X-Ray Hybrid Modalities for Image Guidance

  • Prasheel V. Lillaney
  • Norbert J. Pelc
  • Rebecca Fahrig


Minimally invasive procedures using image guidance have become a popular alternative to their surgical counterparts because they offer reduced patient risk and as a result reduced morbidity. X-ray fluoroscopy can be used to provide real-time image guidance for interventional procedures. Current state-of-the-art fluoroscopy systems consist of a high-power rotating anode x-ray tube and a digital flat-panel detector. However, the lack of three-dimensional visualization has motivated the development of advanced x-ray imaging modalities for image guidance purposes which include real-time tomosynthesis, C-arm computed tomography (CT), and hybrid x-ray/MRI (X-MR).

Tomosynthesis systems traditionally require mechanical motion of the source relative to the detector. Real-time tomosynthesis can be achieved via the scanning beam digital x-ray (SBDX) system, which can obtain a limited range of angular projections without requiring mechanical motion by using a distributed source array. This system has potential applications for three-dimensional tracking of catheters as well as for image guidance for lung nodule biopsy.

C-arm CT can provide three-dimensional imaging capabilities in the interventional suite. Improvements over the past decade to C-arm systems, such as the introduction of large-area flat-panel amorphous silicon detectors and more robust gantry designs, have enabled the development of new intra-procedural applications for this imaging modality. These applications include using the three-dimensional information provided by C-arm CT to obtain brain perfusion parameters in stroke patients and performing image guidance for radio-frequency ablations to treat cardiac arrhythmias.

Hybrid X-MR systems combine the three-dimensional imaging capabilities and excellent soft tissue contrast provided by MRI with the high spatial/temporal resolution and accurate device tracking provided by x-ray. These systems have been used for a variety of intraoperative procedures including shunt deployment in the liver, brain biopsy, chemoembolization of hepatic tumors, hysterosalpingograms, and loopograms. The various hybrid system geometries present different engineering challenges, with those geometries that attempt to place the modalities very close to each other requiring modification of hardware components. The safety and compatibility of interventional devices such as catheters in an MR environment is a concern in these hybrid systems as well, and work has been performed to offer solutions to these problems.


Focal Spot Induction Motor Lung Nodule System Geometry Fringe Field 
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  1. 1.
    Garrison JB, et al. Three dimensional roentgenography. Am J Roentgenol Radium Ther Nucl Med. 1969;105:903–8.PubMedGoogle Scholar
  2. 2.
    Grant DG. Tomosynthesis: a three-dimensional radiographic imaging technique. IEEE Trans Biomed Eng. 1972;19:20–8.PubMedGoogle Scholar
  3. 3.
    Miller ER, et al. An infinite number of laminagrams from a finite number of radiographs. Radiology. 1971;98:249–55.PubMedGoogle Scholar
  4. 4.
    Godfrey D, et al. Optimization of matrix inverse tomosynthesis. SPIE Med Imag. 2001;4320:696–704.Google Scholar
  5. 5.
    Niklason LT, et al. Digital tomosynthesis in breast imaging. Radiology. 1997;205:399–406.PubMedGoogle Scholar
  6. 6.
    Suryanarayanan S, et al. Comparison of tomosynthesis methods used with digital mammography. Acad Radiol. 2000;7:1085–97.PubMedGoogle Scholar
  7. 7.
    Badano A, et al. Anisotropic imaging performance in breast tomosynthesis. Med Phys. 2007;34:4076–91.PubMedGoogle Scholar
  8. 8.
    Chen Y, et al. Importance of point-by-point back projection correction for isocentric motion in digital breast tomosynthesis: relevance to morphology of structures such as microcalcifications. Med Phys. 2007;34:3885–92.PubMedGoogle Scholar
  9. 9.
    Diekmann F, Bick U. Tomosynthesis and contrast-enhanced digital mammography: recent advances in digital mammography. Eur Radiol. 2007;17:3086–92.PubMedGoogle Scholar
  10. 10.
    Gur D. Tomosynthesis: potential clinical role in breast imaging. AJR Am J Roentgenol. 2007;189:614–5.PubMedGoogle Scholar
  11. 11.
    Li B, et al. Optimization of slice sensitivity profile for radiographic tomosynthesis. Med Phys. 2007;34:2907–16.PubMedGoogle Scholar
  12. 12.
    Park JM, et al. Breast tomosynthesis: present considerations and future applications. Radiographics. 2007;27 Suppl 1:S231–40.PubMedGoogle Scholar
  13. 13.
    Poplack SP, et al. Digital breast tomosynthesis: initial experience in 98 women with abnormal digital screening mammography. AJR Am J Roentgenol. 2007;189:616–23.PubMedGoogle Scholar
  14. 14.
    Zeng K, et al. Digital tomosynthesis aided by low-resolution exact computed tomography. J Comput Assist Tomogr. 2007;31:976–83.PubMedGoogle Scholar
  15. 15.
    Zhou J, et al. A computer simulation platform for the optimization of a breast tomosynthesis system. Med Phys. 2007;34:1098–109.PubMedGoogle Scholar
  16. 16.
    Gennaro G, et al. Digital breast tomosynthesis versus digital mammography: a clinical performance study. Eur Radiol. 2010;20:1545–53.PubMedGoogle Scholar
  17. 17.
    Tagliafico A, et al. One-to-one comparison between digital spot compression view and digital breast tomosynthesis. Eur Radiol. 2012;22:539–44.PubMedGoogle Scholar
  18. 18.
    Solomon EG, et al. Low-exposure scanning-beam x-ray fluoroscopy system. Proceedings of SPIE – the international society for optical engineering medical imaging 1996: physics of medical imaging, vol. 2708; 11–13 Feb 1996. Newport Beach; 1996. p. 140–9.Google Scholar
  19. 19.
    Solomon EG, et al. Scanning-beam digital x-ray (SBDX) system for cardiac angiography. Proceedings of SPIE – the international society for optical engineering proceedings of the 1999 medical imaging – physics of medical imaging, vol. 3659; 21–23 Feb 1999. San Diego; 1999. p. 246–57.Google Scholar
  20. 20.
    Sprenger F, et al. Stationary digital breast tomosynthesis with distributed field emission x-ray tube. Proc SPIE. 2011; 79615I-1–79615I-6.Google Scholar
  21. 21.
    Maltz JS, et al. Fixed gantry tomosynthesis system for radiation therapy image guidance based on a multiple source x-ray tube with carbon nanotube cathodes. Med Phys. 2009;36:1624–36.PubMedGoogle Scholar
  22. 22.
    Barrett HH. Limited-angle tomography for the nineties. J Nucl Med. 1990;31:1688–92.PubMedGoogle Scholar
  23. 23.
    Dobbins 3rd JT, Godfrey DJ. Digital x-ray tomosynthesis: current state of the art and clinical potential. Phys Med Biol. 2003;48:R65–106.PubMedGoogle Scholar
  24. 24.
    Speidel MA, et al. Scanning-beam digital x-ray (SBDX) technology for interventional and diagnostic cardiac angiography. Med Phys. 2006;33:2714–27.PubMedGoogle Scholar
  25. 25.
    Speidel MA, et al. Comparison of entrance exposure and signal-to-noise ratio between an SBDX prototype and a wide-beam cardiac angiographic system. Med Phys. 2006;33:2728–43.PubMedGoogle Scholar
  26. 26.
    Wolff MR, et al. Pilot study with a scanning-beam digital x-ray system. Am J Cardiol. 2004;94.Google Scholar
  27. 27.
    Speidel MA, et al. Three-dimensional tracking of cardiac catheters using an inverse geometry x-ray fluoroscopy system. Med Phys. 2010;37:6377–89.PubMedGoogle Scholar
  28. 28.
    National Lung Screening Trial. 2002.
  29. 29.
    Henschke CI, et al. Early lung cancer action project: overall design and findings from baseline screening. Lancet. 1999;354:99–105.PubMedGoogle Scholar
  30. 30.
    Lechtzin N, et al. Patient satisfaction with bronchoscopy. Am J Respir Crit Care Med. 2002;166:1326–31.PubMedGoogle Scholar
  31. 31.
    Suratt PM, et al. Deaths and complications associated with fiberoptic bronchoscopy. Chest. 1976;69:747–51.PubMedGoogle Scholar
  32. 32.
    Geraghty PR, et al. CT-guided transthoracic needle aspiration biopsy of pulmonary nodules: needle size and pneumothorax rate. Radiology. 2003;229:475–81.PubMedGoogle Scholar
  33. 33.
    Gupta S, et al. Small (</=2-cm) subpleural pulmonary lesions: short- versus long-needle-path CT-guided biopsy–comparison of diagnostic yields and complications. Radiology. 2005;234:631–7.PubMedGoogle Scholar
  34. 34.
    Sawabata N, et al. Fine-needle aspiration cytologic technique for lung cancer has a high potential of malignant cell spread through the tract. Chest. 2000;118:936–9.PubMedGoogle Scholar
  35. 35.
    Yeow KM, et al. Risk factors of pneumothorax and bleeding: multivariate analysis of 660 CT-guided coaxial cutting needle lung biopsies. Chest. 2004;126:748–54.PubMedGoogle Scholar
  36. 36.
    Allen MS, et al. Video-assisted thoracic surgical procedures: the Mayo experience. Mayo Clin Proc. 1996;71:351–9.PubMedGoogle Scholar
  37. 37.
    DeCamp Jr MM, et al. The safety and versatility of video-thoracoscopy: a prospective analysis of 895 consecutive cases. J Am Coll Surg. 1995;181:113–20.PubMedGoogle Scholar
  38. 38.
    Wang KP. Transbronchial needle aspiration and percutaneous needle aspiration for staging and diagnosis of lung cancer. Clin Chest Med. 1995;16:535–52.PubMedGoogle Scholar
  39. 39.
    Gay PC, Brutinel WM. Transbronchial needle aspiration in the practice of bronchoscopy. Mayo Clin Proc. 1989;64:158–62.PubMedGoogle Scholar
  40. 40.
    Harrow EM, et al. The utility of transbronchial needle aspiration in the staging of bronchogenic carcinoma. Am J Respir Crit Care Med. 2000;161:601–7.PubMedGoogle Scholar
  41. 41.
    Rong F, Cui B. CT scan directed transbronchial needle aspiration biopsy for mediastinal nodes. Chest. 1998;114:36–9.PubMedGoogle Scholar
  42. 42.
    Schenk DA, et al. Transbronchial needle aspiration in the diagnosis of bronchogenic carcinoma. Chest. 1987;92:83–5.PubMedGoogle Scholar
  43. 43.
    Schenk DA, et al. Utility of the Wang 18-gauge transbronchial histology needle in the staging of bronchogenic carcinoma. Chest. 1989;96:272–4.PubMedGoogle Scholar
  44. 44.
    Wang KP, et al. Flexible transbronchial needle aspiration for staging of bronchogenic carcinoma. Chest. 1983;84:571–6.PubMedGoogle Scholar
  45. 45.
    Schreiber G, McCrory DC. Performance characteristics of different modalities for diagnosis of suspected lung cancer: summary of published evidence. Chest. 2003;123:115S–28.PubMedGoogle Scholar
  46. 46.
    Anantham D, et al. Electromagnetic navigation bronchoscopy-guided fiducial placement for robotic stereotactic radiosurgery of lung tumors: a feasibility study. Chest. 2007;132:930–5.PubMedGoogle Scholar
  47. 47.
    Eberhardt R, et al. Electromagnetic navigation diagnostic bronchoscopy in peripheral lung lesions. Chest. 2007;131:1800–5.PubMedGoogle Scholar
  48. 48.
    Makris D, et al. Electromagnetic navigation diagnostic bronchoscopy for small peripheral lung lesions. Eur Respir J. 2007;29:1187–92.PubMedGoogle Scholar
  49. 49.
    Schwarz Y, et al. Real-time electromagnetic navigation bronchoscopy to peripheral lung lesions using overlaid CT images: the first human study. Chest. 2006;129:988–94.PubMedGoogle Scholar
  50. 50.
    Hautmann H, et al. Electromagnetic catheter navigation during bronchoscopy: validation of a novel method by conventional fluoroscopy. Chest. 2005;128:382–7.PubMedGoogle Scholar
  51. 51.
    Gildea TR, et al. Electromagnetic navigation diagnostic bronchoscopy: a prospective study. Am J Respir Crit Care Med. 2006;174:982–9.PubMedGoogle Scholar
  52. 52.
    Samei E, et al. Detection of subtle lung nodules: relative influence of quantum and anatomic noise on chest radiographs. Radiology. 1999;213:727–34.PubMedGoogle Scholar
  53. 53.
    Samei E, et al. Subtle lung nodules: influence of local anatomic variations on detection. Radiology. 2003;228:76–84.PubMedGoogle Scholar
  54. 54.
    Pineda AR, et al. Optimization of a tomosynthesis system for the detection of lung nodules. Med Phys. 2006;33:1372–9.PubMedGoogle Scholar
  55. 55.
    Fahrig R, et al. Characterization of a C-arm-mounted XRII for 3D image reconstruction during interventional neuroradiology. In: Medical imaging 1996: physics of medical imaging 1996. p. 351–60.Google Scholar
  56. 56.
    Fahrig R, et al. Use of a C-arm system to generate true three-dimensional computed rotational angiograms: preliminary in vitro and in vivo results. AJNR Am J Neuroradiol. 1997;18:1507–14.PubMedGoogle Scholar
  57. 57.
    Rougee A, et al. Geometrical calibration of X-ray imaging chains for three-dimensional reconstruction. Comput Med Imaging Graph. 1993;17:295–300.PubMedGoogle Scholar
  58. 58.
    Navab N, et al. Dynamic geometrical calibration for 3D cerebral angiography. In: Medical imaging 1996: physics of medical imaging. Newport Beach, California; 1996. p. 361–70.Google Scholar
  59. 59.
    Bani-Hashemi A, et al. Applications of Computer Vision, 1998. WACV ‘98. Proceedings, Fourth IEEE Workshop; 19–21 Oct 1998. p. 246–247. ISBN:0-8186-8606-5: Princton; NJ, doi:  10.1109/ACV.1998.732891.
  60. 60.
    Navab N, et al. Medical Image Computing and Computer–Assisted Interventation – MICCAI’98. Lecture Notes in Computer Science. Vol. 1496, 1998, pp. 119–129.Google Scholar
  61. 61.
    Fahrig R, et al. Three-dimensional computed tomographic reconstruction using a C-arm mounted XRII: correction of image intensifier distortion. Med Phys. 1997;24:1097–106.PubMedGoogle Scholar
  62. 62.
    Cerveri P, et al. Distortion correction for x-ray image intensifiers: local unwarping polynomials and RBF neural networks. Med Phys. 2002;29:1759–71.PubMedGoogle Scholar
  63. 63.
    Liu RR, et al. Super-global distortion correction for a rotational C-arm x-ray image intensifier. Med Phys. 1999;26:1802–10.PubMedGoogle Scholar
  64. 64.
    Mitschke MM, Navab N. Mathematical Methords in Biomedical Image Analysis, 2000. Proceedings, IEEE Workshop; 2000. pp. 204–209. ISBN:0-7695-0737-9: INSPEC, Accession Number: 6657281.Google Scholar
  65. 65.
    Schueler BA, et al. Three-dimensional vascular reconstruction with a clinical x-ray angiography system. Acad Radiol. 1997;4:693–9.PubMedGoogle Scholar
  66. 66.
    Wiesent K, et al. Enhanced 3-Dreconstruction algorithm for C-arm systems suitable for interventional procedures. IEEE Trans Med Imaging. 2000;19:391–403.PubMedGoogle Scholar
  67. 67.
    Fahrig R, Holdsworth DW. Three-dimensional computed tomographic reconstruction using a C-arm mounted XRII: image-based correction of gantry motion nonidealities. Med Phys. 2000;27:30–8.PubMedGoogle Scholar
  68. 68.
    Starman J, et al. Estimating 0/sup th/ and 1/sup st/ moments in C-arm CT data for extrapolating truncated projections. Proc SPIE. 2005;5747:378–87.Google Scholar
  69. 69.
    Sourbelle K, et al. Reconstruction from truncated projections in CT using adaptive detruncation. Eur Radiol. 2005;15:1008–14.PubMedGoogle Scholar
  70. 70.
    Ohnesorge B, et al. Efficient correction for CT image artifacts caused by objects extending outside the scan field of view. Med Phys. 2000;27:39–46.PubMedGoogle Scholar
  71. 71.
    Hsieh J, et al. A novel reconstruction algorithm to extend the CT scan field-of-view. Med Phys. 2004;31:2385–91.PubMedGoogle Scholar
  72. 72.
    Bani-Hashemi A, et al. Cone beam X-ray scatter removal via image frequency modulation and filtering. Medical Physics. 2005;32:2093.Google Scholar
  73. 73.
    Bertram M, et al. Potential of software-based scatter corrections in cone-beam volume CT. Proc SPIE. 2005;5745:259–70.Google Scholar
  74. 74.
    Kyriakou Y, Kalender W. Efficiency of antiscatter grids for flat-detector CT. Phys Med Biol. 2007;52:6275–93.PubMedGoogle Scholar
  75. 75.
    Maltz J, et al. Unified algorithm for KV and MV scatter and beam-hardening correction using the convolution-superposition method. Medical Physics. 2006;33:2280.Google Scholar
  76. 76.
    Ning R, Tang X, Conover DL. X-ray scatter suppression algorithm for cone-beam volume CT. Proc SPIE. 2002;4682:774–81.Google Scholar
  77. 77.
    Ning R, et al. X-ray scatter correction algorithm for cone beam CT imaging. Medical Physics. 2004;31:1195–202.PubMedGoogle Scholar
  78. 78.
    Ruhrnschopf EP, et al. A general framework and review of scatter correction methods in cone beam CT. Part 2: scatter estimation approaches. Med Phys. 2011;38:5186–99.Google Scholar
  79. 79.
    Ruhrnschopf EP, Klingenbeck K. Erratum: a general framework and review of scatter correction methods in x-ray cone beam CT. Part 1: scatter compensation approaches [Med. Phys. 38(7), 4296-4311 (2011)]. Med Phys. 2011;38:5830.Google Scholar
  80. 80.
    Ruhrnschopf EP, Klingenbeck K. A general framework and review of scatter correction methods in x-ray cone-beam computerized tomography. Part 1: scatter compensation approaches. Med Phys. 2011;38:4296–311.PubMedGoogle Scholar
  81. 81.
    Siewerdsen JH, et al. A simple, direct method for x-ray scatter estimation and correction in digital radiography and cone-beam CT. Medi Phys. 2006;33:187–97.Google Scholar
  82. 82.
    Zhu L, et al. Scatter correction method for X-ray CT using primary modulation: theory and preliminary results. IEEE Trans Med Imaging. 2006;25:1573–87.PubMedGoogle Scholar
  83. 83.
    Zhu L, et al. X-ray scatter correction for cone-beam CT using moving blocker array. In: Medical imaging 2005: physics of medical imaging. San Diego, California; 2005. p. 251–8.Google Scholar
  84. 84.
    Rinkel J, et al. A new method for x-ray scatter correction: first assessment on a cone-beam CT experimental setup. Phys Med Biol. 2007;52:4633–52.PubMedGoogle Scholar
  85. 85.
    Kyriakou Y, et al. Combining deterministic and Monte Carlo calculations for fast estimation of scatter intensities in CT. Phys Med Biol. 2006;51:4567–86.PubMedGoogle Scholar
  86. 86.
    Mail N, et al. An empirical method for lag correction in cone-beam CT. Med Phys. 2008;35:5187–96.PubMedGoogle Scholar
  87. 87.
    Starman J, et al. Investigation into the optimal linear time-invariant lag correction for radar artifact removal. Med Phys. 2011;38:2398–411.PubMedGoogle Scholar
  88. 88.
    Parker DL. Optimization of short scan convolution reconstruction in fan beam CT. 1982.Google Scholar
  89. 89.
    Feldkamp LA, et al. Practical cone-beam algorithm. J Opt Soc Am A (Opt Imag Sci). 1984;1:612–9.Google Scholar
  90. 90.
    Zellerhoff M, et al. Low contrast 3D reconstruction from C-arm data. Proc SPIE. 2005;5745:646–55.Google Scholar
  91. 91.
    Tuy HK. An inversion formula for cone-beam reconstruction. SIAM J Appl Math. 1983;43:546–52.Google Scholar
  92. 92.
    Hamelin B, et al. Design of iterative ROI transmission tomography reconstruction procedures and image quality analysis. Med Phys. 2010;37:4577–89.PubMedGoogle Scholar
  93. 93.
    Zou Y, et al. Image reconstruction in regions-of-interest from truncated projections in a reduced fan-beam scan. Phys Med Biol. 2005;50:13–27.PubMedGoogle Scholar
  94. 94.
    Ziegler A, et al. Iterative reconstruction of a region of interest for transmission tomography. Med Phys. 2008;35:1317–27.PubMedGoogle Scholar
  95. 95.
    Yu L, et al. Region of interest reconstruction from truncated data in circular cone-beam CT. IEEE Trans Med Imaging. 2006;25:869–81.PubMedGoogle Scholar
  96. 96.
    Courdurier M, et al. Solving the interior problem of computed tomography using a priori knowledge. Inverse Probl. 2008;24:065001.Google Scholar
  97. 97.
    Defrise M, et al. Truncated Hilbert transform and image reconstruction from limited tomographic data. Inverse Probl. 2006;22:1037–53.Google Scholar
  98. 98.
    Endo M, et al. Effect of scattered radiation on image noise in cone beam CT. Med Phys. 2001;28:469–74.PubMedGoogle Scholar
  99. 99.
    Siewerdsen JH, Jaffray DA. Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter. Med Phys. 2001;28:220–31.PubMedGoogle Scholar
  100. 100.
    Siewerdsen JH, Moseley DJ, Bakhtiar B, Richard S, Jaffray D. The influence of antiscatter grids on soft-tissue detectability in cone-beam computed tomography with flat-panel detectors. Med Phys. 2004;31:3506–20.PubMedGoogle Scholar
  101. 101.
    Wiegert J, et al. Performance of standard fluoroscopy antiscatter grids in flat-detector-based cone-beam CT. Proc SPIE. 2004;5368:67–78.Google Scholar
  102. 102.
    Ganguly A, et al. Cerebral CT perfusion using an interventional C-arm imaging system: cerebral blood flow measurements. AJNR Am J Neuroradiol. 2011;32:1525–31.PubMedCentralPubMedGoogle Scholar
  103. 103.
    Orlov MV, et al. Three-dimensional rotational angiography of the left atrium and esophagus-A virtual computed tomography scan in the electrophysiology lab? Heart Rhythm. 2007;4:37–43.PubMedGoogle Scholar
  104. 104.
    Nölker G, et al. Three-dimensional left atrial and esophagus reconstruction using cardiac C-arm computed tomography with image integration into fluoroscopic views for ablation of atrial fibrillation: accuracy of a novel modality in comparison with multislice computed tomography. Heart Rhythm. 2008;5:1651–7.PubMedGoogle Scholar
  105. 105.
    Tognolini A, et al. Intraprocedure visualization of the esophagus using interventional C-arm CT as guidance for left atrial radiofrequency ablation. Acad Radiol. 2011;18:850–7.PubMedCentralPubMedGoogle Scholar
  106. 106.
    Thiagalingam A, et al. Intraprocedural volume imaging of the left atrium and pulmonary veins with rotational x-ray angiography: implications for catheter ablation of atrial fibrillation. J Cardiovasc Electrophysiol. 2008;19:293–300.PubMedGoogle Scholar
  107. 107.
    Dick AJ, et al. Invasive human magnetic resonance imaging: feasibility during revascularization in a combined XMR suite. Catheter Cardiovasc Interv. 2005;64:265–74.PubMedCentralPubMedGoogle Scholar
  108. 108.
    Ladd ME, Debatin JF. Interventional and intravascular MR angiography. Herz. 2000;25:440–51.PubMedGoogle Scholar
  109. 109.
    Ladd ME, et al. Interventional MRA and intravascular imaging. J Magn Reson Imaging. 2000;12:534–46.PubMedGoogle Scholar
  110. 110.
    Vogl TJ, et al. Hybrid MR interventional imaging system: combined MR and angiography suites with single interactive table. Feasibility study in vascular liver tumor procedures. Eur Radiol. 2002;12:1394–400.PubMedGoogle Scholar
  111. 111.
    Wilson MW, et al. Experimental renal artery embolization in a combined MR imaging/angiographic unit. J Vasc Interv Radiol. 2003;14:1169–75.PubMedGoogle Scholar
  112. 112.
    Rhode KS, et al. Registration and tracking to integrate x-ray and MR images in an XMR facility. IEEE Trans Med Imaging. 2003;22:1369–78.PubMedGoogle Scholar
  113. 113.
    Kucharczyk J, et al. Cost-efficacy of MR-guided neurointerventions. Neuroimaging Clin N Am. 2001;11:767–72, xii.PubMedGoogle Scholar
  114. 114.
    Chu RM, et al. Minimally invasive procedures. Interventional MR image-guided functional neurosurgery. Neuroimaging Clin N Am. 2001;11:715–25.PubMedGoogle Scholar
  115. 115.
    Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, 10 (April 30, 2004). doi:  10.1117/12.535103
  116. 116.
    Rhode K, et al. Real-time XMR guidance for cardiac electrophysiology procedures. In: Proceedings of 5th interventional MRI symposium. Boston; 2004.Google Scholar
  117. 117.
    Hegde S, et al. Towards safer cardiac intervention: a novel approach that combines x-ray and magnetic resonance imaging for guidance of stent implantation. In: Proceedings of 5th interventional MRI symposium. Boston; 2004. p. 82–3.Google Scholar
  118. 118.
    Ginks MR, et al. A simultaneous X-Ray/MRI and noncontact mapping study of the acute hemodynamic effect of left ventricular endocardial and epicardial cardiac resynchronization therapy in humans. Circ Heart Fail. 2011;4:170–9.PubMedGoogle Scholar
  119. 119.
    Acher P, et al. An analysis of intraoperative versus post-operative dosimetry with CT, CT-MRI fusion and XMR for the evaluation of permanent prostate brachytherapy implants. Radiother Oncol. 2010;96:166–71.PubMedGoogle Scholar
  120. 120.
    Carlsson M, et al. Magnetic resonance imaging quantification of left ventricular dysfunction following coronary microembolization. Magn Reson Med. 2009;61:595–602.PubMedGoogle Scholar
  121. 121.
    Acher P, et al. Comparison of combined x-ray radiography and magnetic resonance (XMR) imaging-versus computed tomography-based dosimetry for the evaluation of permanent prostate brachytherapy implants. Int J Radiat Oncol Biol Phys. 2008;71:1518–25.PubMedGoogle Scholar
  122. 122.
    Chinchapatnam PP, et al. Anisotropic wave propagation and apparent conductivity estimation in a fast electrophysiological model: application to XMR interventional imaging. Med Image Comput Comput Assist Interv. 2007;10:575–83.PubMedGoogle Scholar
  123. 123.
    Matsumae M, et al. World’s first magnetic resonance imaging/x-ray/operating room suite: a significant milestone in the improvement of neurosurgical diagnosis and treatment. J Neurosurg. 2007;107:266–73.PubMedGoogle Scholar
  124. 124.
    Sermesant M, et al. A fast-marching approach to cardiac electrophysiology simulation for XMR interventional imaging. Med Image Comput Comput Assist Interv. 2005;8:607–15.PubMedGoogle Scholar
  125. 125.
    Fahrig R, et al. A truly hybrid interventional MR/X-ray system: feasibility demonstration. J Magn Reson Imaging. 2001;13:294–300.PubMedGoogle Scholar
  126. 126.
    Wen Z, et al. Shimming with permanent magnets for the x-ray detector in a hybrid x-ray/ MR system. Med Phys. 2008;35:3895–902.PubMedGoogle Scholar
  127. 127.
    Fahrig R, et al. Performance of a static-anode/flat-panel x-ray fluoroscopy system in a diagnostic strength magnetic field: a truly hybrid x-ray/MR imaging system. Med Phys. 2005;32:1775–84.PubMedGoogle Scholar
  128. 128.
    Wen Z, et al. Investigation of electron trajectories of an x-ray tube in magnetic fields of MR scanners. Med Phys. 2007;34:2048–58.PubMedGoogle Scholar
  129. 129.
    Kee ST, et al. MR-guided transjugular intrahepatic portosystemic shunt creation with use of a hybrid radiography/MR system. J Vasc Interv Radiol. 2005;16:227–34.PubMedGoogle Scholar
  130. 130.
    Fahrig R, et al. Truly hybrid interventional MR/X-ray system: investigation of in vivo applications. Acad Radiol. 2001;8:1200–7.PubMedGoogle Scholar
  131. 131.
    Fahrig R, et al. First use of a truly-hybrid X-ray/MR imaging system for guidance of brain biopsy. Acta Neurochir (Wien). 2003;145:995–7; discussion 997.Google Scholar
  132. 132.
    Ganguly A, et al. Truly hybrid X-ray/MR imaging: toward a streamlined clinical system. Acad Radiol. 2005;12:1167–77.PubMedGoogle Scholar
  133. 133.
    Jackson JD. Classical electrodynamics. 3rd ed. New York: Wiley; 1999.Google Scholar
  134. 134.
    Bracken JA, et al. Closed-bore XMR (CBXMR) systems for aortic valve replacement: x-ray tube imaging performance. Med Phys. 2009;36:1086–97.PubMedGoogle Scholar
  135. 135.
    Bracken JA, et al. Closed bore XMR (CBXMR) systems for aortic valve replacement: active magnetic shielding of x-ray tubes. Med Phys. 2009;36:1717–26.PubMedGoogle Scholar
  136. 136.
    Lillaney PV, et al. Electrostatic focal spot correction for x-ray tubes operating in strong magnetic fields. In: American Association of Physicists in Medicine. Vancouver; 2011.Google Scholar
  137. 137.
    Brzozowski L, et al. Compatibility of interventional x-ray and magnetic resonance imaging: feasibility of a closed bore XMR (CBXMR) system. Med Phys. 2006;33:3033–45.PubMedGoogle Scholar
  138. 138.
    Lillaney P, et al. U.S. Patent No. 7,701,215. Washington, DC: U.S. Patent and Trademark Office. 2010.Google Scholar
  139. 139.
    Dumoulin CL, et al. Real-time position monitoring of invasive devices using magnetic resonance. Magn Reson Med. 1993;29:411–5.PubMedGoogle Scholar
  140. 140.
    McKinnon GC, et al. Towards active guidewire visualization in interventional magnetic resonance imaging. MAGMA. 1996;4:13–8.PubMedGoogle Scholar
  141. 141.
    Ladd ME, et al. Active MR visualization of a vascular guidewire in vivo. J Magn Reson Imaging. 1998;8:220–5.PubMedGoogle Scholar
  142. 142.
    Quick HH, et al. Interventional magnetic resonance angiography with no strings attached: wireless active catheter visualization. Magn Reson Med. 2005;53:446–55.PubMedGoogle Scholar
  143. 143.
    Henderson JM, et al. Permanent neurological deficit related to magnetic resonance imaging in a patient with implanted deep brain stimulation electrodes for Parkinson’s disease: case report. Neurosurgery. 2005;57:E1063; discussion E1063.PubMedGoogle Scholar
  144. 144.
    Venook R, et al. Reducing and monitoring resonant heating in MR guidewires. In: The international society for magnetic resonance in medicine. Seattle; 2006.Google Scholar
  145. 145.
    Zanchi MG, et al. An optically coupled system for quantitative monitoring of MRI-induced RF currents into long conductors. IEEE Trans Med Imaging. 2010;29:169–78.PubMedCentralPubMedGoogle Scholar
  146. 146.
    Scott GC, et al. A vector modulation transmit array system. In: The international society for magnetic resonance in medicine. Seattle; 2006.Google Scholar
  147. 147.
    Overall WR, et al. Ensuring safety of implanted devices under MRI using reversed RF polarization. Magn Reson Med. 2010;64:823–33.PubMedCentralPubMedGoogle Scholar
  148. 148.
    Schmidt TG, Fahrig R, Pelc NJ. A three-dimensional reconstruction algorithm for an inverse-geometry volumetric CT system. Med Phys. 2005;32:3234–45.PubMedGoogle Scholar
  149. 149.
    Zhifei W. Investigation of election trajections of an X-ray tube in Magnetic fields of MR scanners. Med Phys. 34(2007):2048.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Prasheel V. Lillaney
    • 1
  • Norbert J. Pelc
    • 2
  • Rebecca Fahrig
    • 2
  1. 1.Departments of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoUSA
  2. 2.Departments of Bioengineering and RadiologyStanford UniversityStanfordUSA

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