{'fraction_non_alphanumeric': 0.015769439912996192, 'fraction_numerical': 0.0, 'mean_word_length': 5.73992673992674, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Exposed and transcutaneous measurement of musculoskeletal tissues using fiber optic coupled Raman spectroscopy

Raman spectroscopic measurement of bone composition has shown promise as a medical diagnostic by measuring the molecular composition of the bone mineral and matrix. We previously demonstrated proof-of-principle transcutaneous Raman spectroscopy bone measurements in human cadavers. In this paper, we discuss further optimization of the instrumental configuration for efficient collection of bone signal using contact fiber-optic probe designs. To optimize collection of Raman signal through overlaying soft tissue, novel geometrically-accurate tissue phantoms were prepared. MRI and CT images of the human cadaveric specimens were used to create solid tissue phantoms with accurate geometric dimensions. In these tissue phantoms, optical properties can be varied systematically. Raman spectra of the prepared tissue phantoms were used to optimize the positions of the fibers in the fiber optic system, and the laser illumination sequence in the measurements. Three fiber optic probes were developed and tested with both novel tissue phantoms and human cadaveric specimens. The contact fiber optic probes were developed for arthroscopic measurements of joints, for transcutaneous measurements of bone in situ, and for contact measurements of exposed bone. By coupling the fiber optic probe to an imaging spectrograph, spectra were collected simultaneously at many positions on the tissue. Furthermore, spectra were collected with several different excitation laser patterns to enhance the effective spatial resolution of the measurements. Finally, a series of improvements were made in the data preprocessing to improve the recovered spectral signal. Together, these modifications improve signal-to-noise and spatial resolution.