Evaluation of automatic image quality assessment in chest CT – A human cadaver study

PurposeThe evaluation of clinical image quality (IQ) is important to optimize CT protocols and to keep patient doses as low as reasonably achievable. Considering the significant amount of effort needed for human observer studies, automatic IQ tools are a promising alternative. The purpose of this study was to evaluate automatic IQ assessment in chest CT using Thiel embalmed cadavers.MethodsChest CT’s of Thiel embalmed cadavers were acquired at different exposures. Clinical IQ was determined by performing a visual grading analysis. Physical-technical IQ (noise, contrast-to-noise and contrast-detail) was assessed in a Catphan phantom. Soft and sharp reconstructions were made with filtered back projection and two strengths of iterative reconstruction. In addition to the classical IQ metrics, an automatic algorithm was used to calculate image quality scores (IQs). To be able to compare datasets reconstructed with different kernels, the IQs values were normalized.ResultsGood correlations were found between IQs and the measured physical-technical image quality: noise (ρ = −1.00), contrast-to-noise (ρ = 1.00) and contrast-detail (ρ = 0.96). The correlation coefficients between IQs and the observed clinical image quality of soft and sharp reconstructions were 0.88 and 0.93, respectively.ConclusionsThe automatic scoring algorithm is a promising tool for the evaluation of thoracic CT scans in daily clinical practice. It allows monitoring of the image quality of a chest protocol over time, without human intervention. Different reconstruction kernels can be compared after normalization of the IQs.     

A cloud-fog software architecture for dental CBCT dose monitoring using the DICOM structured report: Automated establishment of DRL

PurposeThe diagnostic reference level (DRL) has been established to optimize the diagnostic methods and reduce radiation dose during radiographic examinations. The aim of this study was to present a completely new solution based on Cloud-Fog software architecture for automatic establishment of the DRL values during dental cone-beam computed tomography (CBCT) according to digital imaging and communications in medicine (DICOM) structured reports.Methods and MaterialsA Cloud-Fog software architecture was used for automatic data handling. This architecture used the DICOM structured reports as a source for extracting the required information by fog devices in the imaging center. These devices transferred the derived information to the cloud server. The cloud server calculated the value of indication-based DRL in dental CBCT imaging based upon the parameters and adequate quantities of the absorbed dose. The feedback of DRL value was continuously announced to the imaging centers in 6 phases. In each phase, the level of the dose was optimized in imaging centers.ResultsThe DRL value was established for 5-specific indications, including third molar teeth (511 mGy.cm²), implant (719 mGy.cm²), form and position anomalies of the tooth (408 mGy.cm²), dentoalveolar pathologies (612 mGy.cm²), and endodontics (632 mGy.cm²). The determination of the DRL value in each phase revealed a downward trend until stabilization.ConclusionThe new solution presented in this study makes it possible to calculate and update the DRL value nationally and automatically among all centers. Also, the results showed that this approach is successful in establishing stabilized DRL values.     

Radiomics classifier to quantify automatic segmentation quality of cardiac sub-structures for radiotherapy treatment planning

PurposeA radiomics features classifier was implemented to evaluate segmentation quality of heart structures. A robust feature set sensitive to incorrect contouring would provide an ideal quantitative index to drive autocontouring optimization.MethodsTwenty-five cardiac sub-structures were contoured as regions of interest in 36 CTs. Radiomic features were extracted from manually-contoured (MC) and Hierarchical-Clustering automatic-contouring (AC) structures. A robust feature-set was identified from correctly contoured CT datasets. Features variation was analyzed over a MC/AC dataset. A supervised-learning approach was used to train an Artificial-Intelligence (AI) classifier; incorrect contouring cases were generated from the gold-standard MC datasets with translations, expansions and contractions. ROC curves and confusion matrices were used to evaluate the AI-classifier performance.ResultsTwenty radiomics features, were found to be robust across structures, showing a good/excellent intra-class correlation coefficient (ICC) index comparing MC/AC. A significant correlation was obtained with quantitative indexes (Dice-Index, Hausdorff-distance). The trained AI-classifier detected correct contours (CC) and not correct contours (NCC) with an accuracy of 82.6% and AUC of 0.91. True positive rate (TPR) was 85.1% and 81.3% for CC and NCC. Detection of NCC at this point of the development still depended strongly on degree of contouring imperfection.ConclusionsA set of radiomics features, robust on “gold-standard” contour and sensitive to incorrect contouring was identified and implemented in an AI-workflow to quantify segmentation accuracy. This workflow permits an automatic assessment of segmentation quality and may accelerate expansion of an existing autocontouring atlas database as well as improve dosimetric analyses of large treatment plan databases.     

Comparative study of the free amino acid compositions and contents in three different botanical origins of Coptis herb

Free amino acids are important bioactive ingredients in herbs and herbal products. Coptis herbs contain a variety of free amino acids; however, studies have not yet analyzed the relationship between free amino acids and species of Coptis herbs. In the current study, the contents of 20 free amino acids in Coptis chinensis Franch., Coptis teeta Wall., and Coptis deltoidea C.Y. Cheng et Hsiao were determined using an automatic amino acid analyzer to evaluate the differences between the three species. We found that the major amino acids (Asn, Arg, and γ-aminobutyric acid (GABA)) were significantly more in C. chinensis than in the other two species. In addition, Asp content was significantly more in C. deltoidea. The study concluded that Asn can be used to identify C. deltoidea, C. chinensis, and C. teeta, and Gln and Arg can be used to distinguish C. teeta from C. chinensis and C. deltoidea. These findings suggested that free amino acids as active ingredients can be used to identify Coptis herb species.     

Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning

PurposeTo train and evaluate a very deep dilated residual network (DD-ResNet) for fast and consistent auto-segmentation of the clinical target volume (CTV) for breast cancer (BC) radiotherapy with big data.MethodsDD-ResNet was an end-to-end model enabling fast training and testing. We used big data comprising 800 patients who underwent breast-conserving therapy for evaluation. The CTV were validated by experienced radiation oncologists. We performed a fivefold cross-validation to test the performance of the model. The segmentation accuracy was quantified by the Dice similarity coefficient (DSC) and the Hausdorff distance (HD). The performance of the proposed model was evaluated against two different deep learning models: deep dilated convolutional neural network (DDCNN) and deep deconvolutional neural network (DDNN).ResultsMean DSC values of DD-ResNet (0.91 and 0.91) were higher than the other two networks (DDCNN: 0.85 and 0.85; DDNN: 0.88 and 0.87) for both right-sided and left-sided BC. It also has smaller mean HD values of 10.5 mm and 10.7 mm compared with DDCNN (15.1 mm and 15.6 mm) and DDNN (13.5 mm and 14.1 mm). Mean segmentation time was 4 s, 21 s and 15 s per patient with DDCNN, DDNN and DD-ResNet, respectively. The DD-ResNet was also superior with regard to results in the literature.ConclusionsThe proposed method could segment the CTV accurately with acceptable time consumption. It was invariant to the body size and shape of patients and could improve the consistency of target delineation and streamline radiotherapy workflows.     

Analysis of forearm rotational motion using biplane fluoroscopic intensity-based 2D–3D matching

Measuring three-dimensional (3D) forearm rotational motion is difficult. We aimed to develop and validate a new method for analyzing 3D forearm rotational motion. We proposed biplane fluoroscopic intensity-based 2D–3D matching, which employs automatic registration processing using the evolutionary optimization strategy. Biplane fluoroscopy was conducted for forearm rotation at 12.5 frames per second along with computed tomography (CT) at one static position. An arm phantom was embedded with eight stainless steel spheres (diameter, 1.5 mm), and forearm rotational motion measurements using the proposed method were compared with those using radiostereometric analysis, which is considered the ground truth. As for the time resolution analysis, we measured radiohumeral joint motion in a patient with posterolateral rotatory instability and compared the 2D–3D matching method with the simulated multiple CT method, which uses CTs at multiple positions and interpolates between the positions. Rotation errors of the radius and ulna between these two methods were 0.31 ± 0.35° and 0.32 ± 0.33°, respectively, translation errors were 0.43 ± 0.35 mm and 0.29 ± 0.25 mm, respectively. Although the 2D–3D method could detect joint dislocation, the multiple CT method could not detect quick motion during joint dislocation. The proposed method enabled high temporal- and spatial-resolution motion analyses with low radiation exposure. Moreover, it enabled the detection of a sudden motion, such as joint dislocation, and may contribute to 3D motion analysis, including joint dislocation, which currently cannot be analyzed using conventional methods.     

Automatic VMAT planning for post-operative prostate cancer cases using particle swarm optimization: A proof of concept study

ObjectiveTo investigate the potential of Particle Swarm Optimization (PSO) for fully automatic VMAT radiotherapy (RT) treatment planning.Material and MethodsIn PSO a solution space of planning constraints is searched for the best possible RT plan in an iterative, statistical method, optimizing a population of candidate solutions. To identify the best candidate solution and for final evaluation a plan quality score (PQS), based on dose volume histogram (DVH) parameters, was introduced.Automatic PSO-based RT planning was used for N = 10 postoperative prostate cancer cases, retrospectively taken from our clinical database, with a prescribed dose of EUD = 66 Gy in addition to two constraints for rectum and one for bladder. Resulting PSO-based plans were compared dosimetrically to manually generated VMAT plans.ResultsPSO successfully proposed treatment plans comparable to manually optimized ones in 9/10 cases. The median (range) PTV EUD was 65.4 Gy (64.7–66.0) for manual and 65.3 Gy (62.5–65.5) for PSO plans, respectively. However PSO plans achieved significantly lower doses in rectum D_2% 67.0 Gy (66.5–67.5) vs. 66.1 Gy (64.7–66.5, p = 0.016). All other evaluated parameters (PTV D_98% and D_2%, rectum V_40Gy and V_60Gy, bladder D_2% and V_60Gy) were comparable in both plans. Manual plans had lower PQS compared to PSO plans with −0.82 (−16.43–1.08) vs. 0.91 (−5.98–6.25).ConclusionPSO allows for fully automatic generation of VMAT plans with plan quality comparable to manually optimized plans. However, before clinical implementation further research is needed concerning further adaptation of PSO-specific parameters and the refinement of the PQS.     

A genome-wide association study of facial morphology identifies novel genetic loci in Han Chinese

The human face is a heritable surface with many complex sensory organs. In recent years, many genetic loci associated with facial features have been reported in different populations, yet there is a lack of studies on the Han Chinese population. Here, we report a genome-wide association study of 3 D normal human faces of 2,659 Han Chinese with autosegment phenotypes of facial morphology. We identify singlenucleotide polymorphisms(SNPs) encompassing four genomic regions showing significant associations with different facial regions, including SNPs in DENND1 B associated with the chin, SNPs among PISRT1 associated with eyes, SNPs between DCHS2 and SFRP2 associated with the nose, and SNPs in VPS13 B associated with the nose. We replicate 24 SNPs from previously reported genetic loci in different populations, whose candidate genes are DCHS2, SUPT3 H, HOXD1, SOX9, PAX3, and EDAR. These results provide a more comprehensive understanding of the genetic basis of variation in human facial morphology.     

Paint4Net: COBRA Toolbox extension for visualization of stoichiometric models of metabolism

A visual analysis of reconstructions and large stoichiometric models with elastic change of the visualization scope and representation methods becomes increasingly important due to the rapidly growing size and number of available reconstructions. The Paint4Net is a novel COBRA Toolbox extension for automatic generation of a hypergraph layout of defined scope with the steady state rates of reaction fluxes of stoichiometric models. Directionalities and fluxes of reactions are constantly represented in the visualization while detailed information about reaction (ID, name and synonyms, and formula) and metabolite (ID, name and synonyms, and charged formula) appears placing the cursor on the item of interest. Additionally Paint4Net functionality can be used to: (1) get lists of involved metabolites and dead end metabolites of the visualized part of the network, (2) exclude (filter) particular metabolites from representation, (3) find isolated parts of a network and (4) find running cycles when all the substrates are cut down. Layout pictures can be saved in various formats and easily distributed. The Paint4Net is open source software under the GPL v3 license. Relevant documentation and sample data is available at http://www.biosystems.lv/paint4net. The Paint4Net works on MATLAB starting from version of 2009.     

Totomatix: a novel automatic set-up to control diurnal, diel and long-term plant nitrate nutrition

Background

Stand-alone nutritional set-ups are useful tools to grow plants at defined nutrient availabilities and to measure nutrient uptake rates continuously, in particular that for nitrate. Their use is essential when the measurements are meant to cover long time periods. These complex systems have, however, important drawbacks, including poor long-term reliability and low precision at high nitrate concentration. This explains why the information dealing with diel dynamics of nitrate uptake rate is scarce and concerns mainly young plants grown at low nitrate concentration.

Scope

The novel system detailed in this paper has been developed to allow versatile use in growth rooms, greenhouses or open fields at nitrate concentrations ranging from a few micro- to several millimoles per litres. The system controls, at set frequencies, the solution nitrate concentration, pH and volumes. Nitrate concentration is measured by spectral deconvolution of UV spectra. The main advantages of the set-up are its low maintenance (weekly basis), an ability to diagnose interference or erroneous analyses and high precision of nitrate concentration measurements (0·025 % at 3 mm). The paper details the precision of diurnal nitrate uptake rate measurements, which reveals sensitivity to solution volume at low nitrate concentration, whereas at high concentration, it is mostly sensitive to the precision of volume estimates.

Conclusions

This novel set-up allows us to measure and characterize the dynamics of plant nitrate nutrition at high temporal resolution (minutes to hours) over long-term experiments (up to 1 year). It is reliable and also offers a novel method to regulate up to seven N treatments by adjusting the daily uptake of test plants relative to controls, in variable environments such as open fields and glasshouses.