Conversion from dose-length product to effective dose in computed tomography venography of the lower extremities

For radiation dose assessement of computed tomography (CT), effective dose (ED) is often estimated by multiplying the dose-length product (DLP), provided automatically by the CT scanner, by a conversion factor. We investigated such conversion in CT venography of the lower extremities performed in conjunction with CT pulmonary angiography. The study subjects consisted of eight groups imaged using different scanners and different imaging conditions (five and three groups for the GE and Siemens scanners, respectively). Each group included 10 men and 10 women. The scan range was divided into four anatomical regions (trunk, proximal thigh, knee and distal leg), and DLP was calculated for each region (regional DLP). Regional DLP was multiplied by a conversion factor for the respective region, to convert it to ED. The sum of the ED values for the four regions was obtained as standard ED. Additionally, the sum of the four regional DLP values, an approximate of the scanner-derived DLP, was multiplied by the conversion factor for the trunk (0.015 mSv/mGy/cm), as a simplified method to obtain ED. When using the simplified method, ED was overestimated by 32.3%−70.2% and 56.5%−66.2% for the GE and Siemens scanners, respectively. The degree of overestimation was positively and closely correlated with the contribution of the middle and distal portions of the lower extremities to total radiation exposure. ED/DLP averaged within each group, corresponding to the conversion factor, was 0.0089−0.0114 and 0.0091−0.0096 mSv/mGy/cm for the GE and Siemens scanners, respectively. In CT venography of the lower extremities, ED is greatly overestimated by multiplying the scanner-derived DLP by the conversion factor for the trunk. The degree of overestimation varies widely depending on the imaging conditions. It is recommended to divide the scan range and calculate ED as a sum of regional ED values.

Elastic least-squares reverse time migration of steeply dipping structures using prismatic reflections

The migration of prismatic reflections can be used to delineate steeply dipping structures, which is crucial for oil and gas exploration and production. Elastic least-squares reverse time migration (ELSRTM), which considers the effects of elastic wave propagation, can be used to obtain reasonable subsurface reflectivity estimations and interpret multicomponent seismic data. In most cases, we can only obtain a smooth migration model. Thus, conventional ELSRTM, which is based on the first-order Born approximation, considers only primary reflections and cannot resolve steeply dipping structures. To address this issue, we develop an ELSRTM framework, called Pris-ELSRTM, which can jointly image primary and prismatic reflections in multicomponent seismic data. When Pris-ELSRTM is directly applied to multicomponent records, near-vertical structures can be resolved. However, the application of imaging conditions established for prismatic reflections to primary reflections destabilizes the process and leads to severe contamination of the results. Therefore, we further improve the Pris-ELSRTM framework by separating prismatic reflections from recorded multicomponent data. By removing artificial imaging conditions from the normal equation, primary and prismatic reflections can be imaged based on unique imaging conditions. The results of synthetic tests and field data applications demonstrate that the improved Pris-ELSRTM framework produces high-quality images of steeply dipping P- and S-wave velocity structures. However, it is difficult to delineate steep density structures because of the insensitivity of the density to prismatic reflections.

MYLPHerb-1: A Dataset of Malaysian Local Perennial Herbs for the Study of Plant Images Classification under Uncontrolled Environment

Research in the medicinal plants’ recognition field has received great attention due to the need of producing a reliable and accurate system that can recognise medicinal plants under various imaging conditions. Nevertheless, the standard medicinal plant datasets publicly available for research are very limited. This paper proposes a dataset consisting of 34200 images of twelve different high medicinal value local perennial herbs in Malaysia. The images were captured under various imaging conditions, such as different scales, illuminations, and angles. It will enable larger interclass and intraclass variability, creating abundant opportunities for new findings in leaf classification. The complexity of the dataset is investigated through automatic classification using several high-performance deep learning algorithms. The experiment results showed that the dataset creates more opportunities for advanced classification research due to the complexity of the images. The dataset can be accessed through https://www.mylpherbs.com/.

Caught in the Act: Live-Cell Imaging of Plant Meiosis

Live-cell imaging is a powerful method to obtain insights into cellular processes, particularly with respect to their dynamics. This is especially true for meiosis, where chromosomes and other cellular components such as the cytoskeleton follow an elaborate choreography over a relatively short period of time. Making these dynamics visible expands understanding of the regulation of meiosis and its underlying molecular forces. However, the analysis of meiosis by live-cell imaging is challenging; specifically in plants, a temporally resolved understanding of chromosome segregation and recombination events is lacking. Recent advances in live-cell imaging now allow the analysis of meiotic events in plants in real time. These new microscopy methods rely on the generation of reporter lines for meiotic regulators and on the establishment of ex vivo culture and imaging conditions, which stabilize the specimen and keep it alive for several hours or even days. In this review, we combine an overview of the technical aspects of live-cell imaging in plants with a summary of outstanding questions that can now be addressed to promote live-cell imaging in Arabidopsis and other plant species and stimulate ideas on the topics that can be addressed in the context of plant meiotic recombination.

A Review of Keypoints’ Detection and Feature Description in Image Registration

For image registration, feature detection and description are critical steps that identify the keypoints and describe them for the subsequent matching to estimate the geometric transformation parameters between two images. Recently, there has been a large increase in the research methods of detection operators and description operators, from traditional methods to deep learning methods. To solve the problem, that is, which operator is suitable for specific application problems under different imaging conditions, the paper systematically reviewed commonly used descriptors and detectors from artificial methods to deep learning methods, and the corresponding principle, analysis, and comparative experiments are given as well. We introduce the handcrafted detectors including FAST, BRISK, ORB, SURF, SIFT, and KAZE and the handcrafted descriptors including BRISK, FREAK, BRIEF, SURF, ORB, SIFT, KAZE. At the same time, we review detectors based on deep learning technology including DetNet, TILDE, LIFT, multiscale detector, SuperPoint, and descriptors based on deep learning including pretrained descriptor, Siamese descriptor, LIFT, triplet network, and SuperPoint. Two group of comparison experiments are compared comprehensively and objectively on representative datasets. Finally, we concluded with insightful discussions and conclusions of descriptor and detector selection for specific application problem and hope this survey can be a reference for researchers and engineers in image registration and related fields.

Semi-Supervised Training for Positioning of Welding Seams

Robotic welding often uses vision-based measurement to find the correct placement of the welding seam. Traditional machine vision methods work well in many cases but lack robustness when faced with variations in the manufacturing process or in the imaging conditions. While supervised deep neural networks have been successful in increasing accuracy and robustness in many real-world measurement applications, their success relies on labeled data. In this paper, we employ semi-supervised learning to simultaneously increase accuracy and robustness while avoiding expensive and time-consuming labeling efforts by a domain expert. While semi-supervised learning approaches for various image classification tasks exist, we purpose a novel algorithm for semi-supervised key-point detection for seam placement by a welding robot. We demonstrate that our approach can work robustly with as few as fifteen labeled images. In addition, our method utilizes full image resolution to enhance the accuracy of the key-point detection in seam placement.

Nanoscopic resolution within a single imaging frame

Mean-Shift Super Resolution (MSSR) is a principle based on the Mean Shift theory that improves the spatial resolution in fluorescence images beyond the diffraction limit. MSSR works on low- and high-density fluorophore images, is not limited by the architecture of the detector (EM-CCD, sCMOS, or photomultiplier-based laser scanning systems) and is applicable to single images as well as temporal series. The theoretical limit of spatial resolution, based on optimized real-world imaging conditions and analysis of temporal image series, has been measured to be 40 nm. Furthermore, MSSR has denoising capabilities that outperform other analytical super resolution image approaches. Altogether, MSSR is a powerful, flexible, and generic tool for multidimensional and live cell imaging applications.

Nanoscopic resolution within a single imaging frame

Mean-Shift Super Resolution (MSSR) is a principle based on the Mean Shift theory that improves the spatial resolution in fluorescence images beyond the diffraction limit. MSSR works on low- and high-density fluorophore images, is not limited by the architecture of the detector (EM-CCD, sCMOS, or photomultiplier-based laser scanning systems) and is applicable to single images as well as temporal series. The theoretical limit of spatial resolution, based on optimized real-world imaging conditions and analysis of temporal image series, has been measured to be 40 nm. Furthermore, MSSR has denoising capabilities that outperform other analytical super resolution image approaches. Altogether, MSSR is a powerful, flexible, and generic tool for multidimensional and live cell imaging applications.

Resolving Gas Bubbles Ascending in Liquid Metal from Low-SNR Neutron Radiography Images

We demonstrate a new image processing methodology for resolving gas bubbles travelling through liquid metal from dynamic neutron radiography images with an intrinsically low signal-to-noise ratio. Image pre-processing, denoising and bubble segmentation are described in detail, with practical recommendations. Experimental validation is presented—stationary and moving reference bodies with neutron-transparent cavities are radiographed with imaging conditions representative of the cases with bubbles in liquid metal. The new methods are applied to our experimental data from previous and recent imaging campaigns, and the performance of the methods proposed in this paper is compared against our previously achieved results. Significant improvements are observed as well as the capacity to reliably extract physically meaningful information from measurements performed under highly adverse imaging conditions. The showcased image processing solution and separate elements thereof are readily extendable beyond the present application, and have been made open-source.