Nuclear medicine | EPFL Graph Search

Nuclear medicine or nucleology is a medical specialty involving the application of radioactive substances in the diagnosis and treatment of disease. Nuclear imaging, in a sense, is "radiology done inside out" because it records radiation emitting from within the body rather than radiation that is generated by external sources like X-rays. In addition, nuclear medicine scans differ from radiology, as the emphasis is not on imaging anatomy, but on the function. For such reason, it is called a physiological imaging modality. Single photon emission computed tomography (SPECT) and positron emission tomography (PET) scans are the two most common imaging modalities in nuclear medicine. Diagnostic medical imaging Diagnostic In nuclear medicine imaging, radiopharmaceuticals are taken internally, for example, through inhalation, intravenously or orally. Then, external detectors (gamma cameras) capture and form images from the radiation emitted by the radiopharmaceuticals.

Normalisation of Histogrammed List Mode Data

Many PET scanners nowadays have the possibility to record event-by-event information, known as list mode data. This has the advantage of keeping the data in the highest possible resolution (both temporal and spatial). In most cases, list mode data are then binned into sinogram format before reconstruction. In this paper, we discuss at which stage normalisation factors should be introduced. It is shown that noise is greatly reduced by performing the normalisation after the binning. We illustrate this with acquired and simulated data for the quad HiDAC camera.

2008

Data-Driven Predictive Models: Calculational Bias in Characterization of Spent Nuclear Fuel

Characteristics of the spent nuclear fuel (SNF) are typically calculated, requiring validation a priori. The validation process relies on the difference between calculations and measurements, namely the bias. Usually, predicting the bias based on benchmarks is essential, which motivated the present research, focusing on SNF decay heat and Cs-137, U-235, and Pu-239 concentrations.The validation benchmarks are from open-literature, i.e., SNF design and irradiation specifications, as well as the measurements of their characteristics. For the decay heat, they correspond to 262 measurements, conducted at the Clab and the GE-Morris facilities. For the radionuclide concentrations, they are 285 post-irradiation-examination samples, obtained from the SFCOMPO database. The calculations rely on the SCALE code system, namely the Polaris code and the SCALE-based nuclear data.Uncertainties of nuclear data and SNF design and operational history are propagated to the calculated quantities, for two purposes: (1) to assess if the biases are statistically significant, given the calculated uncertainties, and (2) to obtain correlation matrices between the benchmarks. Statistical analyses, resampling and z-tests, are applied on the validation and uncertainty analyses data. They indicate that the biases in several of the analyzed characteristics are significant with respect to uncertainties in the calculated values. For the decay heat case, the biases are considered not significant considering both the calculated and experimental uncertainties. It is also shown that it is crucial to include the correlations between the benchmarks into the hypothesis testing.Then, a novel approach is followed, by applying machine learning (ML) methods to predict the bias of calculated SNF characteristics. The predictive performance is analyzed by comparing the ML-based bias predictions and the validation-based biases. The analyzed ML models predict the bias using highly similar benchmarks or neighbors of the benchmarks, namely Random Forests (RF) and Weighted k-Nearest Neighbors (KKNN). Also, the linear model is analyzed.This research shows that the bias of the decay heat and Pu-239 concentration can be predicted with a reasonable accuracy, relying on specific features of validation benchmarks, or their correlations. The predicted biases bear statistically significant similarities to the observed ones from the validation procedure, using both the RF and the KKNN models. The variances in the original validation data are significantly reduced. The models predict the bias using the spectral index for the decay heat and the hydrogen-to-fissile atom ratio for the Pu-239 concentration. Also, the correlation matrices show that they are informative in predicting the bias of both characteristics. In the case of the U-235 and Cs-137 concentrations, biases could not be satisfactorily predicted. Additionally, the linear models have shown unsatisfactory performance.

EPFL2022

Scanning Probe Microscopy Characterization of Organic Self-Assembled Monolayers

Evangelia-Nefeli Athanasopoulou

As technology advances, surface coatings become more and more important to assure materials performances. In recent years, molecular coatings have found larger acceptance and uses. Among them, self-assembled monolayers (SAMs) are attractive because they are versatile and their manufacturing approach is easy to scale up. Their mechanical properties, such as elasticity, are generally considered an important quality indicator. The molecular structure and ordering of SAMs is believed to be one of the key causes for their mechanical properties. A direct set of structure-property relationships has not been obtained yet. This is mainly due to the difficulty in achieving mechanical and structural information about SAMs at the same time. Most information currently available on intermolecular interactions in SAMs pertains to highly ordered systems on ideal flat surfaces, i.e. the easiest systems to study. This thesis presents a novel approach to address the question of determining structure and mechanical properties of self-assembled monolayers at the same time. The effective Youngâs modulus, E*, of SAMs was measured using the Atomic Force Microscope operated in the bimodal excitation and detection scheme (bimodal AFM) while at the same time imaging at high resolution. Bimodal AFM was first used on alkanethiol molecules self-assembled on Au (111), a model system for SAMs. Surface elasticity has been reliably determined and found to be ligand-length dependent. An interpretation of this behavior is provided in the thesis. A similar investigation has been extended to the characterization of octadecylphosphonic acid SAMs on Al2O3, an industrially relevant system. The monolayer ordering as a function of monolayer formation time was explored, together with the evolution of surface elasticity. The latter allows distinguishing between the consecutive steps of ligand adsorption, monolayer ordering and multilayer formation. The method developed, i.e. simultaneous imaging and mechanical property derivation, was extended to provide localization of the chemical species present in thiolated binary SAMs. Within the systems tested phase separation down to ~10 nm domains could be observed both in the topography and in the elasticity channel. Furthermore, we show in this thesis that this approach can be extended to extract information from more complex biological systems as collagen fibrils. Specifically, in this thesis it is shown that Î² cyclodextrin addition to collagen fibrils affects the tropocollagen packing density. In conclusion, the results shown in this thesis demonstrate that bimodal AFM allows for accurate characterization of surface nanomechanical properties in organic self-assembled systems, as well as the way those scale with varying molecular ordering.

EPFL2018

Data-Driven Predictive Models: Calculational Bias in Characterization of Spent Nuclear Fuel

EPFL2022

Scanning Probe Microscopy Characterization of Organic Self-Assembled Monolayers

Evangelia-Nefeli Athanasopoulou

EPFL2018