Collective and independent-particle motion in two-electron artificial atoms

Investigations of the exactly solvable excitation spectra of two-electron quantum dots with a parabolic confinement, for different values of the parameter R_W expressing the relative magnitudes of the interelectron repulsion and the zero-point kinetic energy of the confined electrons, reveal for large R_W a remarkably well-developed ro-vibrational spectrum associated with formation of a linear trimeric rigid molecule composed of the two electrons and the infinitely heavy confining dot. This spectrum transforms to one characteristic of a "floppy" molecule for smaller values of R_W. The conditional probability distribution calculated for the exact two-electron wave functions allows for the identification of the ro-vibrational excitations as rotations and stretching/bending vibrations, and provides direct evidence pertaining to the formation of such molecules.Comment: Published version. Latex/Revtex, 5 pages with 2 postscript figures embedded in the text. For related papers, see http://www.prism.gatech.edu/~ph274c

surface interpolation and 3d relatability

Although the role of surface-level processes has been demonstrated, visual interpolation models often emphasize contour relationships. We report two experiments on geometric constraints governing 3D interpolation between surface patches without visible edges. Observers were asked to classify pairs of planar patches specified by random dot disparities and visible through circular apertures (aligned or misaligned) in a frontoparallel occluder. On each trial, surfaces appeared in parallel or converging planes with vertical (in Experiment 1) or horizontal (in Experiment 2) tilt and variable amounts of slant. We expected the classification task to be facilitated when patches were perceived as connected. We found enhanced sensitivity and speed for 3D relatable vs. nonrelatable patches. Here 3D relatability does not involve oriented edges but rather inducing patches' orientations computed from stereoscopic information. Performance was markedly affected by slant anisotropy: both sensitivity and speed were worse for patches with horizontal tilt. We found nearly identical advantages of 3D relatability on performance, suggesting an isotropic unit formation process. Results are interpreted as evidence that inducing slant constrains surface interpolation in the absence of explicit edge information: 3D contour and surface interpolation processes share common geometric constraints as formalized by 3D relatability

DATA ACQUISITION AND PROTECTION FOR NEW DII-D IN-VESSEL COILS

OAK-B135 The installation of new internal magnetic coils (I-Coils) in the DIII-D tokamak at General Atomics required extensive additions to the experiment data acquisition and protection capabilities. This set of 12 coils (up to 7 kA each) is designed to allow improved feedback stabilization of resistive wall modes which limit the plasma performance. The acquisition and signal conditions needs of the I-Coil power system presented an opportunity to try a new data acquisition approach which increased both the sampling rate and sample size per channel compared to the standard DIII-D CAMAC acquisition equipment. A 96 channel Compact-PCI (cPCI) digitizer system was purchased for the I-Coil project to acquire up to approximately 380 MB of power supply and coil current data per plasma discharge. Additional instrumentation and control was provided to protect personnel, the new coils, the tokamak, the facility and improve machine availability. This paper will present discussions of technical and programmatic requirements, based for requirements, the design selection outcome, installation experience, integration issues, commissioning experience, and lessons learned. The data acquisition system is described in detail including a conservative signal isolation scheme, signal grounding standards, anti-aliasing filters, and synchronization of acquisition. Protection interlocks are described, including high voltage isolation, water flow measurement, and the coil grounding-shorting switches

Dynamical tunneling in molecules: Quantum routes to energy flow

Dynamical tunneling, introduced in the molecular context, is more than two decades old and refers to phenomena that are classically forbidden but allowed by quantum mechanics. On the other hand the phenomenon of intramolecular vibrational energy redistribution (IVR) has occupied a central place in the field of chemical physics for a much longer period of time. Although the two phenomena seem to be unrelated several studies indicate that dynamical tunneling, in terms of its mechanism and timescales, can have important implications for IVR. Examples include the observation of local mode doublets, clustering of rotational energy levels, and extremely narrow vibrational features in high resolution molecular spectra. Both the phenomena are strongly influenced by the nature of the underlying classical phase space. This work reviews the current state of understanding of dynamical tunneling from the phase space perspective and the consequences for intramolecular vibrational energy flow in polyatomic molecules.Comment: 37 pages and 23 figures (low resolution); Int. Rev. Phys. Chem. (Review to appear in Oct. 2007

Use of artificial intelligence to automatically predict the optimal patient-specific inversion time for late gadolinium enhancement imaging. Tool development and clinical validation

Introduction With the worldwide diffusion of cardiac magnetic resonance (CMR), demand on image quality has grown. CMR late gadolinium enhancement (LGE) imaging provides critical diagnostic and prognostic information, and guides management. The identification of optimal Inversion Time (TI), a time-sensitive parameter closely linked to contrast kinetics, is pivotal for correct myocardium nulling. However, determining the optimal TI can be challenging in some diseases and for less experienced operators. Purpose To develop and test an artificial intelligence tool to automatically predict the personalised optimal TI in LGE imaging. Methods The tool, named THAITI, consists of a Random Forest regression model. It considers, as input parameters, patient-specific TI determinants (age, gender, weight, height, kidney function, heart rate) and CMR scan-specific TI determinants (B0, contrast type and dose, time elapsed from contrast injection). THAITI was trained on 219 patients (3585 images) with mixed conditions who underwent CMR (1.5T; Gadobutrol; averaged, MOCO, free-breathing true-FISP IR [1]) for clinical reasons. The dataset was split with a 90–10 policy: 90% of data for training, and 10% for testing. THAITI’s hyperparameters were optimised by embedding k-fold cross validation into an evolutionary computation algorithm, and the best performing model was finally evaluated on the test set. A graphical user interface was also developed. Clinical validation was performed on 55 consecutive patients, randomised to experimental (THAITI-set TI) vs control (operator-set TI) group. Image quality was assessed blindly by 2 independent experienced operators by a 4-points Likert scale, and by means of the contrast/enhancement ratio (CER) (i.e., signal intensity of enhanced/remote myocardium ratio). Results In the testing set, the TI predicted by THAITI differed from the ground truth by ≥ 5ms in 16% of cases. At clinical validation, myocardial nulling quality did not differ between the experimental vs the control group either by CER or visual assessment, with an overall "optimal" or "good" nulling in 96% vs 93%, respectively. Conclusions Using main determinants of contrast kinetics, THAITI efficiently predicted the optimal TI for CMR-LGE imaging. The tool works as a stand-alone on laptops/mobile devices, not requiring adjunctive scanner technology and thus has great potential for diffusion, including in small or recently opened CMR services, and in low-resource settings. Additional development is ongoing to increase generalisability (multi-vendor, multi-sequence, multi-contrast) and to test its potential to further improve CMR-LGE image quality and reduce the need for repeated imaging for inexperienced operators. Figure 1. Top: THAITI interface. Bottom: examples of experimental group CMR-LGE imaging. Table 1. Control vs experimental group. Data expressed as absolute number (%), mean ± SD, median [IQR]. ⧧ T-test; * Chi-square

Detection of metallic cobalt and chromium liver deposition following failed hip replacement using T2* and R2 magnetic resonance

BACKGROUND: Failed hip prostheses can cause elevated circulating cobalt and chromium levels, with rare reports of fatal systemic organ deposition, including cobalt cardiomyopathy. Although blood cobalt and chromium levels are easily measured, organ deposition is difficult to detect without invasive biopsy. The T2* magnetic resonance (MR) method is used to quantify tissue iron deposition, and plays an important role in the management of iron-loading conditions. Cobalt and chromium, like iron, also affect magnetism and are proposed MR contrast agents. CASE PRESENTATION: We describe a case of a 44-year-old male with a failed hip implant and very elevated blood cobalt and chromium levels. Despite normal cardiac MR findings, liver T2* and R2 values were abnormal, triggering tissue biopsy. Liver tissue analysis, including X-ray fluorescence, demonstrated heavy elemental cobalt and chromium deposition in macrophages, and no detectable iron. CONCLUSIONS: Our case demonstrates T2* and R2 quantification of liver metal deposition in a patient with a failed hip implant. Further work is needed to investigate the role of T2* and R2 MR in the detection of metal deposition from metal on metal hip prostheses

Three dimensional first-pass myocardial perfusion imaging at 3T: feasibility study

<p>Abstract</p> <p>Background</p> <p>In patients with ischemic heart disease, accurate assessment of the extent of myocardial perfusion deficit may be important in predicting prognosis of clinical cardiac outcomes. The aim of this study was to compare the ability of three dimensional (3D) and of two dimensional (2D) multi-slice myocardial perfusion imaging (MPI) using cardiovascular magnetic resonance (CMR) in determining the size of defects, and to demonstrate the feasibility of 3D MPI in healthy volunteers at 3 Tesla.</p> <p>Methods</p> <p>A heart phantom was used to compare the accuracy of 3D and 2D multi-slice MPI in estimating the volume fraction of seven rubber insets which simulated transmural myocardial perfusion defects. Three sets of cross-sectional planes were acquired for 2D multi-slice imaging, where each set was shifted along the partition encoding direction by ± 10 mm. 3D first-pass contrast-enhanced (0.1 mmol/kg Gd-DTPA) MPI was performed in three volunteers with sensitivity encoding for six-fold acceleration. The upslope of the myocardial time-intensity-curve and peak SNR/CNR values were calculated.</p> <p>Results</p> <p>Mean/standard deviation of errors in estimating the volume fraction across the seven defects were -0.44/1.49%, 2.23/2.97%, and 2.59/3.18% in 3D, 2D 4-slice, and 2D 3-slice imaging, respectively. 3D MPI performed in healthy volunteers produced excellent quality images with whole left ventricular (LV) coverage. Peak SNR/CNR was 57.6 ± 22.0/37.5 ± 19.7 over all segments in the first eight slices.</p> <p>Conclusion</p> <p>3D performed better than 2D multi-slice MPI in estimating the size of perfusion defects in phantoms. Highly accelerated 3D MPI at 3T was feasible in volunteers, allowing whole LV coverage with excellent image quality and high SNR/CNR.</p

Automated Detection of Left Ventricle in Arterial Input Function Images for Inline Perfusion Mapping using Deep Learning: A study of 15,000 Patients

Quantification of myocardial perfusion has the potential to improve detection of regional and global flow reduction. Significant effort has been made to automate the workflow, where one essential step is the arterial input function (AIF) extraction. Since failure here invalidates quantification, high accuracy is required. For this purpose, this study presents a robust AIF detection method using the convolutional neural net (CNN) model. CNN models were trained by assembling 25,027 scans (N=12,984 patients) from three hospitals, seven scanners. A test set of 5,721 scans (N=2,805 patients) evaluated model performance. The 2D+T AIF time series was inputted into CNN. Two variations were investigated: a) Two Classes (2CS) for background and foreground (LV mask); b) Three Classes (3CS) for background, foreground LV and RV. Final model was deployed on MR scanners via the Gadgetron InlineAI. Model loading time on MR scanner was ~340ms and applying it took ~180ms. The 3CS model successfully detect LV for 99.98% of all test cases (1 failed out of 5,721 cases). The mean Dice ratio for 3CS was 0.87+/-0.08 with 92.0% of all test cases having Dice ratio >0.75, while the 2CS model gave lower Dice of 0.82+/-0.22 (P<1e-5). Extracted AIF signals using CNN were further compared to manual ground-truth for foot-time, peak-time, first-pass duration, peak value and area-under-curve. No significant differences were found for all features (P>0.2). This study proposed, validated, and deployed a robust CNN solution to detect the LV for the extraction of the AIF signal used in fully automated perfusion flow mapping. A very large data cohort was assembled and resulting models were deployed to MR scanners for fully inline AI in clinical hospitals.Comment: Accepted by Magnetic Resonance in Medicine on March 30, 202