Environmental Sustainability and AI in Radiology: A Double-Edged Sword.

According to the World Health Organization, climate change is the single biggest health threat facing humanity. The global health care system, including medical imaging, must manage the health effects of climate change while at the same time addressing the large amount of greenhouse gas (GHG) emissions generated in the delivery of care. Data centers and computational efforts are increasingly large contributors to GHG emissions in radiology. This is due to the explosive increase in big data and artificial intelligence (AI) applications that have resulted in large energy requirements for developing and deploying AI models. However, AI also has the potential to improve environmental sustainability in medical imaging. For example, use of AI can shorten MRI scan times with accelerated acquisition times, improve the scheduling efficiency of scanners, and optimize the use of decision-support tools to reduce low-value imaging. The purpose of this Radiology in Focus article is to discuss this duality at the intersection of environmental sustainability and AI in radiology. Further discussed are strategies and opportunities to decrease AI-related emissions and to leverage AI to improve sustainability in radiology, with a focus on health equity. Co-benefits of these strategies are explored, including lower cost and improved patient outcomes. Finally, knowledge gaps and areas for future research are highlighted.

Energy and Greenhouse Gas Emission Savings Associated with Implementation of an Abbreviated Cardiac MRI Protocol.

Supplemental material is available for this article. See also the article by Lenkinski and Rofsky in this issue. See also the article by McKee et al in this issue.

Environmental Sustainability and MRI: Challenges, Opportunities, and a Call for Action.

The environmental impact of magnetic resonance imaging (MRI) has recently come into focus. This includes its enormous demand for electricity compared to other imaging modalities and contamination of water bodies with anthropogenic gadolinium related to contrast administration. Given the pressing threat of climate change, addressing these challenges to improve the environmental sustainability of MRI is imperative. The purpose of this review is to discuss the challenges, opportunities, and the need for action to reduce the environmental impact of MRI and prepare for the effects of climate change. The approaches outlined are categorized as strategies to reduce greenhouse gas (GHG) emissions from MRI during production and use phases, approaches to reduce the environmental impact of MRI including the preservation of finite resources, and development of adaption plans to prepare for the impact of climate change. Co-benefits of these strategies are emphasized including lower GHG emission and reduced cost along with improved heath and patient satisfaction. Although MRI is energy-intensive, there are many steps that can be taken now to improve the environmental sustainability of MRI and prepare for the effects of climate change. On-going research, technical development, and collaboration with industry partners are needed to achieve further reductions in MRI-related GHG emissions and to decrease the reliance on finite resources. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

Interventional Imaging Systems in Radiology, Cardiology, and Urology: Energy Consumption, Carbon Emissions, and Electricity Costs.

BACKGROUND. The energy demand of interventional imaging systems has historically been estimated using manufacturer-provided specifications rather than directly measured. OBJECTIVE. The purpose of this study was to investigate the energy consumption of interventional imaging systems and estimate potential savings in the carbon emissions and electricity costs of such systems through hypothetical operational adjustments. METHODS. An interventional radiology suite, neurointerventional suite, radiology fluoroscopy unit, two cardiology laboratories, and two urology fluoroscopy units were equipped with power sensors. Power measurement logs were extracted for a single 4-week period for each radiology and cardiology system (all between June 1, 2022, and November 28, 2022) and for the 2-week period from July 31, 2023, to August 13, 2023, for each urology system. Power statuses, procedure time stamps, and fluoroscopy times were extracted from various sources. System activity was divided into off, idle (no patient in room), active (patient in room for procedure), and net-imaging (active fluoroscopic image acquisition) states. Projected annual energy consumption was calculated. Potential annual savings in carbon emissions and electricity costs through hypothetical operational adjustments were estimated using published values for Switzerland. RESULTS. Across the seven systems, the mean power draw was 0.3-1.1, 0.7-7.4, 0.9-7.6, and 1.9-12.5 kW in the off, idle, active, and net-imaging states, respectively. Across systems, the off state, in comparison with the idle state, showed a decrease in the mean power draw of 0.2-6.9 kW (relative decrease, 22.2-93.2%). The systems had a combined projected annual energy consumption of 115,684 kWh (range, 3646-26,576 kWh per system). The systems' combined projected energy consumption occurring outside the net-imaging state accounted for 93.3% (107,978/115,684 kWh) of projected total energy consumption (range, 89.2-99.4% per system). A hypothetical operational adjustment whereby all systems would be switched from the idle state to the off state overnight and on weekends (versus being operated in idle mode 24 hours a day, 7 days a week) would yield the following potential annual savings: for energy consumption, 144,640 kWh; for carbon emissions, 18.6 metric tons of CO2 equivalent; and for electricity costs, US$37,896. CONCLUSION. Interventional imaging systems are energy intensive, having high consumption outside of image acquisition periods. CLINICAL IMPACT. Strategic operational adjustments (e.g., powering down idle systems) can substantially decrease the carbon emissions and electricity costs of interventional imaging systems.

Modern acceleration in musculoskeletal MRI: applications, implications, and challenges.

Magnetic resonance imaging (MRI) is crucial for accurately diagnosing a wide spectrum of musculoskeletal conditions due to its superior soft tissue contrast resolution. However, the long acquisition times of traditional two-dimensional (2D) and three-dimensional (3D) fast and turbo spin-echo (TSE) pulse sequences can limit patient access and comfort. Recent technical advancements have introduced acceleration techniques that significantly reduce MRI times for musculoskeletal examinations. Key acceleration methods include parallel imaging (PI), simultaneous multi-slice acquisition (SMS), and compressed sensing (CS), enabling up to eightfold faster scans while maintaining image quality, resolution, and safety standards. These innovations now allow for 3- to 6-fold accelerated clinical musculoskeletal MRI exams, reducing scan times to 4 to 6 min for joints and spine imaging. Evolving deep learning-based image reconstruction promises even faster scans without compromising quality. Current research indicates that combining acceleration techniques, deep learning image reconstruction, and superresolution algorithms will eventually facilitate tenfold accelerated musculoskeletal MRI in routine clinical practice. Such rapid MRI protocols can drastically reduce scan times by 80-90% compared to conventional methods. Implementing these rapid imaging protocols does impact workflow, indirect costs, and workload for MRI technologists and radiologists, which requires careful management. However, the shift from conventional to accelerated, deep learning-based MRI enhances the value of musculoskeletal MRI by improving patient access and comfort and promoting sustainable imaging practices. This article offers a comprehensive overview of the technical aspects, benefits, and challenges of modern accelerated musculoskeletal MRI, guiding radiologists and researchers in this evolving field.

Turning radiology reports into epidemiological data to track seasonal pulmonary infections and the COVID-19 pandemic.

OBJECTIVES: To automatically label chest radiographs and chest CTs regarding the detection of pulmonary infection in the report text, to calculate the number needed to image (NNI) and to investigate if these labels correlate with regional epidemiological infection data. MATERIALS AND METHODS: All chest imaging reports performed in the emergency room between 01/2012 and 06/2022 were included (64,046 radiographs; 27,705 CTs). Using a regular expression-based text search algorithm, reports were labeled positive/negative for pulmonary infection if described. Data for regional weekly influenza-like illness (ILI) consultations (10/2013-3/2022), COVID-19 cases, and hospitalization (2/2020-6/2022) were matched with report labels based on calendar date. Positive rate for pulmonary infection detection, NNI, and the correlation with influenza/COVID-19 data were calculated. RESULTS: Between 1/2012 and 2/2020, a 10.8-16.8% per year positive rate for detecting pulmonary infections on chest radiographs was found (NNI 6.0-9.3). A clear and significant seasonal change in mean monthly detection counts (102.3 winter; 61.5 summer; p < .001) correlated moderately with regional ILI consultations (weekly data r = 0.45; p < .001). For 2020-2021, monthly pulmonary infection counts detected by chest CT increased to 64-234 (23.0-26.7% per year positive rate, NNI 3.7-4.3) compared with 14-94 (22.4-26.7% positive rate, NNI 3.7-4.4) for 2012-2019. Regional COVID-19 epidemic waves correlated moderately with the positive pulmonary infection CT curve for 2020-2022 (weekly new cases: r = 0.53; hospitalizations: r = 0.65; p < .001). CONCLUSION: Text mining of radiology reports allows to automatically extract diagnoses. It provides a metric to calculate the number needed to image and to track the trend of diagnoses in real time, i.e., seasonality and epidemic course of pulmonary infections. CLINICAL RELEVANCE: Digitally labeling radiology reports represent previously neglected data and may assist in automated disease tracking, in the assessment of physicians' clinical reasoning for ordering radiology examinations and serve as actionable data for hospital workflow optimization. KEY POINTS: • Radiology reports, commonly not machine readable, can be automatically labeled with the contained diagnoses using a regular-expression based text search algorithm. • Chest radiograph reports positive for pulmonary infection moderately correlated with regional influenza-like illness consultations (weekly data; r = 0.45; p < .001) and chest CT reports with the course of the regional COVID-19 pandemic (new cases: r = 0.53; hospitalizations: r = 0.65; p < 0.001). • Rendering radiology reports into data labels provides a metric for automated disease tracking, the assessment of ordering physicians clinical reasoning and can serve as actionable data for workflow optimization.

Investigating the impact of structured reporting on the linguistic standardization of radiology reports through natural language processing over a 10-year period.

OBJECTIVES: To investigate how a transition from free text to structured reporting affects reporting language with regard to standardization and distinguishability. METHODS: A total of 747,393 radiology reports dictated between January 2011 and June 2020 were retrospectively analyzed. The body and cardiothoracic imaging divisions introduced a reporting concept using standardized language and structured reporting templates in January 2016. Reports were segmented by a natural language processing algorithm and converted into a 20-dimension document vector. For analysis, dimensionality was reduced to a 2D visualization with t-distributed stochastic neighbor embedding and matched with metadata. Linguistic standardization was assessed by comparing distinct report types' vector spreads (e.g., run-off MR angiography) between reporting standards. Changes in report type distinguishability (e.g., CT abdomen/pelvis vs. MR abdomen) were measured by comparing the distance between their centroids. RESULTS: Structured reports showed lower document vector spread (thus higher linguistic similarity) compared with free-text reports overall (21.9 [free-text] vs. 15.9 [structured]; - 27.4%; p < 0.001) and for most report types, e.g., run-off MR angiography (15.2 vs. 1.8; - 88.2%; p < 0.001) or double-rule-out CT (26.8 vs. 10.0; - 62.7%; p < 0.001). No changes were observed for reports continued to be written in free text, e.g., CT head reports (33.2 vs. 33.1; - 0.3%; p = 1). Distances between the report types' centroids increased with structured reporting (thus better linguistic distinguishability) overall (27.3 vs. 54.4; + 99.3 ± 98.4%) and for specific report types, e.g., CT abdomen/pelvis vs. MR abdomen (13.7 vs. 37.2; + 171.5%). CONCLUSION: Structured reporting and the use of factual language yield more homogenous and standardized radiology reports on a linguistic level, tailored to specific reporting scenarios and imaging studies. CLINICAL RELEVANCE: Information transmission to referring physicians, as well as automated report assessment and content extraction in big data analyses, may benefit from standardized reporting, due to consistent report organization and terminology used for pathologies and normal findings. KEY POINTS: • Natural language processing and t-distributed stochastic neighbor embedding can transform radiology reports into numeric vectors, allowing the quantification of their linguistic standardization. • Structured reporting substantially increases reports' linguistic standardization (mean: - 27.4% in vector spread) and distinguishability (mean: + 99.3 ± 98.4% increase in vector distance) compared with free-text reports. • Higher standardization and homogeneity outline potential benefits of structured reporting for information transmission and big data analyses.

[New clinical applications for low-field magnetic resonance imaging : Technical and physical aspects]. / Neue klinische Anwendungsbereiche der Niederfeld-Magnetresonanztomographie : Technische und physikalische Aspekte.

BACKGROUND: Low-field magnetic resonance imaging (MRI) is experiencing a renaissance due to technical innovations. The new-generation devices offer new applications for imaging and a possible solution to increasing cost pressures in the healthcare system. OBJECTIVES: Effects of field strength on technique, physics, image acquisition, and diagnostic quality of examinations are presented. METHODS: Important basic physical parameters for image acquisition and quality are summarized. Initial clinical experience with a new 0.55 T low-field scanner is presented. RESULTS: Field strengths that are lower than the currently used 1.5 T and 3 T field strengths are characterized by an expected lower signal-to-noise ratio in image acquisition. Whether this is a diagnostic limitation needs to be evaluated in studies, as there are several options to offset this perceived drawback, including increasing measurement time or artificial intelligence (AI) postprocessing techniques. In addition, it is necessary to meticulously investigate whether low-field systems allow diagnostically adequate image quality to be achieved in different body regions and different disease entities. Initial studies in our clinic are promising and show, for example, diagnostic quality without relevant loss of time for examinations of the lumbar spine. Advantages of low-field MRI include reduced susceptibility artifacts when imaging the lungs and in patients with metallic implants. CONCLUSION: Low-field scanners offer a variety of new fields of application with field strength-related advantages. In most other clinical examination fields, at least diagnostic quality can be expected.

[Economic aspects of low-field magnetic resonance imaging : Acquisition, installation, and maintenance costs of 0.55 T systems]. / Ökonomische Aspekte der Niederfeld-Magnetresonanztomographie : Anschaffung, Installation und Unterhaltskosten von 0,55 T-Geräten.

BACKGROUND: Low-field magnetic resonance imaging (MRI) scanners offer an opportunity for cost reduction in the healthcare system. This is due to lower manufacturing costs and reduced construction requirements for installation and operation. OBJECTIVES: To discuss potential cost reductions in acquisition, installation, and maintenance by using new low-field MRI systems. METHODS: We provide an overview of key cost drivers and an evaluation of the potential savings of a recent generation 0.55T low-field MRI compared to conventional 1.5T and 3T MRI systems in routine clinical practice. RESULTS: In terms of purchase price, the savings potential of a 0.55T MRI compared to a 1.5T MRI system is about 40-50%. The 25% lower weight of the system reduces the transportation costs incurred, and the smaller size of the unit allows for installation by a remotely controlled mobile robotic system without opening the exterior façade, if the operating site is at ground level. Together with the lack of need to install a quench pipe, this reduces the total cost of installation by up to 70%. The maintenance cost of a 0.55T MRI is approximately 45% less than that of a 1.5T unit with a comparable service contract. Further cost reductions result from the smaller room size and potentially lower energy consumption for examinations and cooling. CONCLUSION: The use of lower field strength MRI systems offers enormous economic and environmental potential for both hospitals and practice operators, as well as for the healthcare system as a whole.

Quantifying Radiology Resident Fatigue: Analysis of Preliminary Reports.

Background Workloads in radiology departments have constantly increased over the past decades. The resulting radiologist fatigue is considered a rising problem that affects diagnostic accuracy. Purpose To investigate whether data mining of quantitative parameters from the report proofreading process can reveal daytime and shift-dependent trends in report similarity as a surrogate marker for resident fatigue. Materials and Methods Data from 117 402 radiology reports written by residents between September 2017 and March 2020 were extracted from a report comparison tool and retrospectively analyzed. Through calculation of the Jaccard similarity coefficient between residents' preliminary and staff-reviewed final reports, the amount of edits performed by staff radiologists during proofreading was quantified on a scale of 0 to 1 (1: perfect similarity, no edits). Following aggregation per weekday and shift, data were statistically analyzed by using simple linear regression or one-way analysis of variance (significance level, P < .05) to determine relationships between report similarity and time of day and/or weekday reports were dictated. Results Decreasing report similarity with increasing work hours was observed for day shifts (r = -0.93 [95% CI: -0.73, -0.98]; P < .001) and weekend shifts (r = -0.72 [95% CI: -0.31, -0.91]; P = .004). For day shifts, negative linear correlation was strongest on Fridays (r = -0.95 [95% CI: -0.80, -0.99]; P < .001), with a 16% lower mean report similarity at the end of shifts (0.85 ± 0.24 at 8 am vs 0.69 ± 0.32 at 5 pm). Furthermore, mean similarity of reports dictated on Fridays (0.79 ± 0.35) was lower than that on all other weekdays (range, 0.84 ± 0.30 to 0.86 ± 0.27; P < .001). For late shifts, report similarity showed a negative correlation with the course of workweeks, showing a continuous decrease from Monday to Friday (r = -0.98 [95% CI: -0.70, -0.99]; P = .007). Temporary increases in report similarity were observed after lunch breaks (day and weekend shifts) and with the arrival of a rested resident during overlapping on-call shifts. Conclusion Decreases in report similarity over the course of workdays and workweeks suggest aggravating effects of fatigue on residents' report writing performances. Periodic breaks within shifts potentially foster recovery. © RSNA, 2021.

Revealing the most common reporting errors through data mining of the report proofreading process.

OBJECTIVES: To investigate the most common errors in residents' preliminary reports, if structured reporting impacts error types and frequencies, and to identify possible implications for resident education and patient safety. MATERIAL AND METHODS: Changes in report content were tracked by a report comparison tool on a word level and extracted for 78,625 radiology reports dictated from September 2017 to December 2018 in our department. Following data aggregation according to word stems and stratification by subspecialty (e.g., neuroradiology) and imaging modality, frequencies of additions/deletions were analyzed for findings and impression report section separately and compared between subgroups. RESULTS: Overall modifications per report averaged 4.1 words, with demonstrably higher amounts of changes for cross-sectional imaging (CT: 6.4; MRI: 6.7) than non-cross-sectional imaging (radiographs: 0.2; ultrasound: 2.8). The four most frequently changed words (right, left, one, and none) remained almost similar among all subgroups (range: 0.072-0.117 per report; once every 9-14 reports). Albeit representing only 0.02% of analyzed words, they accounted for up to 9.7% of all observed changes. Subspecialties solely using structured reporting had substantially lower change ratios in the findings report section (mean: 0.2 per report) compared with prose-style reporting subspecialties (mean: 2.0). Relative frequencies of the most changed words remained unchanged. CONCLUSION: Residents' most common reporting errors in all subspecialties and modalities are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). Structured reporting reduces overall error rates, but does not affect occurrence of the most common errors. Increased error awareness and measures improving report correctness and ensuring patient safety are required. KEY POINTS: • The two most common reporting errors in residents' preliminary reports are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). • Structured reporting reduces the overall the error frequency in the findings report section by a factor of 10 (structured reporting: mean 0.2 per report; prose-style reporting: 2.0) but does not affect the occurrence of the two major errors. • Staff radiologist review behavior noticeably differs between radiology subspecialties.

Response prediction of hepatocellular carcinoma undergoing transcatheter arterial chemoembolization: unlocking the potential of CT texture analysis through nested decision tree models.

OBJECTIVES: To investigate if nested multiparametric decision tree models based on tumor size and CT texture parameters from pre-therapeutic imaging can accurately predict hepatocellular carcinoma (HCC) lesion response to transcatheter arterial chemoembolization (TACE). MATERIALS AND METHODS: This retrospective study (January 2011-September 2017) included consecutive pre- and post-therapeutic dynamic CT scans of 37 patients with 92 biopsy-proven HCC lesions treated with drug-eluting bead TACE. Following manual segmentation of lesions according to modified Response Evaluation Criteria in Solid Tumors criteria on baseline arterial phase CT images, tumor size and quantitative texture parameters were extracted. HCCs were grouped into lesions undergoing primary TACE (VT-lesions) or repeated TACE (RT-lesions). Distinct multiparametric decision tree models to predict complete response (CR) and progressive disease (PD) for the two groups were generated. AUC and model accuracy were assessed. RESULTS: Thirty-eight of 72 VT-lesions (52.8%) and 8 of 20 RT-lesions (40%) achieved CR. Sixteen VT-lesions (22.2%) and 8 RT-lesions (40%) showed PD on follow-up imaging despite TACE treatment. Mean of positive pixels (MPP) was significantly higher in VT-lesions compared to RT-lesions (180.5 vs 92.8, p = 0.001). The highest AUC in ROC curve analysis and accuracy was observed for the prediction of CR in VT-lesions (AUC 0.96, positive predictive value 96.9%, accuracy 88.9%). Prediction of PD in VT-lesions (AUC 0.88, accuracy 80.6%), CR in RT-lesions (AUC 0.83, accuracy 75.0%), and PD in RT-lesions (AUC 0.86, accuracy 80.0%) was slightly inferior. CONCLUSIONS: Nested multiparametric decision tree models based on tumor heterogeneity and size can predict HCC lesion response to TACE treatment with high accuracy. They may be used as an additional criterion in the multidisciplinary treatment decision-making process. KEY POINTS: • HCC lesion response to TACE treatment can be predicted with high accuracy based on baseline tumor heterogeneity and size. • Complete response of HCC lesions undergoing primary TACE was correctly predicted with 88.9% accuracy and a positive predictive value of 96.9%. • Progressive disease was correctly predicted with 80.6% accuracy for lesions undergoing primary TACE and 80.0% accuracy for lesions undergoing repeated TACE.

Multiparametric MR Imaging Depicts Glycosaminoglycan Change in the Achilles Tendon during Ciprofloxacin Administration in Healthy Men: Initial Observation.

PURPOSE: To determine if quantitative magnetic resonance (MR) imaging techniques (sodium MR imaging, glycosaminoglycan [GAG] chemical exchange saturation transfer [CEST], and T2* mapping) could be used as potential markers for biochemical changes in the Achilles tendon induced by ciprofloxacin intake. MATERIALS AND METHODS: The ethics committee of the Medical University of Vienna approved the protocol (number 1225/2012), and all patients gave written informed consent. Fourteen ankles from seven men (mean age, 32 years ± 12 [standard deviation]) were included in the study. All patients underwent 7-T MR imaging examinations of the Achilles tendon at baseline and 10 days and 5 months after ciprofloxacin intake. Sodium signal and T2* maps were acquired with the variable echo-time sequence and the GAG CEST values were acquired with a three-dimensional radiofrequency spoiled gradient-recalled-echo sequence. RESULTS: The mean sodium signal was significantly decreased by 25% in the whole tendon (from baseline to 10 days after ciprofloxacin intake, 130 arbitrary units [au] ± 8 to 98 au ± 5, respectively; P = .023) and returned to baseline after 5 months (116 au ± 10), as observed also at the tendon insertion (baseline, 10 days after ciprofloxacin intake, and 5 months after ciprofloxacin intake, 134 au ± 8, 105 au ± 5, and 119 au ± 9, respectively; P = .034). The mean GAG CEST value in the whole tendon was parallel to the sodium signal with a decrease from baseline to 10 days after ciprofloxacin intake, 4.74% ± 0.75 to 4.50% ± 0.23, respectively (P = .028) and an increase at 5 months after ciprofloxacin intake to 4.88% ± 1.02. CONCLUSION: In conclusion, this study demonstrates a ciprofloxacin-induced reversible reduction of the normalized sodium MR imaging signal and the GAG CEST effect in the Achilles tendon of healthy volunteers. Changes in sodium MR imaging and GAG CEST in men may reflect a decrease of GAG content in the Achilles tendon after ciprofloxacin intake.

[Accelerated musculoskeletal magnetic resonance imaging with deep learning-based image reconstruction at 0.55 T-3 T]. / Beschleunigte muskuloskeletale Magnetresonanztomographie mit Deep-Learning-gestützter Bildrekonstruktion bei 0,55 T­3 T.

CLINICAL/METHODICAL ISSUE: Magnetic resonance imaging (MRI) is a central component of musculoskeletal imaging. However, long image acquisition times can pose practical barriers in clinical practice. STANDARD RADIOLOGICAL METHODS: MRI is the established modality of choice in the diagnostic workup of injuries and diseases of the musculoskeletal system due to its high spatial resolution, excellent signal-to-noise ratio (SNR), and unparalleled soft tissue contrast. METHODOLOGICAL INNOVATIONS: Continuous advances in hardware and software technology over the last few decades have enabled four-fold acceleration of 2D turbo-spin-echo (TSE) without compromising image quality or diagnostic performance. The recent clinical introduction of deep learning (DL)-based image reconstruction algorithms helps to minimize further the interdependency between SNR, spatial resolution and image acquisition time and allows the use of higher acceleration factors. PERFORMANCE: The combined use of advanced acceleration techniques and DL-based image reconstruction holds enormous potential to maximize efficiency, patient comfort, access, and value of musculoskeletal MRI while maintaining excellent diagnostic accuracy. ACHIEVEMENTS: Accelerated MRI with DL-based image reconstruction has rapidly found its way into clinical practice and proven to be of added value. Furthermore, recent investigations suggest that the potential of this technology does not yet appear to be fully harvested. PRACTICAL RECOMMENDATIONS: Deep learning-reconstructed fast musculoskeletal MRI examinations can be reliably used for diagnostic work-up and follow-up of musculoskeletal pathologies in clinical practice.

Radiology weather forecast: A retrospective analysis of predictability of median daily polytrauma-CT occurrence based on weather data.

OBJECTIVES: Resource planning is a crucial component in hospitals, particularly in radiology departments. Since weather conditions are often described to correlate with emergency room visits, we aimed to forecast the amount of polytrauma-CTs using weather information. DESIGN: All polytrauma-CTs between 01/01/2011 and 12/31/2022 (n = 6638) were retrieved from the radiology information system. Local weather data was downloaded from meteoblue.com. The data was normalized and smoothened. Daily polytrauma-CT occurrence was stratified into below median and above median number of daily polytrauma-CTs. Logistic regression and machine learning algorithms (neural network, random forest classifier, support vector machine, gradient boosting classifier) were employed as prediction models. Data from 2012 to 2020 was used for training, data from 2021 to 2022 for validation. RESULTS: More polytrauma-CTs were acquired in summer compared with winter months, demonstrating a seasonal change (median: 2.35; IQR 1.60-3.22 vs. 2.08; IQR 1.36-3.03; p <.001). Temperature (rs = 0.45), sunshine duration (rs = 0.38) and ultraviolet light amount (rs = 0.37) correlated positively, wind velocity (rs = -0.57) and cloudiness (rs = -0.28) correlated negatively with polytrauma-CT occurrence (all p <.001). The logistic regression model for identification of days with above median number of polytrauma-CTs achieved an accuracy of 87 % on training data from 2011 to 2020. When forecasting the years 2021-2022 an accuracy of 65 % was achieved. A neural network and a support vector machine both achieved a validation accuracy of 72 %, whereas all classifiers regarded wind velocity and ultraviolet light amount as the most important parameters. CONCLUSION: It is possible to forecast above or below median daily number of polytrauma-CTs using weather data. CLINCICAL RELEVANCE STATEMENT: Prediction of polytrauma-CT examination volumes may be used to improve resource planning.

Comparing Metal Artifact Severity and Ability to Assess Near-Metal Anatomy Between 0.55 T and 1.5 T MRI in Patients with Metallic Spinal Implants-A Scanner Comparison Study.

RATIONALE AND OBJECTIVES: To compare image quality and metal artifact severity at 0.55 T and 1.5 T MRI in patients with spinal implants following posterior fusion surgery. MATERIALS AND METHODS: 50 consecutive patients (mean age: 69 ±â€¯12 years) who underwent 0.55 T and 1.5 T MRI following posterior fusion surgery of the lumbar or thoracolumbar spine were included. Examinations used metal artifact reduction protocols from clinical routine. Images were rated by two fellowship-trained musculoskeletal radiologists for image quality, ability to assess the spinal canal and the neural foramina, and artifact severity on 5-point Likert scales. Additionally, differences in artifact severity and visibility of near-metal anatomy among implant sizes (1-level vs. 2-level vs. >2-levels) were evaluated. RESULTS: Signal/contrast (mean: 4.0 ±â€¯0.3 [0.55 T] vs. 4.4 ±â€¯0.6 [1.5 T]; p < .001) and resolution (3.8 ±â€¯0.5 vs. 4.2 ±â€¯0.7; p < .001) were rated lower at 0.55 T. The ability to assess the spinal canal (4.4 ±â€¯0.5 vs. 4.2 ±â€¯0.9; p = .69) and the neural foramina (3.8 ±â€¯0.5 vs. 3.8 ±â€¯0.9; p = .19) were however rated equally good with excellent interrater agreement (range: 0.84-0.94). Susceptibility artifacts were rated milder at 0.55 T (1.8 ±â€¯0.5 vs. 3.0 ±â€¯0.6; p < .001). For implant size-based subgroups, the visibility of near-metal anatomy decreased with implant length at 1.5 T, but remained unchanged at 0.55 T. In consequence, the spinal canal and neural foramina could be better assessed at 0.55 T in patients with multi-level implants (4.4 ±â€¯0.5 vs. 3.6 ±â€¯1.1; p < .001). CONCLUSION: Metal artifacts of spinal implants are substantially less pronounced at 0.55 T MRI. When examining patients with multi-level posterior fusion, this translates into a superior ability to assess near-metal anatomy, where 1.5 T MRI reaches diagnostic limitations.

New-Generation 0.55 T MRI of the Knee-Initial Clinical Experience and Comparison With 3 T MRI.

OBJECTIVES: The aim of this study was to compare the detection rate of and reader confidence in 0.55 T knee magnetic resonance imaging (MRI) findings with 3 T knee MRI in patients with acute trauma and knee pain. MATERIALS AND METHODS: In this prospective study, 0.55 T and 3 T knee MRI of 25 symptomatic patients (11 women; median age, 38 years) with suspected internal derangement of the knee was obtained in 1 setting. On the 0.55 T system, a commercially available deep learning image reconstruction algorithm was used (Deep Resolve Gain and Deep Resolve Sharp; Siemens Healthineers), which was not available on the 3 T system. Two board-certified radiologists reviewed all images independently and graded image quality parameters, noted MRI findings and their respective reporting confidence level for the presence or absence, as well as graded the bone, cartilage, meniscus, ligament, and tendon lesions. Image quality and reader confidence levels were compared ( P < 0.05 = significant), and clinical findings were correlated between 0.55 T and 3 T MRI by calculation of the intraclass correlation coefficient (ICC). RESULTS: Image quality was rated higher at 3 T compared with 0.55 T studies (each P ≤ 0.017). Agreement between 0.55 T and 3 T MRI for the detection and grading of bone marrow edema and fractures, ligament and tendon lesions, high-grade meniscus and cartilage lesions, Baker cysts, and joint effusions was perfect for both readers. Overall identification and grading of cartilage and meniscal lesions showed good agreement between high- and low-field MRI (each ICC > 0.76), with lower agreement for low-grade cartilage (ICC = 0.77) and meniscus lesions (ICC = 0.49). There was no difference in readers' confidence levels for reporting lesions of bone, ligaments, tendons, Baker cysts, and joint effusions between 0.55 T and 3 T (each P > 0.157). Reader reporting confidence was higher for cartilage and meniscal lesions at 3 T (each P < 0.041). CONCLUSIONS: New-generation 0.55 T knee MRI, with deep learning-aided image reconstruction, allows for reliable detection and grading of joint lesions in symptomatic patients, but it showed limited accuracy and reader confidence for low-grade cartilage and meniscal lesions in comparison with 3 T MRI.