Brown Tumor as a Result of Hyperparathyroidism in an End-Stage Renal Disease Patient

A 49-year-old male with known history of end-stage renal disease (ESRD) presents with an intraoral exophytic mass of the right mandible. This lesion was given a histologic diagnosis of a Brown tumor. Purpose. To allow physicians to include this lesion in a differential diagnosis when evaluating patients with primary, secondary, or tertiary hyperparathyroidism

Mapping breast cancer blood flow index, composition, and metabolism in a human subject using combined diffuse optical spectroscopic imaging and diffuse correlation spectroscopy

Diffuse optical spectroscopic imaging (DOSI) and diffuse correlation spectroscopy (DCS) are modelbased near-infrared (NIR) methods that measure tissue optical properties (broadband absorption, mu(a), and reduced scattering, mu(s)) and blood flow (blood flow index, BFI), respectively. DOSI-derived mu(a) values are used to determine composition by calculating the tissue concentration of oxy- and deoxyhemoglobin(HbO2,HbR), water, and lipid. We developed and evaluated a combined, coregistered DOSI/ DCS handheld probe for mapping and imaging these parameters. We show that uncertainties of 0.3 mm(-1) (37%) in mu(s) and 0.003 mm(-1) (33%) in mu(a) lead to similar to 53% and 9% errors in BFI, respectively. DOSI/ DCS imaging of a solid tissue-simulating flow phantom and a breast cancer patient reveals well-defined spatial distributions of BFI and composition that clearly delineates both the flow channel and the tumor. BFI reconstructed with DOSI-corrected mu(a) and mu(s) values had a tumor/ normal contrast of 2.7, 50% higher than the contrast using commonly assumed fixed optical properties. In conclusion, spatially coregistered imaging of DOSI and DCS enhances intrinsic tumor contrast and information content. This is particularly important for imaging diseased tissues where there are significant spatial variations in mu(a) and mu(s) as well as potential uncoupling between flow and metabolism. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication,Funding Agencies|National Institute of Biomedical Imaging and Bioengineering [P41EB015890]; National Cancer Institute [R01CA142989, U54CA136400]; Chao Family Comprehensive Cancer Center [P30CA62203]; Arnold and Mabel Beckman Foundation; Fulbright Visiting Scholar grant; Swedish Governmental Agency for Innovation Systems (VINNOVA) [2015-0153]; NIH [P41-EB015893, 1R01NS060653]</p