High Resolution Time Resolved Contrast Enhanced MR Angiography Using k-t FOCUSS

k-t FOCUSS 알고리듬을 이용한 고분해능 4-D MR 혈관 조영 영상 기법

  • Jung, Hong (Bio-Imaging & Signal Processing Lab, Dept. Bio/Brain Engineering, Korea Advanced Institute of Science & Technology (KAIST)) ;
  • Kim, Eung-Yeop (Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine) ;
  • Ye, Jong-Chul (Bio-Imaging & Signal Processing Lab, Dept. Bio/Brain Engineering, Korea Advanced Institute of Science & Technology (KAIST))
  • 정홍 (한국과학기술원 바이오및뇌공학과 바이오 영상 및 신호처리 연구실) ;
  • 김응엽 (연세대학교 의과대학 영상의학과) ;
  • 예종철 (한국과학기술원 바이오및뇌공학과 바이오 영상 및 신호처리 연구실)
  • Received : 2009.12.07
  • Accepted : 2010.03.01
  • Published : 2010.06.30

Abstract

Purpose : Recently, the Recon Challenge at the 2009 ISMRM workshop on Data Sampling and Image Reconstruction at Sedona, Arizona was held to evaluate feasibility of highly accelerated acquisition of time resolved contrast enhanced MR angiography. This paper provides the step-by-step description of the winning results of k-t FOCUSS in this competition. Materials and Methods : In previous works, we proved that k-t FOCUSS algorithm successfully solves the compressed sensing problem even for less sparse cardiac cine applications. Therefore, using k-t FOCUSS, very accurate time resolved contrast enhanced MR angiography can be reconstructed. Accelerated radial trajectory data were synthetized from X-ray cerebral angiography images and provided by the organizing committee, and radiologists double blindly evaluated each reconstruction result with respect to the ground-truth data. Results : The reconstructed results at various acceleration factors demonstrate that each components of compressed sensing, such as sparsifying transform and incoherent sampling patterns, etc can have profound effects on the final reconstruction results. Conclusion : From reconstructed results, we see that the compressed sensing dynamic MR imaging algorithm, k-t FOCUSS enables high resolution time resolved contrast enhanced MR angiography.

목적: 최근, 미국 애리조나 세도나에서 열린 국제자기공명학회 (ISMRM) 주관의 2009년 데이터 샘플링과 영상 복원에 관한 워크샵에서 자기공명영상 복원 대회가 열렸다. 이 대회는 time resolved contrast enhanced MR angiography 에 대한 고속 촬영의 실제 활용 가능성을 평가하기 위한 것이었다. 본 논문은 이 대회의 우승 결과를 얻은 k-t FOCUSS 알고리듬을 단계별로 자세히 묘사하도록 한다. 대상 및 방법: 본 그룹은 앞선 연구에서 비교적 덜 스파스한 심장 영상에 대해 k-t FOCUSS 알고리듬이 성공적으로 압축센싱 문제를 풀수 있음을 증명했다. 따라서 k-t FOCUSS 알고리듬을 time resolved contrast enhanced MR angiography 에 적용함으로써, 매우 정확한 영상 복원이 가능할 것이다. 영상 복원을 위해 X-ray 대뇌 혈관조영 영상으로부터 구성된 다운 샘플링된 데이터가 대회 주최측으로부터 공통으로 제시되었고, 방사선과 의사들이 각 복원된 영상에 대한 사전 정보 없이, 원래 영상과 복원된 결과를 비교함으로써, 영상의 질을 평가하였다. 결과: 다양한 다운샘플링에 대해 얻어진 결과들은 영상의 스파스 변환이나 샘플링 형태와 같은 압축센싱의 중요한 요소들에 의해 크게 영향을 받는다는 것을 보여주었다. 결론: 복원된 결과로부터, 압축센싱 동적자기공명영상 기법인 k-t FOCUSS 가 고해상도의 time resolved contrast enhanced MR angiography 를 가능하게 할 수 있음을 확인하였다.

Keywords

References

  1. B. Urban, L. Ratner, and E. Fishman, Three-dimensional Volume-rendered CT Angiography of the Renal Arteries and Veins: Normal Anatomy, Variants, and Clinical Application 1, Radiographics, 2001;21(2):373-386 https://doi.org/10.1148/radiographics.21.2.g01mr19373
  2. P. Zanzonico, L. Rothenberg, and H. Strauss, Radiation Exposure of Computed Tomography and Direct Intracoronary Angiography Risk has its Reward, Journal of the American College of Cardiology, 2006;47(9):1846-1849 https://doi.org/10.1016/j.jacc.2005.10.075
  3. V. S. Lee, Cardiovascular MRI: Physical principles to practical protocols. Philadelpia: Lippincott Williams & Wilkins, 2006.
  4. K. P. Pruessmann, M. Weigher, M. B. Scheidegger, and P. Boesiger, SENSE: Sensitivity encoding for fast MRI, Magn. Reson. Med, 1999;42(5):952-962 https://doi.org/10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S
  5. D. K. Sodickwon and W. J. Manning, Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays, Magn. Reson. Med, 1997;38(4)591- 603. https://doi.org/10.1002/mrm.1910380414
  6. M. A. Griswold, P. M. Jakob, R. M. Heidemann, M. Nittka, V. Jellus, J. Wang, B. Kiefer, and A. Haase, Generalized autocalibrating partially parallel acquisitions (GRAPPA), Magn. Reson. Med, 2002;47(6):1202-1210 https://doi.org/10.1002/mrm.10171
  7. D. Sodickson, C. McKenzie, W. Li, S. Wolff, W. Manning, and R. Edelman, "Contrast-enhanced 3D MR Angiography with Simultaneous Acquisition of Spatial Harmonics: A Pilot Study 1, Radiology, 2000;217(1):284-289 https://doi.org/10.1148/radiology.217.1.r00se47284
  8. G. Wilson, R. Hoogeveen, W. Willinek, R. Muthupillai, and J. Maki, "Parallel Imaging in MR Angiography," Topics in Magnetic Resonance Imaging, 2004;15(3):169-185. https://doi.org/10.1097/01.rmr.0000134199.94874.70
  9. M. Lustig, D. Donoho, and J. Pauly, Sparse MRI: The application of compressed sensing for rapid MR imaging, Magn. Reson. Med, 2007;58(6):1182-1195 https://doi.org/10.1002/mrm.21391
  10. D. L. Donoho, Compressed sensing, IEEE Trans. on Information Theory, 2006;52(5):1289-1306 https://doi.org/10.1109/TIT.2006.871582
  11. H. Jung, J. C. Ye, and E. Y. Kim, Improved k-t BLAST and k-t SENSE using FOCUSS, Physics in Medicine and Biology, 2007;52(11)3201-3226 https://doi.org/10.1088/0031-9155/52/11/018
  12. 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, Magn. Reson. Med, 2009;61:103-116 https://doi.org/10.1002/mrm.21757
  13. S. Winkelmann, T. Schaeffter, T. Koehler, H. Eggers, and O. Doessel, An optimal radial profile order based on the Golden Ratio for time-resolved MRI, IEEE Transactions on Medical Imaging, 2007;26(1):68-76 https://doi.org/10.1109/TMI.2006.885337
  14. H. V. Poor, An Introduction of Signal Detection and Estimation, 2nd ed. New York: Springer-Verlag, 1994.
  15. Z. Liang, Spatiotemporal Imaging with Partially Separable Functions, in 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007, p. 988-991
  16. C. Mistretta, O. Wieben, J. Velikina, W. Block, J. Perry, Y. Wu, K. Johnson, and Y. Wu, Highly constrained backprojection for time-resolved MRI, Magn. Reson. Med, 2006;55(1)30- 40 https://doi.org/10.1002/mrm.20772
  17. R. O'Halloran, Z. Wen, J. Holmes, and S. Fain, Iterative projection reconstruction of time-resolved images using highly-constrained back-proejction (HYPR), Magn. Reson. Med, 2008;59(1):132-139 https://doi.org/10.1002/mrm.21439
  18. J. Wild, M. Paley, L. Kasuboski, A. Swift, S. Fichele, N. Woodhouse, P. Griffiths, and E. van Beek, Dynamic radial projection MRI of inhaled hyperpolarized 3 He gas, Magn. Reson. Med, 2003;49(6):991-997 https://doi.org/10.1002/mrm.10477
  19. Z. P. Liang and P. C. Lauterbur, Principles of magnetic resonance imaging: A signal processing perspective, New York: IEEE press, 2000.