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Results for “level set methods”

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DOE OSTI2015

SU-F-207-16: CT Protocols Optimization Using Model Observer

Purpose: To quantitatively evaluate the performance of different CT protocols using task-based measures of image quality. This work studies the task of size and the contrast estimation of different iodine concentration rods inserted in head- and body-sized phantoms using different imaging protocols. These protocols are designed to have the same dose level (CTDIvol) but using different X-ray tube voltage settings (kVp). Methods: Different concentrations of iodine objects inserted in a head size phantom and a body size phantom are imaged on a 64-slice commercial CT scanner. Scanning protocols with various tube voltages (80, 100, and 120 kVp) and current settings are selected, which output the same absorbed dose level (CTDIvol). Because the phantom design (size of the iodine objects, the air gap between the inserted objects and the phantom) is not ideal for a model observer study, the acquired CT images are used to generate simulation images with four different sizes and five different contracts iodine objects. For each type of the objects, 500 images (100 x 100 pixels) are generated for the observer study. The observer selected in this study is the channelized scanning linear observer which could be applied to estimate the size and the contrast. The figure of merit used is the correct estimation ratio. The mean and the variance are estimated by the shuffle method. Results: The results indicate that the protocols with 100 kVp tube voltage setting provides the best performance for iodine insert size and contrast estimation for both head and body phantom cases. Conclusion: This work presents a practical and robust quantitative approach using channelized scanning linear observer to study contrast and size estimation performance from different CT protocols. Different protocols at same CTDIvol setting could Result in different image quality performance. The relationship between the absorbed dose and the diagnostic image quality is not linear.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

First model-independent Dalitz analysis of B 0 → DK* 0 , D→K$^{0}_{S}$π + π – decay

We report a measurement of the amplitude ratio r S of B 0 → D 0 K* 0 and B 0 → D 0 ¯K* 0 decays with a Dalitz analysis of D → K$^{0}_{S}$π + π – decays, for the first time using a model-independent method. We set an upper limit rS<0.87 at the 68\% confidence level, using the full data sample of 772×10 6 BB¯ pairs collected at the Υ(4S) resonance with the Belle detector at the KEKB e + e – collider. This result is obtained from observables x–= +0.4$^{+1.0+0.0}_{–0.6–0.1}$ ± 0.0, y– = –0.6$^{+0.8+0.1}_{–1.0–0.0}$ ± 0.1, x + = +0.1$^{+0.7+0.0}_{–0.4–0.1}$ ± 0.1 and y + = +0.3$^{+0.5+0.0}_{–0.8–0.1}$ ± 0.1, where x ± = r S cos(δ S ± Φ 3 ), y ± = r S sin(δ S ± Φ 3 ) and Φ 3 (δ S ) is the weak (strong) phase difference between B 0 → D 0 K* 0 and B 0 → D¯ 0 K* 0 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2014

Generalized conditional symmetries and related solutions of the Grad-Shafranov equation

The generalized conditional symmetry (GCS) method is applied to a specific case of the Grad–Shafranov (GS) equation, in cylindrical geometry assuming the existence of an axial symmetry. We investigate the conditions that yield the GS equation admitting a special class of second-order GCSs. The determining system for the unknown arbitrary functions is solved in several special cases and new exact solutions, including solitary waves, different in form and structure from the ones obtained using other nonclassical symmetry methods, are pointed out. Several plots of the level sets or flux surfaces of the new solutions as well as surfaces with vanishing flow are displayed. The obtained solutions can be useful for studying plasma equilibrium, transport phenomena, and magnetohydrodynamic stability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Multiclass Classification Using Bayesian Multivariate Adaptive Regression Splines

We present a new Bayesian model for the problem of multiclass classification. In this model, the probabilities of class membership of a given observation are determined by the mean of a latent Gaussian distribution. The mean functions of this latent distribution consist of combinations of highly flexible basis functions of the inputs: multivariate adaptive regression splines (MARS), first developed for multiple regression. We use reversible jump Markov chain Monte Carlo to make inference on the classification model, including the number of basis functions. We compare the probabilistic classification performance of our proposed approach to existing methods on simulated and benchmark data, and compare uncertainty estimates on simulated data. Our proposed method compares favorably with existing Bayesian and frequentist multiclass classification methods in out-of-sample probabilistic classification, and uncertainty estimation of these probabilistic classifications. We examine the fit of the proposed method to a data set of hurricane storm surge levels near Delaware Bay, US, and conclude that sea level rise is a key contributor to damage delivered by storm surge.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Global variance reduction for Monte Carlo reactor physics calculations

Over the past few decades, hybrid Monte-Carlo-Deterministic (MC-DT) techniques have been mostly focusing on the development of techniques primarily with shielding applications in mind, i.e. problems featuring a limited number of responses. This paper focuses on the application of a new hybrid MC-DT technique: the SUBSPACE method, for reactor analysis calculation. The SUBSPACE method is designed to overcome the lack of efficiency that hampers the application of MC methods in routine analysis calculations on the assembly level where typically one needs to execute the flux solver in the order of 10{sup 3}-10{sup 5} times. It places high premium on attaining high computational efficiency for reactor analysis application by identifying and capitalizing on the existing correlations between responses of interest. This paper places particular emphasis on using the SUBSPACE method for preparing homogenized few-group cross section sets on the assembly level for subsequent use in full-core diffusion calculations. A BWR assembly model is employed to calculate homogenized few-group cross sections for different burn-up steps. It is found that using the SUBSPACE method significant speedup can be achieved over the state of the art FW-CADIS method. While the presented speed-up alone is not sufficient to render the MC method competitive with the DT method, we believe this work will become a major step on the way of leveraging the accuracy of MC calculations for assembly calculations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2016

Generic and robust method for automatic segmentation of PET images using an active contour model

Purpose: Although positron emission tomography (PET) images have shown potential to improve the accuracy of targeting in radiation therapy planning and assessment of response to treatment, the boundaries of tumors are not easily distinguishable from surrounding normal tissue owing to the low spatial resolution and inherent noisy characteristics of PET images. The objective of this study is to develop a generic and robust method for automatic delineation of tumor volumes using an active contour model and to evaluate its performance using phantom and clinical studies. Methods: MASAC, a method for automatic segmentation using an active contour model, incorporates the histogram fuzzy C-means clustering, and localized and textural information to constrain the active contour to detect boundaries in an accurate and robust manner. Moreover, the lattice Boltzmann method is used as an alternative approach for solving the level set equation to make it faster and suitable for parallel programming. Twenty simulated phantom studies and 16 clinical studies, including six cases of pharyngolaryngeal squamous cell carcinoma and ten cases of nonsmall cell lung cancer, were included to evaluate its performance. Besides, the proposed method was also compared with the contourlet-based active contour algorithm (CAC) and Schaefer’s thresholding method (ST). The relative volume error (RE), Dice similarity coefficient (DSC), and classification error (CE) metrics were used to analyze the results quantitatively. Results: For the simulated phantom studies (PSs), MASAC and CAC provide similar segmentations of the different lesions, while ST fails to achieve reliable results. For the clinical datasets (2 cases with connected high-uptake regions excluded) (CSs), CAC provides for the lowest mean RE (−8.38% ± 27.49%), while MASAC achieves the best mean DSC (0.71 ± 0.09) and mean CE (53.92% ± 12.65%), respectively. MASAC could reliably quantify different types of lesions assessed in this work with good accuracy, resulting in a mean RE of −13.35% ± 11.87% and −11.15% ± 23.66%, a mean DSC of 0.89 ± 0.05 and 0.71 ± 0.09, and a mean CE of 19.19% ± 7.89% and 53.92% ± 12.65%, for PSs and CSs, respectively. Conclusions: The authors’ results demonstrate that the developed novel PET segmentation algorithm is applicable to various types of lesions in the authors’ study and is capable of producing accurate and consistent target volume delineations, potentially resulting in reduced intraobserver and interobserver variabilities observed when using manual delineation and improved accuracy in treatment planning and outcome evaluation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Digital zero noise extrapolation for quantum error mitigation

Zero-noise extrapolation (ZNE) is an increasingly popular technique for mitigating errors in noisy quantum computations without using additional quantum resources. We review the fundamentals of ZNE and propose several improvements to noise scaling and extrapolation, the two key components in the technique. We introduce unitary folding and parameterized noise scaling. Furthermore, these are digital noise scaling frameworks, i.e. one can apply them using only gate-level access common to most quantum instruction sets. We also study different extrapolation methods, including a new adaptive protocol that uses a statistical inference framework. Benchmarks of our techniques show error reductions of 18X to 24X over non-mitigated circuits and demonstrate ZNE effectiveness at larger qubit numbers than have been tested previously. In addition to presenting new results, this work is a self-contained introduction to the practical use of ZNE by quantum programmers.

quantum computing↗

DOE OSTI2012

Aerial Scene Recognition using Efficient Sparse Representation

Advanced scene recognition systems for processing large volumes of high-resolution aerial image data are in great demand today. However, automated scene recognition remains a challenging problem. Efficient encoding and representation of spatial and structural patterns in the imagery are key in developing automated scene recognition algorithms. We describe an image representation approach that uses simple and computationally efficient sparse code computation to generate accurate features capable of producing excellent classification performance using linear SVM kernels. Our method exploits unlabeled low-level image feature measurements to learn a set of basis vectors. We project the low-level features onto the basis vectors and use simple soft threshold activation function to derive the sparse features. The proposed technique generates sparse features at a significantly lower computational cost than other methods~\cite{Yang10, newsam11}, yet it produces comparable or better classification accuracy. We apply our technique to high-resolution aerial image datasets to quantify the aerial scene classification performance. We demonstrate that the dense feature extraction and representation methods are highly effective for automatic large-facility detection on wide area high-resolution aerial imagery.

Computer Vision↗

DOE OSTI2021

Extending quantum probabilistic error cancellation by noise scaling

Here, we propose a general framework for quantum error mitigation that combines and generalizes two techniques: probabilistic error cancellation (PEC) and zero-noise extrapolation (ZNE). Similar to PEC, the proposed method represents ideal operations as linear combinations of noisy operations that are implementable on hardware. However, instead of assuming a fixed level of hardware noise, we extend the set of implementable operations by noise scaling. By construction, this method encompasses both PEC and ZNE as particular cases and allows us to investigate a larger set of hybrid techniques. For example, gate extrapolation can be used to implement PEC without requiring knowledge of the device’s noise model, e.g., avoiding gate-set tomography. Alternatively, probabilistic error reduction can be used to estimate expectation values at intermediate virtual noise strengths (below the hardware level), leading to partially mitigated results at a lower sampling cost. Moreover, multiple results obtained with different noise-reduction factors can be further postprocessed with ZNE to better approximate the zero-noise limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Enhanced Uranium Ore Concentrate Analysis by Handheld Raman Sensor: FY15 Status Report

High-purity uranium ore concentrates (UOC) represent a potential proliferation concern. A cost-effective, “point and shoot” in-field analysis capability to identify ore types, phases of materials present, and impurities, as well as estimate the overall purity would be prudent. Handheld, Raman-based sensor systems are capable of identifying chemical properties of liquid and solid materials. While handheld Raman systems have been extensively applied to many other applications, they have not been broadly studied for application to UOC, nor have they been optimized for this class of chemical compounds. PNNL was tasked in Fiscal Year 2015 by the Office of International Safeguards (NA-241) to explore the use of Raman for UOC analysis and characterization. This report summarizes the activities in FY15 related to this project. The following tasks were included: creation of an expanded library of Raman spectra of a UOC sample set, creation of optimal chemometric analysis methods to classify UOC samples by their type and level of impurities, and exploration of the various Raman wavelengths to identify the ideal instrument settings for UOC sample interrogation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Automatic microseismic event picking via unsupervised machine learning

SUMMARY Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

Chen, Yangkang↗

DOE OSTI2018

The clustering of theSDSS-IV extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample: measuring the anisotropic baryon acoustic oscillations with redshift weights

We present an anisotropic analysis of baryon acoustic oscillation (BAO) signal from the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey Data Release 14 quasar sample. The sample consists of 147 000 quasars distributed over a redshift range of 0.8 < z < 2.2. We apply the redshift weights technique to the clustering of quasars in this sample and achieve a 4.6 per cent measurement of the angular distance measurement DM at z = 2.2 and Hubble parameter H at z = 0.8. We parametrize the distance-redshift relation, relative to a fiducial model, as a Taylor series. The coefficients of this expansion are used to reconstruct the distance-redshift relation and obtain distance and Hubble parameter measurements at all redshifts within the redshift range of the sample. Reporting the result at two characteristic redshifts, we determine DM(z = 1) = 3405 ± 305 (rd/rd, fid) Mpc, H(z=1) = 120.7± 7.3 (r_d,fid / r_d) km s^{-1} Mpc^{-1} and DM(z = 2) = 5325 ± 249 (rd/rd, fid) Mpc, H(z=2) = 189.9± 32.9 (r_d,fid / r_d) km s^{-1} Mpc^{-1}. These measurements are highly correlated. We assess the outlook of BAO analysis from the final quasar sample by testing the method on a set of mocks that mimic the noise level in the final sample. We demonstrate on these mocks that redshift weighting shrinks the measurement error by over 25 per cent on average. We conclude redshift weighting can bring us closer to the cosmological goal of the final quasar sample.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Population of the giant pairing vibration

Background: We present that the giant pairing vibration (GPV), a correlated two-nucleon mode in the second shell above the Fermi surface, has long been predicted and expected to be strongly populated in two-nucleon transfer cross sections similar to those of the normal pairing vibration. Recent experiments have provided evidence for this mode in 14,15 C but, despite sensitive studies, it has not been definitively identified in either Sn or Pb nuclei where pairing correlations are known to play a crucial role. Purpose: Our aim is to test whether features inherent to the mixing of bound and unbound levels might account for this and to study the effect in a simple and intuitively clear approach. Method: We study the mixing of unbound levels in a set of toy models that capture the essential physics of the GPV, along with a more realistic calculation including distorted-wave Born approximation transfer amplitudes. Results: The calculated (relative) cross section to populate a simulated GPV state is effectively low, compared to the case of bound levels with no widths Conclusions: The mixing turns out to be only a minor contributor to the weak population. Rather, the main reason is the melting of the GPV peak due to the width it acquires from the low orbital angular momentum single-particle states playing a dominant role in two-nucleon transfer amplitudes. Lastly, this effect, in addition to a severe Q-value mismatch, may account for the elusive nature of this mode in (t,p) and (p,t) reactions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Reconstructing magnetic deflections from sets of proton images using differential evolution

Proton imaging is a powerful technique for imaging electromagnetic fields within an experimental volume, in which spatial variations in proton fluence are a result of deflections to proton trajectories due to interaction with the fields. When deflections are large, proton trajectories can overlap, and this nonlinearity creates regions of greatly increased proton fluence on the image, known as caustics. The formation of caustics has been a persistent barrier to reconstructing the underlying fields from proton images. We have developed a new method for reconstructing the path-integrated magnetic fields, which begins to address the problem posed by caustics. Our method uses multiple proton images of the same object, each image at a different energy, to fill in the information gaps and provide some uniqueness when reconstructing caustic features. We use a differential evolution algorithm to iteratively estimate the underlying deflection function, which accurately reproduces the observed proton fluence at multiple proton energies simultaneously. We test this reconstruction method using synthetic proton images generated for three different, cylindrically symmetric field geometries at various field amplitudes and levels of proton statistics and present reconstruction results from a set of experimental images. Here, the method we propose requires no assumption of deflection linearity and can reliably solve for fields underlying linear, nonlinear, and caustic proton image features for the selected geometries and is shown to be fairly robust to noise in the input proton intensity.

47 OTHER INSTRUMENTATION↗

DOE OSTI2013

Eulerian adaptive finite-difference method for high-velocity impact and penetration problems

Owing to the complex processes involved, faithful prediction of high-velocity impact events demands a simulation method delivering efficient calculations based on comprehensively formulated constitutive models. Such an approach is presented herein, employing a weighted essentially non-oscillatory (WENO) method within an adaptive mesh refinement (AMR) framework for the numerical solution of hyperbolic partial differential equations. Applied widely in computational fluid dynamics, these methods are well suited to the involved locally non-smooth finite deformations, circumventing any requirement for artificial viscosity functions for shock capturing. Application of the methods is facilitated through using a model of solid dynamics based upon hyper-elastic theory comprising kinematic evolution equations for the elastic distortion tensor. The model for finite inelastic deformations is phenomenologically equivalent to Maxwell s model of tangential stress relaxation. Closure relations tailored to the expected high-pressure states are proposed and calibrated for the materials of interest. Sharp interface resolution is achieved by employing level-set functions to track boundary motion, along with a ghost material method to capture the necessary internal boundary conditions for material interactions and stress-free surfaces. The approach is demonstrated for the simulation of high velocity impacts of steel projectiles on aluminium target plates in two and three dimensions.

Barton, Philip T.↗

DOE OSTI2022

A critical comparison of CH∙∙∙π versus π∙∙∙π interactions in the benzene dimer: obtaining benchmarks at the CCSD(T) level and assessing the accuracy of lower scaling methods

In this study, we have established CCSD(T)/CBS (Complete Basis Set) limits for 3 stationary points on the benzene dimer potential energy surface, corresponding to the π∙∙∙π (parallel displaced or PD(C 2h ), minimum) and CH∙∙∙π (T-shaped or T(C 2v ), transition state) and tilted T-shaped (or TT(Cs), minimum) bonding scenarios considering both the structure and binding energy. The CCSD(T)/CBS binding energies are -2.65 ± 0.02 (PD), -2.74 ± 0.03 (T), and -2.83 ± 0.01 kcal mol -1 (TT). To this end, the CH∙∙∙π is ~0.2 kcal mol -1 stronger than the π∙∙∙π interaction, whereas the tilting of the CH donating benzene molecule with respect to the other benzene is worth 0.1 kcal mol -1 . As previously discussed in the literature, the MP2 level of theory does not provide a close match for either the energy or structure, yet the SCS-MP2 yields structures in excellent agreement with respect to the CCSD(T) result. It is found that the SCS-MI-MP2 also gives optimized structures very close to SCS-MP2 (within ~0.01 Å of the benchmark). Despite the closer match in structure, the spin-biased MP2 methods (SCS-, SCS-MI-, and SOS-MP2) incorrectly predict the relative stabilities of the isomers. That said, none of the spin biased MP2 methods offers a good compromise between energy and structure for the systems examined. Finally, the CCSD(T)/CBS benchmarks were used to assess the performance of 13 DFT functionals selected from different rungs of Jacob's ladder. Several functionals such as TPSS-D3, B3LYP-D3, B97-D, B97-D3, and B2PLYP-D3 provided a good description of the binding energies for both CH∙∙∙π and π∙∙∙π interactions, yielding values within 6% of the CCSD(T)/CBS benchmark values. Unlike the MP2 methods, these functionals correctly predict the relative stability of the PD(C 2h ) and T(C 2v ) dimers. Further, we find that there is no systematic improvement as Jacob's ladder is ascended (increased complexity of functional). The best functionals that result in a good compromise between structure and energy accuracy are B97-D3 and B2PLYP-D3 for both the CH∙∙∙π and π∙∙∙π interaction. Despite the impressive performance of these functionals, a challenge that remains is ensuring the transferability of these density functionals in accurately describing the interaction between dimers of larger aromatic molecules, the latter requiring high-level benchmarks for these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics with Level Set Transformation.

A new approach is developed to delineate the spatial distribution of discrete facies (geological units that have unique distributions of hydraulic, physical, and/or chemical properties) conditioned not only on direct data (measurements directly related to facies properties, e.g., grain size distribution obtained from borehole samples) but also on indirect data (observations indirectly related to facies distribution, e.g., hydraulic head and tracer concentration). Our method integrates for the first time ensemble data assimilation with traditional transition probability-based geostatistics. The concept of level set is introduced to build shape parameterization that allows transformation between discrete facies indicators and continuous random variables. The spatial structure of different facies is simulated by indicator models using conditioning points selected adaptively during the iterative process of data assimilation. To evaluate the new method, a two-dimensional semi-synthetic example is designed to estimate the spatial distribution and permeability of two distinct facies from transient head data induced by pumping tests. The example demonstrates that our new method adequately captures the spatial pattern of facies distribution by imposing spatial continuity through conditioning points. The new method also reproduces the overall response in hydraulic head field with better accuracy compared to data assimilation with no constraints on spatial continuity on facies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

Purpose: A previously proposed method to reduce radiation dose to patient in dynamic contrast-enhanced (DCE) CT is enhanced by principal component analysis (PCA) filtering which improves the signal-to-noise ratio (SNR) of time-concentration curves in the DCE-CT study. The efficacy of the combined method to maintain the accuracy of kinetic parameter estimates at low temporal resolution is investigated with pixel-by-pixel kinetic analysis of DCE-CT data. Methods: The method is based on DCE-CT scanning performed with low temporal resolution to reduce the radiation dose to the patient. The arterial input function (AIF) with high temporal resolution can be generated with a coarsely sampled AIF through a previously published method of AIF estimation. To increase the SNR of time-concentration curves (tissue curves), first, a region-of-interest is segmented into squares composed of 3 × 3 pixels in size. Subsequently, the PCA filtering combined with a fraction of residual information criterion is applied to all the segmented squares for further improvement of their SNRs. The proposed method was applied to each DCE-CT data set of a cohort of 14 patients at varying levels of down-sampling. The kinetic analyses using the modified Tofts’ model and singular value decomposition method, then, were carried out for each of the down-sampling schemes between the intervals from 2 to 15 s. The results were compared with analyses done with the measured data in high temporal resolution (i.e., original scanning frequency) as the reference. Results: The patients’ AIFs were estimated to high accuracy based on the 11 orthonormal bases of arterial impulse responses established in the previous paper. In addition, noise in the images was effectively reduced by using five principal components of the tissue curves for filtering. Kinetic analyses using the proposed method showed superior results compared to those with down-sampling alone; they were able to maintain the accuracy in the quantitative histogram parameters of volume transfer constant [standard deviation (SD), 98th percentile, and range], rate constant (SD), blood volume fraction (mean, SD, 98th percentile, and range), and blood flow (mean, SD, median, 98th percentile, and range) for sampling intervals between 10 and 15 s. Conclusions: The proposed method of PCA filtering combined with the AIF estimation technique allows low frequency scanning for DCE-CT study to reduce patient radiation dose. The results indicate that the method is useful in pixel-by-pixel kinetic analysis of DCE-CT data for patients with cervical cancer.

61 RADIATION PROTECTION AND DOSIMETRY↗