Background To combat one of the leading causes of death worldwide, lung cancer treatment techniques and modalities are advancing, and the treatment options are becoming increasingly individualized. Modern cancer treatment includes the option for the patient to be treated with proton therapy, which can in some cases spare healthy tissue from excessive dose better than conventional photon radiotherapy. However, to assess the benefit of proton therapy compared to photon therapy, it is necessary to make both treatment plans to get information about the Tumour Control Probability (TCP) and the Normal Tissue Complication Probability (NTCP). This requires excessive treatment planning time and increases the workload for planners.  Aim This project aims to investigate the possibility for automated prediction of the treatment dose distribution using a deep learning network for lung cancer patients treated with photon radiotherapy. This is an initial step towards decreasing the overall planning time and would allow for efficient estimation of the NTCP for each treatment plan and lower the workload of treatment planning technicians. The purpose of the current work was also to understand which features of the input data and training specifics were essential for producing accurate predictions.  Methods Three different deep learning networks were developed to assess the difference in performance based on the complexity of the input for the network. The deep learning models were applied for predictions of the dose distribution of lung cancer treatment and used data from 95 patient treatments. The networks were trained with a U-net architecture using input data from the planning Computed Tomography (CT) and volume contours to produce an output of the dose distribution of the same image size. The network performance was evaluated based on the error of the predicted mean dose to Organs At Risk (OAR) as well as the shape of the predicted Dose-Volume Histogram (DVH) and individual dose distributions.  Results  The optimal input combination was the CT scan and lung, mediastinum envelope and Planning Target Volume (PTV) contours. The model predictions showed a homogenous dose distribution over the PTV with a steep fall-off seen in the DVH. However, the dose distributions had a blurred appearance and the predictions of the doses to the OARs were therefore not as accurate as of the doses to the PTV compared to the manual treatment plans. The performance of the network trained with the Houndsfield Unit input of the CT scan had similar performance as the network trained without it.  Conclusions As one of the novel attempts to assess the potential for a deep learning-based prediction model for the dose distribution based on minimal input, this study shows promising results. To develop this kind of model further a larger data set would be needed and the training method could be expanded as a generative adversarial network or as a more developed U-net network.

Background. Classification based PET/MRI attenuation correction methods utilize a PET/CT derived method of identifying tissues and assigning them attenuation values. Phantoms for PET/MRI generally use theoretical attenuation maps.Aim. This paper investigates the creation of fluids that can be used in phantoms for PET, CT, and MRI. The focus lies on creating properties that result in the attenuation to be correctly estimated with CT and MRI systems. This is a step toward the development of hybrid phantoms with tissue equivalency in all modalities.Method. Attenuation and fat fraction is altered in water to produce a fluid that generates correct attenuation values when measured with both CT and MRI. The fat fraction influences what type of tissue the fluid is classified as in the PET/MRI system used. In turn, the tissue type has an attenuation value associated with it. An emulsion of vegetable oil and water is investigated for the manipulation of fat fractions. The CT attenuation measurement in Hounsfield is modified by altering the density of the fluid. The concentration of sodium chloride is used to modify the Hounsfield value. In the end, the phantom of oil, water, salt solution, and emulsion is validated by adding PET-isotopes. The measured attenuation-corrected radioactivity concentration in PET/CT and PET/MRI are compared.Results. The results show that modification of salt concentration is an effective and easily applicable method of altering the Hounsfield attenuation value of a liquid. The use of emulsions results in expected classification based on fat fraction. Long- and short-term stability of the emulsions may be improved. Analysis of radioactivity concentration shows comparable values between PET/CT and PET/MRI proving that the concept works. / Bakgrund. Klassificeringsbaserad attenueringskorrektion i PET/MR utnyttjar en metod för PET/CT där vävnad identifieras och tilldelas ett attenueringsvärde. Fantomer för PET/MR använder vanligtvis teoretiska attenueringskartor.Mål. Den här rapporten undersöker skapandet av vätskor som kan användas i fantomer för PET, CT och MR. Fokuset ligger på att skapa egenskaper som ger samma korrekta resultat för attenuering i både CT- och MR-system. Detta är ett steg mot utvecklingen av hybridfantom med vävnadsekvivalens för alla modaliteter.Metod. Attenuering och fettfraktion ändras i vatten för att skapa en vätska som ger korrekta attenuringsvärden i både CT och MR. Fettfraktionen bestämmer vilken typ av vävnad som vätskan klassificeras som i det använda PET/MR-systemet. Vävnadsklassificeringen har ett associerat attenueringsvärde. En emulsion av vegetabilisk olja och vatten undersöks för manipulering av fettfraktioner. Attenueringsvärdet som mätt av en CT modifieras genom att ändra densiteten på vätskan. Koncentrationen av natriumklorid ändras för att påverka Hounsfield-attenueringsvärdet. Slutligen så undersöks fantomet med olja, vatten, saltlösning och en emulsion genom tillsatsen av PET-isotoper. Den attenueringskorrigerade radioaktiva koncentrationen som uppmättes i PET/CT och PET/MR jämförs.Resultat. Resultaten visar att modifikation av saltkoncentration är en effektiv och enkelt tillämpbar metod för att påverka Hounsfield-värdet på en vätska. Användningen av emulsioner leder till en förväntad klassificering baserad på fettfraktionen. Lång- och kortvarig stabilitet av emulsionerna kan förbättras. En analys av radioaktivitetskoncentrationen visar jämförbara värden mellan PET/CT och PET/MR vilket visar att konceptet fungerar.

Ethics in health care is a topic that has been around since ancient times. It was the basis for the Hippocratic Oath. With the development of modern nursing and specialties in nursing, ethics in nursing becomes an important topic for consideration. This article gives an overview of the history of ethics, with particular considerations to nursing ethics. It provides an overview of moral distress within nursing and how ethical decisions affect care. It also provides examples of ethics within radiology nursing.

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