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Journal articles on the topic 'Non-Cartesian imaging'

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Author: Grafiati

Published: 7 July 2024

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1

Wright, Katherine L., Jesse I. Hamilton, Mark A. Griswold, Vikas Gulani, and Nicole Seiberlich. "Non-Cartesian parallel imaging reconstruction." Journal of Magnetic Resonance Imaging 40, no. 5 (January 10, 2014): 1022–40. http://dx.doi.org/10.1002/jmri.24521.

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Yeh, Ernest N., Matthias Stuber, Charles A. McKenzie, Rene M. Botnar, Tim Leiner, Michael A. Ohliger, Aaron K. Grant, Jacob D. Willig-Onwuachi, and Daniel K. Sodickson. "Inherently self-calibrating non-cartesian parallel imaging." Magnetic Resonance in Medicine 54, no. 1 (2005): 1–8. http://dx.doi.org/10.1002/mrm.20517.

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3

Heidemann, Robin M., Mark A. Griswold, Nicole Seiberlich, Mathias Nittka, Stephan A. R. Kannengiesser, Berthold Kiefer, and Peter M. Jakob. "Fast method for 1D non-cartesian parallel imaging using GRAPPA." Magnetic Resonance in Medicine 57, no. 6 (2007): 1037–46. http://dx.doi.org/10.1002/mrm.21227.

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4

Song, Jiayu, and Qing Huo Liu. "Improving Non-Cartesian MRI Reconstruction through Discontinuity Subtraction." International Journal of Biomedical Imaging 2006 (2006): 1–9. http://dx.doi.org/10.1155/ijbi/2006/87092.

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Non-Cartesian sampling is widely used for fast magnetic resonance imaging (MRI). Accurate and fast image reconstruction from non-Cartesiank-space data becomes a challenge and gains a lot of attention. Images provided by conventional direct reconstruction methods usually bear ringing, streaking, and other leakage artifacts caused by discontinuous structures. In this paper, we tackle these problems by analyzing the principal point spread function (PSF) of non-Cartesian reconstruction and propose a leakage reduction reconstruction scheme based on discontinuity subtraction. Data fidelity ink-space is enforced during each iteration. Multidimensional nonuniform fast Fourier transform (NUFFT) algorithms are utilized to simulate thek-space samples as well as to reconstruct images. The proposed method is compared to the direct reconstruction method on computer-simulated phantoms and physical scans. Non-Cartesian sampling trajectories including 2D spiral, 2D and 3D radial trajectories are studied. The proposed method is found useful on reducing artifacts due to high image discontinuities. It also improves the quality of images reconstructed from undersampled data.

5

Zhang, Jingxin. "Simulation of translational motion correction during cartesian brain MRI." Applied and Computational Engineering 48, no. 1 (March 19, 2024): 280–85. http://dx.doi.org/10.54254/2755-2721/48/20241658.

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Brain Magnetic Resonance Imaging (MRI) is invaluable for non-invasively capturing detailed anatomical and functional information. However, motion artifacts, particularly during brain imaging, can compromise the precision of scans. This study explores motion correction techniques, focusing on the widely-used PROPELLER method and its application to Golden-angle Cartesian Randomized Time-resolved (GOCART) acquisition. While PROPELLER effectively corrects in-plane translation and rotation, its use with cartesian data demands increased sampling. GOCART, a high-speed cartesian sampling scheme, has shown promise in Dynamic Contrast-Enhanced (DCE) MRI, yet its specific artifacts in brain imaging remain underexplored. Our simulation framework assesses PROPELLER correction for translational motion in GOCART-sampled data, examining two motion directions, varied frequencies, and different temporal resolutions. Serving as a vital pre-clinical testing tool, this platform contributes to the optimization of motion correction algorithms, addressing challenges and refining imaging protocols for enhanced diagnostic reliability in advanced brain MRI.

6

Chen, Zhifeng, Ling Xia, Feng Liu, Qiuliang Wang, Yi Li, Xuchen Zhu, and Feng Huang. "An improved non-Cartesian partially parallel imaging by exploiting artificial sparsity." Magnetic Resonance in Medicine 78, no. 1 (August 8, 2016): 271–79. http://dx.doi.org/10.1002/mrm.26360.

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7

Goolaub, Datta Singh, and Christopher K. Macgowan. "Reducing clustering of readouts in non-Cartesian cine magnetic resonance imaging." Journal of Cardiovascular Magnetic Resonance 26, no. 1 (2024): 101003. http://dx.doi.org/10.1016/j.jocmr.2024.101003.

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8

Kashyap, Satyananda, Zhili Yang, and Mathews Jacob. "Non-Iterative Regularized reconstruction Algorithm for Non-CartesiAn MRI: NIRVANA." Magnetic Resonance Imaging 29, no. 2 (February 2011): 222–29. http://dx.doi.org/10.1016/j.mri.2010.08.017.

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9

Amor, Zaineb, Philippe Ciuciu, Chaithya G. R., Guillaume Daval-Frérot, Franck Mauconduit, Bertrand Thirion, and Alexandre Vignaud. "Non-Cartesian 3D-SPARKLING vs Cartesian 3D-EPI encoding schemes for functional Magnetic Resonance Imaging at 7 Tesla." PLOS ONE 19, no. 5 (May 13, 2024): e0299925. http://dx.doi.org/10.1371/journal.pone.0299925.

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The quest for higher spatial and/or temporal resolution in functional MRI (fMRI) while preserving a sufficient temporal signal-to-noise ratio (tSNR) has generated a tremendous amount of methodological contributions in the last decade ranging from Cartesian vs. non-Cartesian readouts, 2D vs. 3D acquisition strategies, parallel imaging and/or compressed sensing (CS) accelerations and simultaneous multi-slice acquisitions to cite a few. In this paper, we investigate the use of a finely tuned version of 3D-SPARKLING. This is a non-Cartesian CS-based acquisition technique for high spatial resolution whole-brain fMRI. We compare it to state-of-the-art Cartesian 3D-EPI during both a retinotopic mapping paradigm and resting-state acquisitions at 1mm3 (isotropic spatial resolution). This study involves six healthy volunteers and both acquisition sequences were run on each individual in a randomly-balanced order across subjects. The performances of both acquisition techniques are compared to each other in regards to tSNR, sensitivity to the BOLD effect and spatial specificity. Our findings reveal that 3D-SPARKLING has a higher tSNR than 3D-EPI, an improved sensitivity to detect the BOLD contrast in the gray matter, and an improved spatial specificity. Compared to 3D-EPI, 3D-SPARKLING yields, on average, 7% more activated voxels in the gray matter relative to the total number of activated voxels.

10

Baron, Corey A., Nicholas Dwork, John M. Pauly, and Dwight G. Nishimura. "Rapid compressed sensing reconstruction of 3D non‐Cartesian MRI." Magnetic Resonance in Medicine 79, no. 5 (September 23, 2017): 2685–92. http://dx.doi.org/10.1002/mrm.26928.

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11

Seiberlich, Nicole, Felix A. Breuer, Martin Blaimer, Kestutis Barkauskas, Peter M. Jakob, and Mark A. Griswold. "Non-Cartesian data reconstruction using GRAPPA operator gridding (GROG)." Magnetic Resonance in Medicine 58, no. 6 (2007): 1257–65. http://dx.doi.org/10.1002/mrm.21435.

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12

Ozaslan, A. A., A. Alacaoglu, O. B. Demirel, T. Çukur, and E. U. Saritas. "Fully automated gridding reconstruction for non-Cartesian x-space magnetic particle imaging." Physics in Medicine & Biology 64, no. 16 (August 21, 2019): 165018. http://dx.doi.org/10.1088/1361-6560/ab3525.

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13

Chieh, Seng‐Wei, Mostafa Kaveh, Mehmet Akçakaya, and Steen Moeller. "Self‐calibrated interpolation of non‐Cartesian data with GRAPPA in parallel imaging." Magnetic Resonance in Medicine 83, no. 5 (November 13, 2019): 1837–50. http://dx.doi.org/10.1002/mrm.28033.

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14

Qu, Peng, Kai Zhong, Bida Zhang, Jianmin Wang, and Gary X. Shen. "Convergence behavior of iterative SENSE reconstruction with non-Cartesian trajectories." Magnetic Resonance in Medicine 54, no. 4 (2005): 1040–45. http://dx.doi.org/10.1002/mrm.20648.

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15

Qian, Yongxian, Zhenghui Zhang, Yi Wang, and Fernando E. Boada. "Decomposed direct matrix inversion for fast non-cartesian SENSE reconstructions." Magnetic Resonance in Medicine 56, no. 2 (2006): 356–63. http://dx.doi.org/10.1002/mrm.20974.

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16

Brodsky, Ethan K., James H. Holmes, Huanzhou Yu, and Scott B. Reeder. "Generalizedk-space decomposition with chemical shift correction for non-cartesian water-fat imaging." Magnetic Resonance in Medicine 59, no. 5 (2008): 1151–64. http://dx.doi.org/10.1002/mrm.21580.

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17

Shragge, Jeffrey. "Solving the 3D acoustic wave equation on generalized structured meshes: A finite-difference time-domain approach." GEOPHYSICS 79, no. 6 (November 1, 2014): T363—T378. http://dx.doi.org/10.1190/geo2014-0172.1.

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The key computational kernel of most advanced 3D seismic imaging and inversion algorithms used in exploration seismology involves calculating solutions of the 3D acoustic wave equation, most commonly with a finite-difference time-domain (FDTD) methodology. Although well suited for regularly sampled rectilinear computational domains, FDTD methods seemingly have limited applicability in scenarios involving irregular 3D domain boundary surfaces and mesh interiors best described by non-Cartesian geometry (e.g., surface topography). Using coordinate mapping relationships and differential geometry, an FDTD approach can be developed for generating solutions to the 3D acoustic wave equation that is applicable to generalized 3D coordinate systems and (quadrilateral-faced hexahedral) structured meshes. The developed numerical implementation is similar to the established Cartesian approaches, save for a necessary introduction of weighted first- and mixed second-order partial-derivative operators that account for spatially varying geometry. The approach was validated on three different types of computational meshes: (1) an “internal boundary” mesh conforming to a dipping water bottom layer, (2) analytic “semiorthogonal cylindrical” coordinates, and (3) analytic semiorthogonal and numerically specified “topographic” coordinate meshes. Impulse response tests and numerical analysis demonstrated the viability of the approach for kernel computations for 3D seismic imaging and inversion experiments for non-Cartesian geometry scenarios.

18

Oh, Changheun, Jun-Young Chung, and Yeji Han. "An End-to-End Recurrent Neural Network for Radial MR Image Reconstruction." Sensors 22, no. 19 (September 26, 2022): 7277. http://dx.doi.org/10.3390/s22197277.

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Recent advances in deep learning have contributed greatly to the field of parallel MR imaging, where a reduced amount of k-space data are acquired to accelerate imaging time. In our previous work, we have proposed a deep learning method to reconstruct MR images directly from k-space data acquired with Cartesian trajectories. However, MRI utilizes various non-Cartesian trajectories, such as radial trajectories, with various numbers of multi-channel RF coils according to the purpose of an MRI scan. Thus, it is important for a reconstruction network to efficiently unfold aliasing artifacts due to undersampling and to combine multi-channel k-space data into single-channel data. In this work, a neural network named ‘ETER-net’ is utilized to reconstruct an MR image directly from k-space data acquired with Cartesian and non-Cartesian trajectories and multi-channel RF coils. In the proposed image reconstruction network, the domain transform network converts k-space data into a rough image, which is then refined in the following network to reconstruct a final image. We also analyze loss functions including adversarial and perceptual losses to improve the network performance. For experiments, we acquired k-space data at a 3T MRI scanner with Cartesian and radial trajectories to show the learning mechanism of the direct mapping relationship between the k-space and the corresponding image by the proposed network and to demonstrate the practical applications. According to our experiments, the proposed method showed satisfactory performance in reconstructing images from undersampled single- or multi-channel k-space data with reduced image artifacts. In conclusion, the proposed method is a deep-learning-based MR reconstruction network, which can be used as a unified solution for parallel MRI, where k-space data are acquired with various scanning trajectories.

19

Nita, Nicoleta, Johannes Kersten, Alexander Pott, Fabian Weber, Temsgen Tesfay, Marius-Tudor Benea, Patrick Metze, et al. "Real-Time Spiral CMR Is Superior to Conventional Segmented Cine-Imaging for Left-Ventricular Functional Assessment in Patients with Arrhythmia." Journal of Clinical Medicine 11, no. 8 (April 8, 2022): 2088. http://dx.doi.org/10.3390/jcm11082088.

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(1) Background: Segmented Cartesian Cardiovascular magnetic resonance (CMR) often fails to deliver robust assessment of cardiac function in patients with arrhythmia. We aimed to assess the performance of a tiny golden-angle spiral real-time CMR sequence at 1.5 T for left-ventricular (LV) volumetry in patients with irregular heart rhythm; (2) Methods: We validated the real-time sequence against the standard breath-hold segmented Cartesian sequence in 32 patients, of whom 11 presented with arrhythmia. End-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF) were assessed. In arrhythmic patients, real-time and standard Cartesian acquisitions were compared against a reference echocardiographic modality; (3) Results: In patients with sinus rhythm, good agreements and correlations were found between the segmented and real-time methods, with only minor, non-significant underestimation of EDV for the real-time sequence (135.95 ± 30 mL vs. 137.15 ± 31, p = 0.164). In patients with arrhythmia, spiral real-time CMR yielded superior image quality to the conventional segmented imaging, allowing for excellent agreement with the reference echocardiographic volumetry. In contrast, in this cohort, standard Cartesian CMR showed significant underestimation of LV-ESV (106.72 ± 63.51 mL vs. 125.47 ± 72.41 mL, p = 0.026) and overestimation of LVEF (42.96 ± 10.81% vs. 39.02 ± 11.72%, p = 0.039); (4) Conclusions: Real-time spiral CMR improves image quality in arrhythmic patients, allowing reliable assessment of LV volumetry.

20

KAZAMA, Ryo, Kazuki SEKINE, and Satoshi ITO. "Compressed Sensing in Magnetic Resonance Imaging Using Non-Randomly Under-Sampled Signal in Cartesian Coordinates." IEICE Transactions on Information and Systems E102.D, no. 9 (September 1, 2019): 1851–59. http://dx.doi.org/10.1587/transinf.2019edp7016.

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21

Hoult, D. I., D. Foreman, G. Kolansky, and D. Kripiakevich. "Overcoming high-field RF problems with non-magnetic Cartesian feedback transceivers." Magnetic Resonance Materials in Physics, Biology and Medicine 21, no. 1-2 (November 17, 2007): 15–29. http://dx.doi.org/10.1007/s10334-007-0089-8.

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22

Zhang, Yufei, Huajun She, and Yiping P. Du. "Dynamic MRI of the abdomen using parallel non‐Cartesian convolutional recurrent neural networks." Magnetic Resonance in Medicine 86, no. 2 (March 21, 2021): 964–73. http://dx.doi.org/10.1002/mrm.28774.

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23

Brodsky, Ethan, David Isaacs, Thomas M. Grist, and Walter F. Block. "3D fluoroscopy with real-time 3D non-cartesian phased-array contrast-enhanced MRA." Magnetic Resonance in Medicine 56, no. 2 (2006): 247–54. http://dx.doi.org/10.1002/mrm.20957.

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24

Simpson, Robin, Jennifer Keegan, Peter Gatehouse, Michael Hansen, and David Firmin. "Spiral tissue phase velocity mapping in a breath-hold with non-cartesian SENSE." Magnetic Resonance in Medicine 72, no. 3 (October 7, 2013): 659–68. http://dx.doi.org/10.1002/mrm.24971.

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25

Liang, Da, Heng Zhang, Tingzhu Fang, Haoyu Lin, Dacheng Liu, and Xiaoxue Jia. "A Modified Cartesian Factorized Backprojection Algorithm Integrating with Non-Start-Stop Model for High Resolution SAR Imaging." Remote Sensing 12, no. 22 (November 20, 2020): 3807. http://dx.doi.org/10.3390/rs12223807.

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High resolution synthetic aperture radar (SAR) imaging has extensive application value especially in military reconnaissance and disaster monitoring. The motion of the satellite during the transmission and reception of the signal introduces notable errors in the high resolution SAR spotlight mode, which will lead to a defocused SAR image if not handled. To address this problem, an accurate correct echo model based on non-start-stop model is derived to describe the property of the SAR signal in the paper. Then, in the imaging processing, an azimuth-time-varying range frequency modulation rate is used for range compression. The range history and compensation phase are also derived based on the correct echo model. Then, combining the correct echo model and Cartesian factorized backprojection (CFBP) algorithm, a modified CFBP algorithm is proposed for SAR imaging to improve the accuracy and efficiency of processing. Besides, the influence of residual error due to mismatch is analyzed in detail. In the end, the simulation experiment and Gaofen-3 (GF-3) data experiment are carried out to demonstrate the feasibility of the proposed algorithm.

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Brodsky, Ethan K., Alexey A. Samsonov, and Walter F. Block. "Characterizing and correcting gradient errors in non-cartesian imaging: Are gradient errors linear time-invariant (LTI)?" Magnetic Resonance in Medicine 62, no. 6 (December 2009): 1466–76. http://dx.doi.org/10.1002/mrm.22100.

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27

Meng, Yuguang, and Hao Lei. "An efficient gridding reconstruction method for multishot non-Cartesian imaging with correction of off-resonance artifacts." Magnetic Resonance in Medicine 63, no. 6 (April 30, 2010): 1691–97. http://dx.doi.org/10.1002/mrm.22336.

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28

Smith, David S., Saikat Sengupta, Seth A. Smith, and E. Brian Welch. "Trajectory optimized NUFFT: Faster non‐Cartesian MRI reconstruction through prior knowledge and parallel architectures." Magnetic Resonance in Medicine 81, no. 3 (October 17, 2018): 2064–71. http://dx.doi.org/10.1002/mrm.27497.

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29

Sun, Changyu, Yang Yang, Xiaoying Cai, Michael Salerno, Craig H. Meyer, Daniel Weller, and Frederick H. Epstein. "Non‐Cartesian slice‐GRAPPA and slice‐SPIRiT reconstruction methods for multiband spiral cardiac MRI." Magnetic Resonance in Medicine 83, no. 4 (September 30, 2019): 1235–49. http://dx.doi.org/10.1002/mrm.28002.

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30

Thürauf, Sabine, Oliver Hornung, Mario Körner, Florian Vogt, Alois Knoll, and M. Ali Nasseri. "Model-Based Calibration of a Robotic C-Arm System Using X-Ray Imaging." Journal of Medical Robotics Research 03, no. 03n04 (September 2018): 1841002. http://dx.doi.org/10.1142/s2424905x18410027.

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In interventional radiology or surgery, C-arm systems are typical imaging modalities. Apart from 2D X-ray images, C-arm systems are able to perform 2D/3D overlays. For this application, a previously recorded 3D volume is projected on a 2D X-ray image for providing additional information to the clinician. The required accuracy for this application is 1.5[Formula: see text]mm. Such a spatial accuracy is only achievable with C-arms, if a calibration is performed. State-of-the-art approaches interpolate between values of lookup tables of a sampled Cartesian volume. However, due to the non-linear system behavior in Cartesian space, a trade-off between the calibration effort and the calibrated volume is necessary. This leads to the calibration of the most relevant subvolume and high calibration times. We discuss a new model-based calibration approach for C-arm systems which potentially leads to a smaller calibration effort and simultaneously to an increased calibrated volume. In this work, we demonstrate that it is possible to calibrate a robotic C-arm system using X-ray images and that a static model of the system is required to achieve the desired accuracy for 2D/3D overlays, if re-orientations of the system are performed.

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Mani, Prasad, Chris S. Hanson, and Shravan Hanasoge. "Imaging the Sun’s Near-surface Flows Using Mode-coupling Analysis." Astrophysical Journal 926, no. 2 (February 1, 2022): 127. http://dx.doi.org/10.3847/1538-4357/ac474e.

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Abstract The technique of normal-mode coupling is a powerful tool with which to seismically image non-axisymmetric phenomena in the Sun. Here we apply mode coupling in the Cartesian approximation to probe steady, near-surface flows in the Sun. Using Doppler cubes obtained from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, we perform inversions on mode-coupling measurements to show that the resulting divergence and radial vorticity maps at supergranular length scales (∼30 Mm) near the surface compare extremely well with those obtained using the local correlation tracking method. We find that the Pearson correlation coefficient is ≥0.9 for divergence flows, while ≥0.8 is obtained for the radial vorticity.

32

Konuk, Tugrul, and Jeffrey Shragge. "Tensorial elastodynamics for anisotropic media." GEOPHYSICS 86, no. 4 (July 1, 2021): T293—T303. http://dx.doi.org/10.1190/geo2020-0156.1.

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Elastic wavefield solutions computed by finite-difference (FD) methods in complex anisotropic media are essential elements of elastic reverse time migration and full-waveform inversion analyses. Cartesian formulations of such solution methods, though, face practical challenges when aiming to represent curved interfaces (including free-surface topography) with rectilinear elements. To forestall such issues, we have developed a general strategy for generating solutions of tensorial elastodynamics for anisotropic media (i.e., tilted transversely isotropic or even lower symmetry) in non-Cartesian computational domains. For the specific problem of handling free-surface topography, we define an unstretched coordinate mapping that transforms an irregular physical domain to a regular computational grid on which FD solutions of the modified equations of elastodynamics are straightforward to calculate. Our fully staggered grid (FSG) with a mimetic FD (MFD) (FSG + MFD) approach solves the velocity-stress formulation of the tensorial elastic wave equation in which we compute the stress-strain constitutive relationship in Cartesian coordinates and then transform the resulting stress tensor to generalized coordinates to solve the equations of motion. The resulting FSG + MFD numerical method has a computational complexity comparable with Cartesian scenarios using a similar FSG + MFD numerical approach. Numerical examples demonstrate that our solution can simulate anisotropic elastodynamic field solutions on irregular geometries; thus, it is a reliable tool for anisotropic elastic modeling, imaging, and inversion applications in generalized computational domains including handling free-surface topography.

33

Radhakrishna, Chaithya Giliyar, and Philippe Ciuciu. "Jointly Learning Non-Cartesian k-Space Trajectories and Reconstruction Networks for 2D and 3D MR Imaging through Projection." Bioengineering 10, no. 2 (January 24, 2023): 158. http://dx.doi.org/10.3390/bioengineering10020158.

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Compressed sensing in magnetic resonance imaging essentially involves the optimization of (1) the sampling pattern in k-space under MR hardware constraints and (2) image reconstruction from undersampled k-space data. Recently, deep learning methods have allowed the community to address both problems simultaneously, especially in the non-Cartesian acquisition setting. This work aims to contribute to this field by tackling some major concerns in existing approaches. Particularly, current state-of-the-art learning methods seek hardware compliant k-space sampling trajectories by enforcing the hardware constraints through additional penalty terms in the training loss. Through ablation studies, we rather show the benefit of using a projection step to enforce these constraints and demonstrate that the resulting k-space trajectories are more flexible under a projection-based scheme, which results in superior performance in reconstructed image quality. In 2D studies, our novel method trajectories present an improved image reconstruction quality at a 20-fold acceleration factor on the fastMRI data set with SSIM scores of nearly 0.92–0.95 in our retrospective studies as compared to the corresponding Cartesian reference and also see a 3–4 dB gain in PSNR as compared to earlier state-of-the-art methods. Finally, we extend the algorithm to 3D and by comparing optimization as learning-based projection schemes, we show that data-driven joint learning-based method trajectories outperform model-based methods such as SPARKLING through a 2 dB gain in PSNR and 0.02 gain in SSIM.

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Freitas, Andreia C., Matthieu Ruthven, Redha Boubertakh, and Marc E. Miquel. "Real-time speech MRI: Commercial Cartesian and non-Cartesian sequences at 3T and feasibility of offline TGV reconstruction to visualise velopharyngeal motion." Physica Medica 46 (February 2018): 96–103. http://dx.doi.org/10.1016/j.ejmp.2018.01.014.

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35

Seiberlich, Nicole, Felix A. Breuer, Philipp Ehses, Hisamoto Moriguchi, Martin Blaimer, Peter M. Jakob, and Mark A. Griswold. "Using the GRAPPA operator and the generalized sampling theorem to reconstruct undersampled non-Cartesian data." Magnetic Resonance in Medicine 61, no. 3 (March 2009): 705–15. http://dx.doi.org/10.1002/mrm.21891.

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36

Brodsky, Ethan K., Jessica L. Klaers, Alexey A. Samsonov, Richard Kijowski, and Walter F. Block. "Rapid measurement and correction of phase errors fromB0eddy currents: Impact on image quality for non-cartesian imaging." Magnetic Resonance in Medicine 69, no. 2 (April 5, 2012): 509–15. http://dx.doi.org/10.1002/mrm.24264.

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37

Hedderich, Dennis, Kilian Weiss, Judith Spiro, Daniel Giese, Gabriele Beck, David Maintz, and Thorsten Persigehl. "Clinical Evaluation of Free-Breathing Contrast-Enhanced T1w MRI of the Liver using Pseudo Golden Angle Radial k-Space Sampling." RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren 190, no. 07 (March 13, 2018): 601–9. http://dx.doi.org/10.1055/s-0044-101263.

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Purpose Contrast-enhanced T1-weighted MR imaging of the liver is typically acquired using breath-hold techniques to reduce motion artifacts and to allow for optimal diagnostic image quality. Insufficient breath-holds during MR data collection can cause severe reduction of image quality up to the point of being non-diagnostic. The aim of this study was to evaluate the subjective and objective clinical image quality of a novel free-breathing radial k-space sampling MR technique. Materials and Methods Consent for this study was given by the local IRB committee. 86 patients who underwent both breath-hold (BH) and free-breathing (FB) late-phase contrast T1w-FS-FFE liver MRI using conventional BH Cartesian (Cartesian-eTHRIVE) and FB “pseudo golden angle” radial k-space sampling (Radial-eTHRIVE) were included in this retrospective analysis. Subjective analysis comprised 5-point Likert scale ratings (1 = very good; 5 = non-diagnostic) for “artifact impact”, “anatomic sharpness”, “vessel sharpness”, “contrast impression”, and “overall diagnostic quality”. Relative signal intensities in different ROIs were compared between Cartesian-eTHRIVE and Radial-eTHRIVE. For statistical differences paired Wilcoxon test and paired t-test have been performed (p < 0.05). Results The MR scan time was significantly longer for FB Radial-eTHRIVE (2 min, 54 s) compared to BH Cartesian-eTHRIVE (0 min 15 s). Cartesian-eTHRIVE demonstrated a superior subjective contrast impression and objective measurements revealed an increased lesion-to-liver-contrast for hypointense liver lesions (Hypo-LTLC: 0.33 ± 0.19 vs. 0.20 ± 0.11; p = 0.000), while no difference was observed for hyperintense liver lesions (Hyper-LTLC). Subjective evaluation showed superior anatomic sharpness ratings by both readers for Radial-eTHRIVE. Most importantly, in a subgroup analysis of patients who were unable to perform adequate breath-holds, free-breathing Radial-eTHRIVE still demonstrated good subjective image quality. Conclusion Free-breathing, radial k-space sampling T1w MRI of the liver delivers high diagnostic image quality, especially in patients who are unable to adequately perform breath-hold maneuvers. Thus, Radial-eTHRIVE can be an important clinical alternative in patients with impaired respiration status. Key points Citation Format

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Lin, Bowen, Shujun Fu, Yuting Lin, Ronny L. Rotondo, Weizhang Huang, Harold H. Li, Ronald C. Chen, and Hao Gao. "An adaptive spot placement method on Cartesian grid for pencil beam scanning proton therapy." Physics in Medicine & Biology 66, no. 23 (December 2, 2021): 235012. http://dx.doi.org/10.1088/1361-6560/ac3b65.

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Abstract:

Abstract Pencil beam scanning proton radiotherapy (RT) offers flexible proton spot placement near treatment targets for delivering tumoricidal radiation dose to tumor targets while sparing organs-at-risk. Currently the spot placement is mostly based on a non-adaptive sampling (NS) strategy on a Cartesian grid. However, the spot density or spacing during NS is a constant for the Cartesian grid that is independent of the geometry of tumor targets, and thus can be suboptimal in terms of plan quality (e.g. target dose conformality) and delivery efficiency (e.g. number of spots). This work develops an adaptive sampling (AS) spot placement method on the Cartesian grid that fully accounts for the geometry of tumor targets. Compared with NS, AS places (1) a relatively fine grid of spots at the boundary of tumor targets to account for the geometry of tumor targets and treatment uncertainties (setup and range uncertainty) for improving dose conformality, and (2) a relatively coarse grid of spots in the interior of tumor targets to reduce the number of spots for improving delivery efficiency and robustness to the minimum-minitor-unit (MMU) constraint. The results demonstrate that (1) AS achieved comparable plan quality with NS for regular MMU and substantially improved plan quality from NS for large MMU, using merely about 10% of spots from NS, where AS was derived from the same Cartesian grid as NS; (2) on the other hand, with similar number of spots, AS had better plan quality than NS consistently for regular and large MMU.

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Huang, Jianping, Wenlong Song, Lihui Wang, and Yuemin Zhu. "The Influence of Radial Undersampling Schemes on Compressed Sensing in Cardiac DTI." Sensors 18, no. 7 (July 23, 2018): 2388. http://dx.doi.org/10.3390/s18072388.

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Diffusion tensor imaging (DTI) is known to suffer from long acquisition time, which greatly limits its practical and clinical use. Undersampling of k-space data provides an effective way to reduce the amount of data to acquire while maintaining image quality. Radial undersampling is one of the most popular non-Cartesian k-space sampling schemes, since it has relatively lower sensitivity to motion than Cartesian trajectories, and artifacts from linear reconstruction are more noise-like. Therefore, radial imaging is a promising strategy of undersampling to accelerate acquisitions. The purpose of this study is to investigate various radial sampling schemes as well as reconstructions using compressed sensing (CS). In particular, we propose two randomly perturbed radial undersampling schemes: golden-angle and random angle. The proposed methods are compared with existing radial undersampling methods, including uniformity-angle, randomly perturbed uniformity-angle, golden-angle, and random angle. The results on both simulated and real human cardiac diffusion weighted (DW) images show that, for the same amount of k-space data, randomly sampling around a random radial line results in better reconstruction quality for DTI indices, such as fractional anisotropy (FA), mean diffusivities (MD), and that the randomly perturbed golden-angle undersampling yields the best results for cardiac CS-DTI image reconstruction.

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Rahmer, Jürgen, Ingo Schmale, Peter Mazurkewitz, Oliver Lips, and Peter Börnert. "Non‐Cartesian k‐space trajectory calculation based on concurrent reading of the gradient amplifiers’ output currents." Magnetic Resonance in Medicine 85, no. 6 (February 18, 2021): 3060–70. http://dx.doi.org/10.1002/mrm.28725.

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Jung, Youngkyoo, Yogesh Jashnani, Richard Kijowski, and Walter F. Block. "Consistent non-cartesian off-axis MRI quality: Calibrating and removing multiple sources of demodulation phase errors." Magnetic Resonance in Medicine 57, no. 1 (2006): 206–12. http://dx.doi.org/10.1002/mrm.21092.

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Jiang, Wenwen, Peder E. Z. Larson, and Michael Lustig. "Simultaneous auto‐calibration and gradient delays estimation (SAGE) in non‐Cartesian parallel MRI using low‐rank constraints." Magnetic Resonance in Medicine 80, no. 5 (March 9, 2018): 2006–16. http://dx.doi.org/10.1002/mrm.27168.

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Liu, Chunlei, Michael E. Moseley, and Roland Bammer. "Simultaneous phase correction and SENSE reconstruction for navigated multi-shot DWI with non-cartesian k-space sampling." Magnetic Resonance in Medicine 54, no. 6 (2005): 1412–22. http://dx.doi.org/10.1002/mrm.20706.

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Sartoretti, Thomas, Luuk van Smoorenburg, Elisabeth Sartoretti, Árpád Schwenk, Christoph A. Binkert, Zsolt Kulcsár, Anton S. Becker, Nicole Graf, Michael Wyss, and Sabine Sartoretti-Schefer. "Ultrafast Intracranial Vessel Imaging With Non-Cartesian Spiral 3-Dimensional Time-of-Flight Magnetic Resonance Angiography at 1.5 T." Investigative Radiology 55, no. 5 (May 2020): 293–303. http://dx.doi.org/10.1097/rli.0000000000000641.

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Hanhela, Matti, Antti Paajanen, Mikko J. Nissi, and Ville Kolehmainen. "Embedded Quantitative MRI T1ρ Mapping Using Non-Linear Primal-Dual Proximal Splitting." Journal of Imaging 8, no. 6 (May 31, 2022): 157. http://dx.doi.org/10.3390/jimaging8060157.

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Quantitative MRI (qMRI) methods allow reducing the subjectivity of clinical MRI by providing numerical values on which diagnostic assessment or predictions of tissue properties can be based. However, qMRI measurements typically take more time than anatomical imaging due to requiring multiple measurements with varying contrasts for, e.g., relaxation time mapping. To reduce the scanning time, undersampled data may be combined with compressed sensing (CS) reconstruction techniques. Typical CS reconstructions first reconstruct a complex-valued set of images corresponding to the varying contrasts, followed by a non-linear signal model fit to obtain the parameter maps. We propose a direct, embedded reconstruction method for T1ρ mapping. The proposed method capitalizes on a known signal model to directly reconstruct the desired parameter map using a non-linear optimization model. The proposed reconstruction method also allows directly regularizing the parameter map of interest and greatly reduces the number of unknowns in the reconstruction, which are key factors in the performance of the reconstruction method. We test the proposed model using simulated radially sampled data from a 2D phantom and 2D cartesian ex vivo measurements of a mouse kidney specimen. We compare the embedded reconstruction model to two CS reconstruction models and in the cartesian test case also the direct inverse fast Fourier transform. The T1ρ RMSE of the embedded reconstructions was reduced by 37–76% compared to the CS reconstructions when using undersampled simulated data with the reduction growing with larger acceleration factors. The proposed, embedded model outperformed the reference methods on the experimental test case as well, especially providing robustness with higher acceleration factors.

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Knopp, Tobias, Stefan Kunis, and Daniel Potts. "A Note on the Iterative MRI Reconstruction from Nonuniformk-Space Data." International Journal of Biomedical Imaging 2007 (2007): 1–9. http://dx.doi.org/10.1155/2007/24727.

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In magnetic resonance imaging (MRI), methods that use a non-Cartesian grid ink-space are becoming increasingly important. In this paper, we use a recently proposed implicit discretisation scheme which generalises the standard approach based on gridding. While the latter succeeds for sufficiently uniform sampling sets and accurate estimated density compensation weights, the implicit method further improves the reconstruction quality when the sampling scheme or the weights are less regular. Both approaches can be solved efficiently with the nonequispaced FFT. Due to several new techniques for the storage of an involved sparse matrix, our examples include also the reconstruction of a large 3D data set. We present four case studies and report on efficient implementation of the related algorithms.

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Malavé, Mario O., Corey A. Baron, Srivathsan P. Koundinyan, Christopher M. Sandino, Frank Ong, Joseph Y. Cheng, and Dwight G. Nishimura. "Reconstruction of undersampled 3D non‐Cartesian image‐based navigators for coronary MRA using an unrolled deep learning model." Magnetic Resonance in Medicine 84, no. 2 (February 3, 2020): 800–812. http://dx.doi.org/10.1002/mrm.28177.

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Akçakaya, Mehmet, Seunghoon Nam, Tamer A. Basha, Keigo Kawaji, Vahid Tarokh, and Reza Nezafat. "An Augmented Lagrangian Based Compressed Sensing Reconstruction for Non-Cartesian Magnetic Resonance Imaging without Gridding and Regridding at Every Iteration." PLoS ONE 9, no. 9 (September 12, 2014): e107107. http://dx.doi.org/10.1371/journal.pone.0107107.

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Wang, Fei, Jürgen Hennig, and Pierre LeVan. "Time‐domain principal component reconstruction (tPCR): A more efficient and stable iterative reconstruction framework for non‐Cartesian functional MRI." Magnetic Resonance in Medicine 84, no. 3 (February 18, 2020): 1321–35. http://dx.doi.org/10.1002/mrm.28208.

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Qian, Yongxian, Jiarui Lin, and Deqin Jin. "Direct reconstruction of MR images from data acquired on a non-Cartesian grid using an equal-phase-line algorithm." Magnetic Resonance in Medicine 47, no. 6 (June 2002): 1228–33. http://dx.doi.org/10.1002/mrm.10165.

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