Syn2Real: synthesis of CT image ring artifacts for deep learning-based correction.

We strive to overcome the challenges posed by ring artifacts in X-ray computed tomography (CT) by developing a novel approach for generating training data for deep learning-based methods. Training such networks require large, high quality, datasets that are often generated in the data domain, time-consuming and expensive. Our objective is to develop a technique for synthesizing realistic ring artifacts directly in the image domain, enabling scalable production of training data without relying on specific imaging system physics.

The Scania Accelerated Intermittent Theta-burst Implementation Study (SATIS)-Lessons from an accelerated treatment protocol.

Background And Objective: The Scania Accelerated Intermittent Theta-burst Implementation Study (SATIS) aimed to investigate the tolerability, preliminary effectiveness, and practical feasibility of an accelerated intermittent theta burst stimulation (aTBS) protocol in treating depression.

Methods: We used an open-label observational design, recruiting 20 patients (aged 19-84 years) from two public brain stimulation centers in Sweden. During the five-day treatment period and at a follow-up visit after 30 days we closely monitored adverse events and collected self-rated side effect data.

Navigating menstrual stigma and norms: a qualitative study on young people's menstrual experiences and strategies for improving menstrual health.

Background: Menstrual health is a recognised important public health issue and is essential for the realisation of gender equality and the achievement of Sustainable Development Goals. This study aimed to explore the menstrual health experiences of young people in Sweden and how the menstrual cycle affects their health and lives. The study also aimed to identify the facilitators and barriers to achieving menstrual health.

Can robots lie? A posthumanist approach to robotic animals and deceptive practices in dementia care.

Robotic animals are designed to resemble real, living animals, but at the same time, dementia care guidelines and policies often emphasize the value of transparency in relation to robots-people should not be led to believe that robots have capacities that they in fact lack. However, it is not obvious how to separate truth from lies in everyday care practice. Based on participant observations and interviews with certified assistant nurses and nursing assistants in Swedish nursing homes for people with dementia, this article studies how robotic animals become "real" in care practice.

Deep learning estimation of proton stopping power with photon-counting computed tomography: a virtual study.

Purpose: Proton radiation therapy may achieve precise dose delivery to the tumor while sparing non-cancerous surrounding tissue, owing to the distinct Bragg peaks of protons. Aligning the high-dose region with the tumor requires accurate estimates of the proton stopping power ratio (SPR) of patient tissues, commonly derived from computed tomography (CT) image data. Photon-counting detectors for CT have demonstrated advantages over their energy-integrating counterparts, such as improved quantitative imaging, higher spatial resolution, and filtering of electronic noise.