Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

Microscopic simulation of xenon-based optical TPCs in the presence of molecular additives

[EN] We introduce a simulation framework for the transport of high and low energy electrons in xenon-based optical time projection chambers (OTPCs). The simulation relies on elementary cross sections (electron-atom and electron-molecule) and incorporates, in order to compute the gas scintillation, the reaction/quenching rates (atom-atom and atom-molecule) of the first 41 excited states of xenon and the relevant associated excimers, together with their radiative cascade. The results compare positively with observations made in pure xenon and its mixtures with CO2 and CF4 in a range of pressures from 0.1 to 10 bar. This work sheds some light on the elementary processes responsible for the primary and secondary xenon-scintillation mechanisms in the presence of additives, that are of interest to the OTPC technology.DGD is supported by the Ramon y Cajal program (Spain) under contract number RYC-2015-18820. The authors want to acknowledge the RD51 collaboration for encouragement and support during the elaboration of this work, and in particular discussions with F. Resnati, A. Milov, V. Peskov, M. Suzuki and A. F. Borghesani. The NEXT Collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the Ministerio de Economia y Competitividad of Spain under grants FIS2014-53371-C04 and the Severo Ochoa Program SEV-2014-0398; the GVA of Spain under grant PROM-ETEO/2016/120; the Portuguese FCT and FEDER through the program COMPETE, project PTDC/FIS-NUC/2525/2014 and UID/FIS/04559/2013; the U.S. Department of Energy under contracts number DE-AC02-07CH11359 (Fermi National Accelerator Laboratory) and DE-FG02-13ER42020 (Texas A& and the University of Texas at Arlington.Azevedo, C.; Gonzalez-Diaz, D.; Biagi, SF.; Oliveira, CAB.; Henriques, CAO.; Escada, J.; Monrabal, F.... (2018). Microscopic simulation of xenon-based optical TPCs in the presence of molecular additives. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 877:157-172. https://doi.org/10.1016/j.nima.2017.08.049S15717287

Uncovering Frustrations: A Qualitative Needs Assessment of Academic General Internists as Geriatric Care Providers and Teachers

BACKGROUND: General internists commonly provide medical care for older adults and geriatric education to trainees, but lack the necessary knowledge and skills to fulfill these tasks. OBJECTIVE: Assess the geriatric training needs of academic general internists in 3 hospital systems in Portland, OR. DESIGN: Ten focus groups and 1 semi-structured interview. Interview transcripts were analyzed using thematic analysis, a well-recognized qualitative technique. PARTICIPANTS: A convenience sample of 22 academic general internists and 8 geriatricians from 3 different teaching hospitals. MEASUREMENTS: We elicited stories of frustration and success in caring for elderly patients and in teaching about their care. We asked geriatricians to recount their experiences as consultants to general internists and to comment on the training of Internists in geriatrics. RESULTS: In addition to deficits in their medical knowledge and skills, our Internists reported frustration with the process of delivering care to older adults. In particular, they felt ill prepared to guide care transitions for patients, use multidisciplinary teams effectively, and were frustrated with health care system issues. Additionally, general internists' approach to medical care, which largely relies on the medical model, is different from that of geriatricians, which focuses more on social and functional issues. CONCLUSIONS: Although our findings may not be broadly representative, improving our general internists' abilities to care for the elderly and to teach learners how to do the same should address deficits in medical knowledge and skills, barriers to the processes of delivering care, and philosophical approaches to care. Prioritizing and quantifying these needs and measuring the effectiveness of curricula to address them are areas for future research