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BioMed Research International
Volume 2014, Article ID 365812, 17 pages
http://dx.doi.org/10.1155/2014/365812
Review Article

Pushing CT and MR Imaging to the Molecular Level for Studying the “Omics”: Current Challenges and Advancements

Healthcare Sector, Siemens Limited Taiwan, 2F No. 3, Yuan Qu Street, Nan Gang District, Taipei 11503, Taiwan

Received 18 October 2013; Revised 26 December 2013; Accepted 24 January 2014; Published 13 March 2014

Academic Editor: Yu-Hua Dean Fang

Copyright © 2014 Hsuan-Ming Huang and Yi-Yu Shih. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. N. G. Costouros, D. Lorang, Y. Zhang et al., “Microarray gene expression analysis of murine tumor heterogeneity defined by dynamic contrast-enhanced MRI,” Molecular Imaging, vol. 1, no. 3, pp. 301–308, 2002. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Guccione, Y.-S. Yang, G. Shi, D. Y. Lee, K. C. P. Li, and M. D. Bednarski, “Functional genomics guided with MR imaging: mouse tumor model study,” Radiology, vol. 228, no. 2, pp. 560–568, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. Y.-S. Yang, S. Guccione, and M. D. Bednarski, “Comparing genomic and histologic correlations to radiographic changes in tumors: a murine SCC VII model study,” Academic Radiology, vol. 10, no. 10, pp. 1165–1175, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. S. K. Hobbs, G. Shi, R. Homer, G. Harsh, S. W. Atlas, and M. D. Bednarski, “Magnetic resonance image-guided proteomics of human glioblastoma multiforme,” Journal of Magnetic Resonance Imaging, vol. 18, no. 5, pp. 530–536, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. M. D. Kuo, J. Gollub, C. B. Sirlin, C. Ooi, and X. Chen, “Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma,” Journal of Vascular and Interventional Radiology, vol. 18, no. 7, pp. 821–830, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. E. Segal, C. B. Sirlin, C. Ooi et al., “Decoding global gene expression programs in liver cancer by noninvasive imaging,” Nature Biotechnology, vol. 25, no. 6, pp. 675–680, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Diehn, C. Nardini, D. S. Wang et al., “Identification of noninvasive imaging surrogates for brain tumor gene-expression modules,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 13, pp. 5213–5218, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. E. Segal, M. Shapira, A. Regev et al., “Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data,” Nature Genetics, vol. 34, no. 2, pp. 166–176, 2003. View at Publisher · View at Google Scholar · View at Scopus
  9. N. J. Serkova and M. S. Brown, “Quantitative analysis in magnetic resonance spectroscopy: from metabolic profiling to in vivo biomarkers,” Bioanalysis, vol. 4, no. 3, pp. 321–341, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Tiziani, Y. Kang, R. Harjanto et al., “Metabolomics of the tumor microenvironment in pediatric acute lymphoblastic leukemia,” PLoS ONE, vol. 8, no. 12, Article ID e82859, 2013. View at Publisher · View at Google Scholar
  11. G. Hassan-Smith, G. R. Wallace, M. R. Douglas, and A. J. Sinclair, “The role of metabolomics in neurological disease,” Journal of Neuroimmunology, vol. 248, no. 1-2, pp. 48–52, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. E.-M. Spur, E. A. Decelle, and L. L. Cheng, “Metabolomic imaging of prostate cancer with magnetic resonance spectroscopy and mass spectrometry,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 40, supplement 1, pp. 60–71, 2013. View at Publisher · View at Google Scholar
  13. R. Popovtzer, A. Agrawal, N. A. Kotov et al., “Targeted gold nanoparticles enable molecular CT imaging of cancer,” Nano Letters, vol. 8, no. 12, pp. 4593–4596, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. T. Reuveni, M. Motiei, Z. Romman, A. Popovtzer, and R. Popovtzer, “Targeted gold nanoparticles enable molecular CT imaging of cancer: an in vivo study,” International Journal of Nanomedicine, vol. 6, pp. 2859–2864, 2011. View at Publisher · View at Google Scholar
  15. O. Rabin, J. M. Perez, J. Grimm, G. Wojtkiewicz, and R. Weissleder, “An X-ray computed tomography imaging agent based on long-circulating bismuth sulphide nanoparticles,” Nature Materials, vol. 5, no. 2, pp. 118–122, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. B. Cohen, K. Ziv, V. Plaks et al., “MRI detection of transcriptional regulation of gene expression in transgenic mice,” Nature Medicine, vol. 13, no. 4, pp. 498–503, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. A. A. Gilad, M. T. McMahon, P. Walczak et al., “Artificial reporter gene providing MRI contrast based on proton exchange,” Nature Biotechnology, vol. 25, no. 2, pp. 217–219, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. C. H. Liu, S. Huang, J. Cui et al., “MR contrast probes that trace gene transcripts for cerebral ischemia in live animals,” FASEB Journal, vol. 21, no. 11, pp. 3004–3015, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Y. Louie, M. M. Hüber, E. T. Ahrens et al., “In vivo visualization of gene expression using magnetic resonance imaging,” Nature Biotechnology, vol. 18, no. 3, pp. 321–325, 2000. View at Publisher · View at Google Scholar · View at Scopus
  20. J. B. Weaver, “Simultaneous multislice acquisition of MR images,” Magnetic Resonance in Medicine, vol. 8, no. 3, pp. 275–284, 1988. View at Google Scholar · View at Scopus
  21. F. A. Breuer, M. Blaimer, M. F. Mueller et al., “Controlled aliasing in volumetric parallel imaging (2D CAIPIRINHA),” Magnetic Resonance in Medicine, vol. 55, no. 3, pp. 549–556, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. S. A. Graham, D. J. Moseley, J. H. Siewerdsen, and D. A. Jaffray, “Compensators for dose and scatter management in cone-beam computed tomography,” Medical Physics, vol. 34, no. 7, pp. 2691–2703, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. T. L. Toth, “Dose reduction opportunities for CT scanners,” Pediatric Radiology, vol. 32, no. 4, pp. 261–267, 2002. View at Publisher · View at Google Scholar · View at Scopus
  24. C. H. McCollough, A. N. Primak, O. Saba et al., “Dose performance of a 64-channel dual-source CT scanner,” Radiology, vol. 243, no. 3, pp. 775–784, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. S. S. Hsieh and N. J. Pelc, “The feasibility of a piecewise-linear dynamic bowtie filter,” Medical Physics, vol. 40, no. 3, Article ID 031910, 2013. View at Publisher · View at Google Scholar
  26. T. P. Szczykutowicz and C. A. Mistretta, “Design of a digital beam attenuation system for computed tomography: part I. System design and simulation framework,” Medical Physics, vol. 40, no. 2, Article ID 021905, 2013. View at Publisher · View at Google Scholar
  27. K. Perisinakis, A. E. Papadakis, and J. Damilakis, “The effect of X-ray beam quality and geometry on radiation utilization efficiency in multidetector CT imaging,” Medical Physics, vol. 36, no. 4, pp. 1258–1266, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. A. Tzedakis, J. Damilakis, K. Perisinakis, J. Stratakis, and N. Gourtsoyiannis, “The effect of z overscanning on patient effective dose from multidetector helical computed tomography examinations,” Medical Physics, vol. 32, no. 6, pp. 1621–1629, 2005. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Tzedakis, J. Damilakis, K. Perisinakis, A. Karantanas, S. Karabekios, and N. Gourtsoyiannis, “Influence of z overscanning on normalized effective doses calculated for pediatric patients undergoing multidetector CT examinations,” Medical Physics, vol. 34, no. 4, pp. 1163–1175, 2007. View at Publisher · View at Google Scholar · View at Scopus
  30. J. A. Christner, V. A. Zavaletta, C. D. Eusemann, A. I. Walz-Flannigan, and C. H. McCollough, “Dose reduction in helical CT: dynamically adjustable z-axis X-ray beam collimation,” American Journal of Roentgenology, vol. 194, no. 1, pp. W49–W55, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. P. D. Deak, O. Langner, M. Lell, and W. A. Kalender, “Effects of adaptive section collimation on patient radiation dose in multisection spiral CT,” Radiology, vol. 252, no. 1, pp. 140–147, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. P. M. Shikhaliev, T. Xu, and S. Molloi, “Photon counting computed tomography: concept and initial results,” Medical Physics, vol. 32, no. 2, pp. 427–436, 2005. View at Publisher · View at Google Scholar · View at Scopus
  33. X. Wang, A. Zamyatin, and D. Shi, “Dose reduction potential with photon counting computed tomography,” in Medical Imaging: Physics of Medical Imaging, N. J. Pelc, R. M. Nishikawa, and B. R. Whiting, Eds., vol. 8313 of Proceedings of SPIE, International Society for Optics and Photonics, February 2012. View at Publisher · View at Google Scholar
  34. L. F. N. D. Carramate, F. Nachtrab, M. Firsching et al., “Energy resolving CT systems using Medipix2 and MHSP detectors,” Journal of Instrumentation, vol. 8, no. 03, Article ID C03022, 2013. View at Publisher · View at Google Scholar
  35. J. Giersch, D. Niederlöhner, and G. Anton, “The influence of energy weighting on X-ray imaging quality,” Nuclear Instruments and Methods in Physics Research A, vol. 531, no. 1-2, pp. 68–74, 2004. View at Publisher · View at Google Scholar
  36. K. S. Kalluri, M. Mahd, and S. J. Glick, “Investigation of energy weighting using an energy discriminating photon counting detector for breast CT,” Medical Physics, vol. 40, no. 8, Article ID 081923, 2013. View at Publisher · View at Google Scholar
  37. L. Wielopolski and R. P. Gardner, “Prediction of the pulse-height spectral distortion caused by the peak pile-up effect,” Nuclear Instruments and Methods, vol. 133, no. 2, pp. 303–309, 1976. View at Google Scholar · View at Scopus
  38. X. Wang, D. Meier, S. Mikkelsen et al., “MicroCT with energy-resolved photon-counting detectors,” Physics in Medicine and Biology, vol. 56, no. 9, pp. 2791–2816, 2011. View at Publisher · View at Google Scholar · View at Scopus
  39. A. Mohammadi, M. Baba, M. Nakhostin, H. Ohuchi, and M. Abe, “Compton spectroscopy for rotation-mode computed tomography,” Journal of X-Ray Science and Technology, vol. 20, no. 2, pp. 131–140, 2012. View at Google Scholar
  40. J. Bennett, A. Opie, Q. Xu et al., “Hybrid spectral micro-CT: system design, implementation & preliminary results,” IEEE Transactions on Bio-Medical Engineering, vol. 61, no. 2, pp. 246–253, 2014. View at Publisher · View at Google Scholar
  41. W. A. Kalender, H. Wolf, and C. Suess, “Dose reduction in CT by anatomically adapted tube current modulation. II. Phantom measurements,” Medical Physics, vol. 26, no. 11, pp. 2248–2253, 1999. View at Publisher · View at Google Scholar · View at Scopus
  42. J. R. Haaga, F. Miraldi, and W. MacIntyre, “The effect of mAs variation upon computed tomography image quality as evaluated by in vivo and in vitro studies,” Radiology, vol. 138, no. 2, pp. 449–454, 1981. View at Google Scholar · View at Scopus
  43. M. K. Kalra, M. M. Maher, T. L. Toth et al., “Techniques and applications of automatic tube current modulation for CT,” Radiology, vol. 233, no. 3, pp. 649–657, 2004. View at Google Scholar · View at Scopus
  44. H. Greeß, H. Wolf, U. Baum, W. A. Kalender, and W. Bautz, “Dose reduction in computed tomography by means of modulation of tube current according to anothing: first clinical results,” RoFo, vol. 170, no. 3, pp. 246–250, 1999. View at Google Scholar · View at Scopus
  45. M. Sderberg and M. Gunnarsson, “Automatic exposure control in computed tomography an evaluation of systems from different manufacturers,” Acta Radiologica, vol. 51, no. 6, pp. 625–634, 2010. View at Publisher · View at Google Scholar · View at Scopus
  46. W. Huda, E. M. Scalzetti, and G. Levin, “Technique factors and image quality as functions of patient weight at abdominal CT,” Radiology, vol. 217, no. 2, pp. 430–435, 2000. View at Google Scholar · View at Scopus
  47. B. B. Ertl-Wagner, R.-T. Hoffmann, R. Bruning et al., “Multi-detector row CT angiography of the brain at various kilovoltage settings,” Radiology, vol. 231, no. 2, pp. 528–535, 2004. View at Publisher · View at Google Scholar · View at Scopus
  48. Y. Funama, K. Awai, Y. Nakayama et al., “Radiation dose reduction without degradation of low-contrast detectability at abdominal multisection CT with a low-tube voltage technique: phantom study,” Radiology, vol. 237, no. 3, pp. 905–910, 2005. View at Publisher · View at Google Scholar · View at Scopus
  49. B. Wintersperger, T. Jakobs, P. Herzog et al., “Aorto-iliac multidetector-row CT angiography with low kV settings: improved vessel enhancement and simultaneous reduction of radiation dose,” European Radiology, vol. 15, no. 2, pp. 334–341, 2005. View at Publisher · View at Google Scholar · View at Scopus
  50. C. Schueller-Weidekamm, C. M. Schaefer-Prokop, M. Weber, C. J. Herold, and M. Prokop, “CT angiography of pulmonary arteries to detect pulmonary embolism: improvement of vascular enhancement with low kilovoltage settings,” Radiology, vol. 241, no. 3, pp. 899–907, 2006. View at Publisher · View at Google Scholar · View at Scopus
  51. A. Waaijer, M. Prokop, B. K. Velthuis, C. J. G. Bakker, G. A. P. De Kort, and M. S. Van Leeuwen, “Circle of Willis at CT angiography: dose reduction and image quality—reducing tube voltage and increasing tube current settings,” Radiology, vol. 242, no. 3, pp. 832–839, 2007. View at Publisher · View at Google Scholar · View at Scopus
  52. Y. Nakayama, K. Awai, Y. Funama et al., “Abdominal CT with low tube voltage: preliminary observations about radiation dose, contrast enhancement, image quality, and noise,” Radiology, vol. 237, no. 3, pp. 945–951, 2005. View at Publisher · View at Google Scholar · View at Scopus
  53. J. Nuyts, B. De Man, P. Dupont, M. Defrise, P. Suetens, and L. Mortelmans, “Iterative reconstruction for helical CT: a simulation study,” Physics in Medicine and Biology, vol. 43, no. 4, pp. 729–737, 1998. View at Publisher · View at Google Scholar · View at Scopus
  54. J.-B. Thibault, K. D. Sauer, C. A. Bouman, and J. Hsieh, “A three-dimensional statistical approach to improved image quality for multislice helical CT,” Medical Physics, vol. 34, no. 11, pp. 4526–4544, 2007. View at Publisher · View at Google Scholar · View at Scopus
  55. I. A. Elbakri and J. A. Fessler, “Statistical image reconstruction for polyenergetic X-ray computed tomography,” IEEE Transactions on Medical Imaging, vol. 21, no. 2, pp. 89–99, 2002. View at Publisher · View at Google Scholar · View at Scopus
  56. A. K. Hara, R. G. Paden, A. C. Silva, J. L. Kujak, H. J. Lawder, and W. Pavlicek, “Iterative reconstruction technique for reducing body radiation dose at CT: feasibility study,” American Journal of Roentgenology, vol. 193, no. 3, pp. 764–771, 2009. View at Publisher · View at Google Scholar · View at Scopus
  57. J. S. Kole and F. J. Beekman, “Evaluation of accelerated iterative X-ray CT image reconstruction using floating point graphics hardware,” Physics in Medicine and Biology, vol. 51, no. 4, pp. 875–889, 2006. View at Publisher · View at Google Scholar · View at Scopus
  58. F. Xu and K. Mueller, “Real-time 3D computed tomographic reconstruction using commodity graphics hardware,” Physics in Medicine and Biology, vol. 52, no. 12, article 006, pp. 3405–3419, 2007. View at Publisher · View at Google Scholar · View at Scopus
  59. G. C. Sharp, N. Kandasamy, H. Singh, and M. Folkert, “GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration,” Physics in Medicine and Biology, vol. 52, no. 19, article 003, pp. 5771–5783, 2007. View at Publisher · View at Google Scholar · View at Scopus
  60. H. Kudo, T. Suzuki, and E. A. Rashed, “Image reconstruction for sparse-view CT and interior CT-introduction to compressed sensing and differentiated backprojection,” Quantitative Imaging in Medicine and Surgery, vol. 3, no. 3, pp. 147–161, 2013. View at Google Scholar
  61. E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory, vol. 52, no. 2, pp. 489–509, 2006. View at Publisher · View at Google Scholar · View at Scopus
  62. D. L. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289–1306, 2006. View at Publisher · View at Google Scholar · View at Scopus
  63. G.-H. Chen, J. Tang, and S. Leng, “Prior Image Constrained Compressed Sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets,” Medical Physics, vol. 35, no. 2, pp. 660–663, 2008. View at Publisher · View at Google Scholar · View at Scopus
  64. E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Physics in Medicine and Biology, vol. 53, no. 17, pp. 4777–4807, 2008. View at Publisher · View at Google Scholar · View at Scopus
  65. K. Choi, J. Wang, L. Zhu, T.-S. Suh, S. Boyd, and L. Xing, “Compressed sensing based cone-beam computed tomography reconstruction with a first-order method,” Medical Physics, vol. 37, no. 9, pp. 5113–5125, 2010. View at Publisher · View at Google Scholar · View at Scopus
  66. L. Ritschl, F. Bergner, C. Fleischmann, and M. Kachelriess, “Improved total variation-based CT image reconstruction applied to clinical data,” Physics in Medicine and Biology, vol. 56, no. 6, pp. 1545–1561, 2011. View at Publisher · View at Google Scholar · View at Scopus
  67. E. Y. Sidky, C.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” Journal of X-Ray Science and Technology, vol. 14, no. 2, pp. 119–139, 2006. View at Google Scholar · View at Scopus
  68. F. Noo, R. Clackdoyle, and J. D. Pack, “A two-step Hilbert transform method for 2D image reconstruction,” Physics in Medicine and Biology, vol. 49, no. 17, pp. 3903–3923, 2004. View at Publisher · View at Google Scholar · View at Scopus
  69. X. Pan, Y. Zou, and D. Xia, “Image reconstruction in peripheral and central regions-of-interest and data redundancy,” Medical Physics, vol. 32, no. 3, pp. 673–684, 2005. View at Publisher · View at Google Scholar · View at Scopus
  70. K. Ogawa, M. Nakajima, and S. Yuta, “A reconstruction algorithm from truncated projections,” IEEE Transactions on Medical Imaging, vol. 3, no. 1, pp. 34–40, 1984. View at Google Scholar · View at Scopus
  71. B. Ohnesorge, T. Flohr, K. Schwarz, J. P. Heiken, and K. T. Bae, “Efficient correction for CT image artifacts caused by objects extending outside the scan field of view,” Medical Physics, vol. 27, no. 1, pp. 39–46, 2000. View at Publisher · View at Google Scholar · View at Scopus
  72. S. Kappler and S. Wirth, “Comparison Of dual-kVp and dual-layer CT in simulations and real CT system measurements,” in Proceedings of the IEEE Nuclear Science Symposium Conference Record (NSS/MIC '08), pp. 4828–4831, IEEE, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  73. L. Goshen, J. Sosna, R. Carmi, G. Kafri, I. Iancu, and A. Altman, “An iodine-calcium separation analysis and virtually non-contrasted image generation obtained with single source dual energy MDCT,” in Proceedings of the IEEE Nuclear Science Symposium Conference Record (NSS/MIC '08), pp. 3868–3870, IEEE, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  74. Y. Zou and M. D. Silver, “Analysis of fast kV-switching in dual energy CT using a pre-reconstruction decomposition technique,” in Medical Imaging: Physics of Medical Imaging, vol. 6913 of Proceedings of SPIE, International Society for Optics and Photonics, February 2008. View at Publisher · View at Google Scholar · View at Scopus
  75. T. R. C. Johnson, B. Krauss, M. Sedlmair et al., “Material differentiation by dual energy CT: initial experience,” European Radiology, vol. 17, no. 6, pp. 1510–1517, 2007. View at Publisher · View at Google Scholar
  76. T. G. Flohr, H. Bruder, K. Stierstorfer, M. Petersilka, B. Schmidt, and C. H. McCollough, “Image reconstruction and image quality evaluation for a dual source CT scanner,” Medical Physics, vol. 35, no. 12, pp. 5882–5897, 2008. View at Publisher · View at Google Scholar · View at Scopus
  77. A. N. Primak, J. C. R. Giraldo, X. Liu, L. Yu, and C. H. McCollough, “Improved dual-energy material discrimination for dual-source CT by means of additional spectral filtration,” Medical Physics, vol. 36, no. 4, pp. 1359–1369, 2009. View at Publisher · View at Google Scholar · View at Scopus
  78. M. Karçaaltıncaba and A. Aktaş, “Dual-energy CT revisited with multidetector CT: review of principles and clinical applications,” Diagnostic and Interventional Radiology, vol. 17, no. 3, pp. 181–194, 2011. View at Google Scholar
  79. T. Kraśnicki, P. Podgórski, M. Guziński et al., “Novel clinical applications of dual energy computed tomography,” Advances in Clinical and Experimental Medicine, vol. 21, no. 6, pp. 831–841, 2012. View at Google Scholar
  80. T. Heye, R. C. Nelson, L. M. Ho, D. Marin, and D. T. Boll, “Dual-energy CT applications in the abdomen,” American Journal of Roentgenology, vol. 199, supplement 5, pp. 64–70, 2012. View at Publisher · View at Google Scholar
  81. H. Scheffel, P. Stolzmann, T. Frauenfelder et al., “Dual-energy contrast-enhanced computed tomography for the detection of urinary stone disease,” Investigative Radiology, vol. 42, no. 12, pp. 823–829, 2007. View at Publisher · View at Google Scholar · View at Scopus
  82. N. Takahashi, R. P. Hartman, T. J. Vrtiska et al., “Dual-energy CT iodine-subtraction virtual unenhanced technique to detect urinary stones in an iodine-filled collecting system: a phantom study,” American Journal of Roentgenology, vol. 190, no. 5, pp. 1169–1173, 2008. View at Publisher · View at Google Scholar · View at Scopus
  83. G. Ascenti, A. Mileto, M. Gaeta, A. Blandino, S. Mazziotti, and E. Scribano, “Single-phase dual-energy CT urography in the evaluation of haematuria,” Clinical Radiology, vol. 68, no. 2, pp. 87–94, 2013. View at Publisher · View at Google Scholar
  84. A. Graser, T. R. C. Johnson, E. M. Hecht et al., “Dual-energy CT in patients suspected of having renal masses: can virtual nonenhanced images replace true nonenhanced images?” Radiology, vol. 252, no. 2, pp. 433–440, 2009. View at Publisher · View at Google Scholar · View at Scopus
  85. A. Graser, C. R. Becker, M. Staehler et al., “Single-phase dual-energy CT allows for characterization of renal masses as benign or malignant,” Investigative Radiology, vol. 45, no. 7, pp. 399–405, 2010. View at Publisher · View at Google Scholar · View at Scopus
  86. S. H. Lee, J. Hur, Y. J. Kim, H.-J. Lee, Y. J. Hong, and B. W. Choi, “Additional value of dual-energy CT to differentiate between benign and malignant mediastinal tumors: an initial experience,” European Journal of Radiology, vol. 82, no. 11, pp. 2043–2049, 2013. View at Publisher · View at Google Scholar
  87. G. Schmid-Bindert, T. Henzler, T. Q. Chu et al., “Functional imaging of lung cancer using dual energy CT: how does iodine related attenuation correlate with standardized uptake value of 18FDG-PET-CT?” European Radiology, vol. 22, no. 1, pp. 93–103, 2012. View at Publisher · View at Google Scholar · View at Scopus
  88. M. Meyer, P. Hohenberger, P. Apfaltrer et al., “CT-based response assessment of advanced gastrointestinal stromal tumor: dual energy CT provides a more predictive imaging biomarker of clinical benefit than RECIST or Choi criteria,” European Journal of Radiology, vol. 82, no. 6, pp. 923–928, 2013. View at Publisher · View at Google Scholar
  89. K. A. Miles and M. R. Griffiths, “Perfusion CT: a worthwhile enhancement?” British Journal of Radiology, vol. 76, no. 904, pp. 220–231, 2003. View at Publisher · View at Google Scholar · View at Scopus
  90. A. R. Kambadakone and D. V. Sahani, “Body perfusion CT: technique, clinical applications, and advances,” Radiologic Clinics of North America, vol. 47, no. 1, pp. 161–178, 2009. View at Publisher · View at Google Scholar · View at Scopus
  91. C.-J. Sun, C. Li, H.-B. Lv et al., “Comparing CT perfusion with oxygen partial pressure in a rabbit VX2 soft-tissue tumor model,” Journal of Radiation Research, vol. 55, no. 1, pp. 183–190, 2014. View at Publisher · View at Google Scholar
  92. G.-L. Ma, R.-J. Bai, H.-J. Jiang et al., “Early changes of hepatic hemodynamics measured by functional CT perfusion in a rabbit model of liver tumor,” Hepatobiliary & Pancreatic Diseases International, vol. 11, no. 4, pp. 407–411, 2012. View at Publisher · View at Google Scholar
  93. R. Jain, “Perfusion CT imaging of brain tumors: an overview,” American Journal of Neuroradiology, vol. 32, no. 9, pp. 1570–1577, 2011. View at Publisher · View at Google Scholar · View at Scopus
  94. D. Arandjic, F. Bonutti, E. Biasizzo et al., “Radiation doses in cerebral perfusion computed tomography: patient and phantom study,” Radiation Protection Dosimetry, vol. 154, no. 4, pp. 459–464, 2013. View at Publisher · View at Google Scholar
  95. N. Negi, T. Yoshikawa, Y. Ohno et al., “Hepatic CT perfusion measurements: a feasibility study for radiation dose reduction using new image reconstruction method,” European Journal of Radiology, vol. 81, no. 11, pp. 3048–3054, 2012. View at Publisher · View at Google Scholar
  96. R. Krissak, C. A. Mistretta, T. Henzler et al., “Noise reduction and image quality improvement of low dose and ultra low dose brain perfusion CT by HYPR-LR processing,” PLoS ONE, vol. 6, no. 2, Article ID e17098, 2011. View at Publisher · View at Google Scholar · View at Scopus
  97. M. Supanich, Y. Tao, B. Nett et al., “Radiation dose reduction in time-resolved CT angiography using highly constrained back projection reconstruction,” Physics in Medicine and Biology, vol. 54, no. 14, pp. 4575–4593, 2009. View at Publisher · View at Google Scholar · View at Scopus
  98. L. He, B. Orten, S. Do et al., “A spatio-temporal deconvolution method to improve perfusion CT quantification,” IEEE Transactions on Medical Imaging, vol. 29, no. 5, pp. 1182–1191, 2010. View at Publisher · View at Google Scholar · View at Scopus
  99. H. Yu, S. Zhao, E. A. Hoffman, and G. Wang, “Ultra-low dose lung CT perfusion regularized by a previous scan,” Academic Radiology, vol. 16, no. 3, pp. 363–373, 2009. View at Publisher · View at Google Scholar · View at Scopus
  100. J. J. S. Shankar, C. Lum, and M. Sharma, “Whole-brain perfusion imaging with 320-MDCT scanner: reducing radiation dose by increasing sampling interval,” American Journal of Roentgenology, vol. 195, no. 5, pp. 1183–1186, 2010. View at Publisher · View at Google Scholar · View at Scopus
  101. A. Tognolini, R. Schor-Bardach, O. S. Pianykh, C. J. Wilcox, V. Raptopoulos, and S. N. Goldberg, “Body tumor CT perfusion protocols: optimization of acquisition scan parameters in a rat tumor model,” Radiology, vol. 251, no. 3, pp. 712–720, 2009. View at Publisher · View at Google Scholar · View at Scopus
  102. P. C. Lauterbur, “Image formation by induced local interactions: examples employing nuclear magnetic resonance,” Nature, vol. 242, no. 5394, pp. 190–191, 1973. View at Publisher · View at Google Scholar · View at Scopus
  103. H. Pedersen, S. Kozerke, S. Ringgaard, K. Nehrke, and Y. K. Won, “K-t PCA: temporally constrained k-t BLAST reconstruction using principal component analysis,” Magnetic Resonance in Medicine, vol. 62, no. 3, pp. 706–716, 2009. View at Publisher · View at Google Scholar · View at Scopus
  104. L. Ge, A. Kino, M. Griswold, C. Mistretta, J. C. Carr, and D. Li, “Myocardial perfusion MRI with sliding-window conjugate-gradient HYPR,” Magnetic Resonance in Medicine, vol. 62, no. 4, pp. 835–839, 2009. View at Publisher · View at Google Scholar · View at Scopus
  105. B. Jung, M. Honal, J. Hennig, and M. Markl, “k-t-space accelerated myocardial perfusion,” Journal of Magnetic Resonance Imaging, vol. 28, no. 5, pp. 1080–1085, 2008. View at Publisher · View at Google Scholar · View at Scopus
  106. P. Kellman, J. A. Derbyshire, K. O. Agyeman, E. R. McVeigh, and A. E. Arai, “Extended coverage first-pass perfusion imaging using slice-interleaved TSENSE,” Magnetic Resonance in Medicine, vol. 51, no. 1, pp. 200–204, 2004. View at Publisher · View at Google Scholar · View at Scopus
  107. J.-S. Hsu, S.-Y. Tsai, M.-T. Wu, H.-W. Chung, and Y.-R. Lin, “Fast dynamic contrast-enhanced lung MR imaging using k-t BLAST: a spatiotemporal perspective,” Magnetic Resonance in Medicine, vol. 67, no. 3, pp. 786–792, 2012. View at Publisher · View at Google Scholar · View at Scopus
  108. C. A. Mistretta, O. Wieben, J. Velikina et al., “Highly constrained backprojection for time-resolved MRI,” Magnetic Resonance in Medicine, vol. 55, no. 1, pp. 30–40, 2006. View at Publisher · View at Google Scholar · View at Scopus
  109. Y. Wu, N. Kim, F. R. Korosec et al., “3D time-resolved contrast-enhanced cerebrovascular MR angiography with subsecond frame update times using radial k-space trajectories and highly constrained projection reconstruction,” American Journal of Neuroradiology, vol. 28, no. 10, pp. 2001–2004, 2007. View at Publisher · View at Google Scholar · View at Scopus
  110. K. K. Vigen, D. C. Peters, T. M. Grist, W. F. Block, and C. A. Mistretta, “Undersampled projection-reconstruction imaging for time-resolved contrast-enhanced imaging,” Magnetic Resonance in Medicine, vol. 43, no. 2, pp. 170–176, 2000. View at Google Scholar
  111. F. R. Korosec, R. Frayne, T. M. Grist, and C. A. Mistretta, “Time-resolved contrast-enhanced 3D MR angiography,” Magnetic Resonance in Medicine, vol. 36, no. 3, pp. 345–351, 1996. View at Google Scholar · View at Scopus
  112. J. F. Utting, S. Kozerke, R. Schnitker, and T. Niendorf, “Comparison of k-t SENSE/k-t BLAST with conventional SENSE applied to BOLD fMRI,” Journal of Magnetic Resonance Imaging, vol. 32, no. 1, pp. 235–241, 2010. View at Publisher · View at Google Scholar · View at Scopus
  113. B. Madore, G. H. Glover, and N. J. Pelc, “Unaliasing by fourier-encoding the overlaps using the temporal dimension (UNFOLD), applied to cardiac imaging and fMRI,” Magnetic Resonance in Medicine, vol. 42, no. 5, pp. 813–828, 1999. View at Google Scholar
  114. D. C. Peters, D. B. Ennis, and E. R. McVeigh, “High-resolution MRI of cardiac function with projection reconstruction and steady-state free precession,” Magnetic Resonance in Medicine, vol. 48, no. 1, pp. 82–88, 2002. View at Publisher · View at Google Scholar · View at Scopus
  115. B. Jung, P. Ullmann, M. Honal, S. Bauer, J. Hennig, and M. Markl, “Parallel MRI with extended and averaged GRAPPA kernels (PEAK-GRAPPA): optimized spatiotemporal dynamic imaging,” Journal of Magnetic Resonance Imaging, vol. 28, no. 5, pp. 1226–1232, 2008. View at Publisher · View at Google Scholar · View at Scopus
  116. P. Kellman, F. H. Epstein, and E. R. McVeigh, “Adaptive sensitivity encoding incorporating temporal filtering (TSENSE),” Magnetic Resonance in Medicine, vol. 45, no. 5, pp. 846–852, 2001. View at Publisher · View at Google Scholar · View at Scopus
  117. J. Tsao, S. Kozerke, P. Boesiger, and K. P. Pruessmann, “Optimizing spatiotemporal sampling for k-t BLAST and k-t SENSE: application to high-resolution real-time cardiac steady-state free precession,” Magnetic Resonance in Medicine, vol. 53, no. 6, pp. 1372–1382, 2005. View at Publisher · View at Google Scholar · View at Scopus
  118. M. A. Griswold, P. M. Jakob, R. M. Heidemann et al., “Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA),” Magnetic Resonance in Medicine, vol. 47, no. 6, pp. 1202–1210, 2002. View at Publisher · View at Google Scholar · View at Scopus
  119. D. K. Sodickson and W. J. Manning, “Simultaneous Acquisition of Spatial Harmonics (SMASH): fast imaging with radiofrequency coil arrays,” Magnetic Resonance in Medicine, vol. 38, no. 4, pp. 591–603, 1997. View at Publisher · View at Google Scholar · View at Scopus
  120. P. M. Jakob, M. A. Griswold, R. R. Edelman, and D. K. Sodickson, “AUTO-SMASH: a self-calibrating technique for SMASH imaging,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 7, no. 1, pp. 42–54, 1998. View at Publisher · View at Google Scholar · View at Scopus
  121. R. Heidemann and M. Griswold, “VD-AUTO-SMASH imaging,” Magnetic Resonance in Medicine, vol. 45, no. 6, pp. 1066–1074, 2000. View at Publisher · View at Google Scholar
  122. P. J. Beatty, A. C. Brau, and S. Chang, “A method for autocalibrating 2-D accelerated volumetric parallel imaging with clinically practical reconstruction times,” in Proceedings of the International Society for Magnetic Resonance (ISMRM '07), vol. 15, p. 1749, Berlin, Germany, 2007.
  123. M. Blaimer, F. A. Breuer, M. Mueller et al., “2D-GRAPPA-operator for faster 3D parallel MRI,” Magnetic Resonance in Medicine, vol. 56, no. 6, pp. 1359–1364, 2006. View at Publisher · View at Google Scholar · View at Scopus
  124. K. Pruessmann, M. Weiger, M. Scheidegger, and P. Boesiger, “SENSE: sensitivity encoding for fast MRI,” Magnetic Resonance in Medicine, vol. 42, pp. 952–962, 1999. View at Google Scholar
  125. K. Ocegueda and A. O. Rodriguez, “Slotted surface coil with reduced g-factor for SENSE imaging,” in Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '06), vol. 1, pp. 1904–1906, September 2006. View at Publisher · View at Google Scholar · View at Scopus
  126. L. T. Muftuler, G. Chen, and O. Nalcioglu, “An inverse method to design RF coil arrays optimized for SENSE imaging,” Physics in Medicine and Biology, vol. 51, no. 24, article 012, pp. 6457–6469, 2006. View at Publisher · View at Google Scholar · View at Scopus
  127. B. Liu, K. King, M. Steckner, J. Xie, J. Sheng, and L. Ying, “Regularized sensitivity encoding (SENSE) reconstruction using bregman iterations,” Magnetic Resonance in Medicine, vol. 61, no. 1, pp. 145–152, 2009. View at Publisher · View at Google Scholar · View at Scopus
  128. D. Liang, H. Wang, and L. Ying, “SENSE reconstruction with nonlocal TV regularization,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '09), pp. 1032–1035, 2009.
  129. F.-H. Lin, K. K. Kwong, J. W. Belliveau, and L. L. Wald, “Parallel imaging reconstruction using automatic regularization,” Magnetic Resonance in Medicine, vol. 51, no. 3, pp. 559–567, 2004. View at Publisher · View at Google Scholar · View at Scopus
  130. A. A. Samsonov, “On optimality of parallel MRI reconstruction in k-space,” Magnetic Resonance in Medicine, vol. 59, no. 1, pp. 156–164, 2008. View at Publisher · View at Google Scholar · View at Scopus
  131. S. O. Schönberg, O. Dietrich, and M. F. Reiser, Parallel Imaging in Clinical MR Applications, Springer, 2007.
  132. M. N. J. Paley, K. J. Lee, J. M. Wild, P. D. Griffiths, and E. H. Whitby, “Simultaneous parallel inclined readout image technique,” Magnetic Resonance Imaging, vol. 24, no. 5, pp. 557–562, 2006. View at Publisher · View at Google Scholar · View at Scopus
  133. F. A. Breuer, M. Blaimer, R. M. Heidemann, M. F. Mueller, M. A. Griswold, and P. M. Jakob, “Controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) for multi-slice imaging,” Magnetic Resonance in Medicine, vol. 53, no. 3, pp. 684–691, 2005. View at Publisher · View at Google Scholar · View at Scopus
  134. D. Stäb, C. O. Ritter, F. A. Breuer, A. M. Weng, D. Hahn, and H. Köstler, “CAIPIRINHA accelerated SSFP imaging,” Magnetic Resonance in Medicine, vol. 65, no. 1, pp. 157–164, 2011. View at Google Scholar · View at Scopus
  135. S. R. Yutzy, N. Seiberlich, J. L. Duerk, and M. A. Griswold, “Improvements in multislice parallel imaging using radial CAIPIRINHA,” Magnetic Resonance in Medicine, vol. 65, no. 6, pp. 1630–1637, 2011. View at Publisher · View at Google Scholar · View at Scopus
  136. H. J. Michaely, J. N. Morelli, J. Budjan et al., “CAIPIRINHA-Dixon-TWIST (CDT)-volume-interpolated breath-hold examination (VIBE): a new technique for fast time-resolved dynamic 3-dimensional imaging of the abdomen with high spatial resolution,” Investigative Radiology, vol. 48, no. 8, pp. 590–597, 2013. View at Publisher · View at Google Scholar
  137. M. H. Yu, J. M. Lee, J.-H. Yoon, B. Kiefer, J. K. Han, and B.-I. Choi, “Clinical application of controlled aliasing in parallel imaging results in a higher acceleration (CAIPIRINHA)-volumetric interpolated breathhold (VIBE) sequence for gadoxetic acid-enhanced liver MR imaging,” Journal of Magnetic Resonance Imaging, vol. 38, no. 5, pp. 1020–1026, 2013. View at Publisher · View at Google Scholar
  138. R. Nunes, J. Hajnal, X. Golay, and D. Larkman, “Simultaneous slice excitation and reconstruction for single shot EPI,” in Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM '06), vol. 14, p. 293, 2006.
  139. K. Setsompop, B. A. Gagoski, J. R. Polimeni, T. Witzel, V. J. Wedeen, and L. L. Wald, “Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty,” Magnetic Resonance in Medicine, vol. 67, no. 5, pp. 1210–1224, 2012. View at Publisher · View at Google Scholar · View at Scopus
  140. S. J. Riederer, T. Tasciyan, F. Farzaneh, J. N. Lee, R. C. Wright, and R. J. Herfkens, “MR fluoroscopy: technical feasibility,” Magnetic Resonance in Medicine, vol. 8, no. 1, pp. 1–15, 1988. View at Google Scholar · View at Scopus
  141. J. J. van Vaals, M. E. Brummer, W. T. Dixon et al., “‘Keyhole” method for accelerating imaging of contrast agent uptake,” Journal of Magnetic Resonance Imaging, vol. 3, no. 4, pp. 671–675, 1993. View at Google Scholar · View at Scopus
  142. M. Doyle, E. G. Walsh, G. G. Blackwell, and G. M. Pohost, “Block regional interpolation scheme for k-space (BRISK): a rapid cardiac imaging technique,” Magnetic Resonance in Medicine, vol. 33, no. 2, pp. 163–170, 1995. View at Publisher · View at Google Scholar · View at Scopus
  143. T. Song, A. F. Laine, Q. Chen et al., “Optimal k-space sampling for dynamic contrast-enhanced MRI with an application to MR renography,” Magnetic Resonance in Medicine, vol. 61, no. 5, pp. 1242–1248, 2009. View at Publisher · View at Google Scholar · View at Scopus
  144. R. P. Lim, M. Shapiro, E. Y. Wang et al., “3D time-resolved MR angiography (MRA) of the carotid arteries with time-resolved imaging with stochastic trajectories: comparison with 3D contrast-enhanced bolus-chase MRA and 3D time-of-flight MRA,” American Journal of Neuroradiology, vol. 29, no. 10, pp. 1847–1854, 2008. View at Publisher · View at Google Scholar · View at Scopus
  145. J. Tsao, P. Boesiger, and K. P. Pruessmann, “k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations,” Magnetic Resonance in Medicine, vol. 50, no. 5, pp. 1031–1042, 2003. View at Publisher · View at Google Scholar · View at Scopus
  146. F. Huang, J. Akao, S. Vijayakumar, G. R. Duensing, and M. Limkeman, “K-t GRAPPA: a k-space implementation for dynamic MRI with high reduction factor,” Magnetic Resonance in Medicine, vol. 54, no. 5, pp. 1172–1184, 2005. View at Publisher · View at Google Scholar · View at Scopus
  147. S. Schnell, M. Markl, P. Entezari et al., “k-t GRAPPA accelerated four-dimensional flow MRI in the aorta: effect on scan time, image quality, and quantification of flow and wall shear stress,” Magnetic Resonance in Medicine, 2013. View at Publisher · View at Google Scholar
  148. P. Lai, F. Huang, A. C. Larson, and D. Li, “Fast four-dimensional coronary MR angiography with k-t GRAPPA,” Journal of Magnetic Resonance Imaging, vol. 27, no. 3, pp. 659–665, 2008. View at Publisher · View at Google Scholar · View at Scopus
  149. M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: the application of compressed sensing for rapid MR imaging,” Magnetic Resonance in Medicine, vol. 58, no. 6, pp. 1182–1195, 2007. View at Publisher · View at Google Scholar · View at Scopus
  150. U. Gamper, P. Boesiger, and S. Kozerke, “Compressed sensing in dynamic MRI,” Magnetic Resonance in Medicine, vol. 59, no. 2, pp. 365–373, 2008. View at Publisher · View at Google Scholar · View at Scopus
  151. M. S. Hansen, C. Baltes, J. Tsao, S. Kozerke, K. P. Pruessmann, and H. Eggers, “k-t BLAST reconstruction from non-Cartesian k-t space sampling,” Magnetic Resonance in Medicine, vol. 55, no. 1, pp. 85–91, 2006. View at Publisher · View at Google Scholar · View at Scopus
  152. C. M. Tsai and D. G. Nishimura, “Reduced aliasing artifacts using variable-density k-space sampling trajectories,” Magnetic Resonance in Medicine, vol. 43, no. 3, pp. 452–458, 2000. View at Google Scholar
  153. K. Scheffler and J. Hennig, “Reduced circular field-of-view imaging,” Magnetic Resonance in Medicine, vol. 40, no. 3, pp. 474–480, 1998. View at Google Scholar · View at Scopus
  154. M. Doneva, P. Börnert, H. Eggers, A. Mertins, J. Pauly, and M. Lustig, “Compressed sensing for chemical shift-based water-fat separation,” Magnetic Resonance in Medicine, vol. 64, no. 6, pp. 1749–1759, 2010. View at Publisher · View at Google Scholar · View at Scopus
  155. C. N. Wiens, C. M. McCurdy, J. D. Willig-Onwuachi, and C. A. McKenzie, “R2*-corrected water-fat imaging using compressed sensing and parallel imaging,” Magnetic Resonance in Medicine, 2013. View at Publisher · View at Google Scholar
  156. R. Otazo, D. Kim, L. Axel, and D. K. Sodickson, “Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI,” Magnetic Resonance in Medicine, vol. 64, no. 3, pp. 767–776, 2010. View at Publisher · View at Google Scholar · View at Scopus
  157. D. Kim, H. A. Dyvorne, R. Otazo, L. Feng, D. K. Sodickson, and V. S. Lee, “Accelerated phase-contrast cine MRI using k-t SPARSE-SENSE,” Magnetic Resonance in Medicine, vol. 67, no. 4, pp. 1054–1064, 2012. View at Publisher · View at Google Scholar · View at Scopus
  158. H. Jung, K. Sung, K. S. Nayak, E. Y. Kim, and J. C. Ye, “K-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI,” Magnetic Resonance in Medicine, vol. 61, no. 1, pp. 103–116, 2009. View at Publisher · View at Google Scholar · View at Scopus
  159. M. Akçakaya, T. A. Basha, R. H. Chan, W. J. Manning, and R. Nezafat, “Accelerated isotropic sub-millimeter whole-heart coronary MRI: compressed sensing versus parallel imaging,” Magnetic Resonance in Medicine, 2013. View at Publisher · View at Google Scholar
  160. A. Hsiao, M. Lustig, M. T. Alley, M. J. Murphy, and S. S. Vasanawala, “Evaluation of valvular insufficiency and shunts with parallel-imaging compressed-sensing 4D phase-contrast MR imaging with stereoscopic 3D velocity-fusion volume-rendered visualization,” Radiology, vol. 265, no. 1, pp. 87–95, 2012. View at Publisher · View at Google Scholar
  161. H. Nguyen and G. Glover, “A modified generalized series approach: application to sparsely sampled FMRI,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 10, pp. 2867–2877, 2013. View at Publisher · View at Google Scholar
  162. D. J. Holland, C. Liu, X. Song et al., “Compressed sensing reconstruction improves sensitivity of variable density spiral fMRI,” Magnetic Resonance in Medicine, vol. 70, no. 6, pp. 1634–1643, 2013. View at Publisher · View at Google Scholar