Implicit and explicit prior information in near-infrared spectral imaging: accuracy, quantification and diagnostic value

Abstract

Near-infrared spectroscopy (NIRS) of tissue provides quantification of absorbers, scattering and luminescent agents in bulk tissue through the use of measurement data and assumptions. Prior knowledge can be critical about things such as (i) the tissue shape and/or structure, (ii) spectral constituents, (iii) limits on parameters, (iv) demographic or biomarker data, and (v) biophysical models of the temporal signal shapes. A general framework of NIRS imaging with prior information is presented, showing that prior information datasets could be incorporated at any step in the NIRS process, with the general workflow being: (i) data acquisition, (ii) pre-processing, (iii) forward model, (iv) inversion/reconstruction, (v) post-processing, and (vi) interpretation/diagnosis. Most of the development in NIRS has used ad hoc or empirical implementations of prior information such as pre-measured absorber or fluorophore spectra, or tissue shapes as estimated by additional imaging tools. A comprehensive analysis would examine what prior information maximizes the accuracy in recovery and value for medical diagnosis, when implemented at separate stages of the NIRS sequence. Individual applications of prior information can show increases in accuracy or improved ability to estimate biochemical features of tissue, while other approaches may not. Most beneficial inclusion of prior information has been in the inversion/reconstruction process, because it solves the mathematical intractability. However, it is not clear that this is always the most beneficial stage.

Near-infrared spectral tomography integrated with digital breast tomosynthesis: Effects of tissue scattering on optical data acquisition design

“…It imposes tissue homogeneity within segmented zones, but when used appropriately, improves lesion detectability assessed by receiver operating characteristic (ROC) analysis compared to no prior information. 31 Homogenous subregion constraints can cause inaccurate reconstructions if region localization is not accurate. 32 Coregistration of tomosynthesis images and near-infrared data mitigates the problem in the present case.…”

“…Inaccurate segmentation adversely affects reconstruction results. 31 Filtering algorithms and auto-segmentation methods have been created to address the problem. 39 Breast tissue has variable composition; hence, simulating it as three homogeneous regions for each chromophore is incomplete.…”

Sensitivity of MRI-guided near-infrared spectroscopy clinical breast exam data and its impact on diagnostic performance

“…Studies have considered both spectral and spatial priors, as well as more sophisticated information such as water maps from simultaneous MR imaging [42][43][44]. While the benefits of incorporating prior information are well-established for NIRS imaging, when prior information is most effectively incorporated remains less clear [21,45,46]. Here, data was reconstructed using spatial and spectral priors but the spectral priors were applied at two different times.…”

Photoacoustic mammography capable of simultaneously acquiring photoacoustic and ultrasound images

“…2,3 Angiogenesis is essential for breast cancer growth and correlated with the malignant potential of a precursor lesion. 4 The microvessel density in tumor tissue decreases significantly in response to PST, starting early in the treatment period.…”