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Готові списки джерел за темами / Spiral k space sampling / Статті в журналах

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Статті в журналах з теми "Spiral k space sampling"

Автор: Grafiati

Опубліковано: 3 вересня 2022

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1

Katoh, Marcus, Elmar Spuentrup, Arno Buecker, Warren J. Manning, Rolf W. Günther, and Rene M. Botnar. "MR coronary vessel wall imaging: Comparison between radial and spiral k-space sampling." Journal of Magnetic Resonance Imaging 23, no. 5 (May 2006): 757–62. http://dx.doi.org/10.1002/jmri.20569.

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2

Speidel, T., P. Metze, and V. Rasche. "Efficient 3D Low-Discrepancy ${k}$ -Space Sampling Using Highly Adaptable Seiffert Spirals." IEEE Transactions on Medical Imaging 38, no. 8 (August 2019): 1833–40. http://dx.doi.org/10.1109/tmi.2018.2888695.

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3

Lorch, Benedikt, Ghislain Vaillant, Christian Baumgartner, Wenjia Bai, Daniel Rueckert, and Andreas Maier. "Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests." Journal of Medical Engineering 2017 (June 11, 2017): 1–9. http://dx.doi.org/10.1155/2017/4501647.

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Анотація:

The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.

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4

Martin, Joe, Matthieu Ruthven, Redha Boubertakh, and Marc E. Miquel. "Realistic Dynamic Numerical Phantom for MRI of the Upper Vocal Tract." Journal of Imaging 6, no. 9 (August 27, 2020): 86. http://dx.doi.org/10.3390/jimaging6090086.

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Dynamic and real-time MRI (rtMRI) of human speech is an active field of research, with interest from both the linguistics and clinical communities. At present, different research groups are investigating a range of rtMRI acquisition and reconstruction approaches to visualise the speech organs. Similar to other moving organs, it is difficult to create a physical phantom of the speech organs to optimise these approaches; therefore, the optimisation requires extensive scanner access and imaging of volunteers. As previously demonstrated in cardiac imaging, realistic numerical phantoms can be useful tools for optimising rtMRI approaches and reduce reliance on scanner access and imaging volunteers. However, currently, no such speech rtMRI phantom exists. In this work, a numerical phantom for optimising speech rtMRI approaches was developed and tested on different reconstruction schemes. The novel phantom comprised a dynamic image series and corresponding k-space data of a single mid-sagittal slice with a temporal resolution of 30 frames per second (fps). The phantom was developed based on images of a volunteer acquired at a frame rate of 10 fps. The creation of the numerical phantom involved the following steps: image acquisition, image enhancement, segmentation, mask optimisation, through-time and spatial interpolation and finally the derived k-space phantom. The phantom was used to: (1) test different k-space sampling schemes (Cartesian, radial and spiral); (2) create lower frame rate acquisitions by simulating segmented k-space acquisitions; (3) simulate parallel imaging reconstructions (SENSE and GRAPPA). This demonstrated how such a numerical phantom could be used to optimise images and test multiple sampling strategies without extensive scanner access.

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5

Swami, Vimarsha G., Mihir Katlariwala, Sukhvinder Dhillon, Zaid Jibri, and Jacob L. Jaremko. "Magnetic Resonance Imaging in Patients with Mechanical Low Back Pain Using a Novel Rapid-Acquisition Three-Dimensional SPACE Sequence at 1.5-T: A Pilot Study Comparing Lumbar Stenosis Assessment with Routine Two-Dimensional Magnetic Resonance Sequences." Canadian Association of Radiologists Journal 67, no. 4 (November 2016): 368–78. http://dx.doi.org/10.1016/j.carj.2015.11.005.

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Purpose To minimize the burden of overutilisation of lumbar spine magnetic resonance imaging (MRI) on a resource-constrained public healthcare system, it may be helpful to image some patients with mechanical low-back pain (LBP) using a simplified rapid MRI screening protocol at 1.5-T. A rapid-acquisition 3-dimensional (3D) SPACE (Sampling Perfection with Application-optimized Contrasts using different flip angle Evolution) sequence can demonstrate common etiologies of LBP. We compared lumbar spinal canal stenosis (LSCS) and neural foraminal stenosis (LNFS) assessment on 3D SPACE against conventional 2-dimensional (2D) MRI. Methods We prospectively performed 3D SPACE and 2D spin-echo MRI sequences (axial or sagittal T1-weighted or T2-weighted) at 1.5-T in 20 patients. Two blinded readers assessed levels L3-4, L4-5 and L5-S1 using: 1) morphologic grading systems, 2) global impression on the presence or absence of clinically significant stenosis (n = 60 disc levels for LSCS, n = 120 foramina for LNFS). Reliability statistics were calculated. Results Acquisition time was ∼5 minutes for SPACE and ∼20 minutes for 2D MRI sequences. Interobserver agreement of LSCS was substantial to near perfect on both sequences (morphologic grading: kappa [k] = 0.71 SPACE, k = 0.69 T2-weighted; global impression: k = 0.85 SPACE, k = 0.78 T2-weighted). LNFS assessment had superior interobserver reliability using SPACE than T1-weighted (k = 0.54 vs 0.37). Intersequence agreement of findings between SPACE and 2D MRI was substantial to near perfect by global impression (LSCS: k = 0.78 Reader 1, k = 0.85 Reader 2; LNFS: k = 0.63 Reader 1, k = 0.66 Reader 2). Conclusions 3D SPACE was acquired in one-quarter the time as the conventional 2D MRI protocol, had excellent agreement with 2D MRI for stenosis assessment, and had interobserver reliability superior to 2D MRI. These results justify future work to explore the role of 3D SPACE in a rapid MRI screening protocol at 1.5-T for mechanical LBP.

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6

Glover, Gary H. "Simple analytic spiral K-space algorithm." Magnetic Resonance in Medicine 42, no. 2 (August 1999): 412–15. http://dx.doi.org/10.1002/(sici)1522-2594(199908)42:2<412::aid-mrm25>3.0.co;2-u.

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7

Hennig, J. "K-space sampling strategies." European Radiology 9, no. 6 (July 22, 1999): 1020–31. http://dx.doi.org/10.1007/s003300050788.

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8

Tan, Hao, and Craig H. Meyer. "Estimation of k -space trajectories in spiral MRI." Magnetic Resonance in Medicine 61, no. 6 (April 7, 2009): 1396–404. http://dx.doi.org/10.1002/mrm.21813.

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9

Noll, Douglas C. "Methodologic considerations for spiral k-space functional MRI." International Journal of Imaging Systems and Technology 6, no. 2-3 (1995): 175–83. http://dx.doi.org/10.1002/ima.1850060207.

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10

Noll, Douglas C., Jonathan D. Cohen, Craig H. Meyer, and Walter Schneider. "Spiral K-space MR imaging of cortical activation." Journal of Magnetic Resonance Imaging 5, no. 1 (January 1995): 49–56. http://dx.doi.org/10.1002/jmri.1880050112.

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11

Meyer, Craig, and Albert Macovski. "5485086 Continuous fluoroscopic MRI using spiral k-space scanning." Magnetic Resonance Imaging 14, no. 5 (January 1996): XII. http://dx.doi.org/10.1016/s0730-725x(96)90042-9.

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12

Chatnuntawech, Itthi, Borjan Gagoski, Berkin Bilgic, Stephen F. Cauley, Kawin Setsompop, and Elfar Adalsteinsson. "Accelerated1H MRSI using randomly undersampled spiral-based k-space trajectories." Magnetic Resonance in Medicine 74, no. 1 (July 30, 2014): 13–24. http://dx.doi.org/10.1002/mrm.25394.

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13

Ding, Xiaoping, Jean Tkach, Paul Ruggieri, John Perl, and Thomas Masaryk. "Improvement of spiral MRI with the measured k-space trajectory." Journal of Magnetic Resonance Imaging 7, no. 5 (September 1997): 938–40. http://dx.doi.org/10.1002/jmri.1880070525.

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14

Lauzon, M. Louis, and Brian K. Rutt. "Polar sampling in k-space: Reconstruction effects." Magnetic Resonance in Medicine 40, no. 5 (November 1998): 769–82. http://dx.doi.org/10.1002/mrm.1910400519.

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15

Regan, Michael W., and Debra Meloy Elmegreen. "K-Band observations of barred spiral galaxies." Astronomical Journal 114 (September 1997): 965. http://dx.doi.org/10.1086/118527.

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16

FLOHR, P., and J. C. VASSILICOS. "Accelerated scalar dissipation in a vortex." Journal of Fluid Mechanics 348 (October 10, 1997): 295–317. http://dx.doi.org/10.1017/s0022112097006927.

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Анотація:

A scalar patch forms spiral structure when it wraps around an isolated vortex. It is shown that this wind-up process leads to accelerated diffusion during a time range TS<t<TD. The lower limit TS is the time needed to create a well-defined spiral, and the upper limit TD is the diffusive time scale of the scalar field θ in the vortex. Whereas the scaling TD∼Pe1/3 is independent of the particular spiral topology, the accelerated decay of the scalar variance θ2[bar](t) for earlier times is directly linked to the space-filling property of the spiral and is found to scale as θ2[bar](0)−θ2[bar](t)∼ (Pe1/3t) 3(1−D′K). D′K is the Kolmogorov capacity of the spiral; it is defined in the range 1/2<D′K<1 and it is the most suitable measure of the spiral's space-filling property.

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17

Nisha, K. L., B. Shaji, and N. R. Rammohan. "Sparse Space Replica Based Image Reconstruction via Cartesian and Spiral Sampling Strategies." Research Journal of Applied Sciences, Engineering and Technology 11, no. 8 (September 20, 2014): 1340–49. http://dx.doi.org/10.19026/rjaset.8.1105.

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18

Milin Šipuš, Željka, Ivana Protrka, and Ljiljana Primorac Gajčić. "Generalized Helices on a Lightlike Cone in 3-Dimensional Lorentz-Minkowski Space." KoG, no. 24 (2020): 41–46. http://dx.doi.org/10.31896/k.24.4.

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Анотація:

In this paper we provide characterizations and give some properties of generalized helices in 3-dimensional Lorentz-Minkowski space that lie on a lightlike cone. Furthermore, by analyzing their projections, which turn out to be Euclidean or Lorentzian logarithmic spiral, we present their parametrizations. In particular, we also analyze planar generalized helices, that is planar intersections of a lightlike cone.

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19

Boudjella, Aissa, Brahim Belhouari Samir, and Omar Kassem Khalil. "Handwritten Character Recognition Based on a Multiple Fermat's Spiral." Advanced Materials Research 774-776 (September 2013): 1629–35. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1629.

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This paper describes a new feature extraction method which can be used very effectively in combination with Cluster K-Nearest Neighbor (CKNN) and KNN Classifier for image recognition. We propose handwritten English character recognition using Fermat's spiral approach to convert an image space into a parameter space. The system is implemented and simulated in MATLAB, and its performance is tested on real alphabet handwriting image. Fifteen (15) alphabet classes were created to carry out the experiment. Each class contains 9 alphabets {a, b, c, d, e, f, g, h, i}. The total of 15x9=135 alphabet images are captured under fixed camera position and controlled energy light intensity. The experimental results give a better recognition rate, 76.19% for KNN and 95.16% for C-KNN with reducing the overall data size of the transformed image. The relationship between the accuracy and k is investigated. It seems that when k goes from 1 to 9, the accuracy decreases linearly. The result of this investigation is a high performance character recognition system with significantly improved recognition rates and real-time.

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20

Taehoon Shin, J.-F. Nielsen, and K. S. Nayak. "Accelerating Dynamic Spiral MRI by Algebraic Reconstruction From Undersampled $k\hbox{--}t$ Space." IEEE Transactions on Medical Imaging 26, no. 7 (July 2007): 917–24. http://dx.doi.org/10.1109/tmi.2007.895450.

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21

Shang, Tian, Emmanuel Canévet, Mickaël Morin, Denis Sheptyakov, María Teresa Fernández-Díaz, Ekaterina Pomjakushina, and Marisa Medarde. "Design of magnetic spirals in layered perovskites: Extending the stability range far beyond room temperature." Science Advances 4, no. 10 (October 2018): eaau6386. http://dx.doi.org/10.1126/sciadv.aau6386.

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In insulating materials with ordered magnetic spiral phases, ferroelectricity can emerge owing to the breaking of inversion symmetry. This property is of both fundamental and practical interest, particularly with a view to exploiting it in low-power electronic devices. Advances toward technological applications have been hindered, however, by the relatively low ordering temperatures Tspiral of most magnetic spiral phases, which rarely exceed 100 K. We have recently established that the ordering temperature of a magnetic spiral can be increased up to 310 K by the introduction of chemical disorder. Here, we explore the design space opened up by this novel mechanism by combining it with a targeted lattice control of some magnetic interactions. In Cu-Fe layered perovskites, we obtain Tspiral values close to 400 K, comfortably far from room temperature and almost 100 K higher than using chemical disorder alone. Moreover, we reveal a linear relationship between the spiral’s wave vector and the onset temperature of the spiral phase. This linear law ends at a paramagnetic-collinear-spiral triple point, which defines the highest spiral ordering temperature that can be achieved in this class of materials. On the basis of these findings, we propose a general set of rules for designing magnetic spirals in layered perovskites using external pressure, chemical substitutions, and/or epitaxial strain, which should guide future efforts to engineer magnetic spiral phases with ordering temperatures suitable for technological applications.

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22

Contijoch, Francisco, Yuchi Han, Srikant Kamesh Iyer, Peter Kellman, Gene Gualtieri, Mark A. Elliott, Sebastian Berisha, et al. "Closed-loop control of k-space sampling via physiologic feedback for cine MRI." PLOS ONE 15, no. 12 (December 29, 2020): e0244286. http://dx.doi.org/10.1371/journal.pone.0244286.

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Background Segmented cine cardiac MRI combines data from multiple heartbeats to achieve high spatiotemporal resolution cardiac images, yet predefined k-space segmentation trajectories can lead to suboptimal k-space sampling. In this work, we developed and evaluated an autonomous and closed-loop control system for radial k-space sampling (ARKS) to increase sampling uniformity. Methods The closed-loop system autonomously selects radial k-space sampling trajectory during live segmented cine MRI and attempts to optimize angular sampling uniformity by selecting views in regions of k-space that were not previously well-sampled. Sampling uniformity and the ability to detect cardiac phase in vivo was assessed using ECG data acquired from 10 normal subjects in an MRI scanner. The approach was then implemented with a fast gradient echo sequence on a whole-body clinical MRI scanner and imaging was performed in 4 healthy volunteers. The closed-loop k-space trajectory was compared to random, uniformly distributed and golden angle view trajectories via measurement of k-space uniformity and the point spread function. Lastly, an arrhythmic dataset was used to evaluate a potential application of the approach. Results The autonomous trajectory increased k-space sampling uniformity by 15±7%, main lobe point spread function (PSF) signal intensity by 6±4%, and reduced ringing relative to golden angle sampling. When implemented, the autonomous pulse sequence prescribed radial view angles faster than the scan TR (0.98 ± 0.01 ms, maximum = 1.38 ms) and increased k-space sampling mean uniformity by 10±11%, decreased uniformity variability by 44±12%, and increased PSF signal ratio by 6±6% relative to golden angle sampling. Conclusion The closed-loop approach enables near-uniform radial sampling in a segmented acquisition approach which was higher than predetermined golden-angle radial sampling. This can be utilized to increase the sampling or decrease the temporal footprint of an acquisition and the closed-loop framework has the potential to be applied to patients with complex heart rhythms.

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23

Zhu, Yan-Chun, Jiang Du, Wen-Chao Yang, Chai-Jie Duan, Hao-Yu Wang, Song Gao, and Shang-Lian Bao. "Reduced aliasing artifacts using shaking projection k -space sampling trajectory." Chinese Physics B 23, no. 3 (March 2014): 038702. http://dx.doi.org/10.1088/1674-1056/23/3/038702.

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24

Senel, L. Kerem, Toygan Kilic, Alper Gungor, Emre Kopanoglu, H. Emre Guven, Emine U. Saritas, Aykut Koc, and Tolga Cukur. "Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI." IEEE Transactions on Medical Imaging 38, no. 7 (July 2019): 1701–14. http://dx.doi.org/10.1109/tmi.2019.2892378.

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25

Hansen, Michael S., Christof Baltes, Jeffrey Tsao, Sebastian Kozerke, Klaas P. Pruessmann, and Holger Eggers. "k-t BLAST reconstruction from non-Cartesiank-t space sampling." Magnetic Resonance in Medicine 55, no. 1 (2005): 85–91. http://dx.doi.org/10.1002/mrm.20734.

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26

Plevritis, Sylvia K., and Albert Macovski. "MRS imaging using anatomically based K-space sampling and extrapolation." Magnetic Resonance in Medicine 34, no. 5 (November 1995): 686–93. http://dx.doi.org/10.1002/mrm.1910340506.

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27

Gumus, Kazim, Mehmet Sait Dundar, Serkan Senol, and Mehmet Bilgen. "Shell trajectory sampling of k-space in magnetic resonance imaging." Journal of Biotechnology 231 (August 2016): S104. http://dx.doi.org/10.1016/j.jbiotec.2016.05.364.

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28

Fielden, Samuel W., and Craig H. Meyer. "A simple acquisition strategy to avoid off-resonance blurring in spiral imaging with redundant spiral-in/out k-space trajectories." Magnetic Resonance in Medicine 73, no. 2 (March 6, 2014): 704–10. http://dx.doi.org/10.1002/mrm.25172.

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29

Sarkar, Shantanu, Keith Heberlein, and Xiaoping Hu. "Truncation artifact reduction in spectroscopic imaging using a dual-density spiral k-space trajectory." Magnetic Resonance Imaging 20, no. 10 (December 2002): 743–57. http://dx.doi.org/10.1016/s0730-725x(02)00608-2.

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30

Taylor, Andrew M., Jennifer Keegan, Permi Jhooti, Peter D. Gatehouse, David N. Firmin, and Dudley J. Pennell. "A comparison between segmented k-space FLASH and interleaved spiral MR coronary angiography sequences." Journal of Magnetic Resonance Imaging 11, no. 4 (April 2000): 394–400. http://dx.doi.org/10.1002/(sici)1522-2586(200004)11:4<394::aid-jmri7>3.0.co;2-s.

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31

Karachentsev, I. D., and V. E. Karachentseva. "‘Scraggy’ dark haloes around bulge-less spiral." Monthly Notices of the Royal Astronomical Society 486, no. 3 (April 12, 2019): 3697–701. http://dx.doi.org/10.1093/mnras/stz1047.

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ABSTRACT We use a sample of 220 face-on bulge-less galaxies situated in the low-density environment to estimate their total mass via orbital motions of supposed rare satellites. Our inspection reveals 43 dwarf companions having the mean projected separation of 130 kpc and the mean-square velocity difference of 96 km s−1. For them, we obtain the mean orbital-mass-to-K-band luminosity ratio of 20 ± 3. Seven bulge-less spirals in the Local Volume are also characterized by the low mean ratio, Morb/LK = 22 ± 5. We conclude that bulge-less Sc–Scd–Sd galaxies have poor dark haloes, about two times lower than that of bulgy spiral galaxies of the same stellar mass.

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32

Huo, Donglai, Kyle A. Salem, Yuhao Jiang, and David L. Wilson. "Optimization of Spiral MRI Using a Perceptual Difference Model." International Journal of Biomedical Imaging 2006 (2006): 1–11. http://dx.doi.org/10.1155/ijbi/2006/35290.

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We systematically evaluated a variety of MR spiral imaging acquisition and reconstruction schemes using a computational perceptual difference model (PDM) that models the ability of humans to perceive a visual difference between a degraded “fast” MRI image with subsampling ofk-space and a “gold standard” image mimicking full acquisition. Human subject experiments performed using a modified double-stimulus continuous-quality scale (DSCQS) correlated well with PDM, over a variety of images. In a smaller set of conditions, PDM scores agreed very well with human detectability measurements of image quality. Having validated the technique, PDM was used to systematically evaluate 2016 spiral image conditions (six interleave patterns, seven sampling densities, three density compensation schemes, four reconstruction methods, and four noise levels). Voronoi (VOR) with conventional regridding gave the best reconstructions. At a fixed sampling density, more interleaves gave better results. With noise present more interleaves and samples were desirable. With PDM, conditions were determined where equivalent image quality was obtained with 50% sampling in noise-free conditions. We conclude that PDM scoring provides an objective, useful tool for the assessment of fast MR image quality that can greatly aid the design of MR acquisition and signal processing strategies.

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33

Parasoglou, Prodromos, A. J. Sederman, J. Rasburn, H. Powell, and M. L. Johns. "Optimal k-space sampling for single point imaging of transient systems." Journal of Magnetic Resonance 194, no. 1 (September 2008): 99–107. http://dx.doi.org/10.1016/j.jmr.2008.06.005.

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34

Sharma, Shubham, and K. V. S. Hari. "Four-shot non-cartesian trajectories for k-space sampling in MRI." CSI Transactions on ICT 6, no. 1 (November 14, 2017): 11–16. http://dx.doi.org/10.1007/s40012-017-0180-x.

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35

Speidel, Tobias, Jan Paul, Stefan Wundrak, and Volker Rasche. "Quasi-Random Single-Point Imaging Using Low-Discrepancy $k$ -Space Sampling." IEEE Transactions on Medical Imaging 37, no. 2 (February 2018): 473–79. http://dx.doi.org/10.1109/tmi.2017.2760919.

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36

Hugg, James W., Andrew A. Maudsley, Michael W. Weiner, and Gerald B. Matson. "Comparison of k-space sampling schemes for multidimensional MR spectroscopic imaging." Magnetic Resonance in Medicine 36, no. 3 (December 12, 2005): 469–73. http://dx.doi.org/10.1002/mrm.1910360321.

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37

Heikal, A. A., K. Wachowicz, and B. G. Fallone. "Correlation between k -space sampling pattern and MTF in compressed sensing MRSI." Medical Physics 43, no. 10 (September 23, 2016): 5626–34. http://dx.doi.org/10.1118/1.4962930.

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38

Hardy, Christopher J., Harvey E. Cline, and Paul A. Bottomley. "Correcting for nonuniform k-space sampling in two-dimensional NMR selective excitation." Journal of Magnetic Resonance (1969) 87, no. 3 (May 1990): 639–45. http://dx.doi.org/10.1016/0022-2364(90)90323-2.

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39

Len, P. M., S. Thevuthasan, A. P. Kaduwela, M. A. Van Hove, and C. S. Fadley. "Optimization of k-space sampling in atomic imaging by electron emission holography." Surface Science 365, no. 2 (September 1996): 535–46. http://dx.doi.org/10.1016/0039-6028(96)00719-4.

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40

Mortari, Daniele, and Jonathan Rogers. "A k-Vector Approach to Sampling, Interpolation, and Approximation." Journal of the Astronautical Sciences 60, no. 3-4 (December 2013): 686–706. http://dx.doi.org/10.1007/s40295-015-0065-x.

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41

Cyr, Isabelle H., C. E. Jones, A. C. Carciofi, C. Steckel, C. Tycner, and A. T. Okazaki. "Spiral density enhancements in Be binary systems." Monthly Notices of the Royal Astronomical Society 497, no. 3 (July 27, 2020): 3525–36. http://dx.doi.org/10.1093/mnras/staa2176.

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ABSTRACT We use a smoothed particle hydrodynamics (SPH) code to examine the effects of a binary companion on a Be star disc for a range of disc viscosities and misalignment angles, i.e. the angle between the orbital plane and the primary star’s spin axis. The density structures in the disc due to the tidal interaction with the binary companion are investigated. Expanding on our previous work, the shape and density structure of density enhancements due to the binary companion are analysed and the changes in observed interferometric features due to these orbiting enhancements are also predicted. We find that larger misalignment angles and viscosity values result in more tightly wound spiral arms with densities that fall-off more slowly with radial distance from the central star. We show that the orbital phase has very little effect on the structure of the spiral density enhancements. We demonstrate that these spiral features can be detected with an interferometer in H α and K-band emission. We also show that the spiral features affect the axis ratios determined by interferometry depending on the orientation of these features and the observer. For example, our simulations show that the axis ratios can vary by 20 per cent for our co-planar binary disc system depending on the location of the disc density enhancements.

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42

Yang, Yuanyu, Dayi Yin, Quan Zhang, and Zhiming Li. "Construction of the Guide Star Catalog for Double Fine Guidance Sensors Based on SSBK Clustering." Sensors 22, no. 13 (July 2, 2022): 4996. http://dx.doi.org/10.3390/s22134996.

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In the Chinese Survey Space Telescope (CSST), the Fine Guidance Sensor (FGS) is required to provide high-precision attitude information of the space telescope. The fine star guide catalog is an essential part of the FGS. It is not only the basis for star identification and attitude determination but also the key to determining the absolute attitude of the space telescope. However, the capacity and uniformity of the fine guide star catalog will affect the performance of the FGS. To build a guide star catalog with uniform distribution of guide stars and catalog capacity that is as small as possible, and to effectively improve the speed of star identification and the accuracy of attitude determination, the spherical spiral binary K-means clustering algorithm (SSBK) is proposed. Based on the selection criteria, firstly, the spherical spiral reference point method is used for global uniform division, and then, the K-means clustering algorithm in machine learning is introduced to divide the stars into several disjoint subsets through the use of angular distance and dichotomy so that the guide stars are uniformly distributed. We assume that the field of view (FOV) is 0.2° × 0.2°, the magnitude range is 9∼15 mag, and the threshold for the number of stars (NOS) in the FOV is 9. The simulation shows that compared with the magnitude filtering method (MFM) and the spherical spiral reference point brightness optimization algorithm (SSRP), the guide star catalog based on the SSBK algorithm has the lowest standard deviation of the NOS in the FOV, and the probability of 5∼15 stars is the highest (over 99.4%), which can ensure a higher identification probability and attitude determination accuracy.

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43

KUBITSCHEK, J. P., and P. D. WEIDMAN. "The effect of viscosity on the stability of a uniformly rotating liquid column in zero gravity." Journal of Fluid Mechanics 572 (January 23, 2007): 261–86. http://dx.doi.org/10.1017/s0022112006003624.

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An investigation of the linear temporal stability of a uniformly rotating viscous liquid column in the absence of gravity is presented. The governing parameters are the rotational Reynolds number Re and the Hocking parameter L, defined as the ratio of surface tension to centrifugal forces. Though the viscosity-independent condition L≥(k2 + n2-1)−1 for stability to disturbances of axial wavenumber k and azimuthal mode number n has been known for some time, the preferred modes, growth rates and frequencies at onset of instability have not been reported. We compute these results over a wide range of L–Re space and determine certain asymptotic behaviours in the limits of L→0, L→∞ and Re→∞. The computations exhibit a continuous evolution toward known inviscid stability results in the large-Re limit and their ultimate transition to an n = 1 spiral mode at small Re. While viscosity is shown to reduce growth rates for axisymmetric disturbances, it also produces a destabilizing effect for n = 2 planar and n = 1 spiral disturbances in certain regions of parameter space. A special feature is the appearance of a tricritical point in L–Re space at which maximum growth rates of the axisymmetric, n = 1 spiral, and n = 2 planar disturbances are equal.

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44

Boccaletti, A., E. Pantin, F. Ménard, R. Galicher, M. Langlois, M. Benisty, R. Gratton, et al. "Investigating point sources in MWC 758 with SPHERE." Astronomy & Astrophysics 652 (August 2021): L8. http://dx.doi.org/10.1051/0004-6361/202141177.

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Context. Spiral arms in protoplanetary disks could be shown to be the manifestation of density waves launched by protoplanets and propagating in the gaseous component of the disk. At least two point sources have been identified in the L band in the MWC 758 system as planetary mass object candidates. Aims. We used VLT/SPHERE to search for counterparts of these candidates in the H and K bands, and to characterize the morphology of the spiral arms. Methods. The data were processed with now-standard techniques in high-contrast imaging to determine the limits of detection, and to compare them to the luminosity derived from L band observations. Results. In considering the evolutionary, atmospheric, and opacity models we were not able to confirm the two former detections of point sources performed in the L band. In addition, the analysis of the spiral arms from a dynamical point of view does not support the hypothesis that these candidates comprise the origin of the spirals. Conclusions. Deeper observations and longer timescales will be required to identify the actual source of the spiral arms in MWC 758.

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45

Solana, A. B., J. A. Hernández-Tamames, E. Manzanedo, R. García-Álvarez, F. O. Zelaya, and F. del Pozo. "Gradient induced artifacts in simultaneous EEG-fMRI: Effect of synchronization on spiral and EPI k-space trajectories." Magnetic Resonance Imaging 32, no. 6 (July 2014): 684–92. http://dx.doi.org/10.1016/j.mri.2014.03.008.

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46

Raja, Rajikha, and Neelam Sinha. "Adaptive k-space sampling design for edge-enhanced DCE-MRI using compressed sensing." Magnetic Resonance Imaging 32, no. 7 (September 2014): 899–912. http://dx.doi.org/10.1016/j.mri.2013.12.022.

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47

Xiao, Dan, and Bruce J. Balcom. "Restricted k-space sampling in pure phase encode MRI of rock core plugs." Journal of Magnetic Resonance 231 (June 2013): 126–32. http://dx.doi.org/10.1016/j.jmr.2013.04.001.

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48

Spuentrup, Elmar, Marcus Katoh, Arno Buecker, Warren J. Manning, Tobias Schaeffter, Trung-Hieu Nguyen, Harald P. Kühl, Matthias Stuber, Rene M. Botnar, and Rolf W. Günther. "Free-breathing 3D Steady-State Free Precession Coronary MR Angiography with Radial k-Space Sampling: Comparison with Cartesian k-Space Sampling and Cartesian Gradient-Echo Coronary MR Angiography—Pilot Study." Radiology 231, no. 2 (May 2004): 581–86. http://dx.doi.org/10.1148/radiol.2312030451.

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49

Tadaki, Ken-ichi, Masanori Iye, Hideya Fukumoto, Masao Hayashi, Cristian E. Rusu, Rhythm Shimakawa, and Tomoka Tosaki. "Spin parity of spiral galaxies II: a catalogue of 80 k spiral galaxies using big data from the Subaru Hyper Suprime-Cam survey and deep learning." Monthly Notices of the Royal Astronomical Society 496, no. 4 (July 2, 2020): 4276–86. http://dx.doi.org/10.1093/mnras/staa1880.

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ABSTRACT We report an automated morphological classification of galaxies into S-wise spirals, Z-wise spirals, and non-spirals using big image data taken from Subaru/Hyper Suprime-Cam (HSC) Survey and a convolutional neural network (CNN)-based deep learning technique. The HSC i-band images are about 36 times deeper than those from the Sloan Digital Sky Survey (SDSS) and have a two times higher spatial resolution, allowing us to identify substructures such as spiral arms and bars in galaxies at z > 0.1. We train CNN classifiers by using HSC images of 1447 S-spirals, 1382 Z-spirals, and 51 650 non-spirals. As the number of images in each class is unbalanced, we augment the data of spiral galaxies by horizontal flipping, rotation, and rescaling of images to make the numbers of three classes similar. The trained CNN models correctly classify 97.5 per cent of the validation data, which is not used for training. We apply the CNNs to HSC images of a half million galaxies with an i-band magnitude of i < 20 over an area of 320 deg2. 37 917 S-spirals and 38 718 Z-spirals are identified, indicating no significant difference between the numbers of two classes. Among a total of 76 635 spiral galaxies, 48 576 are located at z > 0.2, where we are hardly able to identify spiral arms in the SDSS images. Our attempt demonstrates that a combination of the HSC big data and CNNs has a large potential to classify various types of morphology such as bars, mergers, and strongly lensed objects.

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50

Johnston, Katharine G., Melvin G. Hoare, Henrik Beuther, Rolf Kuiper, Nathaniel Dylan Kee, Hendrik Linz, Paul Boley, Luke T. Maud, Aida Ahmadi, and Thomas P. Robitaille. "Spiral arms and instability within the AFGL 4176 mm1 disc." Astronomy & Astrophysics 634 (February 2020): L11. http://dx.doi.org/10.1051/0004-6361/201937154.

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We present high-resolution (30 mas or 130 au at 4.2 kpc) Atacama Large Millimeter/submillimeter Array observations at 1.2 mm of the disc around the forming O-type star AFGL 4176 mm1. The disc (AFGL 4176 mm1-main) has a radius of ∼1000 au and contains significant structure, most notably a spiral arm on its redshifted side. We fitted the observed spiral with logarithmic and Archimedean spiral models. We find that both models can describe its structure, but the Archimedean spiral with a varying pitch angle fits its morphology marginally better. As well as signatures of rotation across the disc, we observe gas arcs in CH3CN that connect to other millimetre continuum sources in the field, supporting the picture of interactions within a small cluster around AFGL 4176 mm1-main. Using local thermodynamic equilibrium modelling of the CH3CN K-ladder, we determine the temperature and velocity field across the disc, and thus produce a map of the Toomre stability parameter. Our results indicate that the outer disc is gravitationally unstable and has already fragmented or is likely to fragment in the future, possibly producing further companions. These observations provide evidence that disc fragmentation is one possible pathway towards explaining the high fraction of multiple systems around high-mass stars.

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