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.

Figure 1.

The inclusion of prior information of several different types into the NIRS imaging process has been implicit in most applications. The reason for using prior information is to improve the final interpretation of the results, through more accurate quantification, and in medical applications to improve the sensitivity or specificity of the technique. In a process manner, this can be thought of as a prior information, P, being convolved into the NIRS steps, N, resulting in a different interpretation and diagnosis, D. Most of the tests are added either to improve sensitivity or specificity, or to reach a better compromise of both, as illustrated by the receiver operating characteristic (ROC) curve.

Figure 2.

Major limitations of NIRS imaging. There are biophysical factors and internal tissue factors that limit the capability of NIRS imaging. (a,b) With respect to spatial issues, it is well known that the shape of the tissue changes the internal fluence (a) and must be accounted for in most cases, requiring prior knowledge of the shape of the tissue. The sensitivity profile of the NIR signal is illustrated in (b) and shows how the details of this internal optical structure may be lost due to the blurring and partial volume averaging that dominate the recovery of internal values. (c,d) In terms of biochemical effects, the spectral features of most biological species are broad (c), leading to an increased value with spectral prior information. Finally, at the microenvironment level (d), the NIRS data are often not sufficient to understand the binding or partition status of biochemical species, requiring biomarker or biological modelling prior information. (Online version in colour.)

Figure 3.

The use of spatial and spectral prior information has been widely studied. Spatial information can come from imaging systems such as X-ray, magnetic resonance imaging (MRI), ultrasound or surgical imaging. The integration of NIRS into these systems has been developed technologically, and the effect of prior spatial information upon imaging accuracy has been studied in niche cases. The application of spectral priors is thought to be quite beneficial, and has been widely adopted. In general, it is believed that using more spectral features, and sampling more wavelengths, improves the specificity of the inversion. Unfortunately, the most specific spectroscopy methods, such as Raman spectroscopy, also tend to correlate inversely with interaction cross section, leading to degraded signal-to-noise ratio. (Online version in colour.)

Figure 4.

(a) Spatial prior inclusion can improve quantitative recovery, as shown in this MRI-guided phantom study, where images show the increasing level of spatial prior information used in the reconstruction algorithm. (b) Spectral prior inclusion can improve both spatial resolution and quantitative recovery, even without explicit tissue structure, as shown in these images. (c) Combining NIRS, spatial and spectral priors maximizes recovery accuracy in large regions, here providing the most benefit for quantifying haemoglobin (HbT), oxygen saturation (StO₂), water and scattering values for adipose tissue and fibroglandular tissue in vivo. (Online version in colour.)

Figure 5.

Images of a single tumour imaged with NIR spectroscopy within the MRI: (a) axial MR image used for segmentation of fat, fibroglandular and tumour regions; (b) total haemoglobin overlay; (c) three-dimensional image of (b); (d) water image overlay on the MRI; (e) oxygen saturation percentage; (f) scatter amplitude; and (g) scatter power. (Online version in colour.)

Figure 6.

The type of data collected for transmitted or emitted signals are illustrated as either (a) harmonic or (b) dynamic transient. If these patterns are known and can be modelled with an explicit numerical function, then it can be incorporated into the prefiltering, inversion or post-processing of the data, leading to output of the model biophysical parameters instead of raw data such as absorption, fluorescence yield or scattering. (Online version in colour.)

Figure 7.

(a) A T1-weighted coronal MRI image of a breast compressed between two plates. Tissue types were segmented and used to generate a two-tissue-region finite element mesh for NIRS modelling as shown in (b,c). Sixteen fibre probes contact the tissue surface in a transmission geometry, as illustrated in (b). Regions that simulate suspicious lesions defined by Gd-MRI were added numerically to the mesh, simulating a positive reading in the MRI image. The size, position and optical contrast of these lesions were varied over a wide range (the lesion sizes and positions are illustrated in (b,c)). The ROC curve in (e) demonstrates the improvement in diagnostic performance when the hard prior approach is used (red curve) versus the approach that uses the MRI information only in the interpretation step of the imaging process (blue curve). If the segmentation of the fibroglandular region is incorrect, as shown in (d), the hard prior approach (red curve) loses much of its diagnostic advantage over the other method (blue curve), as shown in (f). (Online version in colour.)