Ultimately, any convolution of prior information data streams with NIRS must be assessed for diagnostic performance. This is a critical component of the innovation process often missing from NIRS studies. The prior–NIRS convolution must be assessed against other convolution schemes as well as against clinical and pre-clinical diagnostic standards. An important consideration in this analysis is the trade-off between performance and technical complexity. The value of modest gains in diagnostic power realized through extensive, difficult and time-consuming imaging strategies must be considered carefully.
illustrates how a receiver operating characteristic (ROC) curve can be used to compare the diagnostic performance of different prior implementations that can be used for MRI-guided NIRS imaging. In this example, a coronal MRI image of a human breast lightly compressed between two plates (a) was segmented and used to produce a finite element mesh containing two distinct tissue regions, an adipose and a fibroglandular region. Each compression plate is lined with eight optical source–detector fibres for transmitting and receiving light through the tissue, as illustrated by grey rectangles in b. The ROC curve analysis began by generating simulated test domains from the finite element mesh. A suspicious circular legion was numerically added to the mesh in one of two locations, either near the centre of the tissue, as shown in b, or a specified distance from one edge, as illustrated in c. The lesion simulates a region of gadolinium (Gd) enhancement in the MRI image, but may not necessarily be malignant. The radius of this lesion was varied over the range illustrated in b,c, and the optical absorption contrast was varied from 0 to 5 : 1 for each lesion size. Lesions with zero contrast in optical absorption represented an MRI false positive. For each lesion size/contrast, forward data were generated numerically and noise added to the measurements. A total of 600 datasets were considered in this example.
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 (more ...)
The synthetic optical data were then used to recover and interpret absorption images using one of two prior-imaging convolutions. The first method applied the structural prior in the interpretation step, while the second approach considered the hard-prior method, which incorporated the MRI information directly in the image reconstruction step. In the former approach, the optical data were used to perform a ‘no-priors’ image formation. Once the image was recovered, the average value in the region defined by the lesion in the corresponding MRI image was extracted as the diagnostic parameter. The hard-prior approach, on the other hand, encodes the internal structure of the tissue in optically homogeneous regions. Thus, the value recovered in this lesion was extracted directly from the image.
The value of optical absorption in the lesion region was used as the diagnostic parameter in the ROC curve analysis for both methods shown in e. This result demonstrates an improvement in ROC curve performance when the prior was applied in the reconstruction step as opposed to the interpretation step. Area-under-the-curve metrics were 0.85 and 0.73 for the hard-prior and interpretation-only approaches, respectively. However, this analysis assumed perfect image segmentation of the fibroglandular region. Errors in segmenting this region will affect the performance of the hard-prior approach but should not affect the approach that uses the MRI information only in the interpretation step. To explore to what degree the performance is degraded, the segmentation of the fibroglandular region used in the hard-prior image reconstruction was dilated, as shown in d, and the analysis repeated. ROC curves for the two approaches under imperfect segmentation conditions are shown in f, and demonstrate a significant decrease in diagnostic performance for the hard-prior approach due to the segmentation error. Analyses such as this can help determine what imaging strategies are most beneficial under different experimental conditions.
The strategy described above is an effective way to evaluate and compare different implementations of prior information. ROC curve analysis is the preferred method for assessing diagnostic performance in most cases (though specific applications, such as glucose monitoring, have their own standard diagnostic scales). Other types of analyses are commonly reported for NIRS imaging, such as spatial resolution, contrast resolution and contrast detail analysis. While useful for assessing system performance, these analyses should be considered an intermediate step on the path towards a full diagnostic assessment using an ROC curve, which also accounts for biological variability. Establishing truth can be challenging and must be accomplished using accurate gold standards of conventional medicine. Also, the metrics assessed should match the objectives of the diagnostic test. In some cases, the aim of the NIRS technique is to quantify rather than detect diseased tissue, and thus an assessment of detectability would be inappropriate. This is especially true for paradigms in which structural imaging modalities are used to identify suspicious lesions and a prior–NIRS convolution strategy is used to characterize and diagnose the abnormalities. In most cases, a comprehensive ROC curve study should be the ultimate goal of the NIRS researcher to determine whether adoption of the technique in the clinic or research setting is warranted.