Multispectral near-infrared tomography: a case study in compensating for water and lipid content in hemoglobin imaging of the breast

2002 ◽  
Vol 7 (1) ◽  
pp. 72 ◽  
Author(s):  
Troy O. McBride ◽  
Brian W. Pogue ◽  
Steven Poplack ◽  
Sandra Soho ◽  
Wendy A. Wells ◽  
...  
2019 ◽  
Vol 73 (3) ◽  
pp. 218-221
Author(s):  
Kihachiro Bannno
Keyword(s):  

2021 ◽  
Author(s):  
Abhineet Verma ◽  
Sk Saddam Hossain ◽  
Sailaja S Sunkari ◽  
Joseph Reibenspies ◽  
Satyen Saha

Lanthanides (LnIII) are well known for their characteristic emission in the Near-Infrared Region (NIR). However, direct excitation of lanthanides is not feasible as described by Laporte’s parity selection rule. Here,...


2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Ibrahim Shaik ◽  
S. K. Begum ◽  
P. V. Nagamani ◽  
Narayan Kayet

AbstractThe study demonstrates a methodology for mapping various hematite ore classes based on their reflectance and absorption spectra, using Hyperion satellite imagery. Substantial validation is carried out, using the spectral feature fitting technique, with the field spectra measured over the Bailadila hill range in Chhattisgarh State in India. The results of the study showed a good correlation between the concentration of iron oxide with the depth of the near-infrared absorption feature (R2 = 0.843) and the width of the near-infrared absorption feature (R2 = 0.812) through different empirical models, with a root-mean-square error (RMSE) between < 0.317 and < 0.409. The overall accuracy of the study is 88.2% with a Kappa coefficient value of 0.81. Geochemical analysis and X-ray fluorescence (XRF) of field ore samples are performed to ensure different classes of hematite ore minerals. Results showed a high content of Fe > 60 wt% in most of the hematite ore samples, except banded hematite quartzite (BHQ) (< 47 wt%).


2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Gareth F. Difford ◽  
Siri S. Horn ◽  
Katinka R. Dankel ◽  
Bente Ruyter ◽  
Binyam S. Dagnachew ◽  
...  

Abstract Background Product quality and production efficiency of Atlantic salmon are, to a large extent, influenced by the deposition and depletion of lipid reserves. Fillet lipid content is a heritable trait and is unfavourably correlated with growth, thus genetic management of fillet lipid content is needed for sustained genetic progress in these two traits. The laboratory-based reference method for recording fillet lipid content is highly accurate and precise but, at the same time, expensive, time-consuming, and destructive. Here, we test the use of rapid and cheaper vibrational spectroscopy methods, namely near-infrared (NIR) and Raman spectroscopy both as individual phenotypes and phenotypic predictors of lipid content in Atlantic salmon. Results Remarkably, 827 of the 1500 individual Raman variables (i.e. Raman shifts) of the Raman spectrum were significantly heritable (heritability (h2) ranging from 0.15 to 0.65). Similarly, 407 of the 2696 NIR spectral landscape variables (i.e. wavelengths) were significantly heritable (h2 = 0.27–0.40). Both Raman and NIR spectral landscapes had significantly heritable regions, which are also informative in spectroscopic predictions of lipid content. Partial least square predicted lipid content using Raman and NIR spectra were highly concordant and highly genetically correlated with the lipid content values ($${r}_{\text{g}}$$ r g = 0.91–0.98) obtained with the reference method using Lin’s concordance correlation coefficient (CCC = 0.63–0.90), and were significantly heritable ($${h}^{2}$$ h 2 = 0.52–0.67). Conclusions Both NIR and Raman spectral landscapes show substantial additive genetic variation and are highly genetically correlated with the reference method. These findings lay down the foundation for rapid spectroscopic measurement of lipid content in salmonid breeding programmes.


2004 ◽  
Vol 43 (5) ◽  
pp. 1053 ◽  
Author(s):  
Xiaomei Song ◽  
Brian W. Pogue ◽  
Shudong Jiang ◽  
Marvin M. Doyley ◽  
Hamid Dehghani ◽  
...  

2021 ◽  
Author(s):  
Joanna Joiner ◽  
Zachary Fasnacht ◽  
Bo-Cai Gao ◽  
Wenhan Qin

Satellite-based visible and near-infrared imaging of the Earth's surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using multi-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion ofthe panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image. This case study demonstrates that our approach may be useful for many science and applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment as well as disturbance, change, hazard, and disaster detection.


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