Multi-modulated frequency domain high density diffuse optical tomography

Perkins, Guy; Eggebrecht, Adam T.; Dehghani, Hamid

doi:10.1364/boe.467614

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…Using intensity and phase together can alleviate the weaknesses of phase whilst utilising it's benefits, which has been shown before on phantom models. 6 We have designed a workflow to allow multiple paradigms of parameter recovery to be performed simultaneously, namely normal single channel spectroscopy, multi channel tomography, short channel regression and dual slope reconstruction. We have also shown an optical registration method to create subject specific atlas models.…”

Section: Discussionmentioning

confidence: 99%

Multi paradigm frequency domain workflow for human brain imaging

Perkins¹,

Dehghani²

2023

Optical Tomography and Spectroscopy of Tissue XV

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A Frequency Domain (FD)-fNIRS workflow has been designed to demonstrate (i) benefits of phase in FD measurements to offers greater hemodynamic response function (HRF) contrast than intensity alone, (ii) multiple parameter recovery paradigms in dual slopes (DS) which use "nut", not "banana" shaped sensitivity profiles and (iii) subject-atlas registration for source-detector locations and model-based tomography.

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Section: Discussionmentioning

confidence: 99%

Multi paradigm frequency domain workflow for human brain imaging

Perkins¹,

Dehghani²

2023

Optical Tomography and Spectroscopy of Tissue XV

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“…Imaging with randomly scattered light is a significant challenge with a pressing need in non-invasive biomedical diagnosis [ 1 , 2 , 3 , 4 , 5 , 6 ]. One of the more significant applications is a non-invasive functional imaging technique called diffuse optical tomography (DOT), which uses near-infrared (NIR) light to map in 3D the optical characteristics of tissue by penetrating it deeply [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming

2023

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Diffuse optical tomography (DOT) is a non-invasive method for detecting breast cancer; however, it struggles to produce high-quality images due to the complexity of scattered light and the limitations of traditional image reconstruction algorithms. These algorithms can be affected by boundary conditions and have a low imaging accuracy, a shallow imaging depth, a long computation time, and a high signal-to-noise ratio. However, machine learning can potentially improve the performance of DOT by being better equipped to solve inverse problems, perform regression, classify medical images, and reconstruct biomedical images. In this study, we utilized a machine learning model called “XGBoost” to detect tumors in inhomogeneous breasts and applied a post-processing technique based on genetic programming to improve accuracy. The proposed algorithm was tested using simulated DOT measurements from complex inhomogeneous breasts and evaluated using the cosine similarity metrics and root mean square error loss. The results showed that the use of XGBoost and genetic programming in DOT could lead to more accurate and non-invasive detection of tumors in inhomogeneous breasts compared to traditional methods, with the reconstructed breasts having an average cosine similarity of more than 0.97 ± 0.07 and average root mean square error of around 0.1270 ± 0.0031 compared to the ground truth.

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Fundamental effects of array density and modulation frequency on image quality of diffuse optical tomography

Fan,

Trobaugh,

Zhang

et al. 2024

Medical Physics

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BackgroundDiffuse optical tomography (DOT) provides three‐dimensional image reconstruction of chromophore perturbations within a turbid volume. Two leading strategies to optimize DOT image quality include, (i) arrays of regular, interlacing, high‐density (HD) grids of sources and detectors with closest spacing less than 15 mm, or (ii) source modulated light of order ∼100 MHz.PurposeHowever, the general principles for how these crucial design parameters of array density and modulation frequency may interact to provide an optimal system design have yet to be elucidated.MethodsHerein, we systematically evaluated how these design parameters effect image quality via multiple key metrics. Specifically, we simulated 32 system designs with realistic measurement noise and quantified localization error, spatial resolution, signal‐to‐noise, and localization depth of field for each of ∼85 000 point spread functions in each model.ResultsWe found that array density had a far stronger effect on image quality metrics than modulation frequency. Additionally, model fits for image quality metrics revealed that potential improvements diminish with regular arrays denser than 9 mm closest spacing. Further, for a given array density, 300 MHz source modulation provided the deepest reliable imaging compared to other frequencies.ConclusionsOur results indicate that both array density and modulation frequency affect the spatial sampling of tissue, which asymptotically saturates due to photon diffusivity within a turbid volume. In summary, our results provide comprehensive perspectives for optimizing future DOT system designs in applications from wearable functional brain imaging to breast tumor detection.

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Multi-modulated frequency domain high density diffuse optical tomography

Cited by 7 publications

References 33 publications

Multi paradigm frequency domain workflow for human brain imaging

Multi paradigm frequency domain workflow for human brain imaging

Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming

Fundamental effects of array density and modulation frequency on image quality of diffuse optical tomography

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