A back-to-back comparison of a tunable narrow-band SLED (TSLED) and a swept laser are made for OCT applications. Both are 1310 nm sources sweeping at 50 kHz over a 100 nm tuning range and have similar coherence lengths. The TSLED consists of a seed SOA and two amplification SOAs. The ASE is filtered twice by a tunable MEMS Fabry Perot in a polarization multiplexed double-pass arrangement on either side of the middle SOA. This allows very long coherence lengths to be achieved.
A fundamental issue with a SLED is that the RIN is proportional to 1/Linewidth, meaning that the longer the coherence length, the higher the RIN. High RIN also leads to increased clock jitter.
Most swept source SNR calculations assume that the noise is independent of the amplitude of the signal light: The higher the signal, the higher the SNR. We show that in the case of the TSLED, that the high signal RIN and clock jitter give rise to additional noises that scale with signal power. This leads to an SNR limit in the case of the TSLED: The higher the signal, the higher the noise, so the SNR reaches a limit. While the TSLED has respectable sensitivity, the SNR limit causes noise streaks in an image where the A-line has a high reflectivity point. The laser, which is shot noise limited, does not exhibit this effect. This is illustrated with SNR data and side-by-side images taken with the two sources.
Fine needle aspiration biopsy (FNAB) is a rapid and cost-effective method for obtaining a first-line diagnosis of a palpable mass of the breast. However, because it can be difficult to manually discriminate between adipose tissue and the fibroglandular tissue more likely to harbor disease, this technique is plagued by a high number of nondiagnostic tissue draws. We have developed a portable, low coherence interferometry (LCI) instrument for FNAB guidance to combat this problem. The device contains an optical fiber probe inserted within the bore of the fine gauge needle and is capable of obtaining tissue structural information with a spatial resolution of 10 µm over a depth of approximately 1.0 mm. For such a device to be effective clinically, algorithms that use the LCI data must be developed for classifying different tissue types. We present an automated algorithm for differentiating adipose tissue from fibroglandular human breast tissue based on three parameters computed from the LCI signal (slope, standard deviation, spatial frequency content). A total of 260 breast tissue samples from 58 patients were collected from excised surgical specimens. A training set (N=72) was used to extract parameters for each tissue type and the parameters were fit to a multivariate normal density. The model was applied to a validation set (N=86) using likelihood ratios to classify groups. The overall accuracy of the model was 91.9% (84.0 to 96.7) with 98.1% (89.7 to 99.9) sensitivity and 82.4% (65.5 to 93.2) specificity where the numbers in parentheses represent the 95% confidence intervals. These results suggest that LCI can be used to determine tissue type and guide FNAB of the breast.