On Retrospective k-space Subsampling schemes For Deep MRI Reconstruction

Purpose: Acquiring fully-sampled MRI $k$-space data is time-consuming, and collecting accelerated data can reduce the acquisition time. Employing 2D Cartesian-rectilinear subsampling schemes is a conventional approach for accelerated acquisitions; however, this often results in imprecise reconstructions, even with the use of Deep Learning (DL), especially at high acceleration factors. Non-rectilinear or non-Cartesian trajectories can be implemented in MRI scanners as alternative subsampling options. This work investigates the impact of the $k$-space subsampling scheme on the quality of reconstructed accelerated MRI measurements produced by trained DL models. Methods: The Recurrent Variational Network (RecurrentVarNet) was used as the DL-based MRI-reconstruction architecture. Cartesian, fully-sampled multi-coil $k$-space measurements from three datasets were retrospectively subsampled with different accelerations using eight distinct subsampling schemes: four Cartesian-rectilinear, two Cartesian non-rectilinear, and two non-Cartesian. Experiments were conducted in two frameworks: scheme-specific, where a distinct model was trained and evaluated for each dataset-subsampling scheme pair, and multi-scheme, where for each dataset a single model was trained on data randomly subsampled by any of the eight schemes and evaluated on data subsampled by all schemes. Results: In both frameworks, RecurrentVarNets trained and evaluated on non-rectilinearly subsampled data demonstrated superior performance, particularly for high accelerations. In the multi-scheme setting, reconstruction performance on rectilinearly subsampled data improved when compared to the scheme-specific experiments. Conclusion: Our findings demonstrate the potential for using DL-based methods, trained on non-rectilinearly subsampled measurements, to optimize scan time and image quality.Comment: 24 pages, 12 figures, 5 table

Old age pensions and retirement in Spain

In this paper we analyze the influence that incentives play in the timing of the transition to retirement in Spain. We use the Continuous Sample of Working Histories 2006 (CSWH 'Muestra Continua de Vidas Laborales', in Spanish) to construct incentive measures from the Social Security provisions in relation to retiring at old age. We analyse the role played by such incentives and other socio-economic variables on the retirement hazard of men aged between 60 and 70, using a duration model to carry out a dynamic analysis. We assess the effects of the pension system reform that took place in 2002, which set stricter conditions to access an old pension. The results show that both the pension wealth and the substitution effects play a significant role in retirement decisions, but that, after the reform, the latter effects become less important

Conceptual multidimensionality in medical translation

En la comunicación científica, la selección de un término suele conllevar que se resalte una característica más o menos prototípica del concepto, una intención en el emisor o una dimensión del concepto. En la comunicación interlingüística y a menudo intercultural que entraña la traducción en ámbitos biosanitarios, prestar atención a estas dimensiones es importante para que el producto cumpla con las expectativas del receptor meta. El proyecto VARIMED (FFI2011-23120) se plantea estudiar la motivación cognitiva que existe en la selección léxica de distintas formas para dar nombre a conceptos médicos de cara a establecer la presencia de una sistematización en dicha motivación. En este artículo abordaremos estas características desde la descripción de los objetivos del proyecto y la metodología seguida en el mismo.In scientific communication, selecting a term often means highlighting a more or less prototypical feature of a concept, revealing an intention from the issuer or even a certain facet of the concept. It is particularly important to pay due attention to such facets or conceptual dimensions in the process of interlinguistic and intercultural mediation implied by biomedical translation so that the text matches the target audience expectations. The project VariMed (FFI2011-23120) attempts to study the cognitive motivation underlying the lexical selection of different denominative variants for medical concepts in order to find patterns which explain the causes or motivation for terminological variation. This paper deals with the communicative and cognitive motivation from the point of view of the objectives and the methodology followed in VariMed

JSSL: Joint Supervised and Self-supervised Learning for MRI Reconstruction

Magnetic Resonance Imaging represents an important diagnostic modality; however, its inherently slow acquisition process poses challenges in obtaining fully sampled k-space data under motion in clinical scenarios such as abdominal, cardiac, and prostate imaging. In the absence of fully sampled acquisitions, which can serve as ground truth data, training deep learning algorithms in a supervised manner to predict the underlying ground truth image becomes an impossible task. To address this limitation, self-supervised methods have emerged as a viable alternative, leveraging available subsampled k-space data to train deep learning networks for MRI reconstruction. Nevertheless, these self-supervised approaches often fall short when compared to supervised methodologies. In this paper, we introduce JSSL (Joint Supervised and Self-supervised Learning), a novel training approach for deep learning-based MRI reconstruction algorithms aimed at enhancing reconstruction quality in scenarios where target dataset(s) containing fully sampled k-space measurements are unavailable. Our proposed method operates by simultaneously training a model in a self-supervised learning setting, using subsampled data from the target dataset(s), and in a supervised learning manner, utilizing data from other datasets, referred to as proxy datasets, where fully sampled k-space data is accessible. To demonstrate the efficacy of JSSL, we utilized subsampled prostate parallel MRI measurements as the target dataset, while employing fully sampled brain and knee k-space acquisitions as proxy datasets. Our results showcase a substantial improvement over conventional self-supervised training methods, thereby underscoring the effectiveness of our joint approach. We provide a theoretical motivation for JSSL and establish a practical "rule-of-thumb" for selecting the most appropriate training approach for deep MRI reconstruction.Comment: 26 pages, 11 figures, 6 table

Sensitivity to anti-Fas is independent of increased cathepsin D activity and adrenodoxin reductase expression occurring in NOS-3 overexpressing HepG2 cells

© 2015 Elsevier B.V. Stable overexpression of endothelial nitric oxide synthase (NOS-3) in HepG2 cells (4TO-NOS) leads to increased nitro-oxidative stress and upregulation of the cell death mediators p53 and Fas. Thus, NOS-3 overexpression has been suggested as a useful antiproliferative mechanism in hepatocarcinoma cells. We aimed to identify the underlying mechanism of cell death induced by NOS-3 overexpression at basal conditions and with anti-Fas treatment. The intracellular localization of NOS-3, the nitro-oxidative stress and the mitochondrial activity were analysed. In addition, the protein expression profile in 4TO-NOS was screened for differentially expressed proteins potentially involved in the induction of apoptosis. NOS-3 localization in the mitochondrial outer membrane was not associated with changes in the respiratory cellular capacity, but was related to the mitochondrial biogenesis increase and with a higher protein expression of mitochondrial complex IV. Nitro-oxidative stress and cell death in NOS-3 overexpressing cells occurred with the expression increase of pro-apoptotic genes and a higher expression/activity of the enzymes adrenodoxin reductase mitochondrial (AR) and cathepsin D (CatD). CatD overexpression in 4TO-NOS was related to the apoptosis induction independently of its catalytic activity. In addition, CatD activity inhibition by pepstatin A was not effective in blocking apoptosis induced by anti-Fas. In summary, NOS-3 overexpression resulted in an increased sensitivity to anti-Fas induced cell death, independently of AR expression and CatD activity.Instituto de Salud Carlos III (FIS 09/00185). G. Ferrín was supported by the Networked Biomedical Research Center Hepatic and Digestive Diseases (CIBEREHD)Peer Reviewe

Voluntary wheel running preserves lumbar perineuronal nets, enhances motor functions and prevents hyperreflexia after spinal cord injury

Altres ajuts: Fundació La Marató-TV3 (TV3-201736-30-31)Perineuronal nets (PNN) are a promising candidate to harness neural plasticity since their activity-dependent modulation allows to either stabilize the circuits or increase plasticity. Modulation of plasticity is the basis of rehabilitation strategies to reduce maladaptive plasticity after spinal cord injuries (SCI). Hence, it is important to understand how spinal PNN are affected after SCI and rehabilitation. Thus, this work aims to describe functional and PNN changes after thoracic SCI in mice, followed by different activity-dependent therapies: enriched environment, voluntary wheel and forced treadmill running. We found that the contusion provoked thermal hyperalgesia, hyperreflexia and locomotor impairment as measured by thermal plantar test, H wave recordings and the BMS score of locomotion, respectively. In the spinal cord, SCI reduced PNN density around lumbar motoneurons. In contrast, activity-based therapies increased motoneuron activity and reversed PNN decrease. The voluntary wheel group showed full preservation of PNN which also correlated with reduced hyperreflexia and better locomotor recovery. Furthermore, both voluntary wheel and treadmill running reduced hyperalgesia, but this finding was independent of lumbar PNN levels. In the brainstem sensory nuclei, SCI did not modify PNN whereas some activity-based therapies reduced them. The results of the present study highlight the impact of SCI on decreasing PNN at caudal segments of the spinal cord and the potential of physical activity-based therapies to reverse PNN disaggregation and to improve functional recovery. As modulating plasticity is crucial for restoring damaged neural circuits, regulating PNN by activity is an encouraging target to improve the outcome after injury

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Uncertainty-aware multiple-instance learning for reliable classification:Application to optical coherence tomography

Deep learning classification models for medical image analysis often perform well on data from scanners that were used to acquire the training data. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability. We aimed to enhance the reliability of deep learning classification models using a novel method called Uncertainty-Based Instance eXclusion (UBIX). UBIX is an inference-time module that can be employed in multiple-instance learning (MIL) settings. MIL is a paradigm in which instances (generally crops or slices) of a bag (generally an image) contribute towards a bag-level output. Instead of assuming equal contribution of all instances to the bag-level output, UBIX detects instances corrupted due to local artifacts on-the-fly using uncertainty estimation, reducing or fully ignoring their contributions before MIL pooling. In our experiments, instances are 2D slices and bags are volumetric images, but alternative definitions are also possible. Although UBIX is generally applicable to diverse classification tasks, we focused on the staging of age-related macular degeneration in optical coherence tomography. Our models were trained on data from a single scanner and tested on external datasets from different vendors, which included vendor-specific artifacts. UBIX showed reliable behavior, with a slight decrease in performance (a decrease of the quadratic weighted kappa (κw) from 0.861 to 0.708), when applied to images from different vendors containing artifacts; while a state-of-the-art 3D neural network without UBIX suffered from a significant detriment of performance (κw from 0.852 to 0.084) on the same test set. We showed that instances with unseen artifacts can be identified with OOD detection. UBIX can reduce their contribution to the bag-level predictions, improving reliability without retraining on new data. This potentially increases the applicability of artificial intelligence models to data from other scanners than the ones for which they were developed. The source code for UBIX, including trained model weights, is publicly available through https://github.com/qurAI-amsterdam/ubix-for-reliable-classification.</p