Original Scan Research Articles

Pancreas segmentation using a dual-input v-mesh network.

Accurate segmentation of the pancreas from abdomen scans is crucial for the diagnosis and treatment of pancreatic diseases. However, the pancreas is a small, soft and elastic abdominal organ with high anatomical variability and has a low tissue contrast in computed tomography (CT) scans, which makes segmentation tasks challenging. To address this challenge, we propose a dual-input v-mesh fully convolutional network (FCN) to segment the pancreas in abdominal CT images. Specifically, dual inputs, i.e., original CT scans and images processed by a contrast-specific graph-based visual saliency (GBVS) algorithm, are simultaneously sent to the network to improve the contrast of the pancreas and other soft tissues. To further enhance the ability to learn context information and extract distinct features, a v-mesh FCN with an attention mechanism is initially utilized. In addition, we propose a spatial transformation and fusion (SF) module to better capture the geometric information of the pancreas and facilitate feature map fusion. We compare the performance of our method with several baseline and state-of-the-art methods on the publicly available NIH dataset. The comparison results show that our proposed dual-input v-mesh FCN model outperforms previous methods in terms of the Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), average surface distance (ASD) and Hausdorff distance (HD). Moreover, ablation studies show that our proposed modules/structures are critical for effective pancreas segmentation.

Fast Fractal Coding of MRI Images using Deep Reinforcement Learning

This paper presents an algorithm based on Fractal theory by using Iterated Function Systems (IFS). An efficient and fast coding mechanism is proposed by exploiting the self similarity nature in the Brain MRI images. The proposed algorithm utilizes Deep Reinforcement Learning (DRL) technique to learn the transformations required to recreate the original image. We avail of the Adaptive Iterated Function System (AIFS) as the encoding scheme. The proposed algorithm is trained and customized to compress the Medical images, especially Magnetic Resonance Imaging (MRI). The algorithm is tested and evaluated by using the original MR head scan test images. It learns from an existing biomedical dataset viz The Internet Brain Segmentation Repository (IBSR) to predict the new local affine transformations. The empirical analysis shows that the proposed algorithm is at least 4 times faster than the competitive methods and the decoding quality is far distinct with a reduction in the bit rate.

Optimizing Thresholds of the Scan Statistic to Improve Its Worst Case Detection Performance in Sensor Detection Systems

This paper focuses on how to improve the detection performance of distributed sensor systems to detect an emitter using the scan statistic. Considering that the emitter location is often unknown, we adopt a conservative approach and focus on the detection performance under the worst case emitter location. To improve the worst case detection performance, we propose a modified scan statistic: while the original scan statistic considers a single threshold for detection, our modified scan statistic uses a separate threshold for each cluster. The main idea is to reduce the threshold of clusters serving areas with low detection while still satisfying the false alarm constraint by increasing the threshold of clusters serving areas already well-served by other clusters. To optimize the thresholds of each cluster, we use upper and lower bounds for the probabilities of false alarm and detection and use monomial approximations for the bounds to solve the optimization problem. Our results show that our modified scan statistic can improve the worst case detection performance in various scenarios.

Correction: DJ-1 interacts with RACK1 and protects neurons from oxidative-stress-induced apoptosis.

Biochem. J. (2014) 462(3): 489–497. https://doi.org/10.1042/BJ20140235In this article (Biochem. J. 2014, 462, 489–497), which appeared in the Sep 15, 2014 issue of Biochemical Journal (1), the loading control in Figure 2C was inadvertently duplicated from the IgG of Figure 2D during the assembly of the figure. The authors reviewed the original scans and found the correct Western blots of the loading control β-actin for Figure 2C. The corrected Figure 2C appears below.The correction does not affect in any way the article's conclusions as the results are fully consistent with those shown in the original figure. The authors apologize for these errors.

Correction.

The Plant JournalVolume 104, Issue 6 p. 1746-1746 CorrigendumFree Access Correction This article corrects the following: Arabidopsis IRT2 gene encodes a root-periphery iron transporter Grégory Vert, Jean-François Briat, Catherine Curie, Volume 26Issue 2The Plant Journal pages: 181-189 First Published online: December 23, 2001 First published: 21 December 2020 https://doi.org/10.1111/tpj.15103AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL In the article “Arabidopsis IRT2 gene encodes a root-periphery iron transporter” by Vert et al. (2001), the authors would like to correct figure 2 panel a) for which an error was introduced during preparation. The original figure 2 a) is composite and was made of a representative clone out of three independent clones that were tested for each construct. This has inadvertently led to a duplication of some of the drops. The original scans from two decades ago could not be recovered but we provide an image from a different experiment that yields the same conclusions. The results and figure legend are unaltered by this correction. The correction was approved by all authors who apologize for the error. Figure 2Open in figure viewerPowerPoint Iron transport properties of IRT2. (a) Complementation of the fet3fet4 mutant (DEY1453 strain) on synthetic medium supplemented with 10 or 200 µm Fe3+-citrate. REFERENCE Vert, G., Briat, J.-F. and Curie, C. (2001) Arabidopsis IRT2 gene encodes a root-periphery iron transporter. Plant J. 26, 181– 189. Volume104, Issue6December 2020Pages 1746-1746 FiguresReferencesRelatedInformation

Quantitative accuracy remains after shortening of dynamic [18F]flortaucipir PET protocol

AbstractBackgroundTau pathology can be assessed by [18F]flortaucipir positron emission tomography (PET). Dynamic PET protocols allow for accurate quantification of [18F]flortaucipir specific binding. However, dynamic acquisitions can be challenging or burdensome to patients given the long required scan duration of 130 min, especially in persons with cognitive impairment due to Alzheimer’s disease (AD). Therefore, the current study assessed the effect of shorter scan protocols for [18F]flortaucipir PET on its quantitative accuracy.MethodFor this study, two study samples were used: 10 Alzheimer disease (AD) patients and 10 healthy controls (HC) with arterial blood sampling, and 8 AD patients and 6 HCs with a test‐retest scan protocol but without arterial sampling. All subjects underwent a 130‐minute dynamic [18F]flortaucipir PET scan with a 20 min break after 60 minutes. First, the second part of the scan was shortened to 80‐120, 80‐110 and 80‐100 minutes p.i.. Second, the first part of the scan was shortened to 0‐50, 0‐40, and 0‐30 min p.i.. To fill the gap of missing time frames in the reference region (i.e. grey matter cerebellum) time activity curves, four different interpolation methods were tested: 2T4k_VB model using population‐based input‐function (2T4k_POP‐IP), cubic, linear and exponential interpolations. Regional BPND values estimated using SRTM and/or RPM and different scan protocols were compared to the respective values estimated using the original scan acquisition. In addition, test‐retest repeatability (TRT) of the RPM BPND values estimated using the optimal shortest scan duration was assessed.ResultRPM BPND values obtained using a shortened second part of the PET scan had negligible effect on the accuracy of parameter estimates (Table 1). The RPM BPND values using 0‐30/80‐100 min with 2T4k_POP‐IP interpolation had the best correlation with original DVR‐1, SRTM and RPM BPND (r2=0.95, slope=0.91; r2=0.97, slope=0.88; r2=0.99, slope=0.96). The TRT remained similar using the shortest scan duration (Figure 1).ConclusionThe current study demonstrated that accurate [18F]flortaucipir quantification can be obtained using a scan protocol of 0‐30/80‐100 min p.i.. With this reduction in total scan duration the scan will be less burdensome while remaining quantitatively accurate.

I/O efficient structural clustering and maintenance of clusters for large-scale graphs

In recent years, the size of graph data has increased significantly, but most existing graph clustering algorithms do not consider the case where the size of main memory is not sufficient to handle large amount of graph data. Exploring entire region of graph for clustering causes too many random disk accesses to use data that are not loaded into memory, resulting in excessive disk I/O and thrashing. To address this problem, we propose an I/O-efficient algorithm for structural clustering of a graph, called pm-SCAN. In the proposed method, if memory is insufficient, an input graph is partitioned into several subgraphs smaller than memory, and clustering is first performed for each subgraph. And then clusters from the subgraphs are merged based on connectivity between clusters so that global results can be obtained in the point of view of an original input graph. Not only does pm-SCAN produce scalable performance even for very large graphs, i.e., significant shortage of available memory, but also the result of pm-SCAN is the same as that of the original structural clustering algorithm SCAN. We also propose a cluster maintenance method for large-scale dynamic graphs that change over time. Instead of reclustering with a whole graph, only a small set of nodes whose structural connectivities are subject to change by a given update operation is first identified, and we access only those nodes in disk and update their clusters to reduce maintenance costs. This dynamic graph handling mechanism shows significant performance improvement compared to the existing method and the baseline that performs clustering from scratch.

Integration of Risk of Survival Measures Estimated From Pre- and Post-Treatment CT Scans Improves Stratification of Early Stage Non-Small Cell Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy

To predict overall survival of early-stage non-small cell lung cancer (ES-NSCLC), we developed a radiomic model to integrate risk of death estimates based on pre- and post- treatment CT scans in patients receiving stereotactic body radiation therapy (SBRT). We hypothesize this will allow us to distinguish patients with various oncologic outcomes. Two cohorts of ES-NSCLC patients treated with SBRT were studied: 100 patients with treatment planning CTs for training, and 60 patients with pre- and post-treatment CTs for testing. The patients had a median follow-up of 2.0 years. Although all patients were treated uniformly (a median dose of 50Gy in 4-5 fractions), they had different primary tumor outcomes. Each tumor was characterized with 680 radiomics features derived from both the original CT scans and wavelet decomposed data. A survival prediction model was built using random survival forests (RSF) to predict overall survival based on the training cohort with RSF parameters optimized with a 3-fold cross-validation strategy. The survival prediction model was then applied to pre- and post-treatment scans of the testing cohort. The pre-treatment risk of death measures were used to estimate the survival prediction performance measured with concordance-index (c-index), and k-means clustering with 2 clusters was applied to the pre-treatment risk of death measures to identify patients with high and low risks. Changes in risk of death measures from the pre-treatment to post-treatment scans were binarized as increased or decreased to characterize treatment effect. Finally, patients were stratified into 4 groups based on the pre-treatment risk of death measures (high vs. low) and change in risk (increased vs. decreased). All group differences were accessed with log rank tests. The RSF model achieved a c-index of 0.654. Significant difference (χ2 = 5.59, p = 0.018) was observed between the subgroups with high and low pre-treatment death risks. The subgroups with increased and decreased risks were significantly different in overall survival (χ2 = 4.25, p = 0.039). The integration of the pre-treatment risk and change in risk stratified the patients into 4 subgroups (high + increased, high + decreased; low + increased; low + decreased) with significant differences (χ2 = 14.44, p = 0.002). Integration of risk of survival measures estimated from pre- and post-treatment CT scans could help distinguish patients with good survival from those who respond less well to SBRT or develop early nodal or distant metastases. Our study may inform clinical decision to identify early stage NSCLC patients who will most benefit from adjuvant treatment after lung SBRT.

CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation

Background and objective:An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tedious and time-consuming procedure and this represents a high obstacle in clinical practice. In this paper, we propose a novel Convolutional Neural Network for nodule segmentation that combines a light and efficient architecture with innovative loss function and segmentation strategy. Methods:In contrast to most of the standard end-to-end architectures for nodule segmentation, our network learns the context of the nodules by producing two masks representing all the background and secondary-important elements in the Computed Tomography scan. The nodule is detected by subtracting the context from the original scan image. Additionally, we introduce an asymmetric loss function that automatically compensates for potential errors in the nodule annotations. We trained and tested our Neural Network on the public LIDC-IDRI database, compared it with the state of the art and run a pseudo-Turing test between four radiologists and the network. Results:The results proved that the behaviour of the algorithm is very near to the human performance and its segmentation masks are almost indistinguishable from the ones made by the radiologists. Our method clearly outperforms the state of the art on CT nodule segmentation in terms of F1 score and IoU of 3.3% and 4.7%, respectively. Conclusions: The main structure of the network ensures all the properties of the UNet architecture, while the Multi Convolutional Layers give a more accurate pattern recognition. The newly adopted solutions also increase the details on the border of the nodule, even under the noisiest conditions. This method can be applied now for single CT slice nodule segmentation and it represents a starting point for the future development of a fully automatic 3D segmentation software.

Dosimetric evaluation of image-guided radiation therapy for prostate cancer

The aim of this study was to investigate the dosimetric accuracy of imaged-guided radiation therapy for prostate patients using the in-room computed tomography (CT) target localization technique. A Siemens CT-on-rails system was used for patient setup and target localization for intensity-modulated radiation therapy (IMRT) of prostate cancer. Fifteen previously treated prostate patients were included in this retrospective study. CT-on-Rails scans were performed before and after the IMRT treatment under local IRB approval. A total of 15 original simulation CT scans and 98 post-treatment CT scans were contoured by the same oncologist to delineate the prostate target, bladder, and rectum. IMRT plans were generated on the original simulation CTs and the same MUs and leaf sequences were used to compute the dose distributions using post-treatment CTs. Varian Velocity deformable registration was used for the summation of the fractional doses and the cumulative doses were compared with the planned doses. For the 15 patients investigated, the mean isocenter shift was up to 4.0 mm in the left-right direction, 5.9 mm in the anterior-posterior direction and 5.6 mm in the superior-inferior direction due to interfractional organ motion. The mean rectal volume varied from 0.6 to 1.73 times and the mean bladder volume varied from 0.59 to 3.65 times between simulation and the end of treatment. The prescription dose to 95% of the PTV, Dp, was set to 76 Gy for all treatment plans. The dose to 95% of the clinical treatment volume (CTV), D95, was 74.0 to 77.6 Gy and the minimum CTV dose, Dmin, was 61.0 to 71.6 Gy, respectively, in the cumulative dose distributions. Detailed analyses showed that 7.1% of the treatment fractions had cold spots (< 85% of Dp) in the peripheral CTV, leading to Dmin < 64 Gy in the cumulative dose distributions for 4 patients. The rectal dose-volume constraints were violated in 35.7% of the treatment fractions while the bladder dose was much improved in 82.7% of the treatment fractions. The current IGRT procedure for patient setup and target localization using rigid-body registration based on contour/anatomy matching is effective for population-based PTV margins. For a small group of patients, specific PTV margins and/or real-time target monitoring/tracking will be necessary due to significant prostate deformation/rotation caused by inter- and intrafractional bladder and rectal volume variation.

Eski Rusçada dar ünlülerin düşmesi ve Çağdaş Rusçaya etkisi

Yaşayan her olguda olduğu gibi dilde de meydana gelen durağanlık hali dilin gelişim sürecinin bir sonucudur. Günümüz çağdaş dillerin yapısını ve işleyişini daha iyi anlamak için dili tarihsel açıdan incelemek önemlidir. Çağdaş Rusçanın durağan durumu yaklaşık 1300 yıllık umumi gelişiminin bir sonucudur ve günümüz Rusçasındaki yapısal ve işlevsel özelliklerin çoğu Rusçanın tarihsel geçmişinde saklıdır. Yapılan çalışmada, Eski Rusçanın yapısını kökünden değiştiren ve günümüz Rusçasının yapısını belirleyen dar ünlülerin düşmesi hususu araştırılmaktadır. XII.-XIII. yüzyıllarda gerçekleşen /ь/ ve /ъ/ ünlülerinin düşmesi Eski Rusçanın sesbilimsel yapısından başlayarak biçimbirimsel ve sözcükbirimsel düzeye kadar etkisini sürdürmüştür. Sunulan çalışmada dar ünlülerin düşme sürecinin tarihsel yönü incelenerek Çağdaş Rusça üstündeki etkisi ve izleri takip edilmeye çalışılmıştır. Bu amaç doğrultusunda şimdiye dek Rusçada dar ünlülerin düşmesi üzerine yayımlanmış olan araştırmalar göz önünde bulundurularak, özgün inceleme ve taramalara dayanarak, Çağdaş Rusçada bulunan bazı ses değişimleri açıklanmaya çalışılmıştır. Bu bağlamda özellikle yabancı dil olarak Rusça öğretiminde güçlük oluşturan konular arasında yer alan /о/→/ø/ ve /э/→/ø/ değişkelerin ve dar ünlülerin düşmesi süreci arasındaki ilişki üzerinde durulmuştur. Yapılan çalışmanın yenilikçi önemi, çağdaş yabancı dil öğretiminde tarihsel dilbilgisinin ve özellikle tarihsel sesbiliminin önemini vurgulamasıdır. Günümüz yabancı dil olarak Rusça öğretiminde çoğu zaman paradigmadan sapmalar olarak tanıtılan bazı ses ve biçimbirim değişiklikleri, dilin gelişimsel bir sürecin sonucudur. Bu yaklaşım yabancı dil öğretiminde bilişsel zemini desteklemektedir.

Quantified 123I-Thyroid Scan based classification of hyperthyroidism

The original thyroid scan (TS) was widely used to identify typical imaging patterns, suggesting the widely accepted main following clinical diagnoses: Grave's disease, Toxic adenoma, [hetero]-nodular goiters and thyroiditis. With the diffusion of sensitive TSH assays, considerable advances in the comprehension of the molecular mechanisms of hormonosynthesis, and new quantification possibilities especially using 123I, the TS is a textbook of molecular imaging. The image can be finely quantified with, not only as regards the Uptake (123IUp) and related parameters but also, the quantification of the spatial targeting leading to a Spatial Target Index (STI). Using this new molecular 123I-TS, TSH values, and when required, correlation to Multiparametric Ultrasounds (MPUS), we generated a basic classification system of hyperthyroidism, with well-defined indexed criteria (C11-1 to C17-3), that allows reporting 24 distinct etiologies. Selected criteria involve TS and contrast patterns, precocious 123IUp (p123IUp), maximal TSH-dependent physiological Uptake, lobar concentration, Uptake and concentration ratios, STI, 99mTc-MIBI TS and correlative MPUS. This approach allows to identify 4 subtypes of Graves’ disease, including hyperplastic, nodular and common GD variants entangled with Hashimoto's struma, 4 subtypes of Thyroid Functional Autonomy, including Disseminated Functional Autonomy, that cannot be diagnosed with other conventional procedures. Criteria C14-1 to C17-3 report on hyperthyroidism and iodine overload, factitia, main thyroiditis presentations and rare central or tumoral etiologies of hyperthyroidism. This classification, based on 123I-TS molecular imaging, leads to unprecedented diagnostic finesse and paves the way for a personalized theranostic approach in thyroid pathology. Further development towards artificial intelligence networks is under study.

Technical Note: Synthesizing of lung tumors in computed tomography images

When investigating new radiation therapy techniques in the treatment planning stage, it can be extremely time consuming to locate multiple patient scans that match the desired characteristics for the treatment. With the help of machine learning, we propose to bypass the difficulty in finding patient computed tomography (CT) scans that match the treatment requirements. Furthermore, we aim to provide the developed method as a tool that is easily accessible to interested researchers. We propose a generative adversarial network (GAN) to edit individual volumes of interest (VOIs) in pre-existing CT scans, translating features of the healthy VOIs into features of cancerous volumes. Training and testing was done using VOIs from a dataset of 460 diagnostic and lung cancer screening CT scans. Agreement between real tumors and those produced by the editor was tested by comparing the distributions of several histogram parameters and second-order statistics as well as using qualitative analysis. After training, the network was successfully able to map healthy CT segments to realistic looking cancerous volumes. Based on visual inspection, tumors produced by the editor were found to be both realistic and visually consistent with the surrounding anatomy when placed back into the original CT scan. Furthermore, the network was found to be able to extrapolate well beyond the upper size limit of the training set. Lastly, a graphical user interface (GUI) was developed to easily interact with the resulting network. The trained network and associated GUI can serve as a tool to develop an abundance of lung cancer patient data to be used in treatment planning. In addition, this method can be extended to a variety of cancer types if given an appropriate baseline dataset. The GUI and instructions on how to utilize the tool have been made publicly available at https://github.com/teaghan/CT_Editor.

Monte Carlo-based scatter correction for the SMARTZOOM collimator

BackgroundMyocardial perfusion imaging is a commonly performed SPECT protocol and hence it would be beneficial if its scan duration could be shortened. For traditional gamma cameras, two developments have separately shown to allow for a shortened scan duration: (i) reconstructing with Monte Carlo-based scatter correction instead of dual-energy window scatter correction and (ii) acquiring projections with the SMARTZOOM collimator instead of a parallel-hole collimator. This study investigates which reduction in scan duration can be achieved when both methods are combined in a single system.ResultsThe SMARTZOOM collimator was implemented in a Monte Carlo-based reconstruction package and the implementation was validated through image quality phantom experiments. The potential for scan duration reduction was evaluated with a phantom configuration that is realistic for myocardial perfusion imaging. The original reconstruction quality was achieved in 76 ± 8% of the original scan duration when switching from dual-energy window scatter correction to Monte Carlo-based scatter correction. The original reconstruction quality was achieved in 56 ± 13% of the original scan duration when switching from the parallel-hole to the SMARTZOOM collimator. After combining both methods in a single system, the original reconstruction quality was achieved in 34 ± 7% of the original scan duration.ConclusionsMonte Carlo-based scatter correction combined with the SMARTZOOM collimator can further decrease the scan duration in myocardial perfusion imaging.

High prevalence of abdominal aortic aneurysms in patients with lung cancer

BackgroundLung cancer and abdominal aortic aneurysms (AAAs) possess multiple shared risk factors. Whereas both have screening guidelines in place, they vary in methodology despite having significant overlap in populations of patients screened. MethodsOur hospital system's Lung Cancer Program database was used to identify patients diagnosed with primary lung cancer within the past 15 years. Demographic and risk factor data were obtained, and patients' original positron emission tomography-computed tomography scans were re-read for measurements of the abdominal aorta (aortic diameter ≥3.0 cm). A cancer-free control group was obtained for comparison. Multilinear regression modeling was used to evaluate the independent associations of multiple variables on the presence of AAA. ResultsAmong 814 patients with primary lung cancer, 90 (11.1%; 95% confidence interval [CI], 8.9%-13.3%) had AAA compared with 4 of 200 (2%; 95% CI, 0.1%-3.9%) in the control group (P = .0001). Patients who smoked were more likely than nonsmokers to have AAA (11.9% [95% CI, 9.8-14.6] vs 2.2% [95% CI, 0.1-8.1]; P = .0021). In patients with AAA, 12% (11/90) had aneurysms that required treatment, and 76.6% had early-stage lung cancer. Women in our study also had a high prevalence of AAA (4.6%). Logistic regression analysis showed male sex (odds ratio [OR], 3.70; P <.001), increasing age (OR, 1.07 per year; P <.001), smoking amount (OR, 1.01 per pack-year; P = .004), and hypertension (OR, 2.30; P = .020) to be independent risk factors for AAA. ConclusionsPatients with lung cancer have a high prevalence of AAA. If future studies can demonstrate a reduction in AAA mortality by screening for AAA and lung cancer simultaneously, it may prove worthwhile to extend the low-dose computed tomography scan through the lower abdomen in select patients.

인간공학적 제품 설계를 위한 3차원 탬플릿 인체 모델의 활용

Objective: This study aims to provide a comprehensive review of the literature on template model registration technologies and their applications in ergonomic product design. Background: The original 3D scan data of the human body has limitations in terms of its applicability due to lacking quality of mesh (e.g., missed scan areas and noise) and inconsistent postures between participants. Template model registration technologies are used to complement the lacking quality of raw 3D scan data in anthropometric research and ergonomic product design. Method: A literature review was conducted on 66 papers, which were selected by screening titles and abstracts of papers searched by keywords including template model, template matching, and template registration within ergonomics and computer graphics domains in Scopus. Results: A human template model generally consists of five elements: (1) point-cloud and mesh, (2) reference points, (3) skeletal structure of the body, (4) body segment, and (5) skin deformation characteristics. The template model is matched to a target image with a particular size, shape, and posture using a template model registration technology. Conclusion: The template model registration technology has a great potential in its applicability to ergonomic design and evaluation of products for a comfortable fit to the human body. Application: The template model registration technology can be effectively applied to the establishment of parameterized 3D human body database, automatic measurement of body sizes, analysis of the variation of body shapes, predictive analysis of the body posture, and customized design of a product.

Automatic Alignment of Cranial CT Examinations to the Anterior Commissure/Posterior Commissure (ACPC) Reference Plane forReliable Interpretation and Quality Assurance.

Alignment of cranial CT scans (cCTs) to a common reference plane simplifies anatomical-landmark-based orientation and eases follow-up assessment of intracranial findings. We developed and open sourced a fully automated system, which aligns cCTs to the Anterior Commissure/Posterior Commissure (ACPC) line and exports the results to the PACS. FMRIB's Linear Image Registration Tool (FLIRT) with an ACPC-aligned atlas is used in the alignment step. Five mm mean slabs are generated with the top non-air slice as the starting point. For evaluation, 301 trauma cCTs from the CQ500 dataset were processed. In visual comparison with the respective ACPC-aligned atlas, all were successfully aligned. Image quality (IQ) and ease of identification of the central sulcus (CS) were rated on a Likert scale (5 = excellent IQ/immediate CS identification). The median IQ was 4 (range: 2-4) in the original series and 5 (range: 4-5) in the ACPC-aligned series (p < 0.0001). The CS was more easily identified after fatbACPC (original scans: 4 (range: 2-5); ACPC-aligned: 5 (range: 4-5); p < 0.0001). The mean rotation to achieve alignment was|X| = 6.4 ± 5.2° ([-X,+X] = -26.8°-24.2°), |Y| = 2.1 ± 1.7°([-Y,+Y] = -8.7°-9.8°), and |Z| = 3.1 ± 2.4° ([-Z,+Z] = -14.3°-12.5°). The developed system can robustly and automatically align cCTs to the ACPC line. Degrees of deviation from the ideal alignment could be used for quality assurance. KEY POINTS:: · fatbACPC automatically aligns cranial CT scans to the Anterior Commissure/Posterior Commissure plane.. · ACPC-aligned images simplify anatomical-landmark-based orientation.. · fatbACPC does not impact image quality.. · fatbACPC is robust, fully PACS-integrated, and Open Source: https://github.com/BrainImAccs. CITATION FORMAT: · Rubbert C, Turowski B, Caspers J. Automatic Alignment ofCranial CT Examinations to the Anterior Commissure/Posterior Commissure (ACPC) Reference Plane for Reliable Interpretation and Quality Assurance. Fortschr Röntgenstr 2021; 193: 61 - 67.

Automated and efficient powerline extraction from laser scanning data using a voxel-based subsampling with hierarchical approach

For periodic monitoring of power utilities, there has been keen interest by utility companies to extract the powerlines from laser scanning data. However, challenges arise when utilizing large point clouds as well as avoiding false positives or other errors in the extraction due to noise from objects in close proximity to the powerlines. In this study, we propose an efficient and robust approach to overcome these challenges through two main steps: candidate powerline point extraction and refinement. In the candidate powerline point extraction step, a voxel-based subsampling structure temporarily substitutes the original scan points with regularly spaced subsampled points that still preserve key details present within the point cloud but significantly reduce the dataset size. After removing the ground surface and adjacent objects, candidate powerline points are efficiently extracted through a hierarchical, feature-based filtering process. In the refinement step, the link between the subsampled candidate powerline points and original scan point cloud enable the original points to be segmented and grouped into clusters. By fitting mathematical models, an individual powerline is re-clustered and used to reconstruct the broken sections in the powerlines. The proposed approach is evaluated on 30 unique datasets with different powerline configurations acquired at five different sites by either a terrestrial or mobile laser scanning system. The parameters are optimized through a sensitivity analysis with pointwise comparison between the extracted powerlines and ground truth using 10 diverse datasets, demonstrating that only one requisite parameter varied as a function of resolution while the remaining parameters were generally consistent across the datasets. With optimized parameters, the proposed approach achieved F1 scores of 88.87–95.47% with high efficiency ranging from 0.81 and 1.46 million points/sec when tested on 30 datasets.

Improved scanning accuracy with newly designed scan bodies: An in vitro study comparing digital versus conventional impression techniques for complete-arch implant rehabilitation.

To compare the accuracy of an original and two newly designed CAD/CAM scan bodies used in digital impressions with one another as well as conventional implant impressions. A reference model containing four implants was fabricated. Digital impressions were taken using an intraoral scanner with different scan bodies: original scan bodies for Group I (DO), CAD/CAM scan bodies without extensional structure for Group II (DC), and CAD/CAM scan bodies with extensional structure for Group III (DCE). For Group IV, conventional splinted open-tray impressions (CI) were taken. The reference model and conventional stone casts were digitalized with a laboratory reference scanner. The Standard Tessellation Language datasets were imported into an inspection software for trueness and precision assessment. Statistical analysis was performed with a Kruskal-Wallis test and Dunn-Bonferroni test. The level of significance was set at α=.05. The median of trueness was 35.85, 38.50, 28.45, and 25.55μm for Group I, II, III, and IV, respectively. CI was more accurate than DO (p=.015) and DC (p=.002). The median of precision was 48.40, 48.90, 27.30, and 19.00 for Group I, II, III, and IV, respectively. CI was more accurate than DO (p<.001), DC (p<.001), and DCE (p=.007). DCE was more accurate than DC (p<.001) and DO (p<.001). The design of the extensional structure could significantly improve scanning accuracy. Conventional splinted open-tray impressions were more accurate than digital impressions for full-arch implant rehabilitation.

Colobops: a juvenile rhynchocephalian reptile (Lepidosauromorpha), not a diminutive archosauromorph with an unusually strong bite.

Correctly identifying taxa at the root of major clades or the oldest clade-representatives is critical for meaningful interpretations of evolution. A small, partially crushed skull from the Late Triassic (Norian) of Connecticut, USA, originally described as an indeterminate rhynchocephalian saurian, was recently named Colobops noviportensis and reinterpreted as sister to all remaining Rhynchosauria, one of the earliest and globally distributed groups of herbivorous reptiles. It was also interpreted as having an exceptionally reinforced snout and powerful bite based on an especially large supratemporal fenestra. Here, after a re-analysis of the original scan data, we show that the skull was strongly dorsoventrally compressed post-mortem, with most bones out of life position. The cranial anatomy is consistent with that of other rhynchocephalian lepidosauromorphs, not rhynchosaurs. The ‘reinforced snout' region and the ‘exceptionally enlarged temporal region’ are preservational artefacts and not exceptional among clevosaurid rhynchocephalians. Colobops is thus not a key taxon for understanding diapsid feeding apparatus evolution.