis of three different multivariate classifiers.

RESULTS

Retakes, delays, and extended exam duration are associated with perceived complexity (V ≥ 0.2). From the set of possible predictors, patient mobility and communication ability have the most influence on perceived complexity (V > 0.2), followed by special equipment needs (pulse oximetry, intubation, or ECG), protocol details and other patient characteristics. Feasibility of predicting the perceived exam complexity from a multivariate set of exam and patient details known at the time of scheduling has been demonstrated (AUC = 0.73), and suitable classification algorithms have been identified.

CONCLUSION

Perceived exam complexity is associated with various factors. Our results suggest that it can be predicted sufficiently well to support early operational decision making. Some factors, however, may not be readily available in hospital IT systems and must be obtained before scheduling.

IMPLICATIONS FOR PRACTICE

Results and observations of this study could be utilized to assist operational scheduling in the radiology department and reduce MR technologists' stress levels.

Compressed SENSE in Pediatric Brain Tumor MR Imaging : Assessment of Image Quality, Examination Time and Energy Release

Purpose

To compare the image quality, examination time, and total energy release of a standardized pediatric brain tumor magnetic resonance imaging (MRI) protocol performed with and without compressed sensitivity encoding (C-SENSE). Recently introduced as an acceleration technique in MRI, we hypothesized that C‑SENSE would improve image quality, reduce the examination time and radiofrequency-induced energy release compared with conventional examination in a pediatric brain tumor protocol.

Methods

This retrospective study included 22 patients aged 2.33–18.83 years with different brain tumor types who had previously undergone conventional MRI examination and underwent follow-up C‑SENSE examination. Both examinations were conducted with a 3.0-Tesla device and included pre-contrast and post-contrast T1-weighted turbo-field-echo, T2-weighted turbo-spin-echo, and fluid-attenuated inversion recovery sequences. Image quality was assessed in four anatomical regions of interest (tumor area, cerebral cortex, basal ganglia, and posterior fossa) using a 5-point scale. Reader preference between the standard and C‑SENSE images was evaluated. The total examination duration and energy deposit were compared based on scanner log file analysis.

Results

Relative to standard examinations, C‑SENSE examinations were characterized by shorter total examination times (26.1 ± 3.93 vs. 22.18 ± 2.31 min; P = 0.001), reduced total energy deposit (206.0 ± 19.7 vs. 92.3 ± 18.2 J/kg; P < 0.001), and higher image quality (overall P < 0.001).

Conclusion

C‑SENSE contributes to the improvement of image quality, reduction of scan times and radiofrequency-induced energy release relative to the standard protocol in pediatric brain tumor MRI.

Supplementary Information

The online version of this article (10.1007/s00062-021-01112-3) contains supplementary material, which is available to authorized users.

Automated Billing Code Retrieval from MRI Scanner Log Data

Although the level of digitalization and automation steadily increases in radiology, billing coding for magnetic resonance imaging (MRI) exams in the radiology department is still based on manual input from the technologist. After the exam completion, the technologist enters the corresponding exam codes that are associated with billing codes in the radiology information system. Moreover, additional billing codes are added or removed, depending on the performed procedure. This workflow is time-consuming and we showed that billing codes reported by the technologists contain errors. The coding workflow can benefit from an automated system, and thus a prediction model for automated assignment of billing codes for MRI exams based on MRI log data is developed in this work. To the best of our knowledge, it is the first attempt to focus on the prediction of billing codes from modality log data. MRI log data provide a variety of information, including the set of executed MR sequences, MR scanner table movements, and given a contrast medium. MR sequence names are standardized using a heuristic approach and incorporated into the features for the prediction. The prediction model is trained on 9754 MRI exams and tested on 1 month of log data (423 MRI exams) from two MRI scanners of the radiology site for the Swiss medical tariffication system Tarmed. The developed model, an ensemble of classifier chains with multilayer perceptron as a base classifier, predicts medical billing codes for MRI exams with a micro-averaged F1-score of 97.8% (recall 98.1%, precision 97.5%). Manual coding reaches a micro-averaged F1-score of 98.1% (recall 97.4%, precision 98.8%). Thus, the performance of automated coding is close to human performance. Integrated into the clinical environment, this work has the potential to free the technologist from a non-value adding an administrative task, therefore enhance the MRI workflow, and prevent coding errors.

Automatic, log file-based process analysis of a clinical 1.5T MR scanner: a proof-of-concept study

PURPOSE

 In light of the steadily increasing need for economical efficacy and capacity utilization it was the aim of this proof-of-concept work to implement an automated logfile-based analysis tool for MRI scanner utilization and to establish a process analysis. As a primary step, analyses of scanner and protocol utilization, parametrization of protocol processes, their durations, age dependency, and scan efficacy were to be tested.

MATERIALS AND METHODS

 Logfiles were continuously extracted from a 1.5 T MR scanner (Philips Achieva) and automatically explored for relevant scan parameters. Parameters were extracted into a database and logically combined to protocol parameters. Visualization was achieved using PowerBI (Microsoft, USA). Data aggregation comprised a day-based and protocol-based strategy. In addition, age- and regional-based testing was performed. The frequency of protocol usage was evaluated and those protocols with frequent usage compared regarding efficacy to those rarely used.

RESULTS

 After successful technical implementation, 3659 MR exams were available for further analysis. Out of a plethora of parameters, those relevant to the understanding of the scan process were identified. The initial results mirror the daily scanner usage and allow identifying, e. g., shortened scanner usage on Fridays or longer examination times in children. A scan efficacy of 69.6 ± 17.6 % excluding preparation process was identified as a parameter with high potential to be optimized in daily routine.

CONCLUSION

 The logfile-based analysis of MR scanner processes was successfully introduced and holds the promise to be extended into a comprehensive analytic tool for the analysis and optimization of scanner processes. In combination with other variables from the departmental or institutional infrastructure or patient-specific information such tool may be developed into a intelligent steering tool.

KEY POINTS

  · The automated log file analysis of MR-scanner processes was successfully introduced. · The log file-analysis allows for a detailed analysis of scanner processes. · From a log file-analysis, there is potential benefit to users, applications specialists and developers.

CITATION FORMAT

· Frydrychowicz A, Boppel T, Sieber V et al. Automatic, log file-based process analysis of a clinical 1.5T MR scanner: a proof-of-concept study. Fortschr Röntgenstr 2021; 193: 919 - 927.

Understanding Scanner Utilization With Real-Time DICOM Metadata Extraction

Understanding system performance metrics ensures better utilization of the radiology resources with more targeted interventions. The images produced by radiology scanners typically follow the DICOM (Digital Imaging and Communications in Medicine) standard format. The DICOM images consist of textual metadata that can be used to calculate key timing parameters, such as the exact study durations and scanner utilization. However, hospital networks lack the resources and capabilities to extract the metadata from the images quickly and automatically compute the scanner utilization properties. Thus, they resort to using data records from the Radiology Information Systems (RIS). However, data acquired from RIS are prone to human errors, rendering many derived key performance metrics inadequate and inaccurate. Hence, there is motivation to establish a real-time image transfer from the Picture Archiving and Communication Systems (PACS) to receive the DICOM images from the scanners to research clusters to conduct such metadata processing to evaluate scanner utilization metrics efficiently and quickly. This paper analyzes the scanners' utilization by developing a real-time monitoring framework that retrieves radiology images into a research cluster using the DICOM networking protocol and then extracts and processes the metadata from the images. Our proposed approach facilitates a better understanding of scanner utilization across a vast healthcare network by observing properties such as study duration, the interval between the encounters, and the series count of studies. Benchmarks against using the RIS data indicate that our proposed framework based on real-time PACS data estimates the scanner utilization more accurately. Furthermore, our framework has been running stable and performing its computation for more than two years on our extensive healthcare network in pseudo real-time.

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.

An Important and Often Ignored Turnaround Time in Radiology - Clinician Turnaround Time: Implications for Musculoskeletal Radiology

Background:

There has been an increase in routine musculoskeletal (MSK) MRI studies performed on weekends.

Study Aims:

First, to assess whether radiologist interpretation of routine MSK MRI studies on weekends decreases the time to when the clinician reads the radiologist’s report compared to studies performed on the weekend but interpreted the following Monday. Second, to evaluate whether reports are more likely to be read by clinicians if the MRIs are interpreted by radiologists on weekends compared to the following Monday.

Methods:

A random sample of 1765 patients who underwent routine MSK MRIs from January 1, 2015 to December 31, 2016 was evaluated. The radiologist turnaround times (rTATs), clinician turnaround times (cTATs) and the provider turnaround time (pTAT) were calculated. The pTAT was the sum of the rTAT and the cTAT. Fisher’s exact tests were used to compare proportions. Wilcoxon Rank Sum tests were used to compare turnaround time metrics.

Results:

There was no difference in the pTAT for studies performed and interpreted on the weekends compared to those performed on the weekend but interpreted the following Monday (P = 0.750). However, clinicians were significantly less likely to read the reports interpreted on the weekend compared to studies interpreted on weekdays (P = 0.001).

Conclusion:

Routine MSK MRI studies performed on weekends can be interpreted by radiologists on the following weekday (Monday) without affecting the time at which the clinician reads the reports and these reports are more likely to be read by clinicians if the radiologist interprets the study on a weekday.