Similar Precision Research Articles

Validations and Corrections of the SFD and Planck Reddening Maps Based on LAMOST and Gaia Data

Precise correction of dust reddening is fundamental to obtain the intrinsic parameters of celestial objects. The Schlegel et al. (SFD) and the Planck 2D extinction maps are widely used for reddening correction. In this work, using accurate reddening determinations of about 2 million stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Data Release 5 and Gaia DR2, we check and calibrate the SFD and Planck maps in the middle and high Galactic latitudes. The maps show similar precision in reddening correction. We find small yet significant spatially dependent biases for the four maps, which are similar between the SFD and Planck2014-R maps, and between the Planck2014-Tau and Planck2019-Tau maps. The biases show a clear dependence on the dust temperature and extinction for the SFD and Planck2014-R maps. While those of the Planck2014-Tau and Planck2019-Tau maps have a weak dependence on the dust temperature, they both strongly depend on the dust spectral index. Finally, we present corrections of the SFD and Planck extinction maps within the LAMOST footprint, along with empirical relations for corrections outside the LAMOST footprint. Our results provide important clues for the further improvement of the Galactic all-sky extinction maps and lay a significant foundation for accurate extinction correction in the era of precision astronomy.

Short-baseline oscillation scenarios at JUNO and TAO

We study the sensitivity of JUNO and TAO to the oscillations induced by two well-motivated scenarios beyond the standard model: large extra dimensions (LEDs) and light sterile neutrinos in the context of $3+1$ neutrino mixing. We find that $\mathrm{JUNO}+\mathrm{TAO}$ can set competitive bounds on the parameter space of each scenario. In particular, we find that $\mathrm{JUNO}+\mathrm{TAO}$ can be competitive with MINOS, DUNE, or KATRIN in the context of LED. If LEDs are present in nature, we show that the parameters could be measured with a similar precision as the standard oscillation parameters. We also show that $\mathrm{JUNO}+\mathrm{TAO}$ can test nearly all of the parameter space preferred by gallium experiments in the context of $3+1$ mixing. Finally, we discuss the possibility to distinguish the two scenarios from each other.

Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement

This paper describes the development of novel/state-of-art computational framework to accurately predict the degree of binder activity of a reclaimed asphalt pavement sample as a percentage of the indirect tensile strength (ITS) using a reduced number of input variables that are relatively easy to obtain, namely compaction temperature, air voids and ITS. Different machine learning (ML) techniques were applied to obtain the most accurate data representation model. Specifically, three ML techniques were applied: 6th-degree multivariate polynomial regression with regularization, artificial neural network and random forest regression. The three techniques produced models with very similar precision, reporting a mean absolute error ranging from 12.2 to 12.8% of maximum ITS on the test data set. The work presented in this paper is an evolution in terms of data analysis of the results obtained within the interlaboratory tests conducted by Task Group 5 of the RILEM Technical Committee 264 on Reclaimed Asphalt Pavement. Hence, despite it has strong bonds with this framework, this work was developed independently and can be considered as a natural follow-up.

215 Assessment of Equations Relating Carcass Chemical Components in Growing/Finishing Cattle

Abstract Existing equations to estimate chemical composition of cattle were developed over 40 years ago using different cattle genetics. The objective of this analysis was to evaluate existing equations using cattle genetics from then to today. A literature search was performed to identify experiments measuring carcass chemical composition by proximate analysis resulting in 106 treatment means from 10 experiments since 1976. Four sets of equations were used to compute carcass chemical composition assuming carcass water was known: 1) Garrett and Hinman (1969; 10.2527/jas1969.2811), 2) Gil et al. (1970: 10.2527/jas1970.313459x), 3) Preston et al. (1974; 10.2527/jas1974.38147x), and 4) Ferrell et al. (1976; 10.2527/jas1976.4251158x). Mean (SD) carcass water, fat and protein were 57.19 (7.20), 20.49 (8.87), and 17.80 (2.90) % of HCW, respectively. For all equations, simultaneous testing of intercept equal to zero and slope equal to 1 indicated that the intercept and slope for fat, protein, ash and energy were different than zero and one, respectively. The concordance correlation coefficient (CCC) was high (> 0.85) for fat and energy, but low (< 0.40) for protein and ash for all equations. Mean bias for fat, protein, ash, and energy ranged from -8.84 to -0.09%, 2.02 to 8.68%, -13.35 to 12.35%, and -3.50 to 0.79%, respectively, with Eq. 1 having the MB closest to zero for ash, Eq. 3 having the MB closest to zero for fat and protein, and Eq. 4 having the MB closest to zero for energy. Publication year as a continuous variable was a significant (P < 0.05) predictor of the difference between observed and predicted values for all components using all equations. Fat and energy had greater overprediction whereas, protein and ash had greater underprediction in more recent publications. In conclusion, existing equations provide similar precision, but differ in accuracy to predict carcass chemical composition. The trend for greater bias in more recent publication years indicates the need to reevaluate relationships among carcass chemical components using current cattle genetics.

213 Assessment of Equations Relating Shrunk, Empty Body, and Carcass Weight in Growing/Finishing Cattle

Abstract Existing equations to estimate empty body weight of cattle from live or carcass weight were developed over 40 years ago using different cattle genetics. The objective of this analysis was to evaluate existing equations using cattle genetics from that time to today. A literature search was performed to identify experiments measuring shrunk body weight (SBW), empty body weight (EBW), and carcass weight (HCW) resulting in 269 treatment means from 32 experiments since 1976. Two equations were used to compute EBW from SBW: 1) NASEM (2016) and 2) Gil et al. (1970: 10.2527/jas1970.313459x). Eight equations were used to compute HCW from EBW: 1) Lofgreen et al. (1962; 10.2527/jas1962.21120x), 2) Garrett and Hinman (1969; 10.2527/jas1969.2811), 3) Holzer and Levy Eq. 1 (1969; Isr. J. Agric. Res. 19:195), 4) Holzer and Levy Eq. 2, 5) Gil et al. (1970: 10.2527/jas1970.313459x), 6) Alhassan et al. (1975; 10.4141/cjas75-045), 7) Ferrell et al. (1976; 10.2527/jas1976.4251158x), and 8) Fox et al. (1976; 10.2527/jas1976.433566x). Mean (SD) shrunk, empty, and carcass weight were 359 (145), 322 (134), and 210 (92) kg, respectively, and were strongly correlated (> 0.990). For all equations, simultaneous testing of intercept equal to zero and slope equal to 1 indicated that the intercept and slope for EBW and HCW were different than zero and one, respectively. For EBW, the CCC was high (> 0.95) for all equations and mean bias was -1.69 and -4.75% for Eq. 1 and 2, respectively, but became a greater underprediction in more recent publication years for Eq. 1. For HCW, CCC was high (> 0.95) and similar for all equations, but Eq. 7 had the MB nearest to zero (-0.10%) and Eq. 4 furthest from zero (10.74%). For Eq. 2, 4, 6, and 8 there was greater overprediction in more recent publication years. In conclusion, existing equations provide similar precision, but differ in accuracy to predict weight with some equations significantly influenced by year of publication.

214 Assessment of Equations Relating Empty Body Chemical Components in Growing/Finishing Cattle

Abstract Existing equations to estimate empty body chemical composition were developed over 40 years ago using different cattle genetics. The objective of this analysis was to evaluate existing equations using cattle genetics from then to today. A literature search was performed to identify experiments measuring empty body chemical composition by proximate analysis resulting in 198 treatment means from 25 experiments since 1976. Three sets of equations were used to compute empty body chemical composition assuming empty body water was known: 1) Garrett and Hinman (1969; 10.2527/jas1969.2811), 2) Gil et al. (1970: 10.2527/jas1970.313459x), and 3) Ferrell et al. (1976; 10.2527/jas1976.4251158x). Mean (SD) empty body water, fat and protein were 59.91 (6.32), 17.58 (7.89), and 18.24 (2.02) % of EBW, respectively. For all equations, simultaneous testing of intercept equal to zero and slope equal to 1 indicated that the intercept and slope for fat, protein, ash and energy were different than zero and one, respectively. The concordance correlation coefficient (CCC) was high (> 0.85) for fat and energy, but low (< 0.50) for protein and ash for all equations. Mean bias for fat, protein, ash, and energy ranged from -19.58 to -3.50%, 1.62 to 15.84%, -2.22 to 14.15%, and -6.09 to -0.63%, respectively, with Eq. 1 having the MB closest to 0 for protein, and Eq. 3 having the MB closest to 0 for fat, ash, and energy. Publication year as a continuous variable was a significant predictor of the difference between observed and predicted values for all components. Fat and energy had greater overpredicted whereas, protein and ash had greater underprediction in more recent publications. In conclusion, existing equations provide similar precision, but differ in accuracy to predict empty body chemical composition. The trend for greater bias in more recent publication years indicates the need to reevaluate relationships among empty body chemical components using current cattle genetics.

The Case for Case-Cohort: An Applied Epidemiologist's Guide to Reframing Case-Cohort Studies to Improve Usability and Flexibility.

When research questions require the use of precious samples, expensive assays or equipment, or labor-intensive data collection or analysis, nested case-control or case-cohort sampling of observational cohort study participants can often reduce costs. These study designs have similar statistical precision for addressing a singular research question, but case-cohort studies have broader efficiency and superior flexibility. Despite this, case-cohort designs are comparatively underutilized in the epidemiologic literature. Recent advances in statistical methods and software have made analyses of case-cohort data easier to implement, and advances from casual inference, such as inverse probability of sampling weights, have allowed the case-cohort design to be used with a variety of target parameters and populations. To provide an accessible link to this technical literature, we give a conceptual overview of case-cohort study analysis with inverse probability of sampling weights. We show how this general analytic approach can be leveraged to more efficiently study subgroups of interest or disease subtypes or to examine associations independent of case status. A brief discussion of how this framework could be extended to incorporate other related methodologic applications further demonstrates the broad cost-effectiveness and adaptability of case-cohort methods for a variety of modern epidemiologic applications in resource-limited settings.

Joint Models for Estimating Determinants of Cognitive Decline in the Presence of Survival Bias.

Identifying determinants of cognitive decline is crucial for developing strategies to prevent Alzheimer's disease and related dementias. However, determinants of cognitive decline remain elusive, with inconsistent results across studies. One reason could be differential survival. Cognitive decline and many exposures of interest are associated with mortality making survival a collider. Not accounting for informative attrition can result in survival bias. Generalized estimating equations (GEE) and linear mixed-effects model (LME) are commonly used to estimate effects of exposures on cognitive decline, but both assume mortality is not informative. Joint models combine LME with Cox proportional hazards models to simultaneously estimate cognitive decline and the hazard of mortality. Using simulations, we compared estimates of the effect of a binary exposure on rate of cognitive decline from GEE, weighted GEE using inverse-probability-of-attrition weights, and LME to joint models under several causal structures of survival bias. We found that joint models with correctly specified relationship between survival and cognition performed best, producing unbiased estimates and appropriate coverage. Even those with misspecified relationship between survival and cognition showed advantage under causal structures consistent with survival bias. We also compared these models in estimating the effect of education on cognitive decline after dementia diagnosis using Framingham Heart Study data. Estimates of the effect of education on cognitive decline from joint models were slightly attenuated with similar precision compared with LME. In our study, joint models were more robust than LME, GEE, and weighted GEE models when evaluating determinants of cognitive decline.

Challenges and Requirements in High-Precision Nuclear Astrophysics Experiments

In the 21th century astronomical observations, as well as astrophysical models, have become impressively precise. For a better understanding of the processes in stellar interiors, the nuclear physics of astrophysical relevance—known as nuclear astrophysics—must aim for similar precision, as such precision is not reached yet in many cases. This concerns both nuclear theory and experiment. In this paper, nuclear astrophysics experiments are put in focus. Through the example of various parameters playing a role in nuclear reaction studies, the difficulties of reaching high precision and the possible solutions are discussed.

A-LugSeg: Automatic and explainability-guided multi-site lung detection in chest X-ray images

Large variations in anatomical shape and size, too much overlap between anatomical structures, and inconsistent anatomical shapes make accurate lung segmentation in chest x-rays (CXR) a challenging problem. In this paper, we propose an automatic method called A-LugSeg that consists of two subnetworks for lung segmentation in CXRs. The first is a segmentation subnetwork based on a deep learning model (i.e., Mask-RCNN), which completes a coarse segmentation for each input CXR image. The second is a refinement subnetwork designed to optimize the coarse segmentation result by combining an improved closed principal curve method and an enhanced machine learning, where the lung contour’s explainability-guided mathematical model is expressed by the machine learning’s parameters. The performance of the proposed method is evaluated on three public datasets, namely the ShenZhen hospital Chest X-ray dataset (SZCX), Japanese Society of Radiological Technology dataset (JSRT), and Montgomery County chest x-ray dataset (MC), which contain the 662 CXRs, 247 CXRs, and 138 CXRs, respectively. We used different datasets for training/validation (SZCX) and testing (SZCX/JSRT/MC). Furthermore, we used six evaluation metrics to evaluate the performance of our proposed method, including Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (Ω), Accuracy (ACC), Precision, Sensitivity, and Specificity. The obtained results (DSC = 0.973, Ω = 0.958, ACC = 0.972, and p-value for DSC < 0.001) for JSRT, (DSC = 0.971, Ω = 0.955, ACC = 0.97, and p-value for DSC < 0.001) for MC, (DSC = 0.972, Ω = 0.956, and ACC = 0.97) for hybrid datasets (JSRT + MC), and (Precision, Sensitivity, and Specificity are higher than 0.98) show the superior performance of the proposed dual subnetwork segmentation algorithm compared to the existing state of the art approaches.

Numerical Probabilistic Load Flow Analysis in Modern Power Systems with Intermittent Energy Sources

Renewable resources integration through distributed generation (DG) affects conventional consideration of power system performance and confronts deterministic load flow (DLF) analysis with serious challenges. The DLF gives a snapshot of the system state neglecting all of the uncertainties arising from intermittent DG driven by variable weather conditions or volatile consumption. Therefore, with the aim of finer tracking and presentation of system variables, a probabilistic load flow (PLF) approach should be adopted. First, this article gives a literature overview of different PLF techniques. It focuses on numerical techniques examining them for simple random and Latin Hypercube sampling, vastly applied in previous works, and proposes a method combining Monte Carlo simulations with Halton quasi-random numbers. Stochastic modelling is performed for solar and wind power output. For method comparison and confirmation of the applicability of suggested PLF method with Halton sequences, different IEEE test cases were used, all modified by attaching DGs. More profound method assessment is conducted through discussing different renewables penetration levels and processing time. The overall simulation outcomes have shown that results of Halton method are of similar precision as the generally used Latin Hypercube method and therefore indicated the relevance of the proposed method and its potential for application in contemporary system analysis.

The cosmological simulation code CONCEPT 1.0

ABSTRACT We present version 1.0 of the cosmological simulation code $\rm{{\small CO}{{\small N}}{\small CEPT}}$, designed for simulations of large-scale structure formation. $\rm{{\small CO}{\rm{\small N}}{\small CEPT}}$ 1.0 contains a P3M gravity solver, with the short-range part implemented using a novel (sub)tiling strategy, coupled with individual and adaptive particle time-stepping. A primary objective of $\rm{{\small CO}{\rm{\small N}}{\small CEPT}}$ is ease of use. To this end, it has built-in initial condition generation and can produce output in the form of snapshots, power spectra, and direct visualizations. $\rm{{\small CO}{\rm{\small N}}{\small CEPT}}$ is the first massively parallel cosmological simulation code written in Python. Despite of this, excellent performance is obtained, even comparing favourably to other codes such as gadget at similar precision, in the case of low to moderate clustering. By means of power-spectrum comparisons we find extraordinary good agreement between $\rm{{\small CO}{\rm{\small N}}{\small CEPT}}$ 1.0 and gadget. At large and intermediate scales the codes agree to well below the per mille level, while the agreement at the smallest scales probed ($k \sim 13\ h\, {\rm Mpc}^{-1}$) is of the order of 1 per cent. The $\rm{{\small CO}{\rm{\small N}}{\small CEPT}}$ code is openly released and comes with a robust installation script as well as thorough documentation.

A Detection of Red Noise in PSR J1824–2452A and Projections for PSR B1937+21 Using NICER X-Ray Timing Data

Abstract We have used X-ray data from the Neutron Star Interior Composition Explorer (NICER) to search for long-timescale temporal correlations (“red noise”) in the pulse times of arrival (TOAs) from the millisecond pulsars PSR J1824−2452A and PSR B1937+21. These data more closely track intrinsic noise because X-rays are unaffected by the radio-frequency-dependent propagation effects of the interstellar medium. Our search yields strong evidence (natural log Bayes factor of 9.634 ± 0.016) for red noise in PSR J1824−2452A, but the search is inconclusive for PSR B1937+21. In the interest of future X-ray missions, we devise and implement a method to simulate longer and higher-precision X-ray data sets to determine the timing baseline necessary to detect red noise. We find that the red noise in PSR B1937+21 can be reliably detected in a 5 yr mission with a TOA error of 2 μs and an observing cadence of 20 observations per month compared to the 5 μs TOA error and 11 observations per month that NICER currently achieves in PSR B1937+21. We investigate detecting red noise in PSR B1937+21 with other combinations of observing cadences and TOA errors. We also find that time-correlated red noise commensurate with an injected stochastic gravitational-wave background having an amplitude of A GWB = 2 × 10−15 and spectral index of timing residuals of γ GWB = 13/3 can be detected in a pulsar with similar TOA precision to PSR B1937+21. This is with no additional red noise in a 10 yr mission that observes the pulsar 15 times per month and has an average TOA error of 1 μs.

Low-frequency wideband timing of InPTA pulsars observed with the uGMRT

ABSTRACT High-precision measurements of the pulsar dispersion measure (DM) are possible using telescopes with low-frequency wideband receivers. We present an initial study of the application of the wideband timing technique, which can simultaneously measure the pulsar times of arrivals (ToAs) and DMs, for a set of five pulsars observed with the upgraded Giant Metrewave Radio Telescope (uGMRT) as part of the Indian Pulsar Timing Array (InPTA) campaign. We have used the observations with the 300–500 MHz band of the uGMRT for this purpose. We obtain high precision in DM measurements with precisions of the order 10−6 cm−3 pc. The ToAs obtained have sub-μs precision and the rms of the post-fit ToA residuals are in the sub-μs range. We find that the uncertainties in the DMs and ToAs obtained with this wideband technique, applied to low-frequency data, are consistent with the results obtained with traditional pulsar timing techniques and comparable to high-frequency results from other PTAs. This work opens up an interesting possibility of using low-frequency wideband observations for precision pulsar timing and gravitational wave detection with similar precision as high-frequency observations used conventionally.

Caries segmentation on tooth X-ray images with a deep network

ObjectivesDeep learning has been a promising technology in many biomedical applications. In this study, a deep network was proposed aiming for caries segmentation on the clinically collected tooth X-ray images. MethodsThe proposed network inherited the skip connection characteristic from the widely used U-shaped network, and creatively adopted vision Transformer, dilated convolution, and feature pyramid fusion methods to enhance the multi-scale and global feature extraction capability. It was then trained on the clinically self-collected and augmented tooth X-ray image dataset, and the dice similarity and pixel classification precision were calculated for the network's performance evaluation. ResultsExperimental results revealed an average dice similarity of 0.7487 and an average pixel classification precision of 0.7443 on the test dataset, which outperformed the compared networks such as UNet, Trans-UNet, and Swin-UNet, demonstrating the remarkable improvement of the proposed network. ConclusionsThis study contributed to the automatic caries segmentation by using a deep network, and highlighted the potential clinical utility value.

Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears.

The anterior cruciate ligament (ACL) is one of the main stabilizer parts of the knee. ACL injury leads to causes of osteoarthritis risk. ACL rupture is common in the young athletic population. Accurate segmentation at an early stage can improve the analysis and classification of anterior cruciate ligaments tears. This study automatically segmented the anterior cruciate ligament (ACL) tears from magnetic resonance imaging through deep learning. The knee mask was generated on the original Magnetic Resonance (MR) images to apply a semantic segmentation technique with convolutional neural network architecture U-Net. The proposed segmentation method was measured by accuracy, intersection over union (IoU), dice similarity coefficient (DSC), precision, recall and F1-score of 98.4%, 99.0%, 99.4%, 99.6%, 99.6% and 99.6% on 11451 training images, whereas on the validation images of 3817 was, respectively, 97.7%, 93.8%,96.8%, 96.5%, 97.3% and 96.9%. We also provide dice loss of training and test datasets that have remained 0.005 and 0.031, respectively. The experimental results show that the ACL segmentation on JPEG MRI images with U-Nets achieves accuracy that outperforms the human segmentation. The strategy has promising potential applications in medical image analytics for the segmentation of knee ACL tears for MR images.

Improved cardiac T1 mapping accuracy and precision with a new hybrid MOLLI and SASHA technique: MOSHA

PurposeTo develop and validate a new myocardial T1 mapping sequence (MOSHA) which is based on a combination of the modified Look-Locker inversion recovery (MOLLI) and the saturation recovery single-shot acquisition (SASHA) sequences. MethodsPrior studies have shown that myocardial T1 mapping by SASHA is more accurate but less precise than MOLLI. A new myocardial T1 mapping technique (MOSHA) based on single-shot acquisitions is developed by combining the MOLLI and SASHA sequences. Phantom and patient studies on 15 patients (9 males, median age 21 years) were performed to validate and compare MOSHA with the MOLLI and SASHA sequences in terms of accuracy and precision. ResultsIn the phantom study, MOSHA was as accurate as SASHA (P-value = 0.88) and as precise as MOLLI (P-value = 0.59). Similar trends were observed in the patient study. Compared to SASHA, MOSHA accuracy was comparable for blood pre-contrast (P-value≥0.10) and post-contrast (P-value≥0.70), and for myocardium pre-contrast (P-value = 0.70) and post-contrast (P-value = 0.09). Compared to MOLLI, MOSHA precision was lower for blood pre-contrast (P-value<0.01) and higher for blood post-contrast (P-value≤0.01), and comparable for myocardium pre-contrast (P-value = 0.24) and post-contrast (P-value = 0.07). Synthetic Extracellular volume fraction (ECV) calculated by MOSHA was more precise than those of SASHA and MOLLI (P-value≤0.01). ConclusionIn phantom studies and patients, MOSHA has comparable accuracy as SASHA and nearly similar precision as MOLLI for T1 mapping. Precision of MOSHA was better than MOLLI and SASHA in synthetic ECV measurements. Therefore, it may be a superior choice in clinical practice for a precise and accurate calculation of T1 and ECV.

Climate Modeling in Low Precision: Effects of Both Deterministic and Stochastic Rounding

Abstract Motivated by recent advances in operational weather forecasting, we study the efficacy of low-precision arithmetic for climate simulations. We develop a framework to measure rounding error in a climate model, which provides a stress test for a low-precision version of the model, and we apply our method to a variety of models including the Lorenz system, a shallow water approximation for flow over a ridge, and a coarse-resolution spectral global atmospheric model with simplified parameterizations (SPEEDY). Although double precision [52 significant bits (sbits)] is standard across operational climate models, in our experiments we find that single precision (23 sbits) is more than enough and that as low as half precision (10 sbits) is often sufficient. For example, SPEEDY can be run with 12 sbits across the code with negligible rounding error, and with 10 sbits if minor errors are accepted, amounting to less than 0.1 mm (6 h)−1 for average gridpoint precipitation, for example. Our test is based on the Wasserstein metric and this provides stringent nonparametric bounds on rounding error accounting for annual means as well as extreme weather events. In addition, by testing models using both round-to-nearest (RN) and stochastic rounding (SR) we find that SR can mitigate rounding error across a range of applications, and thus our results also provide some evidence that SR could be relevant to next-generation climate models. Further research is needed to test if our results can be generalized to higher resolutions and alternative numerical schemes. However, the results open a promising avenue toward the use of low-precision hardware for improved climate modeling. Significance Statement Weather and climate models provide vital information for decision-making, and will become ever more important in the future with a changed climate and more extreme weather. A central limitation to improved models are computational resources, which is why some weather forecasters have recently shifted from conventional 64-bit to more efficient 32-bit computations, which can provide equally accurate forecasts. Climate models, however, still compute in 64 bits, and adapting to lower precision requires a detailed analysis of rounding errors. We develop methods to quantify rounding error in a climate model, and find similar precision acceptable across weather and climate models, with even 16 bits often sufficient for an accurate climate. This opens a promising avenue for computational efficiency gains in climate modeling.

Fast quasi-automated 3D reconstruction of lower limbs from low dose biplanar radiographs using statistical shape models and contour matching

Three-dimensional bone reconstructions from medical imaging are essential for biomechanical modelling and are growing tools in clinics. Several methods of lower limbs reconstruction from biplanar radiographs have been proposed in the literature but with significant operator dependence. A novel reconstruction method based on reduced manual annotation, statistical shape models and fully automatic adjustments was proposed in this study. While significantly reducing operator intervention, the proposed method demonstrated similar or better precision than previous approaches on clinical parameters. Meanwhile, shape accuracy was improved to around 1mm. By quasi-automating the 3D reconstruction without loss of accuracy and precision, the proposed approach is a considerable step towards extensive use of 3D personalized models in clinical routine and large cohort biomechanical studies.

Improved accuracy and precision with three-parameter simultaneous myocardial T1 and T2 mapping using multiparametric SASHA.

To develop and validate a three-parameter model for improved precision multiparametric SAturation-recovery single-SHot Acquisition (mSASHA) cardiac T1 and T2 mapping with high accuracy in a single breath-hold. The mSASHA acquisition consists of nine images of variable saturation recovery and T2 preparation in 11 heartbeats with T1 and T2 values calculated using a three-parameter model. It was validated in simulations and phantoms at 3 T with comparison to a four-parameter joint T1 -T2 technique. The mSASHA acquisition was compared with MOLLI, SASHA, and T2 -prepared balanced SSFP in 10 volunteers. The mSASHA technique had high accuracy in phantoms compared to spin echo, with -0.2 ± 0.3% T1 error and -2.4 ± 1.3% T2 error. The mSASHA coefficient of variation in phantoms for T1 was similar to MOLLI (0.7 ± 0.2% for both) and T2 -prepared balanced SSFP for T2 (1.3 ± 0.7% vs 1.4 ± 0.3%, adjusted p > .05 for both). In simulations, three-parameter mSASHA had higher precision than four-parameter joint T1 -T2 for both T1 and T2 (46% and 11% reductions in T1 and T2 interquartile range for native myocardium). In vivo myocardial mSASHA T1 was similar to SASHA (1523 ± 18 ms vs 1520 ± 18 ms) with similar coefficient of variation to both MOLLI and SASHA (3.3 ± 0.6% vs 3.1 ± 0.6% and 3.3 ± 0.5% respectively, adjusted p > .05 for all). Myocardial mSASHA T2 was 37.1 ± 1.1 ms with similar precision to T2 -prepared balanced SSFP (6.7 ± 1.7% vs 6.0 ± 1.6%, adjusted p > .05). Three-parameter mSASHA provides high-accuracy cardiac T1 and T2 quantification in a single breath-hold with similar precision to MOLLI and T2 -prepared balanced SSFP. Further study is required to both establish normative values and demonstrate clinical utility in patient populations.