Opodatkowanie wynagrodzenia syndyka, nadzorcy sądowego i zarządcy

Przedmiotem artykułu jest omówienie zasad opodatkowania wynagrodzenia oraz zwrotu wydatków otrzymywanego przez osoby pełniące funkcje w postępowaniu upadłościowym (syndyka, nadzorcę sądowego i zarządcę). W publikacji omówiono opodatkowanie tych świadczeń podatkami: dochodowym od osób fizycznych, dochodowym od osób prawnych oraz podatkiem od towarów i usług.Articles concerns taxation of remuneration granted to authorities conducting bankruptcy proceeding (e.g. trustee in bankruptcy, bankruptcy supervisor, bankruptcy administrator) in Poland. Publication discusses taxation of such income with personal income tax, corporate income tax as well as value added tax

Deconvolution of fMRI BOLD signal in time-domain using an exponential operator and Lasso optimization

International audienceMany techniques have been explored so far in the study of neural activations using the blood oxygenated level dependent (BOLD) signal. Among them, deconvolution methods have been developed in order to explore spontaneous brain activity when the brain is in resting-state. These techniques are powerful since they do not require a priori knowledge about timing and duration of activations [2]. In this work, we propose a regularized deconvolution technique which uses an exponential operator, whose shape and performance can be adjusted by tuning a parameter α, and the Least-Angle Regression (LARS) algorithm, by using the least absolute shrinkage and selection operator (LASSO) model

fMRI Deconvolution via Temporal Regularization using a LASSO model and the LARS algorithm

International audienceIn the context of functional MRI (fMRI), methods based on the deconvolution of the blood oxygenated level dependent (BOLD) signal have been developed to investigate the brain activity, without a need of a priori knowledge about activations occurrence [2]. In this work, we propose a novel temporal regularized deconvolution of the BOLD signal using the Least Absolute Shrinkage and Selection Operator (LASSO) model, solved by means of the Least-Angle Regression (LARS) algorithm. In this way, we were able to recover the underlying neurons activations and their dynamics

L1-Norm Regularized Deconvolution of Functional MRI BOLD Signal

International audienceDeconvolution methods are used to denoise the blood oxygen level-dependent (BOLD) response, the signal that forms the basis of functional MRI (fMRI). In this work we propose a novel approach based on a temporal regularized deconvolution of the BOLD fMRI signal with the least absolute shrinkage and selection operator (LASSO) model, solved using the angle regression algorithm (LARS). In this way we were able to recover the underlying neurons activations and their dynamic

Workshop on reconstruction schemes for magnetic resonance data: summary of findings and recommendations

[EN] The high fidelity reconstruction of compressed and low-resolution magnetic resonance (MR) data is essential for simultaneously improving patient care, accuracy in diagnosis and quality in clinical research. Sponsored by the Royal Society through the Newton Mobility Grant Scheme, we held a half-day workshop on reconstruction schemes for MR data on the 17 of August 2016 to discuss new ideas from related research fields that could be useful to overcome the shortcomings of the conventional reconstruction methods that have been evaluated up to date. Participants were 21 university students, computer scientists, image analysts, engineers and physicists from institutions from 6 different countries. The discussion evolved around exploring new avenues to achieve high resolution, high quality and fast acquisition of MR imaging. In this article, we summarise the topics covered throughout the workshop and make recommendations for ongoing and future works.The workshop was sponsored by the Royal Society through the Newton Mobility Grant NI150340 to E.O.-I. and M.C.V.H. M.C.V.H. is funded by Row Fogo Charitable Trust; R.O.R. is funded by the Ministry of Education, Research, Culture and Sports of Valencia (Spain) under the programme VALi+d 2015; E.O.-I. is funded by Bogazici University, and the research presented at the workshop was supported by TUBITAK Career Development Grant 112E036, EU Marie Curie IRG Grant FP7-PEOPLE-RG-2009 256528, Tubitak 1001 Research Grant 115S219, and Bogazici University BAP Grant 10844SUP; I.M. is funded by core funds from the University of Edinburgh, including the Scottish Funding Council; A.J.V.B. is funded by the Marie Sklodowska Curie scholarship which is part of the European Union's H2020 Framework Programme (H2020-MSCA-ITN-2014) under the grant agreement number 642685 MacSeNet; and V.G.O. and P.F. are privately funded.Ozturk-Isik, E.; Marshall, I.; Filipiak, P.; Benjamin, AJV.; Ones, VG.; Ortiz-Ramón, R.; Valdes Hernandez, MDC. (2017). Workshop on reconstruction schemes for magnetic resonance data: summary of findings and recommendations. Royal Society Open Science. 4(2):1-4. https://doi.org/10.1098/rsos.160731144

Spatio-Temporal dMRI Acquisition Design: Reducing the Number of Samples

International audienceSynopsis Acquisition time is a major limitation in recovering brain white matter microstructure with diffusion magnetic resonance imaging. Finding a sampling scheme that maximizes signal quality and satisfies given time constraints is NP-hard. Therefore, we propose a heuristic method based on genetic algorithm that finds sub-optimal solutions in reasonable time. Our diffusion model is defined in the qτ-space, so that it captures both spacial and temporal phenomena. The experiments on synthetic data and in-vivo diffusion images of the C57Bl6 wild-type mouse corpus callosum reveal superiority of our approach over random sampling and even distribution in the qτ-space. Introduction Brain white matter (WM) microstructure recovery with diffusion Magnetic Resonance Imaging (dMRI) requires lengthy acquisition which is unattainable in clinical practice. Dense scanning schemes studied by researchers [1-5] typically take few hours of imaging time, whereas human subjects can tolerate a little more than one hour [6, 7]. Nonetheless, recent in vivo studies of the WM microstructure [7-9] call for more fine-grained investigation of both space-and time-dependent diffusion. In this work, we aim at bridging the gap between growing demands on spatio-temporal (qτ) probing of dMRI signal [10] and acquisition time limitations. To this end, we propose an acquisition design that reduces the number of samples under adjustable quality loss. Most of the current acquisition schemes assume the fixed τ case, focusing on a dense sampling of the q-space instead [3-5]. However, a pronounced time-dependence in dMRI was recently reported by De Santis et al. [9], Burcaw et al. [11], and Novikov et al. [12]. Their results incline towards paying more attention to temporal phenomena in dMRI signal by incorporating multiple τ variants into acquisition schemes. Methods The main goal of our study is to find a qτ-indexed sampling scheme that best preserves the dMRI signal while satisfying given acquisition time limits [10,13]. We formulate the acquisition design task as an optimization problem. Furthermore, we want our approach to be applicable for real data. To this end, we discretize the spatio-temporal search space by performing a state-of-the-art dense pre-acquisition of dMRI signal. The problem thus boils down to selecting an optimal subset of Diffusion Weighted Images (DWIs), which is NP-hard [13]. Taking into account that the time complexity of our problem grows exponentially with the size of domain, such that global optima cannot be found deterministically within few hours or even few days, we apply a stochastic search engine instead. We use Standard Genetic Algorithm (SGA) [14] for this purpose due to its fast convergence rate, ability to avoid local optima, and the fact that it is based on the mathematically profound Markov Chain model [15]. Experiments For evaluation of our approach, we used both synthetic diffusion data and in vivo dMRI images of the C57Bl6 wild-type mouse. The dense pre-acquisition of signals covered 40 shells, each of which comprised 20 directions and one b 0-image, i.e. 40 × 20 = 800 DWIs plus 40 non-weighted images. We used combinations of 5 separation times Δ ∈ {10.8, 13.1, 15.4, 17.7, 20.0} [ms] and 8 gradient strengths G ∈ {50, 100, 150, 200, 250, 300, 350, 400} [mT/m]. The gradient duration δ = 5 ms remained constant throughout the experiments. We considered four variants of time limits expressed as budget sizes n max = {100, 200, 300, 400} out of 800 DWIs. We compared our method with two alternative sampling schemes. One of them, called random, used the uniform random distribution of qτ samples in the index space {1, …, N}. In the second one, referred to as even, we picked each i-th sample for i = ⌊kN / n max ⌋ and k = 1,. .. , n max. Discussion As Figure 1 shows, our method outperformed the other two in all analyzed cases, assuring lowest mean squared errors (MSEs) and standard deviations (STDs). We verified statistical significance of the results with the two-sample Student's t level α = 10 −

Spatio-Temporal dMRI Acquisition Design: Reducing the Number of qτq\tau Samples Through a Relaxed Probabilistic Model

International audienceAcquisition time is a major limitation in recovering brain white matter microstructure with diffusion Magnetic Resonance Imaging. Finding a sampling scheme that maximizes signal quality and satisfies given time constraints is NP-hard. We alleviate that by introducing a relaxed probabilistic model of the problem, for which sub-optimal solutions can be found effectively. Our model is defined in the $q\tau$ space, so that it captures both spacial and temporal phenomena. The experiments on synthetic data and in-vivo diffusion images of the C57Bl6 wild-type mice reveal superiority of our technique over random sampling and even distribution in the $q\tau$ space

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