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: To investigate the energy consumption of interventional imaging systems and estimate potential savings in such systems' carbon emissions and electricity costs 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 measurements 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 timestamps, 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 kW, 0.7-7.4 kW, 0.9-7.6 kW, 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, exhibited a decrease in 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 of the net-imaging state accounted for 93.0% (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 to off state overnight and on weekends (vs operated in idle mode 24/7) would yield potential annual savings in energy consumption of 144,640 kWh, carbon emissions of 18.6 MtCO2eq, and electricity costs of $37,896. Conclusion: Interventional imaging systems are energy intensive, with high consumption outside of image acquisition periods. Clinical Impact: Strategic operational adjustments (e.g., powering down idle systems) can substantially decrease interventional imaging systems' carbon emissions and electricity costs.

Opportunistic Prostate Cancer Screening with Biparametric Magnetic Resonance Imaging (VISIONING).

BACKGROUND: This study investigates the use of biparametric magnetic resonance imaging (bpMRI) as primary opportunistic screening for prostate cancer (PCa) without using a prostate-specific antigen (PSA) cut-off. OBJECTIVE: The primary endpoint was to assess the efforts and effectiveness of identifying 20 participants with clinically significant prostate cancer (csPCa) using bpMRI. DESIGN, SETTING, AND PARTICIPANTS: Biopsy-naïve men aged over 45 yr were included. All participants underwent 3 Tesla bpMRI, PSA, and digital rectal examination (DRE). Targeted-only biopsy was performed in participants with Prostate Imaging Reporting and Data System (PI-RADS) ≥3. Men with negative bpMRI but suspicious DRE or elevated PSA/PSA density had template biopsies. Preintended protocol adjustments were made after an interim analysis for PI-RADS 3 lesions: no biopsy and follow-up MRI after 6 mo and biopsy only if lesions persisted or upgraded. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Biopsy results underwent a comparison using Fisher's exact test and univariable logistic regression to identify prognostic factors for positive biopsy. RESULTS AND LIMITATIONS: A total of 229 men were enrolled in this study, of whom 79 underwent biopsy. Among these men, 77 displayed suspicious PI-RADS lesions. PCa was detected in 29 participants (12.7%), of whom 21 had csPCa (9.2%). Biparametric MRI detected 21 csPCa cases, while PSA and DRE would have missed 38.1%. Protocol adjustment led to a 54.6% biopsy reduction in PI-RADS 3 lesions. Overall, in this cohort of men with a median PSA value of 1.26 ng/ml, 10.9 bpMRI scans were needed to identify one participant with csPCa. A major limitation of the study is the lack of a control cohort undergoing systematic biopsies. CONCLUSIONS: Opportunistic screening utilising bpMRI as a primary tool has higher sensitivity in detecting csPCa than classical screening methods. PATIENT SUMMARY: Screening with biparametric magnetic resonance imaging (bpMRI) and targeted biopsy identified clinically significant prostate cancer in every 11th man, regardless of the prostate-specific antigen (PSA) levels. Preselecting patients based on PSA >1 ng/ml and a positive family history of prostate cancer, as well as other potential blood tests may further improve the effectiveness of bpMRI in this setting.

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.

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.

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.

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.

TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images.

Purpose: To present a deep learning segmentation model that can automatically and robustly segment all major anatomic structures on body CT images. Materials and Methods: In this retrospective study, 1204 CT examinations (from 2012, 2016, and 2020) were used to segment 104 anatomic structures (27 organs, 59 bones, 10 muscles, and eight vessels) relevant for use cases such as organ volumetry, disease characterization, and surgical or radiation therapy planning. The CT images were randomly sampled from routine clinical studies and thus represent a real-world dataset (different ages, abnormalities, scanners, body parts, sequences, and sites). The authors trained an nnU-Net segmentation algorithm on this dataset and calculated Dice similarity coefficients to evaluate the model's performance. The trained algorithm was applied to a second dataset of 4004 whole-body CT examinations to investigate age-dependent volume and attenuation changes. Results: The proposed model showed a high Dice score (0.943) on the test set, which included a wide range of clinical data with major abnormalities. The model significantly outperformed another publicly available segmentation model on a separate dataset (Dice score, 0.932 vs 0.871; P < .001). The aging study demonstrated significant correlations between age and volume and mean attenuation for a variety of organ groups (eg, age and aortic volume [rs = 0.64; P < .001]; age and mean attenuation of the autochthonous dorsal musculature [rs = -0.74; P < .001]). Conclusion: The developed model enables robust and accurate segmentation of 104 anatomic structures. The annotated dataset (https://doi.org/10.5281/zenodo.6802613) and toolkit (https://www.github.com/wasserth/TotalSegmentator) are publicly available.Keywords: CT, Segmentation, Neural Networks Supplemental material is available for this article. © RSNA, 2023See also commentary by Sebro and Mongan in this issue.

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.

Assessment of hepatic function employing hepatocyte specific contrast agent concentrations to multifactorially evaluate fibrotic remodeling.

Background: Diffuse parenchymal liver diseases are contributing substantially to global morbidity and represent major causes of deaths worldwide. The aim of our study is to assess whether established hepatic fat and iron quantitation and relaxometry-based quantification of hepatocyte-specific contrast material as surrogate for liver function estimation allows to evaluate liver fibrosis. Methods: Retrospective consecutive study. Seventy-two healthy patients (mean age: 53 years) without known liver disease, 21 patients with temporary elevated liver enzymes (mean: 65 years) and 109 patients with biopsy proven liver fibrosis or cirrhosis (mean: 61 years), who underwent liver magnetic resonance imaging (MRI) with a hepatocyte-specific contrast agent [gadoxetate disodium, gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA), 0.25 mmol/mL Primovist, Bayer AG, Leverkusen, Germany] at 1.5 T (n=133) and at 3 T (n=69), were included. Fibrosis was classified using the histopathological meta-analysis of histological data in viral hepatitis (METAVIR) and the clinical Child-Pugh scores. Gd-concentration were quantified using T1 map-based calculations. Gd-concentration mapping was performed by using a Look-Locker approach prior to and 912±159 s after intravenous administration of hepatocyte specific contrast agent. Additionally, parenchymal fat fraction, R2*, bilirubin, gender and age were defined as predicting factors. Diagnostic accuracy was calculated in a monoparametric (linear regression, predictor: Gd-concentration) and multiparametric model (predictors: age, bilirubin level, iron overload, liver fat fraction, Gd concentration in the left and right liver lobe). Results: Mean Gd-concentration in the liver parenchyma was significantly higher for healthy patients ([Gd] =0.51 µmol/L) than for those with liver fibrosis or cirrhosis ([Gd] =0.31 µmol/L; P<0.0001) and with acute liver disease ([Gd] =0.28 µmol/L), though there were no significant differences for the latter two groups. There was a significant moderate negative correlation for the mean Gd-concentration and the METAVIR score (ρ=-0.44, P<0.0001) as well as for the Child-Pugh stage (ρ=-0.35, P<0.0001). There was a significant strong correlation between the bilirubin concentration and the Gd-concentration (ρ=-0.61, P<0.0001). The diagnostic accuracy for the discrimination of healthy patients and patients with known fibrosis or cirrhosis was 0.74 (0.71/0.60 sensitivity/specificity) in a monoparametric and 0.76 (0.85/0.61 sensitivity/specificity) in a machine learning based multiparametric model. Conclusions: T1 mapping-based quantification of hepatic Gd-EOB-DTPA concentrations performed in a multiparametric model shows promising diagnostic accuracy for the detection of fibrotic changes. Liver biopsy might be replaced by imaging examinations.

Sustainability and Climate Protection in Radiology - An Overview. / Nachhaltigkeit und Klimaschutz in der Radiologie ­ Ein Überblick.

BACKGROUND: Sustainability is becoming increasingly important in radiology. Besides climate protection - economic, ecological, and social aspects are integral elements of sustainability. An overview of the scientific background of the sustainability and environmental impact of radiology as well as possibilities for future concepts for more sustainable diagnostic and interventional radiology are presented below.The three elements of sustainability:1. EcologyWith an annually increasing number of tomographic images, Germany is in one of the leading positions worldwide in a per capita comparison. The energy consumption of an MRI system is comparable to 26 four-person households annually. CT and MRI together make a significant contribution to the overall energy consumption of a hospital. In particular, the energy consumption in the idle or inactive state is responsible for a relevant proportion.2. EconomyA critical assessment of the indications for radiological imaging is important not only because of radiation protection, but also in terms of sustainability and "value-based radiology". As part of the "Choosing Wisely" initiative, a total of 600 recommendations for avoiding unnecessary examinations were compiled from various medical societies, including specific indications in radiological diagnostics.3. Social SustainabilityThe alignment of radiology to the needs of patients and referring physicians is a core aspect of the social component of sustainability. Likewise, ensuring employee loyalty by supporting and maintaining motivation, well-being, and job satisfaction is an essential aspect of social sustainability. In addition, sustainable concepts are of relevance in teaching and research, such as the educational curriculum for residents in radiology, RADUCATION or the recommendations of the International Committee of Medical Journal Editors. KEY POINTS: · Sustainability comprises three pillars: economy, ecology and the social component.. · Radiologies have a high optimization potential due to a significant demand of these resources.. · A dialogue between medicine, politics and industry is necessary for a sustainable radiology.. · The discourse, knowledge transfer and public communication of recommendations are part of the sustainability network of the German Roentgen Society (DRG).. CITATION FORMAT: · Palm V, Heye T, Molwitz I et al. Sustainability and Climate Protection in Radiology - An Overview. Fortschr Röntgenstr 2023; 195: 981 - 988.

Estimation of differential renal function on routine abdominal imaging employing compressed-sensed contrast-enhanced MR: a feasibility study referenced against dynamic renal scintigraphy in patients with deteriorating renal retention parameters.

PURPOSE: To assess whether high temporal/spatial resolution GRASP MRI acquired during routine clinical imaging can identify several degrees of renal function impairment referenced against renal dynamic scintigraphy. METHODS: This retrospective study consists of method development and method verification parts. During method development, patients subject to renal imaging using gadoterate meglumine and GRASP post-contrast MRI technique (TR/TE 3.3/1.6 ms; FoV320 × 320 mm; FA12°; Voxel1.1 × 1.1x2.5 mm) were matched into four equally-sized renal function groups (no-mild-moderate-severe impairment) according to their laboratory-determined estimated glomerular filtration rates (eGFR); 60|120 patients|kidneys were included. Regions-of-interest (ROIs) were placed on cortices, medullary pyramids and collecting systems of bilateral kidneys. Cortical perfusion, tubular concentration and collecting system excretion were determined as TimeCortex=Pyramid(sec), SlopeTubuli (sec-1), and TimeCollecting System (sec), respectively, and were measured by a combination of extraction of time intensity curves and respective quantitative parameters. For method verification, patients subject to GRASP MRI and renal dynamic scintigraphy (99mTc-MAG3, 100 MBq/patient) were matched into three renal function groups (no-mild/moderate-severe impairment). Split renal function parameters post 1.5-2.5 min as well as MAG3 TER were correlated with time intensity parameters retrieved using GRASP technique; 15|30 patients|kidneys were included. RESULTS: Method development showed differing values for TimeCortex=Pyramid(71|75|93|122 s), SlopeTubuli(2.6|2.1|1.3|0.5 s-1) and TimeCollecting System(90|111|129|139 s) for the four renal function groups with partial significant tendencies (several p-values < 0.001). In method verification, 29/30 kidneys (96.7%) were assigned to the correct renal function group. CONCLUSION: High temporal and spatial resolution GRASP MR imaging allows to identify several degrees of renal function impairment using routine clinical imaging with a high degree of accuracy.

Quantifying the Effects of Structured Reporting on Report Turnaround Times and Proofreading Workload in Neuroradiology.

RATIONALE AND OBJECTIVES: To assess the effects of a change from free text reporting to structured reporting on resident reports, the proofreading workload and report turnaround times in the neuroradiology daily routine. MATERIALS AND METHODS: Our neuroradiology section introduced structured reporting templates in July 2019. Reports dictated by residents during dayshifts from January 2019 to March 2020 were retrospectively assessed using quantitative parameters from report comparison. Through automatic analysis of text-string differences between report states (i.e. draft, preliminary and final report), Jaccard similarities and edit distances of reports following read-out sessions as well as after report sign-off were calculated. Furthermore, turnaround times until preliminary and final report availability to clinicians were investigated. Parameters were visualized as trending line graphs and statistically compared between reporting standards. RESULTS: Three thousand five hundred thirty-eight reports were included into analysis. Mean Jaccard similarity of resident drafts and staff-reviewed final reports increased from 0.53 ± 0.37 to 0.79 ± 0.22 after the introduction of structured reporting (p < .001). Both mean overall edits on draft reports by residents following read-out sessions (0.30 ± 0.45 vs. 0.09 ± 0.29; p < .001) and by staff radiologists during report sign-off (0.17 ± 0.28 vs. 0.12 ± 0.23, p < .001) decreased. With structured reporting, mean turnaround time until preliminary report availability to clinicians decreased by 20.7 minutes (246.9 ± 207.0 vs. 226.2 ± 224.9; p < .001). Similarly, final reports were available 35.0 minutes faster on average (558.05 ± 15.1 vs. 523.0 ± 497.3; p = .002). CONCLUSION: Structured reporting is beneficial in the neuroradiology daily routine, as resident drafts require fewer edits in the report review process. This reduction in proofreading workload is likely responsible for lower report turnaround times.

Clinical acceptance of advanced visualization methods: a comparison study of 3D-print, virtual reality glasses, and 3D-display.

BACKGROUND: To compare different methods of three-dimensional representations, namely 3D-Print, Virtual Reality (VR)-Glasses and 3D-Display regarding the understanding of the pathology, accuracy of details, quality of the anatomical representation and technical operability and assessment of possible change in treatment in different disciplines and levels of professional experience. METHODS: Interviews were conducted with twenty physicians from the disciplines of cardiology, oral and maxillofacial surgery, orthopedic surgery, and radiology between 2018 and 2020 at the University Hospital of Basel. They were all presented with three different three-dimensional clinical cases derived from CT data from their area of expertise, one case for each method. During this, the physicians were asked for their feedback written down on a pencil and paper questionnaire. RESULTS: Concerning the understanding of the pathology and quality of the anatomical representation, VR-Glasses were rated best in three out of four disciplines and two out of three levels of professional experience. Regarding the accuracy of details, 3D-Display was rated best in three out of four disciplines and all levels of professional experience. As to operability, 3D-Display was consistently rated best in all levels of professional experience and all disciplines. Possible change in treatment was reported using 3D-Print in 33%, VR-Glasses in 44%, and 3D-Display in 33% of participants. Physicians with a professional experience of more than ten years reported no change in treatment using any method. CONCLUSIONS: 3D-Print, VR-Glasses, and 3D-Displays are very well accepted, and a relevant percentage of participants with less than ten years of professional work experience could imagine a possible change in treatment using any of these three-dimensional methods. Our findings challenge scientists, technicians, and physicians to further develop these methods to improve the three-dimensional understanding of pathologies and to add value to the education of young and inexperienced physicians.

Dual-energy CT of acute bowel ischemia.

Acute bowel ischemia is a condition with high mortality and requires rapid intervention to avoid catastrophic outcomes. Swift and accurate imaging diagnosis is essential because clinical findings are commonly nonspecific. Conventional contrast enhanced CT of the abdomen has been the imaging modality of choice to evaluate suspected acute bowel ischemia. However, subtlety of image findings and lack of non-contrast or arterial phase images can make correct diagnosis challenging. Dual-energy CT provides valuable information toward assessing bowel ischemia. Dual-energy CT exploits the differential X-ray attenuation at two different photon energy levels to characterize the composition of tissues and reveal the presence or absence of faint intravenous iodinated contrast to improve reader confidence in detecting subtle bowel wall enhancement. With the same underlying technique, virtual non-contrast images can help to show non-enhancing hyperdense hemorrhage of the bowel wall in intravenous contrast-enhanced scans without the need to acquire actual non-contrast scans. Dual-energy CT derived low photon energy (keV) virtual monoenergetic images emphasize iodine contrast and provide CT angiography-like images from portal venous phase scans to better evaluate abdominal arterial patency. In Summary, dual-energy CT aids diagnosing acute bowel ischemia in multiple ways, including improving visualization of the bowel wall and mesenteric vasculature, revealing intramural hemorrhage in contrast enhanced scans, or possibly reducing intravenous contrast dose.

Respiratory anomalies associated with gadoxetate disodium and gadoterate meglumine: compressed sensing MRI revealing physiologic phenomena during the entire injection cycle.

OBJECTIVES: The goal of this study was to investigate the precise timeline of respiratory events occurring after the administration of two gadolinium-based contrast agents, gadoxetate disodium and gadoterate meglumine. MATERIALS AND METHODS: This retrospective study examined 497 patients subject to hepatobiliary imaging using the GRASP MRI technique (TR/TE = 4/2 ms; ST = 2.5 mm; 384 × 384 mm). Imaging was performed after administration of gadoxetate (N = 338) and gadoterate (N = 159). All GRASP datasets were reconstructed using a temporal resolution of 1 s. Four regions-of-interest (ROIs) were placed in the liver dome, the right and left cardiac ventricle, and abdominal aorta detecting liver displacement and increasing vascular signal intensities over time. Changes in hepatic intensity reflected respiratory dynamics in temporal correlation to the vascular contrast bolus. RESULTS: In total, 216 (67%) and 41 (28%) patients presented with transient respiratory motion after administration of gadoxetate and gadoterate, respectively. The mean duration from start to acme of the respiratory episode was similar (p = 0.4) between gadoxetate (6.0 s) and gadoterate (5.6 s). Its mean onset in reference to contrast arrival in the right ventricle differed significantly (p < 0.001) between gadoxetate (15.3s) and gadoterate (1.8 s), analogously to peak inspiration timepoint in reference to the aortic enhancement arrival (gadoxetate: 0.9s after, gadoterate: 11.2 s before aortic enhancement, p < 0.001). CONCLUSIONS: The timepoint of occurrence of transient respiratory anomalies associated with gadoxetate disodium and gadoterate meglumine differs significantly between both contrast agents while the duration of the event remains similar. KEY POINTS: • Transient respiratory anomalies following the administration of gadoterate meglumine occurred during a time period usually not acquired in MR imaging. • Transient respiratory anomalies following the administration of gadoxetate disodium occurred around the initiation of arterial phase imaging. • The estimated duration of respiratory events was similar between both contrast agents.

Investigating difficult to detect pancreatic lesions: Characterization of benign pancreatic islet cell tumors using multiparametric pancreatic 3-T MRI.

INTRODUCTION: Pancreatic islet-cell tumors (PICT) often present with atypical signal-characteristics and are often missed on preoperative imaging. The aim of this study is to provide a multiparametric PICT characterization and investigate factors impeding PICT detection. MATERIAL AND METHODS: This is a detailed MRI analysis of a prospective, monocenter study, including 49 consecutive patients (37 female, 12 male; median age 50) with symptoms due to endogenous hyperinsulinemic hypoglycemia (EHH) and mostly negative prior-imaging. All patients received a 3-T MRI and a 68Ga-DOTA-exendin-4-PET/CT. Pooled accuracy, sensitivity, specificity and inter-reader agreement were calculated. Reference-standard was histopathology and 68Ga-DOTA-Exendin-4-PET/CT in one patient who refused surgery. For PICT analyses, 34 patients with 49 PICTs (48 histologically proven; one 68Ga-DOTA-exendin-4-PET/CT positive) were assessed. Dynamic contrast-enhanced (DCE) Magnetic Resonance Images (MRI) with Golden-Angle-Radial-Sparse-Parallel (GRASP) reconstruction, enabling imaging at high spatial and temporal resolution, was used to assess enhancement-patterns of PICTs. Tumor-to-background (T2B) ratio for each sequence and the employed quantitative threshold for conspicuity of PICTs were analyzed in regard to prediction of true-positive PICTs. RESULTS: Evaluation of 49 patients revealed a pooled lesion-based accuracy, sensitivity and specificity of 70.3%, 72.9% and 62.5%, respectively. Mean PICT size was 12.9±5.3mm for detected, 9.0±2.9mm for undetected PICTs (p-value 0.0112). In-phase T1w detected the most PICT (67.3%). Depending on the sequence, PICTs were isointense and poorly visible in 29-68%. Only 2/41(4.9%) PICTs showed typical signal-characteristics across T1w, T2w, DWI and ceT1w combined. 66.6% of PICTs enhanced simultaneously to the parenchyma, 17.8% early and 15.6% late. Predictor screening analysis showed number of sequences detecting a PICT, lesion size and in-phase T1w T2B ratio had the highest contribution for detecting a true-positive PICT. CONCLUSION: The majority of PICTs enhance simultaneously to surrounding parenchyma, present with atypical signal-characteristics and thus are poorly visible. In non-enhancing PICTs, radiologists should search for small lesions most likely conspicuous on unenhanced T1w or DWI.