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Intravoxel incoherent motion: model-free determination of tissue type in abdominal organs using machine learning


Ciritsis, Alexander; Rossi, Cristina; Wurnig, Moritz C; Phi van, Valerie Doan; Boss, Andreas (2017). Intravoxel incoherent motion: model-free determination of tissue type in abdominal organs using machine learning. Investigative Radiology:Epub ahead of print.

Abstract

PURPOSE: For diffusion data sets including low and high b-values, the intravoxel incoherent motion model is commonly applied to characterize tissue. The aim of the present study was to show that machine learning allows a model-free approach to determine tissue type without a priori assumptions on the underlying physiology.
MATERIALS AND METHODS: In 8 healthy volunteers, diffusion data sets were acquired using an echo-planar imaging sequence with 16 b-values in the range between 0 and 1000 s/mm. Using the k-nearest neighbors technique, the machine learning algorithm was trained to distinguish abdominal organs (liver, kidney, spleen, muscle) using the signal intensities at different b-values as training features. For systematic variation of model complexity (number of neighbors), performance was assessed by calculation of the accuracy and the kappa coefficient (κ). Most important b-values for tissue discrimination were determined by principal component analysis.
RESULTS: The optimal trade-off between model complexity and overfitting was found in the range between K = 11 to 13. On "real-world" data not previously applied to optimize the algorithm, the k-nearest neighbors algorithm was capable to accurately distinguish tissue types with best accuracy of 94.5% and κ = 0.92 reached for intermediate model complexity (K = 11). The principal component analysis showed that most important b-values are (with decreasing importance): b = 1000 s/mm, b = 970 s/mm, b = 750 s/mm, b = 20 s/mm, b = 620 s/mm, and b = 40 s/mm. Applying a reduced set of 6 most important b-values, still a similar accuracy was achieved on the real-world data set with an average accuracy of 93.7% and a κ coefficient of 0.91.
CONCLUSIONS: Machine learning allows for a model-free determination of tissue type using intra voxel incoherent motion signal decay curves as features. The technique may be useful for segmentation of abdominal organs or distinction between healthy and pathological tissues.

Abstract

PURPOSE: For diffusion data sets including low and high b-values, the intravoxel incoherent motion model is commonly applied to characterize tissue. The aim of the present study was to show that machine learning allows a model-free approach to determine tissue type without a priori assumptions on the underlying physiology.
MATERIALS AND METHODS: In 8 healthy volunteers, diffusion data sets were acquired using an echo-planar imaging sequence with 16 b-values in the range between 0 and 1000 s/mm. Using the k-nearest neighbors technique, the machine learning algorithm was trained to distinguish abdominal organs (liver, kidney, spleen, muscle) using the signal intensities at different b-values as training features. For systematic variation of model complexity (number of neighbors), performance was assessed by calculation of the accuracy and the kappa coefficient (κ). Most important b-values for tissue discrimination were determined by principal component analysis.
RESULTS: The optimal trade-off between model complexity and overfitting was found in the range between K = 11 to 13. On "real-world" data not previously applied to optimize the algorithm, the k-nearest neighbors algorithm was capable to accurately distinguish tissue types with best accuracy of 94.5% and κ = 0.92 reached for intermediate model complexity (K = 11). The principal component analysis showed that most important b-values are (with decreasing importance): b = 1000 s/mm, b = 970 s/mm, b = 750 s/mm, b = 20 s/mm, b = 620 s/mm, and b = 40 s/mm. Applying a reduced set of 6 most important b-values, still a similar accuracy was achieved on the real-world data set with an average accuracy of 93.7% and a κ coefficient of 0.91.
CONCLUSIONS: Machine learning allows for a model-free determination of tissue type using intra voxel incoherent motion signal decay curves as features. The technique may be useful for segmentation of abdominal organs or distinction between healthy and pathological tissues.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Diagnostic and Interventional Radiology
Dewey Decimal Classification:610 Medicine & health
Date:24 July 2017
Deposited On:19 Sep 2017 15:54
Last Modified:20 Sep 2017 07:36
Publisher:Lippincott Williams & Wilkins
ISSN:0020-9996
Publisher DOI:https://doi.org/10.1097/RLI.0000000000000400
PubMed ID:28742733

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