Nonlinear approach to difference imaging in diffuse optical tomography

Mozumder, Meghdoot; Tarvainen, Tanja; Seppänen, Aku; Nissilä, Ilkka; Arridge, Simon; Kolehmainen, Ville

doi:10.1117/1.jbo.20.10.105001

Cited by 11 publications

(8 citation statements)

References 56 publications

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“…Absolute and difference imaging reconstructions using the phantom were earlier presented in Refs. [15,41], and they show similar quality reconstructions. The boundary artefacts are larger in both absorption and scattering estimates obtained with the GN in '3D mesh 2' in Fig.…”

Section: Resultsmentioning

confidence: 75%

A model-based iterative learning approach for diffuse optical tomography

Mozumder¹,

Hauptmann²,

Nissilä³

et al. 2021

Preprint

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Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image reconstruction problem of DOT is an ill-posed inverse problem, due to the non-linear light propagation in tissues and limited boundary measurements. The ill-posedness means that the image reconstruction is sensitive to measurement and modelling errors. The Bayesian approach for the inverse problem of DOT offers the possibility of incorporating prior information about the unknowns, rendering the problem less ill-posed. It also allows marginalisation of modelling errors utilising the so-called Bayesian approximation error method. A more recent trend in image reconstruction techniques is the use of deep learning techniques, which has shown promising results in various applications from image processing to tomographic reconstructions. In this work, we study the non-linear DOT inverse problem of estimating the absorption and scattering coefficients utilising a 'modelbased' learning approach, essentially intertwining learned components with the model equations of DOT. The proposed approach was validated with 2D simulations and 3D experimental data. We demonstrated improved absorption and scattering estimates for targets with a mix of smooth and sharp image features, implying that the proposed approach could learn image features that are difficult to model using standard Gaussian priors. Furthermore, it was shown that the approach can be utilised in compensating for modelling errors due to coarse discretisation enabling computationally efficient solutions. Overall, the approach provided improved computation times compared to a standard Gauss-Newton iteration.

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Section: Resultsmentioning

confidence: 75%

A model-based iterative learning approach for diffuse optical tomography

Mozumder¹,

Hauptmann²,

Nissilä³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Absolute and difference imaging reconstructions using the phantom were earlier presented in Refs. [47], [48], and they show similar quality reconstructions. The boundary artefacts are larger in both absorption and scattering estimates obtained with the GN in '3D mesh 2' in Fig.…”

Section: Methodsmentioning

confidence: 75%

A Model-Based Iterative Learning Approach for Diffuse Optical Tomography

Mozumder

Hauptmann

Nissilä

et al. 2022

IEEE Trans. Med. Imaging

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Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image reconstruction problem of DOT is an ill-posed inverse problem, due to the non-linear light propagation in tissues and limited boundary measurements. The ill-posedness means that the image reconstruction is sensitive to measurement and modelling errors. The Bayesian approach for the inverse problem of DOT offers the possibility of incorporating prior information about the unknowns, rendering the problem less ill-posed. It also allows marginalisation of modelling errors utilising the socalled Bayesian approximation error method. A more recent trend in image reconstruction techniques is the use of deep learning , which has shown promising results in various applications from image processing to tomographic reconstructions. In this work, we study the non-linear DOT inverse problem of estimating the (absolute) absorption and scattering coefficients utilising a 'model-based' learning approach, essentially intertwining learned components with the model equations of DOT. The proposed approach was validated with 2D simulations and 3D experimental data.We demonstrated improved absorption and scattering estimates for targets with a mix of smooth and sharp image features, implying that the proposed approach could learn image features that are difficult to model using standard Gaussian priors. Furthermore, it was shown that the approach can be utilised in compensating for modelling errors due to coarse discretisation enabling computationally efficient solutions. Overall, the approach provided improved computation times compared to a standard Gauss-Newton iteration.

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“…1) Conventional Methods: While absolute imaging relies on a single set of measurements to reconstruct spatially distributed optical coefficients, difference imaging aims at recovery of the change in the tissue optical properties based on measurements before and after the change [9]. The reference measurement can be the previous state (e.g., the rest stage in brain DOT) or a reference tissue or phantom.…”

Section: B Current Dot Image Reconstruction Approachesmentioning

confidence: 99%

Multitask Deep Learning Reconstruction and Localization of Lesions in Limited Angle Diffuse Optical Tomography

Yedder

Cardoen

Shokoufi

et al. 2022

IEEE Trans. Med. Imaging

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Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. DOT image reconstruction is an ill-posed problem due to the highly scattered photons in the medium and the smaller number of measurements compared to the number of unknowns. Limited-angle DOT reduces probe complexity at the cost of increased reconstruction complexity. Reconstructions are thus commonly marred by artifacts and, as a result, it is difficult to obtain an accurate reconstruction of target objects, e.g., malignant lesions. Reconstruction does not always ensure good localization of small lesions. Furthermore, conventional optimization-based reconstruction methods are computationally expensive, rendering them too slow for real-time imaging applications. Our goal is to develop a fast and accurate image reconstruction method using deep learning, where multitask learning ensures accurate lesion localization in addition to improved reconstruction. We apply spatial-wise attention and a distance transform based loss function in a novel multitask learning formulation to improve localization and reconstruction compared to single-task optimized methods. Given the scarcity of realworld sensor-image pairs required for training supervised deep learning models, we leverage physics-based simulation to generate synthetic datasets and use a transfer learning module to align the sensor domain distribution between in silico and real-world data, while taking advantage of cross-domain learning. Applying our method, we find that we can reconstruct and localize lesions faithfully while allowing real-time reconstruction. We also demonstrate that the present algorithm can reconstruct multiple cancer lesions. The results demonstrate that multitask learning provides sharper and more accurate reconstruction.

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Nonlinear approach to difference imaging in diffuse optical tomography

Cited by 11 publications

References 56 publications

A model-based iterative learning approach for diffuse optical tomography

A model-based iterative learning approach for diffuse optical tomography

A Model-Based Iterative Learning Approach for Diffuse Optical Tomography

Multitask Deep Learning Reconstruction and Localization of Lesions in Limited Angle Diffuse Optical Tomography

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