Notes

[Research in Personalized Phantom Based on Three dimensional Producing for Radiotherapy Dose Verification].
The resultant systematic review reveals a change in the type of research carried out, the service design tools used and a shift towards service design from using the co-design tool to other methods. The paper highlights the gaps in the very limited amount of empirical work in the non-clinical healthcare space and accordingly a model is recommended.Diffuse carcinomatosis of the bone marrow (DCBM) is a rare clinical condition characterized by diffuse bone marrow involvement with hematological changes. This case study concerns a patient who presented with DCBM secondary to colon cancer with diffuse intravascular coagulation. This is a rare presentation of DCBM in colon cancer. The case study also elaborates on clinical features, pathogenesis, and therapy of this unique presentation.
Carotid cavernous fistulas (CCFs) are rare, usually follow head trauma or aneurysmal rupture. Recent treatment options include endovascular techniques such as flow diversion devices (FDDs).

To present our case treated with FDD application with transarterial cavernous-sinus coiling and present a systematic review on the use and effectiveness of FDDs in CCF treatment.

We present our case of CCF treatment with FDD. A search was also conducted in PubMed, EMBASE and Cochrane until November 2020. Reference lists were also cross-checked.

Including our case, thirty-eight patients were identified with a CCF that was treated with FDDs in sixteen studies. Twenty-two patients were females, nine were males and the rest unidentified. The mean age was 52,6 years (range 17-86, SD± 19.28). Thirty-six patients suffered from direct and two from indirect CCFs. Single FDD was used in four cases, single FDD with embolic materials in eleven cases, multiple overlapping FDDs were used in six cases and multiple overlapping FDDs with embolic materials were used in seventeen cases. Thirty-five patients (92,1%) had clinical improvement, immediate angiographic occlusion was seen in 44,7% of the cases, while long-term occlusion rate was 100% but with variable follow-up periods. One patient (2,6%) presented with a neurological deficit related to FDD deployment.

Targeted treatment of CCFs with single or overlapping FDDs with or without adjunct embolic agents offers a high success rate, both clinically and long-term angiographically compared to other endovascular methods alone. this website However, further research with multi-center prospective trials is warranted.
Targeted treatment of CCFs with single or overlapping FDDs with or without adjunct embolic agents offers a high success rate, both clinically and long-term angiographically compared to other endovascular methods alone. However, further research with multi-center prospective trials is warranted.
To assess the heterogeneity of transitions toward dependency in older adults and to explore the robustness of results to different operationalizations of dependency.

Using data from people aged 60 years and older from a national representative study in Uruguay (
,
= 5071), we fitted multinomial regressions adjusted by sociodemographic and health characteristics to model transitions into dependency and death. We used a harder operationalization with basic activities of daily living (Katz-dependency) and Comprehensive-dependency with basic, instrumental, and advanced activities.

Increasing age (RRR = 1.08, CI = [1.05; 1.12],
< .001) and having comorbidities (RRR = 2.16, CI = [1.31; 3.57],
= .003) increased the risk of transition from nondependent to dependent using Katz-dependency. Women with at least two chronic conditions have increased risk of Comprehensive-dependency (RRR = 1.79, CI = [1.15; 2.80],
= .010).

Inconsistencies in findings emerged when evaluating transitions into dependency with the different measures, which may have social care implications.
Inconsistencies in findings emerged when evaluating transitions into dependency with the different measures, which may have social care implications.
This study sought to explore perspectives of trans and gender diverse (TGD) people of ways to alleviate gender dysphoria in service provision and to develop a framework for application in health and other areas that can be used by researchers and service providers to design study protocols, assess organisations and enhance everyday practice in ways that are sensitive to TGD people's experiences.

Data from a national Australian survey on TGD people conducted in 2018-2019 (n = 340) were used to develop a framework for alleviating dysphoria. Participants were asked an open-ended question on ways that body discomfort could be minimised in clinical encounters. Inductive thematic analysis was used to develop themes true to participant sentiment, which formed the basis for the development of a framework.

The sample was overall young, with 60.6% aged 18-24, and a strong representation of gender diverse people (42.6%). The most important theme for participants was the context of the experience, which included thtant areas to address centre on the context of the immediate experience, which may be influenced through systems-level characteristics.Spectral Computed Tomography (CT) is an emerging technology that enables us to estimate the concentration of basis materials within a scanned object by exploiting different photon energy spectra. In this work, we aim at efficiently solving a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function using a randomized second order method. By approximating the Newton step using a sketching of the Hessian of the likelihood function, it is possible to reduce the complexity while retaining the complex prior structure given by the data-driven regularizer. We exploit a non-uniform block sub-sampling of the Hessian with inexact but efficient conjugate gradient updates that require only Jacobian-vector products for denoising term. Finally, we show numerical and experimental results for spectral CT materials decomposition. This article is part of the theme issue 'Synergistic tomographic image reconstruction part 1'.Coronary artery disease (CAD) is caused by the formation of plaques in the coronary arteries and is one of the most common cardiovascular diseases. NaF-PET can be used to assess plaque composition, which could be important for therapy planning. One of the main challenges of NaF-PET is cardiac and respiratory motion which can strongly impair diagnostic accuracy. In this study, we investigated the use of a synergistic image registration approach which combined motion-resolved MR and PET data to estimate cardiac and respiratory motion. This motion estimation could then be used to improve the NaF-PET image quality. The approach was evaluated with numerical simulations and in vivo scans of patients suffering from CAD. In numerical simulations, it was shown, that combining MR and PET information can improve the accuracy of motion estimation by more than 15%. For the in vivo scans, the synergistic image registration led to an improvement in uptake visualization. This is the first study to assess the benefit of combining MR and NaF-PET for cardiac and respiratory motion estimation. Further patient evaluation is required to fully evaluate the potential of this approach. This article is part of the theme issue 'Synergistic tomographic image reconstruction part 1'.Fast magnetic resonance imaging (MRI) is crucial for clinical applications that can alleviate motion artefacts and increase patient throughput. K-space undersampling is an obvious approach to accelerate MR acquisition. However, undersampling of k-space data can result in blurring and aliasing artefacts for the reconstructed images. Recently, several studies have been proposed to use deep learning-based data-driven models for MRI reconstruction and have obtained promising results. However, the comparison of these methods remains limited because the models have not been trained on the same datasets and the validation strategies may be different. The purpose of this work is to conduct a comparative study to investigate the generative adversarial network (GAN)-based models for MRI reconstruction. We reimplemented and benchmarked four widely used GAN-based architectures including DAGAN, ReconGAN, RefineGAN and KIGAN. These four frameworks were trained and tested on brain, knee and liver MRI images using twofold, fourfold and sixfold accelerations, respectively, with a random undersampling mask. Both quantitative evaluations and qualitative visualization have shown that the RefineGAN method has achieved superior performance in reconstruction with better accuracy and perceptual quality compared to other GAN-based methods. This article is part of the theme issue 'Synergistic tomographic image reconstruction part 1'.Imaging is omnipresent in modern society with imaging devices based on a zoo of physical principles, probing a specimen across different wavelengths, energies and time. Recent years have seen a change in the imaging landscape with more and more imaging devices combining that which previously was used separately. Motivated by these hardware developments, an ever increasing set of mathematical ideas is appearing regarding how data from different imaging modalities or channels can be synergistically combined in the image reconstruction process, exploiting structural and/or functional correlations between the multiple images. Here we review these developments, give pointers to important challenges and provide an outlook as to how the field may develop in the forthcoming years. This article is part of the theme issue 'Synergistic tomographic image reconstruction part 1'.This special issue focuses on synergistic tomographic image reconstruction in a range of contributions in multiple disciplines and various application areas. The topic of image reconstruction covers substantial inverse problems (Mathematics) which are tackled with various methods including statistical approaches (e.g. Bayesian methods, Monte Carlo) and computational approaches (e.g. machine learning, computational modelling, simulations). The issue is separated in two volumes. This volume focuses mainly on algorithms and methods. Some of the articles will demonstrate their utility on real-world challenges, either medical applications (e.g. cardiovascular diseases, proton therapy planning) or applications in material sciences (e.g. material decomposition and characterization). One of the desired outcomes of the special issue is to bring together different scientific communities which do not usually interact as they do not share the same platforms (such as journals and conferences). This article is part of the theme issue 'Synergistic tomographic image reconstruction part 1'.Abdominal aortic aneurysm (AAA) monitoring and risk of rupture is currently assumed to be correlated with the aneurysm diameter. Aneurysm growth, however, has been demonstrated to be unpredictable. Using PET to measure uptake of [18F]-NaF in calcified lesions of the abdominal aorta has been shown to be useful for identifying AAA and to predict its growth. The PET low spatial resolution, however, can affect the accuracy of the diagnosis. Advanced edge-preserving reconstruction algorithms can overcome this issue. The kernel method has been demonstrated to provide noise suppression while retaining emission and edge information. Nevertheless, these findings were obtained using simulations, phantoms and a limited amount of patient data. In this study, the authors aim to investigate the usefulness of the anatomically guided kernelized expectation maximization (KEM) and the hybrid KEM (HKEM) methods and to judge the statistical significance of the related improvements. Sixty-one datasets of patients with AAA and 11 from control patients were reconstructed with ordered subsets expectation maximization (OSEM), HKEM and KEM and the analysis was carried out using the target-to-blood-pool ratio, and a series of statistical tests.
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