Performance of an Automated Versus a Manual Whole-Body Magnetic Resonance Imaging Workflow

Stocker, Daniel; Finkenstaedt, Tim; Kuehn, Bernd; Nanz, Daniel; Klarhoefer, Markus; Guggenberger, Roman; Andreisek, Gustav; Kiefer, Berthold; Reiner, Caecilia S.

doi:10.1097/rli.0000000000000471

Cited by 8 publications

(7 citation statements)

References 35 publications

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“…Illustration of the 10-point Likert scale as part of the questionnaire analysis covering the points "exertion", "dyspnea", "patient comfort", "tightness", "physical fatigue", "noise", and "duration" of the examination MRI facilitates the automated recognition of individual anatomic landmarks using FastView scan AutoCoverage (Siemens Healthineers) and provides consistent, reproducible image settings to ensure a proper coverage of all body regions. [15][16][17] In our protocol, the breath-hold capacity was set at 20 s.…”

Section: Mri Scan Protocolmentioning

confidence: 99%

“…14 Recently, automated workflow algorithms have been introduced, which allow for shorter scanning times due to personalized, guided, and standardized examination protocols for whole-body imaging. [15][16][17] We hypothesized that free-breathing sequences combined with automated workflow processing may improve patient satisfaction during whole-body MRI at similar image quality and diagnostic confidence compared with conventional MR scan protocols using breath-hold maneuvers.…”

Section: Introductionmentioning

confidence: 99%

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Free‐breathing accelerated whole‐body MRI using an automated workflow: Comparison with conventional breath‐hold sequences

et al. 2022

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Whole‐body magnetic resonance imaging (MRI) has become increasingly popular in oncology. However, the long acquisition time might hamper its widespread application. We sought to assess and compare free‐breathing sequences with conventional breath‐hold examinations in whole‐body MRI using an automated workflow process. This prospective study consisted of 20 volunteers and six patients with a variety of pathologies who had undergone whole‐body 1.5‐T MRI that included T1‐weighted radial and Dixon volumetric interpolated breath‐hold examination sequences. Free‐breathing sequences were operated by using an automated user interface. Image quality, diagnostic confidence, and image noise were evaluated by two experienced radiologists. Additionally, signal‐to‐noise ratio was measured. Diagnostic performance for the overall detection of pathologies was assessed using the area under the receiver operating characteristics curve (AUC). Study participants were asked to rate their examination experiences in a satisfaction survey. MR free‐breathing scans were rated as at least equivalent to conventional MR scans in more than 92% of cases, showing high overall diagnostic accuracy (95% [95% CI 92–100]) and performance (AUC 0.971, 95% CI 0.942–0.988; p < 0.0001) for the assessment of pathologies at simultaneously reduced examination times (25 ± 2 vs. 32 ± 3 min; p < 0.0001). Interrater agreement was excellent for both free‐breathing (ϰ = 0.96 [95% CI 0.88–1.00]) and conventional scans (ϰ = 0.93 [95% CI 0.84–1.00]). Qualitative and quantitative assessment for image quality, image noise, and diagnostic confidence did not differ between the two types of MR image acquisition (all p > 0.05). Scores for patient satisfaction were significantly better for free‐breathing compared with breath‐hold examinations (p = 0.0145), including significant correlations for the grade of noise (r = 0.79, p < 0.0001), tightness (r = 0.71, p < 0.0001), and physical fatigue (r = 0.52, p = 0.0065). In summary, free‐breathing whole‐body MRI in tandem with an automated user interface yielded similar diagnostic performance at equivalent image quality and shorter acquisition times compared to conventional breath‐hold sequences.

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Section: Mri Scan Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Free‐breathing accelerated whole‐body MRI using an automated workflow: Comparison with conventional breath‐hold sequences

et al. 2022

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“… 63 75 76 In contrast, the literature revealed characteristics associated with workflows that are more difficult to automate, summarized in Table 3 . 8 12 18 27 39 54 62 77 78 79 Careful selection with these characteristics in mind can help to prevent some of the documented problems that automation can cause 59 across industries, including health care.…”

Section: Workflow Automation Approaches Across Industriesmentioning

confidence: 99%

Identifying Opportunities for Workflow Automation in Health Care: Lessons Learned from Other Industries

2021

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Background Workflow automation, which involves identifying sequences of tasks that can be streamlined by using technology and modern computing, offers opportunities to address the United States health care system's challenges with quality, safety, and efficiency. Other industries have successfully implemented workflow automation to address these concerns, and lessons learned from those experiences may inform its application in health care. Objective Our aim was to identify and synthesize (1) current approaches in workflow automation across industries, (2) opportunities for applying workflow automation in health care, and (3) considerations for designing and implementing workflow automation that may be relevant to health care. Methods We conducted a targeted review of peer-reviewed and gray literature on automation approaches. We identified relevant databases and terms to conduct the searches across sources and reviewed abstracts to identify 123 relevant articles across 11 disciplines. Results Workflow automation is used across industries such as finance, manufacturing, and travel to increase efficiency, productivity, and quality. We found automation ranged from low to full automation, and this variation was associated with task and technology characteristics. The level of automation is linked to how well a task is defined, whether a task is repetitive, the degree of human intervention and decision-making required, and the sophistication of available technology. We found that identifying automation goals and assessing whether those goals were reached was critical, and ongoing monitoring and improvement would help to ensure successful automation. Conclusion Use of workflow automation in other industries can inform automating health care workflows by considering the critical role of people, process, and technology in design, testing, implementation, use, and ongoing monitoring of automated workflows. Insights gained from other industries will inform an interdisciplinary effort by the Office of the National Coordinator for Health Information Technology to outline priorities for advancing health care workflow automation.

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“…In the context of large cohort studies, this process then becomes both inefficient and costly. Developments leading to automation in workflow and RTs’ decision-making are therefore being undertaken and could result in improved resource allocation and image quality, as well as less discomfort for participants and patients 9,10 …”

mentioning

confidence: 99%

“…Developments leading to automation in workflow and RTs' decision-making are therefore being undertaken and could result in improved resource allocation and image quality, as well as less discomfort for participants and patients. 9,10 The fundamental role of RTs' perception of image quality and its influence on protocol repetition in a large cohort study, as a preceding factor to all further analyses, has not previously been established in the literature. Our study, therefore, aimed to assess protocol repetition frequency as well as the RTs' underlying decision-making process, to determine its correlation with quantitative image quality parameters, and to identify quantitative parameters that are predictive for protocol repetition, with a perspective to objectify and potentially automate this time-consuming task.…”

mentioning

confidence: 99%

Whole-Body Magnetic Resonance Imaging in the Large Population-Based German National Cohort Study

Schuppert

Krüchten²,

Hirsch³

et al. 2022

Invest Radiol

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Background: Reproducible image quality is of high relevance for large cohort studies and can be challenging for magnetic resonance imaging (MRI). Automated image quality assessment may contribute to conducting radiologic studies effectively. Purpose: The aims of this study were to assess protocol repetition frequency in population-based whole-body MRI along with its effect on examination time and to examine the applicability of automated image quality assessment for predicting decision-making regarding repeated acquisitions. Materials and Methods: All participants enrolled in the prospective, multicenter German National Cohort (NAKO) study who underwent whole-body MRI at 1 of 5 sites from 2014 to 2016 were included in this analysis (n = 11,347). A standardized examination program of 12 protocols was used. Acquisitions were carried out by certified radiologic technologists, who were authorized to repeat protocols based on their visual perception of image quality. Eleven image quality parameters were derived fully automatically from the acquired images, and their discrimination ability regarding baseline acquisitions and repetitions was tested.Results: At least 1 protocol was repeated in 12% (n = 1359) of participants, and more than 1 protocol in 1.6% (n = 181). The repetition frequency differed across protocols ( P < 0.001), imaging sites ( P < 0.001), and over the study period ( P < 0.001). The mean total scan time was 62.6 minutes in participants without and 67.4 minutes in participants with protocol repetitions (mean difference, 4.8 minutes; 95% confidence interval, 4.5-5.2 minutes). Ten of the automatically derived image quality parameters were individually retrospectively predictive for the repetition of particular protocols; for instance, "signal-to-noise ratio" alone provided an area under the curve of 0.65 ( P < 0.001) for repetition of the Cardio Cine SSFP SAX protocol. Combinations generally improved prediction ability, as exemplified by "image sharpness" plus "foreground ratio" yielding an area under the curve of 0.89 ( P < 0.001) for repetition of the Neuro T1w 3D MPRAGE protocol, versus 0.85 ( P < 0.001) and 0.68 ( P < 0.001) as individual parameters. Conclusions: Magnetic resonance imaging protocol repetitions were necessary in approximately 12% of scans even in the highly standardized setting of a large cohort study. Automated image quality assessment shows predictive value for the technologists' decision to perform protocol repetitions and has the potential to improve imaging efficiency.

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Performance of an Automated Versus a Manual Whole-Body Magnetic Resonance Imaging Workflow

Abstract: In conclusion, the automated WB-MRI scanner workflow showed a significant reduction of the examination time and the user interaction compared with the manual WB-MRI workflow. Image quality and the coverage of the study volume were comparable in both groups.

Cited by 8 publications

References 35 publications

Free‐breathing accelerated whole‐body MRI using an automated workflow: Comparison with conventional breath‐hold sequences

Free‐breathing accelerated whole‐body MRI using an automated workflow: Comparison with conventional breath‐hold sequences

Identifying Opportunities for Workflow Automation in Health Care: Lessons Learned from Other Industries

Whole-Body Magnetic Resonance Imaging in the Large Population-Based German National Cohort Study

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