Über die Schwierigkeit, an der Erfahrung zu scheitern

Menke C. Über die Schwierigkeit, an der Erfahrung zu scheitern. In: Ajouri P, Mellmann K, Rauen C, eds. Empirie in der Literaturwissenschaft. Poetogenesis. Vol 8. Münster: Mentis; 2013: 75

Conventional and Deep-Learning-Based Image Reconstructions of Undersampled K-Space Data of the Lumbar Spine Using Compressed Sensing in MRI: A Comparative Study on 20 Subjects

Compressed sensing accelerates magnetic resonance imaging (MRI) acquisition by undersampling of the k-space. Yet, excessive undersampling impairs image quality when using conventional reconstruction techniques. Deep-learning-based reconstruction methods might allow for stronger undersampling and thus faster MRI scans without loss of crucial image quality. We compared imaging approaches using parallel imaging (SENSE), a combination of parallel imaging and compressed sensing (COMPRESSED SENSE, CS), and a combination of CS and a deep-learning-based reconstruction (CS AI) on raw k-space data acquired at different undersampling factors. 3D T2-weighted images of the lumbar spine were obtained from 20 volunteers, including a 3D sequence (standard SENSE), as provided by the manufacturer, as well as accelerated 3D sequences (undersampling factors 4.5, 8, and 11) reconstructed with CS and CS AI. Subjective rating was performed using a 5-point Likert scale to evaluate anatomical structures and overall image impression. Objective rating was performed using apparent signal-to-noise and contrast-to-noise ratio (aSNR and aCNR) as well as root mean square error (RMSE) and structural-similarity index (SSIM). The CS AI 4.5 sequence was subjectively rated better than the standard in several categories and deep-learning-based reconstructions were subjectively rated better than conventional reconstructions in several categories for acceleration factors 8 and 11. In the objective rating, only aSNR of the bone showed a significant tendency towards better results of the deep-learning-based reconstructions. We conclude that CS in combination with deep-learning-based image reconstruction allows for stronger undersampling of k-space data without loss of image quality, and thus has potential for further scan time reduction

Conventional and Deep-Learning-Based Image Reconstructions of Undersampled K-Space Data of the Lumbar Spine Using Compressed Sensing in MRI: A Comparative Study on 20 Subjects

Compressed sensing accelerates magnetic resonance imaging (MRI) acquisition by undersampling of the k-space. Yet, excessive undersampling impairs image quality when using conventional reconstruction techniques. Deep-learning-based reconstruction methods might allow for stronger undersampling and thus faster MRI scans without loss of crucial image quality. We compared imaging approaches using parallel imaging (SENSE), a combination of parallel imaging and compressed sensing (COMPRESSED SENSE, CS), and a combination of CS and a deep-learning-based reconstruction (CS AI) on raw k-space data acquired at different undersampling factors. 3D T2-weighted images of the lumbar spine were obtained from 20 volunteers, including a 3D sequence (standard SENSE), as provided by the manufacturer, as well as accelerated 3D sequences (undersampling factors 4.5, 8, and 11) reconstructed with CS and CS AI. Subjective rating was performed using a 5-point Likert scale to evaluate anatomical structures and overall image impression. Objective rating was performed using apparent signal-to-noise and contrast-to-noise ratio (aSNR and aCNR) as well as root mean square error (RMSE) and structural-similarity index (SSIM). The CS AI 4.5 sequence was subjectively rated better than the standard in several categories and deep-learning-based reconstructions were subjectively rated better than conventional reconstructions in several categories for acceleration factors 8 and 11. In the objective rating, only aSNR of the bone showed a significant tendency towards better results of the deep-learning-based reconstructions. We conclude that CS in combination with deep-learning-based image reconstruction allows for stronger undersampling of k-space data without loss of image quality, and thus has potential for further scan time reduction

Reconstruction of 3D knee MRI using deep learning and compressed sensing: a validation study on healthy volunteers

Abstract Background To investigate the potential of combining compressed sensing (CS) and artificial intelligence (AI), in particular deep learning (DL), for accelerating three-dimensional (3D) magnetic resonance imaging (MRI) sequences of the knee. Methods Twenty healthy volunteers were examined using a 3-T scanner with a fat-saturated 3D proton density sequence with four different acceleration levels (10, 13, 15, and 17). All sequences were accelerated with CS and reconstructed using the conventional and a new DL-based algorithm (CS-AI). Subjective image quality was evaluated by two blinded readers using seven criteria on a 5-point-Likert-scale (overall impression, artifacts, delineation of the anterior cruciate ligament, posterior cruciate ligament, menisci, cartilage, and bone). Using mixed models, all CS-AI sequences were compared to the clinical standard (sense sequence with an acceleration factor of 2) and CS sequences with the same acceleration factor. Results 3D sequences reconstructed with CS-AI achieved significantly better values for subjective image quality compared to sequences reconstructed with CS with the same acceleration factor (p ≤ 0.001). The images reconstructed with CS-AI showed that tenfold acceleration may be feasible without significant loss of quality when compared to the reference sequence (p ≥ 0.999). Conclusions For 3-T 3D-MRI of the knee, a DL-based algorithm allowed for additional acceleration of acquisition times compared to the conventional approach. This study, however, is limited by its small sample size and inclusion of only healthy volunteers, indicating the need for further research with a more diverse and larger sample. Trial registration DRKS00024156. Relevance statement Using a DL-based algorithm, 54% faster image acquisition (178 s versus 384 s) for 3D-sequences may be possible for 3-T MRI of the knee. Key points • Combination of compressed sensing and DL improved image quality and allows for significant acceleration of 3D knee MRI. • DL-based algorithm achieved better subjective image quality than conventional compressed sensing. • For 3D knee MRI at 3 T, 54% faster image acquisition may be possible. Graphical Abstrac

Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers

<jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>To investigate the potential of combining compressed sensing (CS) and deep learning (DL) for accelerated two-dimensional (2D) and three-dimensional (3D) magnetic resonance imaging (MRI) of the shoulder.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>Twenty healthy volunteers were examined using at 3-T scanner with a fat-saturated, coronal, 2D proton density-weighted sequence with four acceleration levels (2.3, 4, 6, and 8) and a 3D sequence with three acceleration levels (8, 10, and 13), all accelerated with CS and reconstructed using the conventional algorithm and a new DL-based algorithm (CS-AI). Subjective image quality was evaluated by two blinded readers using 6 criteria on a 5-point Likert scale (overall impression, artifacts, and delineation of the subscapularis tendon, bone, acromioclavicular joint, and glenoid labrum). Objective image quality was measured by calculating signal-to-noise-ratio, contrast-to-noise-ratio, and a structural similarity index measure. All reconstructions were compared to the clinical standard (CS 2D acceleration factor 2.3; CS 3D acceleration factor 8). Additionally, subjective and objective image quality were compared between CS and CS-AI with the same acceleration levels.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>Both 2D and 3D sequences reconstructed with CS-AI achieved on average significantly better subjective and objective image quality compared to sequences reconstructed with CS with the same acceleration factor (<jats:italic>p</jats:italic> ≤ 0.011). Comparing CS-AI to the reference sequences showed that 4-fold acceleration for 2D sequences and 13-fold acceleration for 3D sequences without significant loss of quality (<jats:italic>p</jats:italic> ≥ 0.058).</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>For MRI of the shoulder at 3 T, a DL-based algorithm allowed additional acceleration of acquisition times compared to the conventional approach.</jats:p> </jats:sec><jats:sec> <jats:title>Relevance statement</jats:title> <jats:p>The combination of deep-learning and compressed sensing hold the potential for further scan time reduction in 2D and 3D imaging of the shoulder while providing overall better objective and subjective image quality compared to the conventional approach.</jats:p> </jats:sec><jats:sec> <jats:title>Trial registration</jats:title> <jats:p>DRKS00024156.</jats:p> </jats:sec><jats:sec> <jats:title>Key points</jats:title> <jats:p>• Combination of compressed sensing and deep learning improved image quality and allows for significant acceleration of shoulder MRI.</jats:p> <jats:p>• Deep learning-based algorithm achieved better subjective and objective image quality than conventional compressed sensing.</jats:p> <jats:p>• For shoulder MRI at 3 T, 40% faster image acquisition for 2D sequences and 38% faster image acquisition for 3D sequences may be possible.</jats:p> </jats:sec><jats:sec> <jats:title>Graphical Abstract</jats:title> </jats:sec&gt

Methylene Blue Treatment of Grafts During Cold Ischemia Time Reduces the Risk of Hepatitis C Virus Transmission.

Background: Although organ shortage is a rising problem, organs from hepatitis C virus (HCV) ribonucleic acid (RNA)-positive donors are not routinely transplanted in HCV-negative individuals. Because HCV only infects hepatocytes, other organs such as kidneys are merely contaminated with HCV via the blood. In this study, we established a protocol to reduce HCV virions during the cold ischemic time. Methods: Standard virological assays were used to investigate the effect of antivirals, including methylene blue (MB), in different preservation solutions. Kidneys from mini pigs were contaminated with Jc1 or HCV RNA-positive human serum. Afterwards, organs were flushed with MB. Hypothermic machine perfusion was used to optimize reduction of HCV. Results: Three different antivirals were investigated for their ability to inactivate HCV in vitro. Only MB completely inactivated HCV in the presence of all perfusion solutions. Hepatitis C virus-contaminated kidneys from mini pigs were treated with MB and hypothermic machine perfusion without any negative effect on the graft. Human liver-uPA-SCID mice did not establish HCV infection after inoculation with flow through from these kidneys. Conclusions: This proof-of-concept study is a first step to reduce transmission of infectious HCV particles in the transplant setting and might serve as a model for other relevant pathogens

Involvement of a volatile metabolite during phosphoramide mustard-induced ovotoxicity.

International audienceThe finite ovarian follicle reserve can be negatively impacted by exposure to chemicals including the anti-neoplastic agent, cyclophosphamide (CPA). CPA requires bioactivation to phosphoramide mustard (PM) to elicit its therapeutic effects however; in addition to being the tumor-targeting metabolite, PM is also ovotoxic. In addition, PM can break down to a cytotoxic, volatile metabolite, chloroethylaziridine (CEZ). The aim of this study was initially to characterize PM-induced ovotoxicity in growing follicles. Using PND4 Fisher 344 rats, ovaries were cultured for 4 days before being exposed once to PM (10 or 30 μM). Following eight additional days in culture, relative to control (1% DMSO), PM had no impact on primordial, small primary or large primary follicle number, but both PM concentrations induced secondary follicle depletion (P<0.05). Interestingly, a reduction in follicle number in the control-treated ovaries was observed. Thus, the involvement of a volatile, cytotoxic PM metabolite (VC) in PM-induced ovotoxicity was explored in cultured rat ovaries, with control ovaries physically separated from PM-treated ovaries during culture. Direct PM (60 μM) exposure destroyed all stage follicles after 4 days (P<0.05). VC from nearby wells depleted primordial follicles after 4 days (P<0.05), temporarily reduced secondary follicle number after 2 days, and did not impact other stage follicles at any other time point. VC was determined to spontaneously liberate from PM, which could contribute to degradation of PM during storage. Taken together, this study demonstrates that PM and VC are ovotoxicants, with different follicular targets, and that the VC may be a major player during PM-induced ovotoxicity observed in cancer survivors

The IL17A and IL17F loci have divergent histone modifications and are differentially regulated by prostaglandin E2 in Th17 cells

Prostaglandin E2 (PGE2), IL-23 and IL-1β are implicated in inflammatory bowel disease susceptibility, likely in part by modulating IL-17 producing CD4(+) T helper (Th17) cells. To better understand how these three mediators affect Th17 cell memory responses, we characterized the gene expression profiles of activated human peripheral CD4(+) effector memory T cells and sorted Th17 memory cells from healthy donors concurrent with IL17A mRNA induction mediated by PGE2 and/or IL-23 plus IL-1β. We discovered that PGE2 and IL-23 plus IL-1β differentially regulate Th17 cytokine expression and synergize to induce IL-17A, but not IL-17F. IL-23 plus IL-1β preferentially induce IL-17F expression. The addition of PGE2 to IL-23 plus IL-1β only enhances IL-17A expression as mediated by the PGE2 EP4 receptor, and promotes a switch from an IL-17F to an IL-17A predominant immune response. The human Th17 HuT-102 cell line was also found to constitutively express IL-17A, but not IL-17F. We went on to show that the IL17A and IL17F loci have divergent epigenetic architectures in unstimulated HuT-102 and primary Th17 cells and are poised for preferential expression of IL17A. We conclude that the chromatin for IL17A and IL17F are distinctly regulated, which may play an important role in mucosal health and disease