Comparison of fitting methods and b-value sampling strategies for intravoxel incoherent motion in breast cancer.

TitleComparison of fitting methods and b-value sampling strategies for intravoxel incoherent motion in breast cancer.
Publication TypeJournal Article
Year of Publication2015
AuthorsCho GYoung, Moy L, Zhang JL, Baete S, Lattanzi R, Moccaldi M, Babb JS, Kim S, Sodickson DK, Sigmund EE
JournalMagn Reson Med
Volume74
Issue4
Pagination1077-85
Date Published2015 Oct
ISSN1522-2594
KeywordsAdult, Aged, Breast Neoplasms, Diffusion Magnetic Resonance Imaging, Female, Humans, Image Processing, Computer-Assisted, Middle Aged, Motion
Abstract

PURPOSE: To compare fitting methods and sampling strategies, including the implementation of an optimized b-value selection for improved estimation of intravoxel incoherent motion (IVIM) parameters in breast cancer.

METHODS: Fourteen patients (age, 48.4 ± 14.27 years) with cancerous lesions underwent 3 Tesla breast MRI examination for a HIPAA-compliant, institutional review board approved diffusion MR study. IVIM biomarkers were calculated using "free" versus "segmented" fitting for conventional or optimized (repetitions of key b-values) b-value selection. Monte Carlo simulations were performed over a range of IVIM parameters to evaluate methods of analysis. Relative bias values, relative error, and coefficients of variation (CV) were obtained for assessment of methods. Statistical paired t-tests were used for comparison of experimental mean values and errors from each fitting and sampling method.

RESULTS: Comparison of the different analysis/sampling methods in simulations and experiments showed that the "segmented" analysis and the optimized method have higher precision and accuracy, in general, compared with "free" fitting of conventional sampling when considering all parameters. Regarding relative bias, IVIM parameters fp and Dt differed significantly between "segmented" and "free" fitting methods.

CONCLUSION: IVIM analysis may improve using optimized selection and "segmented" analysis, potentially enabling better differentiation of breast cancer subtypes and monitoring of treatment.

DOI10.1002/mrm.25484
Alternate JournalMagn Reson Med
PubMed ID25302780
PubMed Central IDPMC4439397
Grant ListP41 EB017183 / EB / NIBIB NIH HHS / United States
Related Institute: 
MRI Research Institute (MRIRI)

Weill Cornell Medicine
Department of Radiology
525 East 68th Street New York, NY 10065