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Purpose: To investigate the accuracy of Agatston scoring and potential for radiation dose reduction of a coronary artery calcium scoring (CACS) CT protocol at 100 kV with tin filtration (Sn100kV) and kV-independent iterative reconstruction, compared to standard 120 kV acquisitions., Materials and Methods: With IRB approval and in HIPAA compliance, 114 patients (61.8 ± 9.6 years; 66 men) underwent CACS using a standard 120 kV protocol and an additional Sn100kV CACS scan. The two datasets were reconstructed using a medium sharp convolution algorithm and in addition the Sn100kV scans were reconstructed iteratively based on a kV-independent algorithm. Agatston scores and radiation dose values were compared between the Sn100kV and the standard 120 kV protocol., Results: Median Agatston scores derived from the Sn100kV protocol with the kV-independent algorithm and the standard 120 kV were 21.4 (IQR, 0-173.8) and 24.7 (IQR, 0-171.1) respectively, with no significant differences (p=0.18). Agatston scores derived from the two different protocols had an excellent correlation (r = 0.99). The dose-length-product was 11.5 ± 4.1 mGy × cm using Sn100kV and 50.4 ± 24.9 mGy × cm using the standard 120 kV protocol (p < 0.01), resulting in a significantly lower (77%) effective dose at Sn100kV (0.16 ± 0.06 mSv vs. 0.71 ± 0.35 mSv, p < 0.01). Additionally, 99% of the patients were classified into the same risk category (0, 1-10, 11-100, 101-400, or >400) using the Sn100kV protocol., Conclusion: CACS at Sn100kV using the kV-independent iterative algorithm is feasible and provides high accuracy when compared to standard 120 kV scanning. Furthermore, radiation dose can be significantly reduced for this screening application in a priori healthy individuals., (Copyright © 2020 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)

Objectives: The objective of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for the fully automated per lobe segmentation and emphysema quantification (EQ) on chest-computed tomography as it compares to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) severity classification of chronic obstructive pulmonary disease (COPD) patients., Methods: Patients (n=137) who underwent chest-computed tomography acquisition and spirometry within 6 months were retrospectively included in this Institutional Review Board-approved and Health Insurance Portability and Accountability Act-compliant study. Patient-specific spirometry data, which included forced expiratory volume in 1 second, forced vital capacity, and the forced expiratory volume in 1 second/forced vital capacity ratio (Tiffeneau-Index), were used to assign patients to their respective GOLD stage I to IV. Lung lobe segmentation was carried out using AI-RAD Companion software prototype (Siemens Healthineers), a deep convolution image-to-image network and emphysema was quantified in each lung lobe to detect the low attenuation volume., Results: A strong correlation between the whole-lung-EQ and the GOLD stages was found (ρ=0.88, P<0.0001). The most significant correlation was noted in the left upper lobe (ρ=0.85, P<0.0001), and the weakest in the left lower lobe (ρ=0.72, P<0.0001) and right middle lobe (ρ=0.72, P<0.0001)., Conclusions: AI-based per lobe segmentation and its EQ demonstrate a very strong correlation with the GOLD severity stages of COPD patients. Furthermore, the low attenuation volume of the left upper lobe not only showed the strongest correlation to GOLD severity but was also able to most clearly distinguish mild and moderate forms of COPD. This is particularly relevant due to the fact that early disease processes often elude conventional pulmonary function diagnostics. Earlier detection of COPD is a crucial element for positively altering the course of disease progression through various therapeutic options.

The constantly increasing number of computed tomography (CT) examinations poses major challenges for radiologists. In this article, the additional benefits and potential of an artificial intelligence (AI) analysis platform for chest CT examinations in routine clinical practice will be examined. Specific application examples include AI-based, fully automatic lung segmentation with emphysema quantification, aortic measurements, detection of pulmonary nodules, and bone mineral density measurement. This contribution aims to appraise this AI-based application for value-added diagnosis during routine chest CT examinations and explore future development perspectives.

OBJECTIVE. The purpose of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for fully automated quantification of emphysema on chest CT compared with pulmonary function testing (spirometry). MATERIALS AND METHODS. A total of 141 patients (72 women, mean age ± SD of 66.46 ± 9.7 years [range, 23-86 years]; 69 men, mean age of 66.72 ± 11.4 years [range, 27-91 years]) who underwent both chest CT acquisition and spirometry within 6 months were retrospectively included. The spirometry-based Tiffeneau index (TI; calculated as the ratio of forced expiratory volume in the first second to forced vital capacity) was used to measure emphysema severity; a value less than 0.7 was considered to indicate airway obstruction. Segmentation of the lung based on two different reconstruction methods was carried out by using a deep convolution image-to-image network. This multilayer convolutional neural network was combined with multilevel feature chaining and depth monitoring. To discriminate the output of the network from ground truth, an adversarial network was used during training. Emphysema was quantified using spatial filtering and attenuation-based thresholds. Emphysema quantification and TI were compared using the Spearman correlation coefficient. RESULTS. The mean TI for all patients was 0.57 ± 0.13. The mean percentages of emphysema using reconstruction methods 1 and 2 were 9.96% ± 11.87% and 8.04% ± 10.32%, respectively. AI-based emphysema quantification showed very strong correlation with TI (reconstruction method 1, ρ = -0.86; reconstruction method 2, ρ = -0.85; both p < 0.0001), indicating that AI-based emphysema quantification meaningfully reflects clinical pulmonary physiology. CONCLUSION. AI-based, fully automated emphysema quantification shows good correlation with TI, potentially contributing to an image-based diagnosis and quantification of emphysema severity.

Purpose: The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data., Materials and Methods: Under an IRB waiver and in HIPAA compliance, a total of 194 patients who had undergone CCTA were retrospectively included. Two observers independently evaluated the image quality and recorded the presence of CAC in the right (RCA), the combination of left main and left anterior descending (LM-LAD), and left circumflex (LCx) coronary arteries. Noncontrast CACS scans were allowed to be used in cases of uncertainty. Heart and coronary artery centerline detection and labeling were automatically performed. Presence of CAC was assessed by a RNN-LSTM. The algorithm's overall and per-vessel sensitivity, specificity, and diagnostic accuracy were calculated., Results: CAC was absent in 84 and present in 110 patients. As regards CCTA, the median subjective image quality, signal-to-noise ratio, and contrast-to-noise ratio were 3.0, 13.0, and 11.4. A total of 565 vessels were evaluated. On a per-vessel basis, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 93.1% (confidence interval [CI], 84.3%-96.7%), 82.76% (CI, 74.6%-89.4%), and 86.7% (CI, 76.8%-87.9%), respectively, for the RCA, 93.1% (CI, 86.4%-97.7%), 95.5% (CI, 88.77%-98.75%), and 94.2% (CI. 90.2%-94.6%), respectively, for the LM-LAD, and 89.9% (CI, 80.2%-95.8%), 90.0% (CI, 83.2%-94.7%), and 89.9% (CI, 85.0%-94.1%), respectively, for the LCx. The overall sensitivity, specificity, and diagnostic accuracy were 92.1% (CI, 92.1%-95.2%), 88.9% (CI. 84.9%-92.1%), and 90.3% (CI, 88.0%-90.0%), respectively. When accounting for image quality, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 76.2%, 87.5%, and 82.2%, respectively, for poor-quality data sets and 93.3%, 89.2% and 90.9%, respectively, when data sets rated adequate or higher were combined., Conclusion: The proposed RNN-LSTM demonstrated high diagnostic accuracy for the detection of CAC from CCTA.

Cereals including wheat and barley are of primary importance to ensure food security for the 21st century. A combination of lab- and field-based approaches has led to a considerably improved understanding of the importance of organ and particularly of whole-plant (monocarpic) senescence for wheat and barley yield and quality. A delicate balance between senescence timing, grain nutrient content, nutrient-use efficiency, and yield needs to be considered to (further) improve cereal varieties for a given environment and end use. The recent characterization of the Gpc-1 (NAM-1) genes in wheat and barley demonstrates the interdependence of these traits. Lines or varieties with functional Gpc-1 genes demonstrate earlier senescence and enhanced grain protein and micronutrient content but, depending on the environment, somewhat reduced yields. A major effort is needed to dissect regulatory networks centred on additional wheat and barley transcription factors and signalling pathways influencing the senescence process. Similarly, while important molecular details of nutrient (particularly nitrogen) remobilization from senescing organs to developing grains have been identified, important knowledge gaps remain. The genes coding for the major proteases involved in senescence-associated plastidial protein degradation are largely unknown. Membrane transport proteins involved in the different transport steps occurring between senescing organ (such as leaf mesophyll) cells and protein bodies in the endosperm of developing grains remain to be identified or further characterized. Existing data suggest that an improved understanding of all these steps will reveal additional, important targets for continued cereal improvement., (© The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Leaf senescence is an important process in the developmental life of all plant species. Senescence efficiency influences important agricultural traits such as grain protein content and plant growth, which are often limited by nitrogen use. Little is known about the molecular mechanisms regulating this highly orchestrated process. To enhance our understanding of leaf senescence and its regulation, we have undertaken the structural and functional characterization of previously unknown proteins that are involved in the control of senescence in barley (Hordeum vulgare L.). Previous microarray analysis highlighted several barley genes whose transcripts are differentially expressed during senescence, including a specific gene which is greater than 40-fold up-regulated in the flag leaves of early- as compared to late-senescing near-isogenic barley lines at 14 and 21 days past flowering (anthesis). From inspection of its amino acid sequence, this gene is predicted to encode a glycine-rich RNA-binding protein herein referred to as HvGR-RBP1. HvGR-RBP1 has been expressed as a recombinant protein in Escherichia coli, and preliminary NMR data analysis has revealed that its glycine-rich C-terminal region [residues: 93-162] is structurally disordered whereas its N-terminal region [residues: 1-92] forms a well-folded domain. Herein, we report the complete (1)H, (13)C, and (15)N resonance assignments of backbone and sidechain atoms, and the secondary structural topology of the N-terminal RNA recognition motif (RRM) domain of HvGR-RBP1, as a first step to unraveling its structural and functional role in the regulation of barley leaf senescence.

In this work we test the hypothesis that yield of rice ( Oryza sativa L.) can be enhanced by increasing endosperm activity of ADP-glucose pyrophosphorylase (AGP), a key enzyme in starch biosynthesis. The potential for increases in yield exist because rice initiates more seeds than are taken to maturity and possesses excess photosynthetic capacity that could be utilized if there were more demand for assimilate. Following an approach already shown to be successful in wheat, experiments were designed to increase demand for assimilate by increasing the capacity for starch synthesis in endosperm. This was accomplished by transforming rice with a modified maize AGP large subunit sequence ( Sh2r6hs) under control of an endosperm-specific promoter. This altered subunit confers upon AGP decreased sensitivity to allosteric inhibition by inorganic phosphate (Pi) and enhanced heat stability, potentially leading to higher AGP activity in vivo. The Sh2r6hs transgene increased AGP activity in developing endosperm by 2.7-fold in the presence of Pi. Increases in AGP activity in transgenic seeds compared with controls were maximal between 10-15 days after anthesis. Starch content of individual seeds at harvest was not increased, but seed weight per plant and total plant biomass were each increased by more than 20%. Increased endosperm AGP activity thus stimulates setting of additional seeds and overall plant growth rather than increasing yield of seeds already set. Results demonstrate that deregulation of endosperm AGP increases overall plant sink strength, leading to larger, more productive plants in a manner similar to that in wheat having similar genetic modification.