Deep Learning Diffuse Optical Tomography

Yoo, Jaejun; Sabir, Sohail; Heo, Duchang; Kim, Kee Hyun; Wahab, Abdul; Choi, Yun-Chul; Lee, Seul-I; Chae, Eun Young; Kim, Hak Hee; Bae, Young‐Min; Choi, Young-Wook; Cho, Seungryong; Ye, Jong Chul

doi:10.1109/tmi.2019.2936522

Cited by 102 publications

(106 citation statements)

References 31 publications

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“…The readers were fellowship-trained, dedicated breast radiologists and had more than 7 years of experience with DBT. They also had experience with DOT using biomimetic phantoms and animals, and had participated in previous study involving DOT technology 19 . Each reader independently completed three separate sessions: (1) reconstructed DBT alone, (2) reconstructed DOT alone, and (3) DBT/DOT fusion images.…”

Section: Methodsmentioning

confidence: 99%

Development of digital breast tomosynthesis and diffuse optical tomography fusion imaging for breast cancer detection

Chae

Kim

Sabir

et al. 2020

Sci Rep

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Diffuse optical tomography (DOT) non-invasively measures the functional characteristics of breast lesions using near infrared light to probe tissue optical properties. This study aimed to evaluate a new digital breast tomosynthesis (DBT)/DOT fusion imaging technique and obtain preliminary data for breast cancer detection. Twenty-eight women were prospectively enrolled and underwent both DBT and DOT examinations. DBT/DOT fusion imaging was created after acquisition of both examinations. Two breast radiologists analyzed DBT and DOT images independently, and then finally evaluated the fusion images. The diagnostic performance of each reading session was compared and interobserver agreement was assessed. The technical success rate was 96.4%, with one failure due to an error during DOT data storage. Among the 27 women finally included in the analysis, 13 had breast cancer. The areas under the receiver operating characteristic curve (AUCs) for DBT were 0.783 and 0.854 for readers 1 and 2, respectively. DOT showed comparable diagnostic performance to DBT for both readers. The AUCs were significantly improved (P = 0.004) when the DBT/DOT fusion images were used. Interobserver agreements were highest for the DBT/DOT fusion images. In conclusion, this study suggests that DBT/DOT fusion imaging technique appears to be a promising tool for breast cancer diagnosis.

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Section: Methodsmentioning

confidence: 99%

Development of digital breast tomosynthesis and diffuse optical tomography fusion imaging for breast cancer detection

Chae

Kim

Sabir

et al. 2020

Sci Rep

Self Cite

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“…First condition is that no photons travel in an inward direction at boundary except source photons. Diffusion equation also cannot satisfy this condition precisely, rather it is assumed that total inward directed current is zero [11]- [15].…”

Section: Boundary Conditionsmentioning

confidence: 99%

“…An ill-posed problem has no solutions in desired class or has many solution, or solution procedure is unstable [15] [16] [17] [18]. In this research case, this means that there are a smaller number of independent measurements than unknown pixel values.…”

Section: Ill-posednessmentioning

confidence: 99%

Application of Reconstruction and Optimization Algorithms in Optical Tomography

Hashmi¹,

Muzzammel²,

Arshad³

et al. 2020

WJET

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Optical tomography is a non-invasive technique that uses visible or near infrared radiation to analyze biological tissues. Researchers take immense attention towards advancement in optical tomography because of its low cost and an advantage of providing anatomical information. Based on the information of optical characteristics, forward and inverse problem of tomography are solved. In this research, finite element method is employed for forward problem and gradient-based optimization algorithm is developed for inverse problem of optical tomography. It is found from simulations that information about imaging is processed more distinctly and in less computational time. Normal and abnormal conditions in imaging are readily distinguished. Simulations are carried out in Matlab. Different scenarios are developed and are simulated to validate the performance of reconstruction and optimization algorithms in optical tomography.

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“…While our approach is consistent with the recent trend of using deep learning architectures for image reconstruction [22][23][24][25][26][27][28][29][30], it is fundamentally different in the sense that due to multiple scattering our measurement operator is both nonlinear and object dependent (and hence unknown). Our approach is also related to the recent work on reverse photon migration for diffuse optical tomography [31]. However, our focus is on diffractive imaging, where the light propagation is assumed to be deterministic, rather than stochastic as in [31].…”

Section: Introductionmentioning

confidence: 99%

“…Our approach is also related to the recent work on reverse photon migration for diffuse optical tomography [31]. However, our focus is on diffractive imaging, where the light propagation is assumed to be deterministic, rather than stochastic as in [31]. Finally, we extensively validated the proposed method on several simulated and real datasets by comparing the method against recent optimization-based approaches based on the Lippmann-Schwinger (LS) model and the TV regularizer [12,14].…”

Section: Introductionmentioning

confidence: 99%

Efficient and accurate inversion of multiple scattering with deep learning

2018

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Image reconstruction under multiple light scattering is crucial in a number of applications such as diffraction tomography. The reconstruction problem is often formulated as a nonconvex optimization, where a nonlinear measurement model is used to account for multiple scattering and regularization is used to enforce prior constraints on the object. In this paper, we propose a powerful alternative to this optimization-based view of image reconstruction by designing and training a deep convolutional neural network that can invert multiple scattered measurements to produce a high-quality image of the refractive index. Our results on both simulated and experimental datasets show that the proposed approach is substantially faster and achieves higher imaging quality compared to the state-of-the-art methods based on optimization.

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Deep Learning Diffuse Optical Tomography

Abstract: and AW is currently with the NUTECH

Cited by 102 publications

References 31 publications

Development of digital breast tomosynthesis and diffuse optical tomography fusion imaging for breast cancer detection

Development of digital breast tomosynthesis and diffuse optical tomography fusion imaging for breast cancer detection

Application of Reconstruction and Optimization Algorithms in Optical Tomography

Efficient and accurate inversion of multiple scattering with deep learning

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