2D phase-sensitive inversion recovery imaging to measure in vivo spinal cord gray and white matter areas in clinically feasible acquisition times

Journal of Magnetic Resonance Imaging

Volumn 42, Issue 3, 2015, Pages 698-708

  Papinutto, Nico ^a   Schlaeger, Regina ^a,b   Panara, Valentina ^c   Caverzasi, Eduardo ^a   Ahn, Sinyeob ^d   Johnson, Kevin J ^d   Zhu, Alyssa H ^a   Stern, William A ^a   Laub, Gerhard ^d   Hauser, Stephen L ^a   Henry, Roland G ^a,e  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF BASEL   (Switzerland)

^c UNIVERSITY OF CHIETI   (Italy)

^d Siemens Healthcare   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

anatomy; gray matter; magnetic resonance imaging; morphometry; spinal cord; white matter

Indexed keywords

ADULT; AGED; ARTICLE; CONTRAST TO NOISE RATIO; CONTROLLED STUDY; CROSS-SECTIONAL STUDY; DESCRIPTIVE RESEARCH; EXPLORATORY RESEARCH; FEMALE; GRAY MATTER; HUMAN; HUMAN EXPERIMENT; IMAGE ANALYSIS; IMAGE QUALITY; IMAGE RECONSTRUCTION; IN VIVO STUDY; INTERRATER RELIABILITY; INTRARATER RELIABILITY; MALE; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; PHASE SENSITIVE INVERSION RECOVERY IMAGING; PRIORITY JOURNAL; REPRODUCIBILITY; SPINAL CORD; WHITE MATTER; COHORT ANALYSIS; IMAGE PROCESSING; MIDDLE AGED; NEUROLOGIC DISEASE; PATHOLOGY; PATHOPHYSIOLOGY; REFERENCE VALUE; SIGNAL NOISE RATIO; TIME FACTOR; WHOLE BODY IMAGING;

ADULT; AGED; COHORT STUDIES; CROSS-SECTIONAL STUDIES; FEMALE; GRAY MATTER; HEALTHY VOLUNTEERS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; NERVOUS SYSTEM DISEASES; REFERENCE VALUES; REPRODUCIBILITY OF RESULTS; SIGNAL-TO-NOISE RATIO; SPINAL CORD; TIME FACTORS; WHITE MATTER; WHOLE BODY IMAGING;

EID: 84939261648     PISSN: 10531807     EISSN: 15222586     Source Type: Journal    
DOI: 10.1002/jmri.24819     Document Type: Article

References (53)

1. Do-Dai DD, Brooks MK, Goldkamp A, Erbay S, Bhadelia RA,. Magnetic resonance imaging of intramedullary spinal cord lesions: a pictorial review. Curr Probl Diagn Radiol 2010; 39: 160-185.
2. Wheeler-Kingshott CA, Stroman PW, Schwab JM, et al., The current state-of-the-art of spinal cord imaging: applications. NeuroImage 2014; 84: 1082-1093.
3. Gilmore CP, DeLuca GC, Bo L, et al., Spinal cord neuronal pathology in multiple sclerosis. Brain Pathol 2009; 19: 642-649.
4. Schlaeger R, Papinutto N, Panara V, et al., Spinal cord gray matter atrophy correlates with multiple sclerosis disability. Ann Neurol 2014; 76: 568-580.
5. Valsasina P, Rocca MA, Agosta F, et al., Mean diffusivity and fractional anisotropy histogram analysis of the cervical cord in MS patients. NeuroImage 2005; 26: 822-828.
6. Agosta F, Absinta M, Sormani MP, et al., In vivo assessment of cervical cord damage in MS patients: a longitudinal diffusion tensor MRI study. Brain 2007; 130 (Pt 8): 2211-2219.
7. Wheeler-Kingshott CA, Hickman SJ, Parker GJ, et al., Investigating cervical spinal cord structure using axial diffusion tensor imaging. NeuroImage 2002; 16: 93-102.
8. Freund P, Wheeler-Kingshott C, Jackson J, Miller D, Thompson A, Ciccarelli O,. Recovery after spinal cord relapse in multiple sclerosis is predicted by radial diffusivity. Mult Scler 2010; 16: 1193-1202.
9. van Hecke W, Nagels G, Emonds G, et al., A diffusion tensor imaging group study of the spinal cord in multiple sclerosis patients with and without T2 spinal cord lesions. J Magn Reson Imaging 2009; 30: 25-34.
10. Kearney H, Schneider T, Yiannakas MC, et al., Spinal cord grey matter abnormalities are associated with secondary progression and physical disability in multiple sclerosis. J Neurol Neurosurg Psychiatry 2014 [Epub ahead of print].
11. Kendi AT, Tan FU, Kendi M, Yilmaz S, Huvaj S, Tellioglu S,. MR spectroscopy of cervical spinal cord in patients with multiple sclerosis. Neuroradiology 2004; 46: 764-769.
12. Bjartmar C, Kidd G, Mork S, Rudick R, Trapp BD,. Neurological disability correlates with spinal cord axonal loss and reduced N-acetyl aspartate in chronic multiple sclerosis patients. Ann Neurol 2000; 48: 893-901.
13. Ciccarelli O, Wheeler-Kingshott CA, McLean MA, et al., Spinal cord spectroscopy and diffusion-based tractography to assess acute disability in multiple sclerosis. Brain 2007; 130 (Pt 8): 2220-2231.
14. Yiannakas MC, Kearney H, Samson RS, et al., Feasibility of grey matter and white matter segmentation of the upper cervical cord in vivo: a pilot study with application to magnetisation transfer measurements. NeuroImage 2012; 63: 1054-1059.
15. Agosta F, Pagani E, Caputo D, Filippi M,. Associations between cervical cord gray matter damage and disability in patients with multiple sclerosis. Arch Neurol 2007; 64: 1302-1305.
16. Kearney H, Yiannakas MC, Samson RS, Wheeler-Kingshott CA, Ciccarelli O, Miller DH,. Investigation of magnetization transfer ratio-derived pial and subpial abnormalities in the multiple sclerosis spinal cord. Brain 2014; 137 (Pt 9): 2456-2468.
17. Stroman PW, Wheeler-Kingshott C, Bacon M, et al., The current state-of-the-art of spinal cord imaging: methods. NeuroImage 2014; 84: 1070-1081.
18. Kameyama T, Hashizume Y, Ando T, Takahashi A,. Morphometry of the normal cadaveric cervical spinal cord. Spine 1994; 19: 2077-2081.
19. Kretschmann HJ, Tafesse U, Herrmann A,. Different volume changes of cerebral cortex and white matter during histological preparation. Microsc Acta 1982; 86: 13-24.
20. Dietrich O, Reiser MF, Schoenberg SO,. Artifacts in 3-T MRI: physical background and reduction strategies. Eur J Radiol 2008; 65: 29-35.
21. Klein JP, Arora A, Neema M, et al., A 3T MR imaging investigation of the topography of whole spinal cord atrophy in multiple sclerosis. AJNR Am J Neuroradiol 2011; 32: 1138-1142.
22. Ulbrich EJ, Schraner C, Boesch C, et al., Normative MR cervical spinal canal dimensions. Radiology 2014; 271: 172-182.
23. Kato F, Yukawa Y, Suda K, Yamagata M, Ueta T,. Normal morphology, age-related changes and abnormal findings of the cervical spine. Part II: Magnetic resonance imaging of over 1,200 asymptomatic subjects. Eur Spine J 2012; 21: 1499-1507.
24. Sigmund EE, Suero GA, Hu C, et al., High-resolution human cervical spinal cord imaging at 7 T. NMR Biomed 2012; 25: 891-899.
25. Taso M, Le Troter A, Sdika M, et al., Construction of an in vivo human spinal cord atlas based on high-resolution MR images at cervical and thoracic levels: preliminary results. MAGMA 2014; 27: 257-267.
26. Callot V, Duhamel G, Vignaud A, Cozzone PJ,. Toward a better description of the gray matter spinal cord by using highly resolved diffusion-weighted and morphologic T2∗-weighted MRI. In: Proc 17th Annual Meeting ISMRM, Honolulu; 2009 (abstract 1302).
27. Fonov VS, Le Troter A, Taso M, et al., Framework for integrated MRI average of the spinal cord white and gray matter: the MNI-Poly-AMU template. NeuroImage 2014; 102P2: 817-827.
28. Fradet L, Arnoux PJ, Ranjeva JP, Petit Y, Callot V,. Morphometrics of the entire human spinal cord and spinal canal measured from in vivo high-resolution anatomical magnetic resonance imaging. Spine 2014; 39: E262-269.
29. Bernstein MA, Thomasson DM, Perman WH,. Improved detectability in low signal-to-noise ratio magnetic resonance images by means of a phase-corrected real reconstruction. Med Phys 1989; 16: 813-817.
30. Moran PR, Kumar NG, Karstaedt N, Jackels SC,. Tissue contrast enhancement: image reconstruction algorithm and selection of TI in inversion recovery MRI. Magn Reson Imaging 1986; 4: 229-235.
31. Park HW, Cho MH, Cho ZH,. Real-value representation in inversion-recovery NMR imaging by use of a phase-correction method. Magn Reson Med 1986; 3: 15-23.
32. Kellman P, Arai AE, McVeigh ER, Aletras AH,. Phase-sensitive inversion recovery for detecting myocardial infarction using gadolinium-delayed hyperenhancement. Magn Reson Med 2002; 47: 372-383.
33. Xiang QS,. Inversion recovery image reconstruction with multiseed region-growing spin reversal. J Magn Resonance Imaging 1996; 6: 775-782.
34. Borrello JA, Chenevert TL, Aisen AM,. Regional phase correction of inversion-recovery MR images. Magn Reson Med 1990; 14: 56-67.
35. Wang J, Chen H, Maki JH, et al., Referenceless acquisition of phase-sensitive inversion-recovery with decisive reconstruction (RAPID) imaging. Magn Reson Med 2014; 72: 806-815.
36. Huber AM, Schoenberg SO, Hayes C, et al., Phase-sensitive inversion-recovery MR imaging in the detection of myocardial infarction. Radiology 2005; 237: 854-860.
37. Hou P, Hasan KM, Sitton CW, Wolinsky JS, Narayana PA,. Phase-sensitive T1 inversion recovery imaging: a time-efficient interleaved technique for improved tissue contrast in neuroimaging. AJNR Am J Neuroradiol 2005; 26: 1432-1438.
38. Christophe C, Muller MF, Baleriaux D, et al., Mapping of normal brain maturation in infants on phase-sensitive inversion-recovery MR images. Neuroradiology 1990; 32: 173-178.
39. Sanchez-Panchuelo RM, Besle J, Mougin O, et al., Regional structural differences across functionally parcellated Brodmann areas of human primary somatosensory cortex. NeuroImage 2014; 93 (Pt 2): 221-230.
40. Ishimori T, Nakano S, Mori Y, et al., Preoperative identification of subthalamic nucleus for deep brain stimulation using three-dimensional phase sensitive inversion recovery technique. Magn Reson Med Sci 2007; 6: 225-229.
41. O'Gorman RL, Shmueli K, Ashkan K, et al., Optimal MRI methods for direct stereotactic targeting of the subthalamic nucleus and globus pallidus. Eur Radiol 2011; 21: 130-136.
42. Nelson F, Poonawalla AH, Hou P, Huang F, Wolinsky JS, Narayana PA,. Improved identification of intracortical lesions in multiple sclerosis with phase-sensitive inversion recovery in combination with fast double inversion recovery MR imaging. AJNR Am J Neuroradiol 2007; 28: 1645-1649.
43. Poonawalla AH, Hasan KM, Gupta RK, et al., Diffusion-tensor MR imaging of cortical lesions in multiple sclerosis: initial findings. Radiology 2008; 246: 880-886.
44. Sethi V, Yousry TA, Muhlert N, et al., Improved detection of cortical MS lesions with phase-sensitive inversion recovery MRI. J Neurol Neurosurg Psychiatry 2012; 83: 877-882.
45. Kearney H, Miszkiel KA, Yiannakas MC, Ciccarelli O, Miller DH,. A pilot MRI study of white and grey matter involvement by multiple sclerosis spinal cord lesions. Mult Scler Relat Disord 2013; 2: 103-108.
46. Kearney H, Yiannakas MC, Abdel-Aziz K, et al., Improved MRI quantification of spinal cord atrophy in multiple sclerosis. J Magn Reson Imaging 2014; 39: 617-623.
47. Poonawalla AH, Hou P, Nelson FA, Wolinsky JS, Narayana PA,. Cervical spinal cord lesions in multiple sclerosis: T1-weighted inversion-recovery MR imaging with phase-sensitive reconstruction. Radiology 2008; 246: 258-264.
48. Losseff NA, Webb SL, O'Riordan JI, et al., Spinal cord atrophy and disability in multiple sclerosis. A new reproducible and sensitive MRI method with potential to monitor disease progression. Brain 1996; 119 (Pt 3): 701-708.
49. Mann RS, Constantinescu CS, Tench CR,. Upper cervical spinal cord cross-sectional area in relapsing remitting multiple sclerosis: application of a new technique for measuring cross-sectional area on magnetic resonance images. J Magn Reson Imaging 2007; 26: 61-65.
50. Rashid W, Davies GR, Chard DT, et al., Upper cervical cord area in early relapsing-remitting multiple sclerosis: cross-sectional study of factors influencing cord size. J Magn Reson Imaging 2006; 23: 473-476.
51. Rocca MA, Horsfield MA, Sala S, et al., A multicenter assessment of cervical cord atrophy among MS clinical phenotypes. Neurology 2011; 76: 2096-2102.
52. Lukas C, Sombekke MH, Bellenberg B, et al., Relevance of spinal cord abnormalities to clinical disability in multiple sclerosis: MR imaging findings in a large cohort of patients. Radiology 2013; 269: 542-552.
53. Horsfield MA, Sala S, Neema M, et al., Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: application in multiple sclerosis. NeuroImage 2010; 50: 446-455.