Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging

PLoS ONE

Volumn 8, Issue 4, 2013, Pages

  Lauzon, Carolyn B ^a,b   Asman, Andrew J ^a   Esparza, Michael L ^a   Burns, Scott S ^b   Fan, Qiuyun ^a,b   Gao, Yurui ^a,b   Anderson, Adam W ^a,b   Davis, Nicole ^b   Cutting, Laurie E ^b   Landman, Bennett A ^a,b  

^a VANDERBILT UNIVERSITY   (United States)

^b VANDERBILT UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; DATA ANALYSIS SOFTWARE; DATA PROCESSING; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; DIFFUSION TENSOR IMAGING; FEASIBILITY STUDY; HUMAN; IMAGE ANALYSIS; IMAGE QUALITY; MATHEMATICAL COMPUTING; NEUROIMAGING; PROCESS DESIGN; QUALITY CONTROL; VALIDATION PROCESS; ALGORITHM; CHILD; COMPUTER PROGRAM; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES;

ALGORITHMS; CHILD; DIFFUSION TENSOR IMAGING; HUMANS; PRINCIPAL COMPONENT ANALYSIS; QUALITY CONTROL; SOFTWARE;

EID: 84876952298     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0061737     Document Type: Article

References (46)

1. Basser PJ, Jones DK, (2002) Diffusion-tensor MRI: theory, experimental design and data analysis- a technical review. NMR Biomedicine 15: 456-467.
2. Gallichan D, Scholz J, Bartsch A, Behrens TE, Robson MD, et al. (2010) Addressing a systematic vibration artifact in diffusion-weighted MRI. Hum Brain Mapp 31: 193-202.
3. Basser PJ, Pajevic S, (2000) Statistical artifacts in diffusion tensor MRI (DT-MRI) caused by background noise. Magn Reson Med 44: 41-50.
4. Jones DK, Basser PJ, (2004) "Squashing peanuts and smashing pumpkins": How noise distorts diffusion-weighted MR data. Magn Reson Med 52: 979-993.
5. Mortamet B, Bernstein MA, Jack Jr CR, Gunter JL, Ward C, et al. (2009) Automatic quality assessment in structural brain magnetic resonance imaging. Magn Reson Med 62: 365-372.
6. Stöcker T, Schneider F, Klein M, Habel U, Kellermann T, et al. (2005) Automated quality assurance routines for fMRI data applied to a multicenter study. Hum Brain Mapp 25: 237-246.
7. Friedman L, Glover GH, (2006) Report on a multicenter fMRI quality assurance protocol. J Magn Reson Imaging 23: 827-839.
8. Zijdenbos AP, Forghani R, Evans AC, (2002) Automatic pipeline analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. Medical Imaging, IEEE Transactions on 21: 1280-1291.
9. Hasan KM, (2007) A framework for quality control and parameter optimization in diffusion tensor imaging: theoretical analysis and validation. Magn Reson Imaging 25: 1196-1202.
10. Landman BA, Farrell JAD, Patel N, Mori S, Prince JL (2007) DTI fiber tracking: the importance of adjusting DTI gradient tables for motion correction. CATNAP-a tool to simplify and accelerate DTI analysis. Proc Org Human Brain Mapping 13th Annual Meeting;138.
11. Jiang H, van Zijl P, Kim J, Pearlson GD, Mori S, (2006) DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking. Comput Methods Programs Biomed 81: 106-116.
12. Skare S, Hedehus M, Moseley ME, Li TQ, (2000) Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with MRI. J Magn Reson 147: 340-352.
13. Landman BA, Farrell JA, Huang H, Prince JL, Mori S, (2008) Diffusion tensor imaging at low SNR: nonmonotonic behaviors of tensor contrasts. Magn Reson Imaging 26: 790-800.
14. Landman BA, Farrell JA, Jones CK, Smith SA, Prince JL, et al. (2007) Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5 T. Neuroimage. 36: 1123-1138.
15. Farrell JA, Landman BA, Jones CK, Smith SA, Prince JL, et al. (2007) Effects of signal-to-noise ratio on the accuracy and reproducibility of diffusion tensor imaging-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T. J Magn Reson Imaging 26: 756-767.
16. Dietrich O, Heiland S, Sartor K, (2001) Noise correction for the exact determination of apparent diffusion coefficients at low SNR. Magn Reson Med 45: 448-453.
17. Fox R, Sakaie K, Lee JC, Debbins J, Liu Y, et al. (2012) A Validation Study of Multicenter Diffusion Tensor Imaging: Reliability of Fractional Anisotropy and Diffusivity Values. Am J Neuroradiol 33: 695-700.
18. Ding Z, Gore JC, Anderson AW, (2005) Reduction of noise in diffusion tensor images using anisotropic smoothing. Magn Reson Med 53: 485-490.
19. Fieremans E, Delputte S, Deblaere K, De Deene Y, Truyens B, et al. (2005) A flexible hardware phantom for validation of diffusion imaging sequences. Proc Int Soc Magn Reson Med (ISMRM);13; Miami, FL, USA. 1301.
20. Yanasak N, Allison J, (2006) Use of capillaries in the construction of an MRI phantom for the assessment of diffusion tensor imaging: demonstration of performance. Magn Reson Imaging 24: 1349-1361.
21. Laun F, Stieltjes B, Huff S, Schad L (2007) Investigation of a DTI-phantom with properties similar to in vivo neuronal tissue. Proc Int Soc Magn Reson Med (ISMRM);15; Berlin, Germany. 1526.
22. Papadakis NG, Martin KM, Wilkinson ID, Huang CLH, (2003) A measure of curve fitting error for noise filtering diffusion tensor MRI data. J Magn Reson 164: 1-9.
23. Heim S, Hahn K, Sämann P, Fahrmeir L, Auer D, (2004) Assessing DTI data quality using bootstrap analysis. Magn Reson Med 52: 582-589.
24. Lauzon CB, Crainiceanu C, Caffo BC, Landman BA (2012) Assessment of bias in experimentally measured diffusion tensor imaging parameters using SIMEX. Magn Reson Med. 2012/05/23 ed.
25. Chang LC, Jones DK, Pierpaoli C, (2005) RESTORE: robust estimation of tensors by outlier rejection. Magn Reson Med 53: 1088-1095.
26. Cook P, Bai Y, Nedjati-Gilani S, Seunarine K, Hall M, et al. (2006) Camino: Open-source diffusion-MRI reconstruction and processing. Proc Int Soc Magn Reson Med (ISMRM) 14: 2759.
27. Smith SM, (2002) Fast robust automated brain extraction. Hum Brain Mapp 17: 143-155.
28. Jenkinson M, Bannister P, Brady M, Smith S, (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17: 825-841.
29. Le Bihan D, van Zijl P, (2002) From the diffusion coefficient to the diffusion tensor. NMR Biomed 15: 431-434.
30. Mori S, Zhang J, (2006) Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron 51: 527-539.
31. Rohlfing T, Russakoff DB, Maurer CR, (2004) Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation. IEEE Trans Med Imaging 23: 983-994.
32. Heckemann RA, Hajnal JV, Aljabar P, Rueckert D, Hammers A, (2006) Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33: 115-126.
33. Ardekani BA, Braun M, Hutton BF, Kanno I, Iida H, (1995) A fully automatic multimodality image registration algorithm. J Comput Assisted Tomogr 19: 615-623.
34. Avants B, Epstein C, Grossman M, Gee J, (2008) Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12: 26-41.
35. Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, et al. (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46: 786-802.
36. Warfield SK, Zou KH, Wells WM, (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. Medical Imaging, IEEE Transactions on 23: 903-921.
37. Sabuncu MR, Yeo BTT, Van Leemput K, Fischl B, Golland P, (2010) A generative model for image segmentation based on label fusion. IEEE Trans Med Imaging 29: 1714-1729.
38. Asman AJ, Landman BA (2012) Non-Local STAPLE: An Intensity-Driven Multi-Atlas Rater Model. Medical Image Computing and Computer-Assisted Intervention (MICCAI). Nice, France.
39. Whitcher B, Tuch DS, Wisco JJ, Sorensen AG, Wang L, (2008) Using the wild bootstrap to quantify uncertainty in diffusion tensor imaging. Hum Brain Mapp 29: 346-362.
40. Cook JR, Stefanski LA, (1994) Simulation-extrapolation estimation in parametric measurement error models. J Am Stat Assoc 89: 1314-1328.
41. Carroll RJ, Ruppert D, Stefanski L, Crainiceanu CM (2006) Measurement Error in Nonlinear Models: A Modern Perspective: Chapman and Hall/CRC Press.
42. Gudbjartsson H, Patz S, (1995) The Rician distribution of noisy MRI data. Magn Reson Med 34: 910-914.
43. Lauzon CB, Landman, B A. (2013) Correcting p-Value and Calculations for Bias in Diffusion Tensor Imaging. Magnetic Resonance in Imaging in press.
44. Shen J (2005) Tools for NIfTI and Analyze Image. September 2011 ed.
45. Jenkinson M, Smith S, (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5: 143-156.
46. Ballou DP, Pazer HL (1985) Modeling data and process quality in multi-input, multi-output information systems. Management science: 150-162.