18F-choline in experimental soft tissue infection assessed with autoradiography and high-resolution PET

For each oncological tracer it is important to know the uptake in non-tumorous lesions. The purpose of this study was to measure the accumulation of fluorine-18 choline (FCH), a promising agent for the evaluation of certain tumour types, in infectious tissue. Unilateral thigh muscle abscesses were induced in five rats by intramuscular injection of 0.1ml of a bacterial suspension (Staphylococcus aureus, 1.2×109CFU/ml). In all animals, FCH accumulation was measured with high-resolution positron emission tomography (PET) on day 6. Autoradiography of the abscess and ipsilateral healthy muscle was performed on day 7 (three animals) and day 11 (two animals) and correlated with histology. In addition, 18F-fluorodeoxyglucose (FDG) PET was performed on day 5. Increased FCH uptake was noted in specific layers of the abscess wall which contained an infiltrate of mainly granulocytes on day 7 and mainly macrophages on day 11. The autoradiographic standardised uptake values in the most active part of the abscess wall were 2.99 on day 7 (n=3) and 4.05 on day 11 (n=2). In healthy muscle the corresponding values were 0.99 and 0.64. The abscesses were clearly visualised on the FCH and FDG PET images. In conclusion, this study demonstrated avid FCH accumulation in inflammatory tissue, which limits the specificity of FCH for tumour detection. Future studies are now needed to determine the degree of this limitation in human cancer patient

Associated tears of the lateral meniscus in anterior cruciate ligament injuries: risk factors for different tear patterns

BACKGROUND: The pattern of lateral meniscus tears observed in anterior cruciate ligament (ACL)-injured subjects varies greatly and determines subsequent management. Certain tear patterns with major biomechanical consequences should be repaired in a timely manner. Knowledge about risk factors for such tears may help to identify patients in the early posttraumatic phase and subsequently may improve clinical results. METHODS: A database of 268 patients undergoing primary ACL reconstruction was used to identify all patients with isolated ACL tears and patients with an associated tear of the lateral meniscus. Patients who underwent surgery >6 months after the injury were excluded. Based on the arthroscopic appearance of the lateral meniscus, patients were assorted to one of three groups: ‘no tear,’ ‘minor tear,’ and ‘major tear.’ Tear patterns defined as major included root tears, complete radial tears, and unstable longitudinal tears including bucket-handle tears. Univariate analysis was performed by comparing the three groups with regard to gender, age, height, weight, BMI, type of injury (high-impact sport, low-impact sport, and not sports related), and mechanism of injury (non-contact vs. contact). Multivariate logistic regression was carried out to identify independent risk factors for minor and major meniscal tears and to calculate odds ratios (OR). RESULTS: Two hundred fifteen patients met the inclusion and exclusion criteria. Of those, 56% had isolated ACL tears, 27% had associated minor tears, and 17% had associated major tears of the lateral meniscus. Univariate analysis revealed significant differences between the three groups for gender (p = 0.002), age groups (p = 0.026), and mechanism of injury (p < 0.001). A contact injury mechanism was a risk factor for minor tears (OR: 4.28) and major tears (OR: 18.49). Additional risk factors for major tears were male gender (OR: 7.38) and age <30 years (OR: 5.85). CONCLUSION: Male patients, patients <30 years, and particularly patients who sustained a contact injury have a high risk for an associated major lateral meniscus tear. Special attention is therefore necessary in those patients and early referral to magnetic resonance imaging and/or arthroscopy is recommended to allow meniscus repair in a timely manner

Generation of training data for fault detection and diagnosis algorithms using fault simulation and parameter uncertainty

Over the past decades, many efforts have been made to reduce the consumption of building energy systems. However, upon closer inspection of the building stock, many newly constructed or retro-fitted buildings, do not perform as expected due to faults in construction and equipment. To overcome this problem, methods have been developed to automatically detect faults occurring in the system. In the past years, the focus of research has shifted to applications of machine learning, as the data collected in buildings has risen exponentially, while advances in computing capabilities as well as machine learning research have made advanced data science widely available to non-domain experts. However, the application of these methods requires highly specific training data, which is difficult to obtain for individual and complex building energy systems. The use of simulation models as a data source has been proposed, but did not find application due to the complex and time-intensive modelling process. In this thesis, I investigate how the use of automated model generation and uncertainty in the parameter sets can be applied to generate training data. First, I introduce a fault model library that can be automatically combined with pre-existing building energy system models to enable the simulation of fault behaviour. I then use Uncertainty Analysis to investigate if the application of uncertainty in model parameters during the training process can be used to increase independence of the fault detection and diagnosis (FDD) model from these parameters. Subsequently, I train three common machine learning approaches for FDD with data created with an increasing number of uncertain parameter sets. Finally, I present a field test where I evaluate the proposed method on data collected from a test bench. The developed fault models are capable of qualitatively representing the fault behaviour, and the simulation model of the test bench is subsequently able to correctly represent the unit's reaction to the fault. During the simulation trial, the increasing the number of uncertain parameter sets during the creation of the training data significantly increased the performance of the FDD models. However, during the field test, no such effect could be observed. Generally, the performance on the field test data is comparable to the benchmark performance. In conclusion, generating training data with simulation model is a viable approach to mitigate the problem of missing training data. Automatically adding faults from a library to existing models reduces the manual effort greatly. The use of more parameter sets does not lead to an improvement in performance at the moment, but may become viable once the general FDD performance increases. The investigated FDD model architectures are suitable for simulation data, but need to be further improved for the use on real measurement data