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AbstractThe correction of artifacts in Magnetic Resonance Imaging (MRI) is crucial due to physiological phenomena and technical issues affecting diagnostic quality. Reverting from corrupted to artifact-free images is a complex task. Deep Learning (DL) models have been employed to preserve data characteristics and to identify and correct those artifacts. We proposeMAC, a novel DL-based solution to correct artifacts in multi-contrast brain MRI scans.MACoffers two models: the simulation and the correction models. The simulation model introduces perturbations similar to those occurring in an exam while preserving the original image as ground truth; this is required as publicly available datasets rarely have motion-corrupted images. It allows the addition of three types of artifacts with different degrees of severity. The DL-based correction model adds a fourth contrast to state-of-the-art solutions while improving the overall performance of the models.MACachieved the highest results in the FLAIR contrast, with a Structural Similarity Index Measure (SSIM) of 0.9803 and a Normalized Mutual Information (NMI) of 0.8030. Moreover, the model reduced training time by 63% compared to its predecessor.MACmodel can correct large volumes of images faster and adapt to different levels of artifact severity than current state-ofthe-art models, allowing for better diagnosis.

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Lung cancer represents a significant health concern necessitating diligent monitoring of individuals at risk. While the detection of pulmonary nodules warrants clinical attention, not all cases require immediate surgical intervention, often calling for a strategic approach to follow-up decisions. The Lung-RADS guideline serves as a cornerstone in clinical practice, furnishing structured recommendations based on various nodule characteristics, including size, calcification, and texture, outlined within established reference tables. However, the reliance on labor-intensive manual measurements underscores the potential advantages of integrating decision support systems into this process. Herein, we propose a feature-based methodology aimed at enhancing clinical decision-making by automating the assessment of nodules in computed tomography scans. Leveraging algorithms tailored for nodule calcification, texture analysis, and segmentation, our approach facilitates the automated classification of follow-up recommendations aligned with Lung-RADS criteria. Comparison with a previously reported end-to-end image-based classification method revealed competitive performance, with the feature-based approach achieving an accuracy of 0.701 +/- 0.026, while the end-to-end method attained 0.727 +/- 0.020. The inherent explainability of the feature-based approach offers distinct advantages, allowing clinicians to scrutinize and modify individual features to address disagreements or rectify inaccuracies, thereby tailoring follow-up recommendations to patient profiles.

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PurposeRefugee entrepreneurship is increasingly viewed as a silver bullet being able to promote host countries' economic performance and enable the successful integration of refugees. This study aims to identify the main determinants of entrepreneurial intentions of refugees in Portugal based on the underdog theory.Design/methodology/approachIn this study, the authors scrutinize the entrepreneurial intentions of refugees living in Portugal, an overlooked context, using a purpose-built inquiry responded to by 41 refugees and resorting to fuzzy-set qualitative comparative analysis, complemented with partial least squares path modeling.FindingsSome important results are worth highlighting: the entrepreneurial intentions of the respondent sample of refugees living in Portugal are high; the theoretical arguments underlying the underdog or challenge-based entrepreneurship theory are validated in the context of the respondent sample; and psychological related factors associated with the more standard explanations of entrepreneurial intentions constitute necessary conditions for high refugee entrepreneurial intentions.Originality/valueEntrepreneurial intentions to launch a business have been discussed in the entrepreneurship literature vastly, but it has not yet received much attention when focusing on refugees, often identified as underdogs (potential) entrepreneurs. This study contributes to the literature by testing the challenge-based entrepreneurship theory to identify the primary factors influencing refugee entrepreneurial intentions.