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Level of evidence5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.

PurposeTo assess the ability of a recent, anatomically designed breast phantom incorporating T1 and diffusion elements to serve as a quality control device for quantitative comparison of apparent diffusion coefficient (ADC) measurements calculated from diffusion-weighted MRI (DWI) within and across MRI systems.Materials and methodsA bilateral breast phantom incorporating multiple T1 and diffusion tissue mimics and a geometric distortion array was imaged with DWI on 1.5 Tesla (T) and 3.0T scanners from two different manufacturers, using three different breast coils (three configurations total). Multiple measurements were acquired to assess the bias and variability of different diffusion weighted single-shot echo-planar imaging sequences on the scanner-coil systems.ResultsThe repeatability of ADC measurements was mixed: the standard deviation relative to baseline across scanner-coil-sequences ranged from low variability (0.47, 95% confidence interval [CI]: 0.22-1.00) to high variability (1.69, 95% CI: 0.17-17.26), depending on material, with the lowest and highest variability from the same scanner-coil-sequence. Assessment of image distortion showed that right/left measurements of the geometric distortion array were 1 to 16% larger on the left coil side compared with the right coil side independent of scanner-coil systems, diffusion weighting, and phase-encoding direction.ConclusionThis breast phantom can be used to measure scanner-coil-sequence bias and variability for DWI. When establishing a multisystem study, this breast phantom may be used to minimize protocol differences (e.g., due to available sequences or shimming technique), to correct for bias that cannot be minimized, and to weigh results from each system depending on respective variability. J. Magn. Reson. Imaging 2016. J. MAGN. RESON. IMAGING 2016;44:846-855.

Rationale and objectivesTumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile.Materials and methodsIndustry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers.ResultsIntra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail.ConclusionsNine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm.

To facilitate the characterization of unlabeled induced pluripotent stem cells (iPSCs) during culture and expansion, we developed an AI pipeline for nuclear segmentation and mitosis detection from phase contrast images of individual cells within iPSC colonies. The analysis uses a 2D convolutional neural network (U-Net) plus a 3D U-Net applied on time lapse images to detect and segment nuclei, mitotic events, and daughter nuclei to enable tracking of large numbers of individual cells over long times in culture. The analysis uses fluorescence data to train models for segmenting nuclei in phase contrast images. The use of classical image processing routines to segment fluorescent nuclei precludes the need for manual annotation. We optimize and evaluate the accuracy of automated annotation to assure the reliability of the training. The model is generalizable in that it performs well on different datasets with an average F1 score of 0.94, on cells at different densities, and on cells from different pluripotent cell lines. The method allows us to assess, in a non-invasive manner, rates of mitosis and cell division which serve as indicators of cell state and cell health. We assess these parameters in up to hundreds of thousands of cells in culture for more than 36 hours, at different locations in the colonies, and as a function of excitation light exposure. [ABSTRACT FROM AUTHOR]

1. Introduction As originally conceived, X-ray computed tomography (CT) finds a material- dependent parameter from observations based on line integrals characterizing the absorption of X-rays in a sample, known in [...]

RECIST (Response Evaluation Criteria in Solid Tumors) is a linear measure intended to predict tumor size in medical computed tomography (CT). In this work, using purely geometrical considerations, we estimate [...]

We present methods for measuring errors in the rendering of three-dimensional points, line segments, and polygons in pixel-based computer graphics systems. We present error metrics for each of these three […]

1. Introduction This is the third in a series of articles (1), (2) that describe, through examples, how the Scientific Applications and Visualization Group (SAVG) at the National Institute of […]

1. IntroductionOne of the principal methods for detecting cancer is through the measurement of the growth of nodules. Arguably, the most widely accepted method is that of the Response Evaluation [...]

1. Introduction The National Institute of Standards and Technology (NIST) was established in 1901 as the National Bureau of Standards. Although NIST's mission has expanded over the years, metrology, or […]

This is the second in a series of articles describing a wide variety of projects at NIST that synergistically combine physical science and information science. It describes, through examples, how […]

We investigated an Er(super 3+)//Yb(super 3+) codoped silicate glass as a host material for waveguide lasers operating near 1.5 (mu)m. Spectroscopic properties of the glass are reported. Waveguide lasers were fabricated by K(super +)-ion exchange from a nitrate melt. The waveguides support a single transverse mode at 1.5 (mu)m. An investigation of the laser performance as a function of the Yb:Er ratio was performed, indicating an optimal ratio of approximately 5:1. Slope efficiencies of as great as 6.5% and output powers as high as 19.6 mW at 1.54 (mu)m were realized. The experimental results are compared with a waveguide laser model that is used to extract the Er(super 3+) upconversion coefficients and the yb(super 3+)-Er(super 3+) cross-relaxation coefficients. The results indicate the possibility of obtaining high-performance waveguide lasers from a durable silicate host glass.

An adaptive simulated annealing optimization algorithm is used to derive laser rate equation and waveguiding models with which the best design for a diode-pumped fiber-coupled, Yb:Er glass waveguide laser can be determined. Material parameters that correspond to commercially available laser-glass and diode-pump sources are used in this study. Given a continuous-wave 300-mW pump at 977 nm, approximately 48 mW of power at 1540 nm can be coupled into the [LP.sub.01] mode of an optical fiber. Fabrication and alignment tolerance analyses are presented. [C] 2000 Optical Society of America OCIS codes: 140.3500, 130.3120, 220.4830, 130.2790, 140.5680.

Rationale and Objectives: Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA).Materials and Methods: The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type.Results: Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall.Conclusion: The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria. [ABSTRACT FROM AUTHOR]

We present a new numerical technique for modeling the flow around multiple objects moving in a fluid. The method tracks the dynamic interaction between each particle and the fluid. The movements of the fluid and the object are directly coupled. A background mesh is designed to fit the geometry of the overall domain. The mesh is designed independently of the presence of the particles except in terms of how fine it must be to track particles of a given size. Each particle is represented by a geometric figure that describes its boundary. This figure overlies the mesh. Nodes are added to the mesh where the particle boundaries intersect the background mesh, increasing the number of nodes contained in each element whose boundary is intersected. These additional nodes are then used to describe and track the particle in the numerical scheme. Appropriate element shape functions are defined to approximate the solution on the elements with extra nodes. The particles are moved through the mesh by moving only the overlying nodes defining the particles. The regular finite element grid remains unchanged. In this method, the mesh does not distort as the particles move. Instead, only the placement of particle-defining nodes changes as the particles move. Element shape functions are updated as the nodes move through the elements. This method is especially suited for models of moderate numbers of moderate-size particles, where the details of the fluid-particle coupling are important. Both the complications of creating finite element meshes around appreciable numbers of particles, and extensive remeshing upon movement of the particles are simplified in this method.