A maximum-likelihood method to estimate a single ADC value of lesions using diffusion MRI

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4 Citations (Scopus)

Abstract

Purpose: Design a statistically rigorous procedure to estimate a single apparent diffusion coefficient (ADC) of lesion from the mean lesion signal intensity in diffusion MRI. Theory and Methods: A rigorous maximum-likelihood technique that incorporated the statistics of the mean lesion intensity and accounted for lesion heterogeneity was derived to estimate the ADC value. Performance evaluation included comparison with the conventionally used linear-regression and a statistically rigorous state-of-the-art ADC-map technique using realistic and clinically relevant simulation studies conducted with assistance of patient data for homogeneous and heterogeneous lesion models. Results: The proposed technique outperformed the linear-regression and ADC-map approaches over a large spectrum of signal-to-noise ratio, ADC, lesion size, image-misalignment parameters, including at no image misalignment, and different amounts of lesion heterogeneity. The method was also superior at different sets of b values and in studies from specific patient-image-derived data. The technique took less than a second to execute. Conclusions: A rigorous, computationally fast, easy-to-implement, and convenient-to-use maximum-likelihood technique was proposed to estimate a single ADC value of the lesion. Results provide strong evidence in support of the method.

Original languageEnglish (US)
JournalMagnetic Resonance in Medicine
DOIs
StateAccepted/In press - 2016

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Diffusion Magnetic Resonance Imaging
Linear Models
Signal-To-Noise Ratio

Keywords

  • ADC estimation
  • Maximum-likelihood method
  • Motion misalignment
  • Single ADC value
  • Statistics of Rician-distributed random variables

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

@article{078bfd92580b4b7eba1143b507e8c884,
title = "A maximum-likelihood method to estimate a single ADC value of lesions using diffusion MRI",
abstract = "Purpose: Design a statistically rigorous procedure to estimate a single apparent diffusion coefficient (ADC) of lesion from the mean lesion signal intensity in diffusion MRI. Theory and Methods: A rigorous maximum-likelihood technique that incorporated the statistics of the mean lesion intensity and accounted for lesion heterogeneity was derived to estimate the ADC value. Performance evaluation included comparison with the conventionally used linear-regression and a statistically rigorous state-of-the-art ADC-map technique using realistic and clinically relevant simulation studies conducted with assistance of patient data for homogeneous and heterogeneous lesion models. Results: The proposed technique outperformed the linear-regression and ADC-map approaches over a large spectrum of signal-to-noise ratio, ADC, lesion size, image-misalignment parameters, including at no image misalignment, and different amounts of lesion heterogeneity. The method was also superior at different sets of b values and in studies from specific patient-image-derived data. The technique took less than a second to execute. Conclusions: A rigorous, computationally fast, easy-to-implement, and convenient-to-use maximum-likelihood technique was proposed to estimate a single ADC value of the lesion. Results provide strong evidence in support of the method.",
keywords = "ADC estimation, Maximum-likelihood method, Motion misalignment, Single ADC value, Statistics of Rician-distributed random variables",
author = "Jha, {Abhinav K.} and Rodriguez, {Jeffrey J} and Stopeck, {Alison T}",
year = "2016",
doi = "10.1002/mrm.26072",
language = "English (US)",
journal = "Magnetic Resonance in Medicine",
issn = "0740-3194",
publisher = "John Wiley and Sons Inc.",

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AU - Jha, Abhinav K.

AU - Rodriguez, Jeffrey J

AU - Stopeck, Alison T

PY - 2016

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N2 - Purpose: Design a statistically rigorous procedure to estimate a single apparent diffusion coefficient (ADC) of lesion from the mean lesion signal intensity in diffusion MRI. Theory and Methods: A rigorous maximum-likelihood technique that incorporated the statistics of the mean lesion intensity and accounted for lesion heterogeneity was derived to estimate the ADC value. Performance evaluation included comparison with the conventionally used linear-regression and a statistically rigorous state-of-the-art ADC-map technique using realistic and clinically relevant simulation studies conducted with assistance of patient data for homogeneous and heterogeneous lesion models. Results: The proposed technique outperformed the linear-regression and ADC-map approaches over a large spectrum of signal-to-noise ratio, ADC, lesion size, image-misalignment parameters, including at no image misalignment, and different amounts of lesion heterogeneity. The method was also superior at different sets of b values and in studies from specific patient-image-derived data. The technique took less than a second to execute. Conclusions: A rigorous, computationally fast, easy-to-implement, and convenient-to-use maximum-likelihood technique was proposed to estimate a single ADC value of the lesion. Results provide strong evidence in support of the method.

AB - Purpose: Design a statistically rigorous procedure to estimate a single apparent diffusion coefficient (ADC) of lesion from the mean lesion signal intensity in diffusion MRI. Theory and Methods: A rigorous maximum-likelihood technique that incorporated the statistics of the mean lesion intensity and accounted for lesion heterogeneity was derived to estimate the ADC value. Performance evaluation included comparison with the conventionally used linear-regression and a statistically rigorous state-of-the-art ADC-map technique using realistic and clinically relevant simulation studies conducted with assistance of patient data for homogeneous and heterogeneous lesion models. Results: The proposed technique outperformed the linear-regression and ADC-map approaches over a large spectrum of signal-to-noise ratio, ADC, lesion size, image-misalignment parameters, including at no image misalignment, and different amounts of lesion heterogeneity. The method was also superior at different sets of b values and in studies from specific patient-image-derived data. The technique took less than a second to execute. Conclusions: A rigorous, computationally fast, easy-to-implement, and convenient-to-use maximum-likelihood technique was proposed to estimate a single ADC value of the lesion. Results provide strong evidence in support of the method.

KW - ADC estimation

KW - Maximum-likelihood method

KW - Motion misalignment

KW - Single ADC value

KW - Statistics of Rician-distributed random variables

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