bluuebunny/crossref_metadata_2025_split huggingface dataset:
Strategic Investment Subsidies under Asymmetric Oligopoly
Authors: Jinji Naoto , Toshimitsu Tsuyoshi 
Published: July 2014

AbstractThis paper examines strategic investment subsidies in an international oligopoly. A general oligopoly model is constructed in which firms compete in two stages and governments commit to investment subsidies prior to firms' actions. The paper considers asymmetry among firms that arises from the nature of goods they produce rather than their cost structures. When firms produce asymmetrically differentiated goods, it is found that a change in the number of foreign competitors may alter the sign of the optimal unilateral investment subsidy. An example of policy reversal is provided in the case of strategic research and development subsidies for a quality‐differentiated industry.
The Volatility of Foreign Portfolio Investment and the Access to Finance of Small Listed Firms
Authors: Knill April , Lee Bong Soo 
Published: July 2014

AbstractThis paper examines the impact of foreign portfolio investment (FPI) volatility on the access to capital of small listed firms. The volatility of FPI is significantly associated with decreased access to finance for small listed firms only in years when nations are considered less “creditworthy.” Even in these times, however, the benefits of FPI are not completely depleted. These results underscore the significance of both a good financial system that minimizes capital flow volatility and national creditworthiness in inspiring confidence in foreign investors.
Do the Effects of Corruption upon Growth Differ Between Democracies and Autocracies?
Authors: Assiotis Andreas , Sylwester Kevin 
Published: July 2014

AbstractMany studies examining whether corruption lowers economic growth do not consider if the effects of corruption differ across countries. Whether corruption produces the same effects everywhere or whether its effects are conditional on some country characteristics are important questions. We investigate the association between corruption and growth, where the marginal impact of corruption is allowed to differ across democratic and nondemocratic regimes. Using cross‐country, annual data from 1984 to 2007, we regress growth on corruption, democracy and their interaction. We find that decreases in corruption raise growth but more so in authoritarian regimes. Possible reasons are that in autocracies corruption causes more uncertainty, is of a more pernicious nature, or is less substitutable with other forms of rent seeking.
Susceptibility to imazamox in Italian weedy rice populations and Clearfield® rice varieties
Authors: Andres A , Fogliatto S , Ferrero A , Vidotto F 
Published: July 2014

SummaryThe introduction of imidazolinone‐tolerant rice varieties has made selective Oryza sativa (weedy rice) control possible. We hypothesised that Italian weedy rice populations have variable degrees of susceptibility to imazamox prior to imidazolinone‐tolerant variety introduction. To this end, 149 Italian weedy rice populations collected from fields never before cultivated with imidazolinone‐tolerant varieties were tested in a glasshouse‐based, whole‐plant response screening study. Imazamox was applied to all populations post‐emergence at a rate of 70 g a.i. ha−1, resulting in 70–90% shoot biomass reduction in the majority of cases. The results prompted a second study of the seedling dose response of four weedy rice populations from the initial study group. Three imidazolinone‐tolerant and one conventional rice variety were also included. The seedling roots were cut six days after germination and exposed to different concentrations of imazamox. The root regrowth associated with each concentration‐exposure was then measured. Imazamox concentrations to inhibit weedy rice root growth by 50% varied by about two orders of magnitude, or between 0.0018 and 0.12 mm. Even with this result, imidazolinone‐tolerant varieties were at least 31.8 times less susceptible than weedy rice populations, suggesting that Italian weedy rice populations were not tolerant to imazamox before introduction of these varieties.
A complete software application for automatic registration of x‐ray mammography and magnetic resonance images
Authors: Solves‐Llorens J. A. , Rupérez M. J. , Monserrat C. , Feliu E. , García M. , Lloret M. 
Published: July 2014

Purpose:This work presents a complete and automatic software application to aid radiologists in breast cancer diagnosis. The application is a fully automated method that performs a complete registration of magnetic resonance (MR) images and x‐ray (XR) images in both directions (from MR to XR and from XR to MR) and for both x‐ray mammograms, craniocaudal (CC), and mediolateral oblique (MLO). This new approximation allows radiologists to mark points in the MR images and, without any manual intervention, it provides their corresponding points in both types of XR mammograms and vice versa.Methods:The application automatically segments magnetic resonance images and x‐ray images using the C‐Means method and the Otsu method, respectively. It compresses the magnetic resonance images in both directions, CC and MLO, using a biomechanical model of the breast that distinguishes the specific biomechanical behavior of each one of its three tissues (skin, fat, and glandular tissue) separately. It makes a projection of both compressions and registers them with the original XR images using affine transformations and nonrigid registration methods.Results:The application has been validated by two expert radiologists. This was carried out through a quantitative validation on 14 data sets in which the Euclidean distance between points marked by the radiologists and the corresponding points obtained by the application were measured. The results showed a mean error of 4.2 ± 1.9 mm for the MRI to CC registration, 4.8 ± 1.3 mm for the MRI to MLO registration, and 4.1 ± 1.3 mm for the CC and MLO to MRI registration.Conclusions:A complete software application that automatically registers XR and MR images of the breast has been implemented. The application permits radiologists to estimate the position of a lesion that is suspected of being a tumor in an imaging modality based on its position in another different modality with a clinically acceptable error. The results show that the application can accelerate the mammographic screening process for high risk populations or for dense breasts.
Evaluating the utility of intraprocedural 3D TRUS image information in guiding registration for displacement compensation during prostate biopsy
Authors: De Silva Tharindu , Cool Derek W. , Romagnoli Cesare , Fenster Aaron , Ward Aaron D. 
Published: July 2014

Purpose:In targeted 3D transrectal ultrasound (TRUS)‐guided biopsy, patient and prostate movement during the procedure can cause target misalignments that hinder accurate sampling of preplanned suspicious tissue locations. Multiple solutions have been proposed for displacement compensation via registration of intraprocedural TRUS images to a baseline 3D TRUS image acquired at the beginning of the biopsy procedure. While 2D TRUS images are widely used for intraprocedural guidance, some solutions utilize richer intraprocedural images such as bi‐ or multiplanar TRUS or 3D TRUS, acquired by specialized probes. In this work, the impact of such richer intraprocedural imaging on displacement compensation accuracy was measured to evaluate the tradeoff between cost and complexity of intraprocedural imaging versus improved displacement compensation.Methods:Baseline and intraprocedural 3D TRUS images were acquired from 29 patients at standard sextant‐template biopsy locations. Planes extracted from 3D TRUS images acquired at sextant positions were used to simulate 2D and 3D intraprocedural information available in different potential clinically relevant scenarios for co‐registration with the baseline 3D TRUS image. In practice, intraprocedural 3D information can be acquired either via the use of specialized ultrasound probes (e.g., multiplanar or 3D probes) or via axial rotation of a tracked 2D TRUS probe. Registration accuracy was evaluated by calculating the target registration error (TRE) using manually identified homologous intrinsic fiducial markers (microcalcifications). The TRE was analyzed separately at the base, mid‐gland and apex regions of the prostate.Results:The results indicate that TRE improved gradually as the number of intraprocedural imaging planes used in registration was increased, implying that 3D TRUS information assisted the registration algorithm to robustly converge to more accurate solutions. The acquisition of a partial volume up to the angle of rotation supported more accurate displacement compensation than acquiring biplane configurations. Additional intraprocedural 3D TRUS image information was more beneficial to registration accuracy in the base and apex regions as compared with the mid‐gland region.Conclusions:While the majority of the registrations using 2D TRUS images provided a clinically desired level of accuracy, intraprocedural 3D imaging helped improve the overall registration accuracy and robustness, especially in the base and apex regions of the prostate. These results are helpful for devising image‐based registration methods for displacement compensation when designing 3D TRUS‐guided biopsy systems.
SU‐C‐17A‐05: Quantification of Intra‐Fraction Motion of Breast Tumors Using Cine‐MRI
Authors: van Heijst T , Philippens M , van den Bongard D , van Asselen B , Lagendijk J , Kleijnen J , den Hartogh M 
Published: July 2014

Purpose:Magnetic resonance imaging (MRI) enables direct characterization of intra‐fraction motion ofbreast tumors, due to high softtissue contrast and geometric accuracy. The purpose is to analyzethis motion in early‐stage breast‐cancer patients using pre‐operative supine cine‐MRI.Methods:MRI was performed in 12 female early‐stage breast‐cancer patients on a 1.5‐T Ingenia (Philips)wide‐bore scanner in supine radiotherapy (RT) position, prior to breast‐conserving surgery. Twotwodimensional (2D) T2‐weighted balanced fast‐field echo (cine‐MRI) sequences were added tothe RT protocol, oriented through the tumor. They were alternately acquired in the transverse andsagittal planes, every 0.3 s during 1 min. A radiation oncologist delineated gross target volumes(GTVs) on 3D contrast‐enhanced MRI. Clinical target volumes (CTV = GTV + 15 mm isotropic)were generated and transferred onto the fifth time‐slice of the time‐series, to which subsequents lices were registered using a non‐rigid Bspline algorithm; delineations were transformed accordingly. To evaluate intra‐fraction CTV motion, deformation fields between the transformed delineations were derived to acquire the distance ensuring 95% surface coverage during scanning(P95%), for all in‐plane directions: anteriorposterior (AP), left‐right (LR), and caudal‐cranial(CC). Information on LR was derived from transverse scans, CC from sagittal scans, AP fromboth sets.Results:Time‐series with registration errors – induced by motion artifacts – were excluded by visual inspection. For our analysis, 11 transverse, and 8 sagittal time‐series were taken into account. Themedian P95% calculated in AP (19 series), CC (8), and LR (11) was 1.8 mm (range: 0.9–4.8), 1.7mm (0.8–3.6), and 1.0 mm (0.6–3.5), respectively.Conclusion:Intra‐fraction motion analysis of breast tumors was achieved using cine‐MRI. These first results show that in supine RT position, motion amplitudes are limited. This information can be used for adaptive RT planning, and to develop preoperative partial‐breast RT strategies, such asablative RT for early‐stage breast‐cancer patients.
SU‐C‐18A‐03: Automatic Organ at Risk Delineation with Machine Learning Techniques
Authors: Bernard G , Verleysen M , Lee J 
Published: July 2014

Purpose:Manual delineation of organs at risk (OARs) on CT images consumes much time. Automatic segmentation methods like atlases partly address this issue. However, atlases depend on deformable registration quality. This work proposes an atlas‐like method that relies on machine learning techniques instead of registration.Methods:First, a watershed algorithm segments filtered CT images into superpixels (images patches with similar intensity pixels). Next, two kinds of superpixel features are computed: intrinsic ones (known at all times, like superpixel size, position, and mean intensity) and extrinsic ones (to be inferred from partial delineation results, like the distances to other organs). To build the atlas, a binary classifier is associated with each organ. Training optimizes the classifiers' parameters as well as their sequence, to make the most useful extrinsic features available as soon as possible. After training, the sequence of binary classifiers can process any new image, tagging all superpixels incrementally with an OAR label. The method was applied to 2D CT images of 49 breast‐cancer patients (axial slice passing through the 7th thoracic vertebra). The balanced classification rate (BCR) measures the method's accuracy, by giving the percentage of correctly classified pixels per label.Results:The proposed incremental method (BCR = 94%) is compared to two similar classification procedures, with either no extrinsic features (blind, BCR = 84%) or all of them known beforehand (cheating oracle, BCR = 98%). A preliminary comparison with a registration‐based atlas on synthetic data led to 97% (registration) and 98% (proposed).Conclusion:This abstract demonstrates the feasibility of atlas‐like OAR delineation based on machine learning techniques instead of deformable registration. The proposed method relies on incremental classification (partial classification allows additional highly informative features to be inferred). Involving no elastic deformation, delineation can be easily corrected if needed, just by changing the erroneous superpixel labels.John Aldo Lee is a Research Associate with the Belgian fund of scientific research F.R.S‐FNRS.
SU‐C‐18A‐04: 3D Markerless Registration of Lung Based On Coherent Point Drift: Application in Image Guided Radiotherapy
Authors: Nasehi Tehrani J , Wang J , Guo X , Yang Y 
Published: July 2014

Purpose:This study evaluated a new probabilistic non‐rigid registration method called coherent point drift for real time 3D markerless registration of the lung motion during radiotherapy.Method:4DCT image datasets Dir‐lab (www.dir‐lab.com) have been used for creating 3D boundary element model of the lungs. For the first step, the 3D surface of the lungs in respiration phases T0 and T50 were segmented and divided into a finite number of linear triangular elements. Each triangle is a two dimensional object which has three vertices (each vertex has three degree of freedom). One of the main features of the lungs motion is velocity coherence so the vertices that creating the mesh of the lungs should also have features and degree of freedom of lung structure. This means that the vertices close to each other tend to move coherently. In the next step, we implemented a probabilistic non‐rigid registration method called coherent point drift to calculate nonlinear displacement of vertices between different expiratory phases.Results:The method has been applied to images of 10‐patients in Dir‐lab dataset. The normal distribution of vertices to the origin for each expiratory stage were calculated. The results shows that the maximum error of registration between different expiratory phases is less than 0.4 mm (0.38 SI, 0.33 mm AP, 0.29 mm RL direction). This method is a reliable method for calculating the vector of displacement, and the degrees of freedom (DOFs) of lung structure in radiotherapy.Conclusions:We evaluated a new 3D registration method for distribution set of vertices inside lungs mesh. In this technique, lungs motion considering velocity coherence are inserted as a penalty in regularization function. The results indicate that high registration accuracy is achievable with CPD. This method is helpful for calculating of displacement vector and analyzing possible physiological and anatomical changes during treatment.
SU‐C‐18C‐01: Surrogate Function Evaluation and Comparison for Atlas Selection in Multi‐Atlas‐Based Segmentation
Authors: Xie Q , Ruan D 
Published: July 2014

Purpose:To develop a systematic surrogate evaluation and comparison scheme to select a subset of training samples that best resemble the target in multi‐atlas‐based automatic segmentation, for the purpose of improved accuracy.Methods:In multi‐atlas‐based segmentation, it is desirable to fuse labels from a subset of training samples among the atlases that contribute positively to the final segmentation, for improved accuracy and efficiency. A surrogate metric is needed to prognosticate the contribution of a training image to the target segmentation. We formalize atlas subset selection as a ranking problem and develop a quality index to characterize the efficacy of a surrogate approach. A candidate surrogate is assessed in a supervised learning setting, where images with known segmentation are split into training atlases and testing targets, to enable assess to ground‐truth contribution level from a training atlas to the target, as measured by the Dice similarity coefficient (DSC). The quality index measures the discrepancy between (1) DSC in descending order as from an Oracle and (2) the reordered DSCs based on the surrogate metric. The quality index was applied in the context of automatic prostate and brain segmentations, to assess 6 surrogate candidates, namely, mean square error (MSE), normalized cross correlation (NCC), and mutual information (MI), either measured within a fixed region of interest (ROI), fROI, or adaptive ROI (aROI).Results:Surrogate metrics measured within aROI significantly outperform those within fROI (paired t‐test, p‐values consistently below 0.067), yet no statistically significant difference among MSE/NCC/MI (paired t‐test, 0.014≤p≤0.941).Conclusion:We have developed a quality index to quantitatively evaluate and compare surrogates' efficacy in atlas selection for automatic segmentation. When applied to comparing MSE/NCC/MI within fROI/aROI, it shows that surrogate metrics over aROI instead of fROI significantly improve segmentation performance, while there is no statistical evidence to support the superiority among MSE/NCC/MI.
SU‐D‐18C‐01: A Novel 4D‐MRI Technology Based On K‐Space Retrospective Sorting
Authors: Liu Y , Yin F , Cai J 
Published: July 2014

Purpose:Current 4D‐MRI techniques lack sufficient temporal/spatial resolution and consistent tumor contrast. To overcome these limitations, this study presents the development and initial evaluation of an entirely new framework of 4D‐MRI based on k‐space retrospective sorting.Methods:An important challenge of the proposed technique is to determine the number of repeated scans(NR) required to obtain sufficient k‐space data for 4D‐MRI. To do that, simulations using 29 cancer patients' respiratory profiles were performed to derive the relationship between data acquisition completeness(Cp) and NR, also relationship between NR(Cp=95%) and the following factors: total slice(NS), respiratory phase bin length(Lb), frame rate(fr), resolution(R) and image acquisition starting‐phase(P0). To evaluate our technique, a computer simulation study on a 4D digital human phantom (XCAT) were conducted with regular breathing (fr=0.5Hz; R=256×256). A 2D echo planer imaging(EPI) MRI sequence were assumed to acquire raw k‐space data, with respiratory signal and acquisition time for each k‐space data line recorded simultaneously. K‐space data was re‐sorted based on respiratory phases. To evaluate 4D‐MRI image quality, tumor trajectories were measured and compared with the input signal. Mean relative amplitude difference(D) and cross‐correlation coefficient(CC) are calculated. Finally, phase‐sharing sliding window technique was applied to investigate the feasibility of generating ultra‐fast 4D‐MRI.Result:Cp increased with NR(Cp=100*[1‐exp(‐0.19*NR)], when NS=30, Lb=100%/6). NR(Cp=95%) was inversely‐proportional to Lb (r=0.97), but independent of other factors. 4D‐MRI on XCAT demonstrated highly accurate motion information (D=0.67%, CC=0.996) with much less artifacts than those on image‐based sorting 4D‐MRI. Ultra‐fast 4D‐MRI with an apparent temporal resolution of 10 frames/second was reconstructed using the phase‐sharing sliding window technique.Conclusions:A novel 4D‐MRI technology based on k‐space sorting has been successfully developed and evaluated on the digital phantom. Framework established can be applied to a variety of MR sequences, showing great promises to develop the optimal 4D‐MRI technique for many radiation therapy applications.NIH (1R21CA165384‐01A1)
SU‐D‐18C‐04: The Feasibility of Quantifying MRI Contrast Agent in Pulsatile Flowing Blood Using DCE‐MRI
Authors: N Gwilliam M , J Collins D , O Leach M , R Orton M 
Published: July 2014

Purpose:To assess the feasibility of accurately quantifying the concentration of MRI contrast agent (CA) in pulsatile flowing blood by measuring its T1, as is common for the purposes of obtaining a patientspecific arterial input function (AIF). Dynamic contrast enhanced (DCE) – MRI and pharmacokinetic (PK) modelling is widely used to produce measures of vascular function but accurate measurement of the AIF undermines their accuracy. A proposed solution is to measure the T1 of blood in a large vessel using the Fram double flip angle method during the passage of a bolus of CA. This work expands on previous work by assessing pulsatile flow and the changes in T1 seen with a CA bolus.Methods:A phantom was developed which used a physiological pump to pass fluid of a known T1 (812ms) through the centre of a head coil of a clinical 1.5T MRI scanner. Measurements were made using high temporal resolution sequences suitable for DCE‐MRI and were used to validate a virtual phantom that simulated the expected errors due to pulsatile flow and bolus of CA concentration changes typically found in patients.Results:: Measured and virtual results showed similar trends, although there were differences that may be attributed to the virtual phantom not accurately simulating the spin history of the fluid before entering the imaging volume. The relationship between T1 measurement and flow speed was non‐linear. T1 measurement is compromised by new spins flowing into the imaging volume, not being subject to enough excitations to have reached steady‐state. The virtual phantom demonstrated a range of recorded T1 for various simulated T1 / flow rates.Conclusion:T1 measurement of flowing blood using standard DCE‐MRI sequences is very challenging. Measurement error is non‐linear with relation to instantaneous flow speed. Optimising sequence parameters and lowering baseline T1 of blood should be considered.