Excited-state transition-rate measurements in C-18

Excited states in C-18 were populated by the one-proton knockout reaction of an intermediate energy radioactive N-19 beam. The lifetime of the first 2(+) state was measured with the Koln/NSCL plunger via the recoil distance method to be tau (2(1)(+)) = 22.4 +/- 0.9(stat)(-2.2)(+3.3)(syst) ps, which corresponds to a reduced quadrupole transition strength of B(E2; 2(1)(+) -> 0(1)(+)) = 3.64(-0.14)(+ 0.15)(stat)(-0.47)(+0.40)(syst) e(2)fm(4). In addition, an upper limit on the lifetime of a higher-lying state feeding the 2(1)(+) state was measured to be tau < 4.6 ps. The results are compared to large-scale ab initio no-core shell model calculations using two accurate nucleon-nucleon interactions and the importance-truncation scheme. The comparison provides strong evidence that the inclusion of three-body forces is needed to describe the low-lying excited-state properties of this A = 18 system

Evaluation of a Motion Correction Algorithm for C-Arm Computed Tomography Acquired During Transarterial Chemoembolization

Purpose!#!The aim of this retrospective study was to evaluate the feasibility of a motion correction 3D reconstruction prototype technique for C-arm computed tomography (CACT).!##!Material and methods!#!We included 65 consecutive CACTs acquired during transarterial chemoembolization of 54 patients (47 m,7f; 67 ± 11.3 years). All original raw datasets (CACT!##!Results!#!Objective IQ as defined by an image sharpness metric, increased from 273.5 ± 28 (CACT!##!Conclusion!#!The application of a motion correction algorithm was feasible for all data sets and led to an increase in both objective and subjective IQ parameters.!##!Level of evidence!#!3

Non-occlusive mesenteric ischemia (NOMI): evaluation of 2D-perfusion angiography (2D-PA) for early treatment response assessment

Purpose!#!To evaluate the feasibility of 2D-perfusion angiography (2D-PA) for the analysis of intra-procedural treatment response after intra-arterial prostaglandin E1 therapy in patients with non-occlusive mesenteric ischemia (NOMI).!##!Methods!#!Overall, 20 procedures in 18 NOMI patients were included in this retrospective case-control study. To evaluate intra-procedural splanchnic circulation changes, post-processing of digital subtraction angiography (DSA) series was performed. Regions of interest (ROIs) were placed in the superior mesenteric artery (SMA; reference), the portal vein (PV; ROI!##!Results!#!Vasodilator therapy leads to a significant decrease of the 2D-PA-derived values PD!##!Conclusion!#!2D-PA offers an objective approach to analyze immediate flow and perfusion changes following vasodilatory therapies of NOMI patients and may be a valuable tool for assessing treatment response

Automatisierte Klassifizierung von radiologischen Freitext-Befunden: Analyse verschiedener Feature-Extraction-Methoden zur Identifizierung distaler Fibulafrakturen

Purpose: Radiology reports mostly contain free-text, which makes it challenging to obtain structured data. Natural language processing (NLP) techniques transform free-text reports into machine-readable document vectors that are important for creating reliable, scalable methods for data analysis. The aim of this study is to classify unstructured radiograph reports according to fractures of the distal fibula and to find the best text mining method. Materials & Methods: We established a novel German language report dataset: a designated search engine was used to identify radiographs of the ankle and the reports were manually labeled according to fractures of the distal fibula. This data was used to establish a machine learning pipeline, which implemented the text representation methods bag-of-words (BOW), term frequency-inverse document frequency (TF-IDF), principal component analysis (PCA), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and document embedding (doc2vec). The extracted document vectors were used to train neural networks (NN), support vector machines (SVM), and logistic regression (LR) to recognize distal fibula fractures. The results were compared via cross-tabulations of the accuracy (acc) and area under the curve (AUC). Results: In total, 3268 radiograph reports were included, of which 1076 described a fracture of the distal fibula. Comparison of the text representation methods showed that BOW achieved the best results (AUC = 0.98; acc = 0.97), followed by TF-IDF (AUC = 0.97; acc = 0.96), NMF (AUC = 0.93; acc = 0.92), PCA (AUC = 0.92; acc = 0.9), LDA (AUC = 0.91; acc = 0.89) and doc2vec (AUC = 0.9; acc = 0.88). When comparing the different classifiers, NN (AUC = 0,91) proved to be superior to SVM (AUC = 0,87) and LR (AUC = 0,85). Conclusion: An automated classification of unstructured reports of radiographs of the ankle can reliably detect findings of fractures of the distal fibula. A particularly suitable feature extraction method is the BOW model. Key Points:  - The aim was to classify unstructured radiograph reports according to distal fibula fractures. - Our automated classification system can reliably detect fractures of the distal fibula. - A particularly suitable feature extraction method is the BOW model.Ziel: Radiologische Befundtexte enthalten häufig Freitext, was eine strukturierte Datenauswertung erschwert. Natural language processing (NLP)-Techniken wandeln Freitext in maschinenlesbare Dokumentenvektoren um, die für die Entwicklung zuverlässiger, skalierbarer Methoden zur Datenanalyse wichtig sind. Ziel dieser Studie war es, unstrukturierte Röntgenbefunde nach Frakturen der distalen Fibula zu klassifizieren und die beste Text-Mining-Methode zu finden. Material & Methoden: Zur Erstellung eines eigenen deutschsprachigen Befunddatensatzes wurden mittels einer dedizierten Suchmaschine Sprunggelenks-Röntgenbilder identifiziert und die entsprechenden Befunde manuell nach Frakturen der distalen Fibula sortiert. Anhand der Daten wurde eine Machine-Learning-Pipeline erstellt, die die Textrepräsentationsmethoden Bag-of-Words (BOW), Term Frequency-Inverse Document Frequency (TF-IDF), Principal Component Analysis (PCA), Non-Negative Matrix Factorization (NMF), Latent Dirichlet Allocation (LDA) und Document Embedding (doc2vec) implementierte. Die extrahierten Dokumentvektoren wurden zum Trainieren von neuronalen Netzen (NN), Support Vector Machines (SVM) und logistischer Regression (LR) verwendet, um distale Fibulafrakturen zu erkennen. Die Ergebnisse wurden mittels Kreuztabellen bzgl. der Accuracy (acc) und der area under the curve (AUC) verglichen. Ergebnisse: Insgesamt wurden 3268 Röntgenbefunde inkludiert, von denen 1076 eine distale Fibulafraktur beschrieben. Der Vergleich der Textdarstellungsmethoden zeigte, dass BOW die besten Ergebnisse erzielte (AUC = 0,98; acc = 0,97), gefolgt von TF-IDF (AUC = 0,97; acc = 0,96), NMF (AUC = 0,93; acc = 0,92), PCA (AUC = 0,92; acc = 0,9), LDA (AUC = 0,91; acc = 0,89) und doc2vec (AUC = 0,9; acc = 0,88). Im Vergleich der Klassifikatoren erwiesen sich die NN (AUC = 0,91) gegenüber SVM (AUC = 0,87) und LR (AUC = 0,85) als überlegen. Schlussfolgerung: Durch die automatisierte Klassifikation von unstrukturierten Befunden von Sprunggelenksaufnahmen können Frakturen der distalen Fibula zuverlässig erkannt werden. Eine besonders geeignete Methode zur Feature Extraction ist das BOW-Modell. Kernaussagen:  - Ziel war die automatisierte Klassifizierung unstrukturierter Röntgenbefunde entsprechend distaler Fibulafrakturen. - Eine zuverlässige Detektion von distalen Fibulafrakturen ist durch das automatisierte Klassifizierungssystem gewährleistet. - Eine besonders geeignete Methode zur Feature Extraction ist das BOW-Modell