Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics.

OBJECTIVES: Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation. MATERIALS AND METHODS: The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges. RESULTS: Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows. CONCLUSION: To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized. CRITICAL RELEVANCE STATEMENT: Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics. KEY POINTS: Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community.

Accuracy of a deep learning-based algorithm for the detection of thoracic aortic calcifications in chest computed tomography and cardiovascular surgery planning.

OBJECTIVES: To assess the accuracy of a deep learning-based algorithm for fully automated detection of thoracic aortic calcifications in chest computed tomography (CT) with a focus on the aortic clamping zone. METHODS: We retrospectively included 100 chest CT scans from 91 patients who were examined on second- or third-generation dual-source scanners. Subsamples comprised 47 scans with an electrocardiogram-gated aortic angiography and 53 unenhanced scans. A deep learning model performed aortic landmark detection and aorta segmentation to derive 8 vessel segments. Associated calcifications were detected and their volumes measured using a mean-based density thresholding. Algorithm parameters (calcium cluster size threshold, aortic mask dilatation) were varied to determine optimal performance for the upper ascending aorta that encompasses the aortic clamping zone. A binary visual rating served as a reference. Standard estimates of diagnostic accuracy and inter-rater agreement using Cohen's Kappa were calculated. RESULTS: Thoracic aortic calcifications were observed in 74% of patients with a prevalence of 27-70% by aorta segment. Using different parameter combinations, the algorithm provided binary ratings for all scans and segments. The best performing parameter combination for the presence of calcifications in the aortic clamping zone yielded a sensitivity of 93% and a specificity of 82%, with an area under the receiver operating characteristic curve of 0.874. Using these parameters, the inter-rater agreement ranged from κ 0.66 to 0.92 per segment. CONCLUSIONS: Fully automated segmental detection of thoracic aortic calcifications in chest CT performs with high accuracy. This includes the critical preoperative assessment of the aortic clamping zone.

Cardioprotective effects of vaccination in hospitalized patients with COVID-19.

COVID-19 vaccination has been shown to prevent and reduce the severity of COVID-19 disease. The aim of this study was to explore the cardioprotective effect of COVID-19 vaccination in hospitalized COVID-19 patients. In this retrospective, single-center cohort study, we included hospitalized COVID-19 patients with confirmed vaccination status from July 2021 to February 2022. We assessed outcomes such as acute cardiac events and cardiac biomarker levels through clinical and laboratory data. Our analysis covered 167 patients (69% male, mean age 58 years, 42% being fully vaccinated). After adjustment for confounders, vaccinated hospitalized COVID-19 patients displayed a reduced relative risk for acute cardiac events (RR: 0.33, 95% CI [0.07; 0.75]) and showed diminished troponin T levels (Cohen's d: - 0.52, 95% CI [- 1.01; - 0.14]), compared to their non-vaccinated peers. Type 2 diabetes (OR: 2.99, 95% CI [1.22; 7.35]) and existing cardiac diseases (OR: 4.31, 95% CI [1.83; 10.74]) were identified as significant risk factors for the emergence of acute cardiac events. Our findings suggest that COVID-19 vaccination may confer both direct and indirect cardioprotective effects in hospitalized COVID-19 patients.

Quantification of Aortic Valve Calcification in Contrast-Enhanced Computed Tomography.

Background: The goal of our study is to evaluate a method to quantify aortic valve calcification (AVC) in contrast-enhanced computed tomography for patients with suspected severe aortic stenosis pre-interventionally. Methods: A total of sixty-five patients with aortic stenosis underwent both a native and a contrast-enhanced computed tomography (CECT) scan of the aortic valve (45 in the training cohort and 20 in the validation cohort) using a standardized protocol. Aortic valve calcification was semi-automatically quantified via the Agatston score method for the native scans and was used as a reference. For contrast-enhanced computed tomography, a calcium threshold of the Hounsfield units of the aorta plus four times the standard deviation was used. Results: For the quantification of aortic valve calcification in contrast-enhanced computed tomography, a conversion formula (691 + 1.83 x AVCCECT) was derived via a linear regression model in the training cohort. The validation in the second cohort showed high agreement for this conversion formula with no significant proportional bias (Bland-Altman, p = 0.055) and with an intraclass correlation coefficient in the validation cohort of 0.915 (confidence interval 95% 0.786-0.966) p < 0.001. Conclusions: Calcium scoring in patients with aortic valve stenosis can be performed using contrast-enhanced computed tomography with high validity. Using a conversion factor led to an excellent agreement, thereby obviating an additional native computed tomography scan. This might contribute to a decrease in radiation exposure.

Imaging Markers Derived From MRI-Based Automated Kidney Segmentation­an Analysis of Data From the German National Cohort (NAKO Gesundheitsstudie)

BACKGROUND: Population-wide research on potential new imaging biomarkers of the kidney depends on accurate automated segmentation of the kidney and its compartments (cortex, medulla, and sinus). METHODS: We developed a robust deep-learning framework for kidney (sub-)segmentation based on a hierarchical, three-dimensional convolutional neural network (CNN) that was optimized for multi-scale problems of combined localization and segmentation. We applied the CNN to abdominal magnetic resonance images from the population-based German National Cohort (NAKO) study. RESULTS: There was good to excellent agreement between the model predictions and manual segmentations. The median values for the body-surface normalized total kidney, cortex, medulla, and sinus volumes of 9934 persons were 158, 115, 43, and 24 mL/m2. Distributions of these markers are provided both for the overall study population and for a subgroup of persons without kidney disease or any associated conditions. Multivariable adjusted regression analyses revealed that diabetes, male sex, and a higher estimated glomerular filtration rate (eGFR) are important predictors of higher total and cortical volumes. Each increase of eGFR by one unit (i.e., 1 mL/min per 1.73 m2 body surface area) was associated with a 0.98 mL/m2 increase in total kidney volume, and this association was significant. Volumes were lower in persons with eGFR-defined chronic kidney disease. CONCLUSION: The extraction of image-based biomarkers through CNN-based renal sub-segmentation using data from a population-based study yields reliable results, forming a solid foundation for future investigations.

Serum metabolite signatures of cardiac function and morphology in individuals from a population-based cohort.

BACKGROUND: Changes in serum metabolites in individuals with altered cardiac function and morphology may exhibit information about cardiovascular disease (CVD) pathway dysregulations and potential CVD risk factors. We aimed to explore associations of cardiac function and morphology, evaluated using magnetic resonance imaging (MRI) with a large panel of serum metabolites. METHODS: Cross-sectional data from CVD-free individuals from the population-based KORA cohort were analyzed. Associations between 3T-MRI-derived left ventricular (LV) function and morphology parameters (e.g., volumes, filling rates, wall thickness) and markers of carotid plaque with metabolite profile clusters and single metabolites as outcomes were assessed by adjusted multinomial logistic regression and linear regression models. RESULTS: In 360 individuals (mean age 56.3 years; 41.9% female), 146 serum metabolites clustered into three distinct profiles that reflected high-, intermediate- and low-CVD risk. Higher stroke volume (relative risk ratio (RRR): 0.53, 95%-CI [0.37; 0.76], p-value < 0.001) and early diastolic filling rate (RRR: 0.51, 95%-CI [0.37; 0.71], p-value < 0.001) were most strongly protectively associated against the high-risk profile compared to the low-risk profile after adjusting for traditional CVD risk factors. Moreover, imaging markers were associated with 10 metabolites in linear regression. Notably, negative associations of stroke volume and early diastolic filling rate with acylcarnitine C5, and positive association of function parameters with lysophosphatidylcholines, diacylphosphatidylcholines, and acylalkylphosphatidylcholines were observed. Furthermore, there was a negative association of LV wall thickness with alanine, creatinine, and symmetric dimethylarginine. We found no significant associations with carotid plaque. CONCLUSIONS: Serum metabolite signatures are associated with cardiac function and morphology even in individuals without a clinical indication of CVD.

Ultrashort VIBE-Dixon versus Cine and late gadolinium enhancement MRI sequences for cardiac thrombus detection.

PURPOSE: To determine the diagnostic accuracy of volumetric interpolated breath-hold examination sequences with fat suppression in Dixon technique (VIBE-Dixon) for cardiac thrombus detection. METHOD: From our clinical database, we retrospectively identified consecutive patients between 2014 and 2022 who had definite diagnosis or exclusion of cardiac thrombus confirmed by an independent adjudication committee, serving as the reference standard. All patients received 2D-Cine plus 2D-Late-Gadolinium-Enhancement (Cine + LGE) and VIBE-Dixon sequences. Two blinded readers assessed all images for the presence of cardiac thrombus. The diagnostic accuracy of Cine + LGE and VIBE-Dixon was determined and compared. RESULTS: Among 141 MRI studies (116 male, mean age: 61 years) mean image examination time was 28.8 ± 3.1 s for VIBE-Dixon and 23.3 ± 2.5 min for Cine + LGE. Cardiac thrombus was present in 49 patients (prevalence: 35 %). For both readers sensitivity for thrombus detection was significantly higher in VIBE-Dixon compared with Cine + LGE (Reader 1: 96 % vs.73 %, Reader 2: 96 % vs. 78 %, p < 0.01 for both readers), whereas specificity did not differ significantly (Reader 1: 96 % vs. 98 %, Reader 2: 92 % vs. 93 %, p > 0.1). Overall diagnostic accuracy of VIBE-Dixon was higher than for Cine + LGE (95 % vs. 89 %, p = 0.02) and was non-inferior to the reference standard (Delta ≤ 5 % with probability > 95 %). CONCLUSIONS: Biplanar VIBE-Dixon sequences, acquired within a few seconds, provided a very high diagnostic accuracy for cardiac thrombus detection. They could be used as stand-alone sequences to rapidly screen for cardiac thrombus in patients not amenable to lengthy acquisition times.

Photon-counting CT-angiography in pre-TAVR aortic annulus assessment: effects of retrospective vs. prospective ECG-synchronization on prosthesis valve selection.

To compare the diagnostic value of ultrahigh-resolution CT-angiography (UHR-CTA) compared with high-pitch spiral CTA (HPS-CTA) using a first-generation, dual-source photon-counting CT (PCD-CT) scanner for preprocedural planning of transcatheter aortic valve replacement (TAVR). Clinically referred patients with severe aortic valve stenosis underwent both, retrospective ECG-gated cardiac UHR-CTA (collimation: 120 × 0.2 mm) and prospective ECG-triggered aortoiliac HPS-CTA (collimation: 144 × 0.4 mm, full spectral capabilities) for TAVR planning from August 2022 to March 2023. Radiation dose was extracted from the CT reports, and the effective dose was calculated. Two radiologists analyzed UHR-CTA and HPS-CTA datasets, assessing the image quality of the aortic annulus, with regard to the lumen visibility and margin delineation using a 4-point visual-grading scale (ranges: 4 = "excellent" to 1 = "poor"). Aortic annulus area (AAA) measurements were taken for valve prosthesis sizing, with retrospective UHR-CTA serving as reference standard. A total of 64 patients were included (mean age, 81 years ± 7 SD; 28 women) in this retrospective study. HPS-CTA showed a lower radiation dose, 4.1 mSv vs. 12.6 mSv (p < 0.001). UHR-CTA demonstrated higher image quality to HPS-CTA (median score, 4 [IQR, 3-4] vs. 3 [IQR, 2-3]; p < 0.001). Quantitative assessments of AAA from both CTA datasets were strongly positively correlated (mean 477.4 ± 91.1 mm2 on UHR-CTA and mean 476.5 ± 90.4 mm2 on HPS-CTA, Pearson r2 = 0.857, p < 0.001) with a mean error of 22.3 ± 24.6 mm2 and resulted in identical valve prosthesis sizing in the majority of patients (91%). Patients with lower image quality on HPS-CTA (score value 1 or 2, n = 28) were more likely to receive different sizing recommendations (82%). Both UHR-CTA and HPS-CTA acquisitions using photon-counting CT technology provided reliable aortic annular assessments for TAVR planning. While UHR-CTA offers superior image quality, HPS-CTA is associated with lower radiation exposure. However, severely impaired image quality on HPS-CTA may impact on prosthesis sizing, suggesting that immediate post-scan image evaluations may require complementary UHR-CTA scanning.

Deep learning to estimate impaired glucose metabolism from Magnetic Resonance Imaging of the liver: An opportunistic population screening approach.

AIM: Diabetes is a global health challenge, and many individuals are undiagnosed and not aware of their increased risk of morbidity/mortality although dedicated tests are available, which indicates the need for novel population-wide screening approaches. Here, we developed a deep learning pipeline for opportunistic screening of impaired glucose metabolism using routine magnetic resonance imaging (MRI) of the liver and tested its prognostic value in a general population setting. METHODS: In this retrospective study a fully automatic deep learning pipeline was developed to quantify liver shape features on routine MR imaging using data from a prospective population study. Subsequently, the association between liver shape features and impaired glucose metabolism was investigated in individuals with prediabetes, type 2 diabetes and healthy controls without prior cardiovascular diseases. K-medoids clustering (3 clusters) with a dissimilarity matrix based on Euclidean distance and ordinal regression was used to assess the association between liver shape features and glycaemic status. RESULTS: The deep learning pipeline showed a high performance for liver shape analysis with a mean Dice score of 97.0±0.01. Out of 339 included individuals (mean age 56.3±9.1 years; males 58.1%), 79 (23.3%) and 46 (13.6%) were classified as having prediabetes and type 2 diabetes, respectively. Individuals in the high risk cluster using all liver shape features (n = 14) had a 2.4 fold increased risk of impaired glucose metabolism after adjustment for cardiometabolic risk factors (age, sex, BMI, total cholesterol, alcohol consumption, hypertension, smoking and hepatic steatosis; OR 2.44 [95% CI 1.12-5.38]; p = 0.03). Based on individual shape features, the strongest association was found between liver volume and impaired glucose metabolism after adjustment for the same risk factors (OR 1.97 [1.38-2.85]; p<0.001). CONCLUSIONS: Deep learning can estimate impaired glucose metabolism on routine liver MRI independent of cardiometabolic risk factors and hepatic steatosis.

Vaccination protects against acute respiratory distress syndrome (ARDS) in hospitalized patients with COVID-19.

This study aimed to analyze the effect of COVID-19 vaccination on the occurrence of ARDS in hospitalized COVID-19 patients. The study population of this retrospective, single-center cohort study consisted of hospitalized COVID-19 patients with known vaccination status and chest computed tomography imaging between July 2021 and February 2022. The impact of vaccination on ARDS in COVID-19 patients was assessed through logistic regression adjusting for demographic differences and confounding factors with statistical differences determined using confidence intervals and effect sizes. A total of 167 patients (69% male, average age 58 years, 95% CI [55; 60], 42% fully vaccinated) were included in the data analysis. Vaccinated COVID-19 patients had a reduced relative risk (RR) of developing ARDS (RR: 0.40, 95% CI [0.21; 0.62]). Consequently, non-vaccinated hospitalized patients had a 2.5-fold higher probability of developing ARDS. This risk reduction persisted after adjusting for several confounding variables (RR: 0.64, 95% CI [0.29; 0.94]) in multivariate analysis. The protective effect of COVID-19 vaccination increased with ARDS severity (RR: 0.61, 95% CI [0.37; 0.92]). Particularly, patients under 60 years old were at risk for ARDS onset and seemed to benefit from COVID-19 vaccination (RR: 0.51, 95% CI [0.20; 0.90]). COVID-19 vaccination showed to reduce the risk of ARDS occurrence in hospitalized COVID-19 patients, with a particularly strong effect in patients under 60 years old and those with more severe ARDS.

Ultra-high-resolution photon-counting detector CT in evaluating coronary stent patency: a comparison to invasive coronary angiography.

OBJECTIVES: To determine the diagnostic accuracy of ultra-high-resolution photon-counting detector CT angiography (UHR PCD-CTA) for evaluating coronary stent patency compared to invasive coronary angiography (ICA). METHODS: Consecutive, clinically referred patients with prior coronary stent implantation were prospectively enrolled between August 2022 and March 2023 and underwent UHR PCD-CTA (collimation, 120 × 0.2 mm). Two radiologists independently analyzed image quality of the in-stent lumen using a 5-point Likert scale, ranging from 1 ("excellent") to 5 ("non-diagnostic"), and assessed all coronary stents for the presence of in-stent stenosis (≥ 50% lumen narrowing). The diagnostic accuracy of UHR PCD-CTA was determined, with ICA serving as the standard of reference. RESULTS: A total of 44 coronary stents in 18 participants (mean age, 83 years ± 6 [standard deviation]; 12 women) were included in the analysis. In 3/44 stents, both readers described image quality as non-diagnostic, whereas reader 2 noted a fourth stent to have non-diagnostic image quality. In comparison to ICA, UHR PCD-CTA demonstrated a sensitivity, specificity, and accuracy of 100% (95% CI [confidence interval] 47.8, 100), 92.3% (95% CI 79.1, 98.4), and 93.2% (95% CI 81.3, 98.6) for reader 1 and 100% (95% CI 47.8, 100), 87.2% (95% CI 72.6, 95.7), and 88.6% (95% CI 75.4, 96.2) for reader 2, respectively. Both readers observed a 100% negative predictive value (36/36 stents and 34/34 stents). Stent patency inter-reader agreement was 90.1%, corresponding to a substantial Cohen's kappa value of 0.72. CONCLUSIONS: UHR PCD-CTA enables non-invasive assessment of coronary stent patency with high image quality and diagnostic accuracy. CLINICAL RELEVANCE STATEMENT: Ultra-high-resolution photon-counting detector CT angiography represents a reliable and non-invasive method for assessing coronary stent patency. Its high negative predictive value makes it a promising alternative over invasive coronary angiography for the rule-out of in-stent stenosis. KEY POINTS: • CT-based evaluation of coronary stent patency is limited by stent-induced artifacts and spatial resolution. • Ultra-high-resolution photon-counting detector CT accurately evaluates coronary stent patency compared to invasive coronary angiography. • Photon-counting detector CT represents a promising method for the non-invasive rule-out of in-stent stenosis.

Radiographic complicated and uncomplicated descending aortic dissections: aortic morphological differences by CT angiography and risk factor analysis.

AIMS: To identify radiographic differences between patients with uncomplicated and complicated descending aortic dissections. METHODS AND RESULTS: Between April 2009 and July 2021, 209 patients with acute descending aortic dissections were analysed as complicated (malperfusion, rupture, diameter progress, and diameter ≥ 55 mm) or uncomplicated. Detailed CTA measurements (slice thickness ≤ 3 mm) were taken in multiplanar reconstruction. A composite endpoint (early aortic failure) was defined as reoperation, diameter progression, and early mortality. Seventy-seven patients were female (36.8%) [complicated n = 27 (36.5%); uncomplicated n = 50 (37.0%) P = 1.00]. Seventy-four (35%) patients were categorized as morphologically complicated, and 135 (65%) as uncomplicated. In patients with complicated dissections, the dissection extended more frequently to the aortic bifurcation (P = 0.044), the coeliac trunk (P = 0.003), the superior mesenteric artery (P = 0.007), and both iliac arteries (P < 0.001) originated less frequently from the true lumen. The length of the most proximal communication (entry) in type B aortic dissection was longer, 14.0 mm [12.0 mm; 27.0 mm] vs. 6.0 mm [4,0 mm; 13.0 mm] in complicated cases (P = 0.005). Identified risk factors for adverse aortic events were connective tissue disease [HR 8.0 (1.9-33.7 95% CI HR)], length of the aortic arch [HR 4.7 (1.5-15.1 95% CI HR)], a false lumen diameter > 19.38 mm [HR 3.389 (1.1-10.2 95% CI HR)], and origin of the inferior mesenteric artery from the false lumen [HR 4.2 (1.0-5.5 95% CI HR)]. CONCLUSION: We identified significant morphological differences and predictors for adverse events in patients presenting complicated and uncomplicated descending dissections. Our morphological findings will help guide future aortic therapies, taking a tailored patient approach.

Photon-counting computed tomography - clinical application in oncological, cardiovascular, and pediatric radiology. / Photon-Counting Computertomographie ­ klinische Anwendungen in der onkologischen, kardiovaskulären und pädiatrischen Radiologie.

BACKGROUND: Photon-counting detector computed tomography (PCD-CT) is a promising new technology with the potential to fundamentally change workflows in the daily routine and provide new quantitative imaging information to improve clinical decision-making and patient management. METHOD: The contents of this review are based on an unrestricted literature search of PubMed and Google Scholar using the search terms "photon-counting CT", "photon-counting detector", "spectral CT", "computed tomography" as well as on the authors' own experience. RESULTS: The fundamental difference with respect to the currently established energy-integrating CT detectors is that PCD-CT allows for the counting of every single photon at the detector level. Based on the identified literature, PCD-CT phantom measurements and initial clinical studies have demonstrated that the new technology allows for improved spatial resolution, reduced image noise, and new possibilities for advanced quantitative image postprocessing. CONCLUSION: For clinical practice, the potential benefits include fewer beam hardening artifacts, a radiation dose reduction, and the use of new or combinations of contrast agents. In particular, critical patient groups such as oncological, cardiovascular, lung, and head & neck as well as pediatric patient collectives benefit from the clinical advantages. KEY POINTS: · Photon-counting computed tomography (PCD-CT) is being used for the first time in routine clinical practice, enabling a significant dose reduction in critical patient populations such as oncology, cardiology, and pediatrics.. · Compared to conventional CT, PCD-CT enables a reduction in electronic image noise.. · Due to the spectral data sets, PCD-CT enables fully comprehensive post-processing applications.. CITATION FORMAT: · Hagen F, Soschynski M, Weis M et al. Photon-counting computed tomography - clinical application in oncological, cardiovascular, and pediatric radiology. Fortschr Röntgenstr 2024; 196: 25 - 34.

Strong Association of Depression and Anxiety With the Presence of Back Pain While Impact of Spinal Imaging Findings is Limited: Analysis of an MRI Cohort Study.

Development of back pain is multifactorial, and it is not well understood which factors are the main drivers of the disease. We therefore applied a machine-learning approach to an existing large cohort study data set and sought to identify and rank the most important contributors to the presence of back pain amongst the documented parameters of the cohort. Data from 399 participants in the KORA-MRI (Cooperative health research in the region Augsburg-magnetic resonance imaging) (Cooperative Health Research in the Region Augsburg) study was analyzed. The data set included MRI images of the whole body, including the spine, metabolic, sociodemographic, anthropometric, and cardiovascular data. The presence of back pain was one of the documented items in this data set. Applying a machine-learning approach to this preexisting data set, we sought to identify the variables that were most strongly associated with back pain. Mediation analysis was performed to evaluate the underlying mechanisms of the identified associations. We found that depression and anxiety were the 2 most selected predictors for back pain in our model. Additionally, body mass index, spinal canal width and disc generation, medium and heavy physical work as well as cardiovascular factors were among the top 10 most selected predictors. Using mediation analysis, we found that the effects of anxiety and depression on the presence of back pain were mainly direct effects that were not mediated by spinal imaging. In summary, we found that psychological factors were the most important predictors of back pain in our cohort. This supports the notion that back pain should be treated in a personalized multidimensional framework. PERSPECTIVE: This article presents a wholistic approach to the problem of back pain. We found that depression and anxiety were the top predictors of back pain in our cohort. This strengthens the case for a multidimensional treatment approach to back pain, possibly with a special emphasis on psychological factors.

Pilot study on high-resolution radiological methods for the analysis of cerebrospinal fluid (CSF) shunt valves.

OBJECTIVES: Despite their life-saving capabilities, cerebrospinal fluid (CSF) shunts exhibit high failure rates, with a large fraction of failures attributed to the regulating valve. Due to a lack of methods for the detailed analysis of valve malfunctions, failure mechanisms are not well understood, and valves often have to be surgically explanted on the mere suspicion of malfunction. The presented pilot study aims to demonstrate radiological methods for comprehensive analysis of CSF shunt valves, considering both the potential for failure analysis in design optimization, and for future clinical in-vivo application to reduce the number of required shunt revision surgeries. The proposed method could also be utilized to develop and support in situ repair methods (e.g. by lysis or ultrasound) of malfunctioning CSF shunt valves. MATERIALS AND METHODS: The primary methods described are contrast-enhanced radiographic time series of CSF shunt valves, taken in a favorable projection geometry at low radiation dose, and the machine-learning-based diagnosis of CSF shunt valve obstructions. Complimentarily, we investigate CT-based methods capable of providing accurate ground truth for the training of such diagnostic tools. Using simulated test and training data, the performance of the machine-learning diagnostics in identifying and localizing obstructions within a shunt valve is evaluated regarding per-pixel sensitivity and specificity, the Dice similarity coefficient, and the false positive rate in the case of obstruction free test samples. RESULTS: Contrast enhanced subtraction radiography allows high-resolution, time-resolved, low-dose analysis of fluid transport in CSF shunt valves. Complementarily, photon-counting micro-CT allows to investigate valve obstruction mechanisms in detail, and to generate valid ground truth for machine learning-based diagnostics. Machine-learning-based detection of valve obstructions in simulated radiographies shows promising results, with a per-pixel sensitivity >70%, per-pixel specificity >90%, a median Dice coefficient >0.8 and <10% false positives at a detection threshold of 0.5. CONCLUSIONS: This ex-vivo study demonstrates obstruction detection in cerebro-spinal fluid shunt valves, combining radiological methods with machine learning under conditions compatible to future in-vivo application. Results indicate that high-resolution contrast-enhanced subtraction radiography, possibly including time-series data, combined with machine-learning image analysis, has the potential to strongly improve the diagnostics of CSF shunt valve failures. The presented method is in principle suitable for in-vivo application, considering both measurement geometry and radiological dose. Further research is needed to validate these results on real-world data and to refine the employed methods. In combination, the presented methods enable comprehensive analysis of valve failure mechanisms, paving the way for improved product development and clinical diagnostics of CSF shunt valves.

Automated image quality assessment for selecting among multiple magnetic resonance image acquisitions in the German National Cohort study.

In magnetic resonance imaging (MRI), the perception of substandard image quality may prompt repetition of the respective image acquisition protocol. Subsequently selecting the preferred high-quality image data from a series of acquisitions can be challenging. An automated workflow may facilitate and improve this selection. We therefore aimed to investigate the applicability of an automated image quality assessment for the prediction of the subjectively preferred image acquisition. Our analysis included data from 11,347 participants with whole-body MRI examinations performed as part of the ongoing prospective multi-center German National Cohort (NAKO) study. Trained radiologic technologists repeated any of the twelve examination protocols due to induced setup errors and/or subjectively unsatisfactory image quality and chose a preferred acquisition from the resultant series. Up to 11 quantitative image quality parameters were automatically derived from all acquisitions. Regularized regression and standard estimates of diagnostic accuracy were calculated. Controlling for setup variations in 2342 series of two or more acquisitions, technologists preferred the repetition over the initial acquisition in 1116 of 1396 series in which the initial setup was retained (79.9%, range across protocols: 73-100%). Image quality parameters then commonly showed statistically significant differences between chosen and discarded acquisitions. In regularized regression across all protocols, 'structured noise maximum' was the strongest predictor for the technologists' choice, followed by 'N/2 ghosting average'. Combinations of the automatically derived parameters provided an area under the ROC curve between 0.51 and 0.74 for the prediction of the technologists' choice. It is concluded that automated image quality assessment can, despite considerable performance differences between protocols and anatomical regions, contribute substantially to identifying the subjective preference in a series of MRI acquisitions and thus provide effective decision support to readers.

Photon-counting computed tomography of coronary and peripheral artery stents: a phantom study.

Accurate small vessel stent visualization using CT remains challenging. Photon-counting CT (PCD-CT) may help to overcome this issue. We systematically investigate PCD-CT impact on small vessel stent assessment compared to energy-integrating-CT (EID). 12 water-contrast agent filled stents (3.0-8 mm) were scanned with patient-equivalent phantom using clinical PCD-CT and EID-CT. Images were reconstructed using dedicated vascular kernels. Subjective image quality was evaluated by 5 radiologists independently (5-point Likert-scale; 5 = excellent). Objective image quality was evaluated by calculating multi-row intensity profiles including edge rise slope (ERS) and coefficient-of-variation (CV). Highest overall reading scores were found for PCD-CT-Bv56 (3.6[3.3-4.3]). In pairwise comparison, differences were significant for PCD-CT-Bv56 vs. EID-CT-Bv40 (p ≤ 0.04), for sharpness and blooming respectively (all p < 0.05). Highest diagnostic confidence was found for PCD-CT-Bv56 (p ≤ 0.2). ANOVA revealed a significant effect of kernel strength on ERS (p < 0.001). CV decreased with stronger PCD-CT kernels, reaching its lowest in PCD-CT-Bv56 and highest in EID-CT reconstruction (p ≤ 0.05). We are the first study to verify, by phantom setup adapted to real patient settings, PCD-CT with a sharp vascular kernel provides the most favorable image quality for small vessel stent imaging. PCD-CT may reduce the number of invasive coronary angiograms, however, more studies needed to apply our results in clinical practice.