Diagnostic performance of deep learning-based reconstruction algorithm in 3D MR neurography

OBJECTIVE The study aims to evaluate the diagnostic performance of deep learning-based reconstruction method (DLRecon) in 3D MR neurography for assessment of the brachial and lumbosacral plexus. MATERIALS AND METHODS Thirty-five exams (18 brachial and 17 lumbosacral plexus) of 34 patients undergoing routine clinical MR neurography at 1.5 T were retrospectively included (mean age: 49 ± 12 years, 15 female). Coronal 3D T2-weighted short tau inversion recovery fast spin echo with variable flip angle sequences covering plexial nerves on both sides were obtained as part of the standard protocol. In addition to standard-of-care (SOC) reconstruction, k-space was reconstructed with a 3D DLRecon algorithm. Two blinded readers evaluated images for image quality and diagnostic confidence in assessing nerves, muscles, and pathology using a 4-point scale. Additionally, signal-to-noise ratio (SNR) and contrast-to-noise ratios (CNR) between nerve, muscle, and fat were measured. For comparison of visual scoring result non-parametric paired sample Wilcoxon signed-rank testing and for quantitative analysis paired sample Student's t-testing was performed. RESULTS DLRecon scored significantly higher than SOC in all categories of image quality (p < 0.05) and diagnostic confidence (p < 0.05), including conspicuity of nerve branches and pathology. With regard to artifacts there was no significant difference between the reconstruction methods. Quantitatively, DLRecon achieved significantly higher CNR and SNR than SOC (p < 0.05). CONCLUSION DLRecon enhanced overall image quality, leading to improved conspicuity of nerve branches and pathology, and allowing for increased diagnostic confidence in evaluation of the brachial and lumbosacral plexus

First results of the SOAP project. Open access publishing in 2010

The SOAP (Study of Open Access Publishing) project has compiled data on the present offer for open access publishing in online peer-reviewed journals. Starting from the Directory of Open Access Journals, several sources of data are considered, including inspection of journal web site and direct inquiries within the publishing industry. Several results are derived and discussed, together with their correlations: the number of open access journals and articles; their subject area; the starting date of open access journals; the size and business models of open access publishers; the licensing models; the presence of an impact factor; the uptake of hybrid open access.Comment: Submitted to PLoS ON

Highlights from the SOAP project survey. What Scientists Think about Open Access Publishing

The SOAP (Study of Open Access Publishing) project has run a large-scale survey of the attitudes of researchers on, and the experiences with, open access publishing. Around forty thousands answers were collected across disciplines and around the world, showing an overwhelming support for the idea of open access, while highlighting funding and (perceived) quality as the main barriers to publishing in open access journals. This article serves as an introduction to the survey and presents this and other highlights from a preliminary analysis of the survey responses. To allow a maximal re-use of the information collected by this survey, the data are hereby released under a CC0 waiver, so to allow libraries, publishers, funding agencies and academics to further analyse risks and opportunities, drivers and barriers, in the transition to open access publishing.Comment: Data manual available at http://bit.ly/gI8nct Compressed CSV data file available at http://bit.ly/gSmm71 Alternative data formats: CSV http://bit.ly/ejuvKO XLS http://bit.ly/e6gE7o XLSX http://bit.ly/gTjyv

Improving workflow in prostate MRI: AI-based decision-making on biparametric or multiparametric MRI

OBJECTIVES To develop and validate an artificial intelligence algorithm to decide on the necessity of dynamic contrast-enhanced sequences (DCE) in prostate MRI. METHODS This study was approved by the institutional review board and requirement for study-specific informed consent was waived. A convolutional neural network (CNN) was developed on 300 prostate MRI examinations. Consensus of two expert readers on the necessity of DCE acted as reference standard. The CNN was validated in a separate cohort of 100 prostate MRI examinations from the same vendor and 31 examinations from a different vendor. Sensitivity/specificity were calculated using ROC curve analysis and results were compared to decisions made by a radiology technician. RESULTS The CNN reached a sensitivity of 94.4% and specificity of 68.8% (AUC: 0.88) for the necessity of DCE, correctly assigning 44%/34% of patients to a biparametric/multiparametric protocol. In 2% of all patients, the CNN incorrectly decided on omitting DCE. With a technician reaching a sensitivity of 63.9% and specificity of 89.1%, the use of the CNN would allow for an increase in sensitivity of 30.5%. The CNN achieved an AUC of 0.73 in a set of examinations from a different vendor. CONCLUSIONS The CNN would have correctly assigned 78% of patients to a biparametric or multiparametric protocol, with only 2% of all patients requiring re-examination to add DCE sequences. Integrating this CNN in clinical routine could render the requirement for on-table monitoring obsolete by performing contrast-enhanced MRI only when needed

Improving workflow in prostate MRI: AI-based decision-making on biparametric or multiparametric MRI

Objectives To develop and validate an artificial intelligence algorithm to decide on the necessity of dynamic contrast-enhanced sequences (DCE) in prostate MRI. Methods This study was approved by the institutional review board and requirement for study-specific informed consent was waived. A convolutional neural network (CNN) was developed on 300 prostate MRI examinations. Consensus of two expert readers on the necessity of DCE acted as reference standard. The CNN was validated in a separate cohort of 100 prostate MRI examinations from the same vendor and 31 examinations from a different vendor. Sensitivity/specificity were calculated using ROC curve analysis and results were compared to decisions made by a radiology technician. Results The CNN reached a sensitivity of 94.4% and specificity of 68.8% (AUC: 0.88) for the necessity of DCE, correctly assigning 44%/34% of patients to a biparametric/multiparametric protocol. In 2% of all patients, the CNN incorrectly decided on omitting DCE. With a technician reaching a sensitivity of 63.9% and specificity of 89.1%, the use of the CNN would allow for an increase in sensitivity of 30.5%. The CNN achieved an AUC of 0.73 in a set of examinations from a different vendor. Conclusions The CNN would have correctly assigned 78% of patients to a biparametric or multiparametric protocol, with only 2% of all patients requiring re-examination to add DCE sequences. Integrating this CNN in clinical routine could render the requirement for on-table monitoring obsolete by performing contrast-enhanced MRI only when needed.ISSN:1869-410

Starch Synthesis in Potato Tubers Is Regulated by Post-Translational Redox Modification of ADP-Glucose Pyrophosphorylase: A Novel Regulatory Mechanism Linking Starch Synthesis to the Sucrose Supply

Transcriptional and allosteric regulation of ADP-Glc pyrophosphorylase (AGPase) plays a major role in the regulation of starch synthesis. Analysis of the response after detachment of growing potato tubers from the mother plant revealed that this concept requires extension. Starch synthesis was inhibited within 24 h of tuber detachment, even though the catalytic subunit of AGPase (AGPB) and overall AGPase activity remained high, the substrates ATP and Glc-1-P increased, and the glycerate-3-phosphate/inorganic orthophosphate (the allosteric activator and inhibitor, respectively) ratio increased. This inhibition was abolished in transformants in which a bacterial AGPase replaced the potato AGPase. Measurements of the subcellular levels of each metabolite between Suc and starch established AGPase as the only step whose substrates increase and mass action ratio decreases after detachment of wild-type tubers. Separation of extracts on nonreducing SDS gels revealed that AGPB is present as a mixture of monomers and dimers in growing tubers and becomes dimerized completely in detached tubers. Dimerization led to inactivation of the enzyme as a result of a marked decrease of the substrate affinity and sensitivity to allosteric effectors. Dimerization could be reversed and AGPase reactivated in vitro by incubating extracts with DTT. Incubation of tuber slices with DTT or high Suc levels reduced dimerization, increased AGPase activation, and stimulated starch synthesis in vivo. In intact tubers, the Suc content correlated strongly with AGPase activation across a range of treatments, including tuber detachment, aging of the mother plant, heterologous overexpression of Suc phosphorylase, and antisense inhibition of endogenous AGPase activity. Furthermore, activation of AGPase resulted in a stimulation of starch synthesis and decreased levels of glycolytic intermediates

Open access journals – what publishers offer, what researchers want

The SOAP (Study of Open Access Publishing) project has analyzed the current supply and demand situation in the open access journal landscape. Starting from the Directory of Open Access Journals, several sources of data were considered, including journal websites and direct inquiries within the publishing industry to comprehensively map the present supply of online peer-reviewed OA journals. The demand for open access publishing is summarised, as assessed through a large-scale survey of researchers' opinions and attitudes. Some forty thousand answers were collected across disciplines and around the world, reflecting major support for the idea of open access, while highlighting drivers of and barriers to open access publishing

First results of the SOAP Project

* About the project * Highlights: Gold OA journals today * Results from the large-scale survey of researcher