Joint Deformable Image Registration and ADC Map Regularization: Application to DWI-Based Lymphoma Classification

The Apparent Diffusion Coefficient (ADC) is considered an importantimaging biomarker contributing to the assessment of tissue microstructure and pathophy- siology. It is calculated from Diffusion-Weighted Magnetic Resonance Imaging (DWI) by means of a diffusion model, usually without considering any...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2022-07, Vol.26 (7), p.3151-3162
Main Authors: Kornaropoulos, Evgenios N., Zacharaki, Evangelia I., Zerbib, Pierre, Lin, Chieh, Rahmouni, Alain, Paragios, Nikos
Format: Article
Language:English
Subjects:
ADC
B-cell lymphoma
b-values
Biomarkers
Biomedical imaging
classification
Computation
deformable
Diffusion
Diffusion coefficient
discrete
Engineering and Technology
Estimation
Feature extraction
Formability
Image acquisition
Image classification
Image reconstruction
Image registration
Lymphocytes B
Lymphoma
Magnetic resonance imaging
Medical Engineering
Medical Image Processing
Medicinsk bildbehandling
Medicinteknik
Neoplasms
Optimization
Registration
Regularization
Strain
Teknik
Visualization
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Summary:The Apparent Diffusion Coefficient (ADC) is considered an importantimaging biomarker contributing to the assessment of tissue microstructure and pathophy- siology. It is calculated from Diffusion-Weighted Magnetic Resonance Imaging (DWI) by means of a diffusion model, usually without considering any motion during image acquisition. We propose a method to improve the computation of the ADC by coping jointly with both motion artifacts in whole-body DWI (through group-wise registration) and possible instrumental noise in the diffusion model. The proposed deformable registration method yielded on average the lowest ADC reconstruction error on data with simulated motion and diffusion. Moreover, our approach was applied on whole-body diffusion weighted images obtained with five different b-values from a cohort of 38 patients with histologically confirmed lymphomas of three different types (Hodgkin, diffuse large B-cell lymphoma and follicular lymphoma). Evaluation on the real data showed that ADC-based features, extracted using our joint optimization approach classified lymphomas with an accuracy of approximately 78.6% (yielding a 11% increase in respect to the standard features extracted from unregistered diffusion-weighted images). Furthermore, the correlation between diffusion characteristics and histopathological findings was higher than any other previous approach of ADC computation.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2022.3156009