Multiobjective guided priors improve the accuracy of near-infrared spectral tomography for breast imaging

Feng, Jinchao; Jiang, Shudong; Xu, Jing; Zhao, Yan; Pogue, Brian W.; Paulsen, Keith D.

doi:10.1117/1.jbo.21.9.090506

Cited by 15 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One approach has been to use it as a confirmatory technique, in the hope that identification of additional features may serve to limit the number of false positive findings [ 7 ]. Prior knowledge also has been employed [ 8 ], as, among other things, a basis for monitoring the response to neoadjuvant chemotherapy [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Hemoglobin state-flux: A finite-state model representation of the hemoglobin signal for evaluation of the resting state and the influence of disease

Barbour¹,

Graber²

2018

PLoS ONE

View full text Add to dashboard Cite

SummaryIn this report we introduce a weak-model approach for examination of the intrinsic time-varying properties of the hemoglobin signal, with the aim of advancing the application of functional near infrared spectroscopy (fNIRS) for the detection of breast cancer, among other potential uses. The developed methodology integrates concepts from stochastic network theory with known modulatory features of the vascular bed, and in doing so provides access to a previously unrecognized dense feature space that is shown to have promising diagnostic potential. Notable features of the methodology include access to this information solely from measures acquired in the resting state, and analysis of these by treating the various components of the hemoglobin (Hb) signal as a co-varying interacting system.ApproachThe principal data-transform kernel projects Hb state-space trajectories onto a coordinate system that constitutes a finite-state representation of covariations among the principal elements of the Hb signal (i.e., its oxygenated (ΔoxyHb) and deoxygenated (ΔdeoxyHb) forms and the associated dependent quantities: total hemoglobin (ΔtotalHb = ΔoxyHb + ΔdeoxyHb), hemoglobin oxygen saturation (ΔHbO2Sat = 100Δ(oxyHb/totalHb)), and tissue-hemoglobin oxygen exchange (ΔHbO2Exc = ΔdeoxyHb—ΔoxyHb)). The resulting ten-state representation treats the evolution of this signal as a one-space, spatiotemporal network that undergoes transitions from one state to another. States of the network are defined by the algebraic signs of the amplitudes of the time-varying components of the Hb signal relative to their temporal mean values. This assignment produces several classes of coefficient arrays, most with a dimension of 10×10.Biological motivationMotivating our approach is the understanding that effector mechanisms that modulate blood delivery to tissue operate on macroscopic scales, in a spatially and temporally varying manner. Also recognized is that this behavior is sensitive to nonlinear actions of these effectors, which include the binding properties of hemoglobin. Accessible phenomenology includes measures of the kinetics and probabilities of network dynamics, which we treat as surrogates for the actions of feedback mechanisms that modulate tissue-vascular coupling.FindingsQualitative and quantitative features of this space, and their potential to serve as markers of disease, have been explored by examining continuous-wave fNIRS 3D tomographic time series obtained from the breasts of women who do and do not have breast cancer. Inspection of the coefficient arrays reveals that they are governed predominantly by first-order rate processes, and that each array class exhibits preferred structure that is mainly independent of the others. Discussed are strategies that may serve to extend evaluation of the accessible feature space and how the character of this information holds potential for development of novel clinical and preclinical uses.

show abstract

Section: Introductionmentioning

confidence: 99%

Hemoglobin state-flux: A finite-state model representation of the hemoglobin signal for evaluation of the resting state and the influence of disease

Barbour¹,

Graber²

2018

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The spatial information provided by the MRI will also improve the identification of tumor and normal tissue regions. The advantages provided by increased spatial information 38 , 39 , 64 , 65 and a priori localization 11 , 47 – 51 are well-known and have been previously demonstrated by several other groups.…”

Section: Discussionmentioning

confidence: 73%

“…Hard-prior constraints have been shown to improve quantitative contrast between tumor and normal regions. 46 – 48 However, this technique is very sensitive to systematic inaccuracies in the location of the defined regions. 49 , 50 Therefore, accurate segmentation and spatial coregistration between modalities is imperative.…”

Section: Data Acquisition and Analysismentioning

confidence: 99%

Hybrid time-domain and continuous-wave diffuse optical tomography instrument with concurrent, clinical magnetic resonance imaging for breast cancer imaging

et al. 2019

View full text Add to dashboard Cite

Diffuse optical tomography has demonstrated significant potential for clinical utility in the diagnosis and prognosis of breast cancer, and its use in combination with other structural imaging modalities improves lesion localization and the quantification of functional tissue properties. Here, we introduce a hybrid diffuse optical imaging system that operates concurrently with magnetic resonance imaging (MRI) in the imaging suite, utilizing commercially available MR surface coils. The instrument acquires both continuous-wave and time-domain diffuse optical data in the parallel-plate geometry, permitting both absolute assignment of tissue optical properties and three-dimensional tomography; moreover, the instrument is designed to incorporate diffuse correlation spectroscopic measurements for probing tissue blood flow. The instrument is described in detail here. Image reconstructions of a tissue phantom are presented as an initial indicator of the system's ability to accurately reconstruct optical properties and the concrete benefits of the spatial constraints provided by concurrent MRI. Last, we briefly discuss how various data combinations that the instrument could facilitate, including tissue perfusion, can enable more comprehensive assessment of lesion physiology.

show abstract

“…Although both linear and nonlinear reconstruction algorithms for DOT are available, 14 considerable efforts have been made to develop various reconstruction algorithms to improve quantitative accuracy and image quality. [14][15][16][17][18][19][20][21][22] To date, the illposedness of the inverse problem in DOT can be alleviated by employing a regularization technique, which utilizes a data fitting term together with a regularizer (L 2 or L 1 norm, etc.) to suppress the effect of measurement noise and modeling errors.…”

Section: Introductionmentioning

confidence: 99%

Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography

Feng

Sun

et al. 2018

J. Biomed. Opt.

Self Cite

View full text Add to dashboard Cite

Diffuse optical tomography (DOT) is a promising noninvasive imaging modality and is capable of providing functional characteristics of biological tissue by quantifying optical parameters. The DOT image reconstruction is ill-posed and ill-conditioned, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. The widely used regularization technique for DOT image reconstruction is Tikhonov regularization, which tends to yield oversmoothed and low-quality images containing severe artifacts. It is necessary to accurately choose a regularization parameter for Tikhonov regularization. To overcome these limitations, we develop a noniterative reconstruction method, whereby optical properties are recovered based on a back-propagation neural network (BPNN). We train the parameters of BPNN before DOT image reconstruction based on a set of training data. DOT image reconstruction is achieved by implementing a single evaluation of the trained network. To demonstrate the performance of the proposed algorithm, we compare with the conventional Tikhonov regularization-based reconstruction method. The experimental results demonstrate that image quality and quantitative accuracy of reconstructed optical properties are significantly improved with the proposed algorithm.

show abstract

Multiobjective guided priors improve the accuracy of near-infrared spectral tomography for breast imaging

Abstract: guided priors improve the accuracy of near-infrared spectral tomography for breast imaging," J.

Cited by 15 publications

References 17 publications

Hemoglobin state-flux: A finite-state model representation of the hemoglobin signal for evaluation of the resting state and the influence of disease

Hemoglobin state-flux: A finite-state model representation of the hemoglobin signal for evaluation of the resting state and the influence of disease

Hybrid time-domain and continuous-wave diffuse optical tomography instrument with concurrent, clinical magnetic resonance imaging for breast cancer imaging

Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography

Contact Info

Product

Resources

About