scholarly journals Application of FT-Raman and FTIR measurements using a novel spectral reconstruction algorithm

2003 ◽  
Vol 34 (10) ◽  
pp. 795-805 ◽  
Author(s):  
Su Ying Sin ◽  
Effendi Widjaja ◽  
Liya E. Yu ◽  
Marc Garland
Keyword(s):  
Reconstruction Algorithm ◽  
Spectral Reconstruction ◽  
Ft Raman

scholarly journals A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 644 ◽  
Author(s):  
Shang Zhang ◽  
Yuhan Dong ◽  
Hongyan Fu ◽  
Shao-Lun Huang ◽  
Lin Zhang
Keyword(s):  
Dictionary Learning ◽  
Reconstruction Algorithm ◽  
Sparse Optimization ◽  
Spectral Reconstruction

A spectral reconstruction algorithm for two-plane Compton cameras

2020 ◽  
Vol 65 (2) ◽  
pp. 025011
Author(s):  
Enrique Muñoz ◽  
Luis Barrientos ◽  
José Bernabéu ◽  
Marina Borja-Lloret ◽  
Gabriela Llosá ◽  
...  
Keyword(s):  
Reconstruction Algorithm ◽  
Spectral Reconstruction

scholarly journals Implementation of a Framelet-Based Spectral Reconstruction for Multi-Slice Spiral CT

2021 ◽  
Vol 9 ◽  
Author(s):  
Xin Li ◽  
Yanbo Zhang ◽  
Shuwei Mao ◽  
Jiehua Zhu ◽  
Yangbo Ye
Keyword(s):  
Color Image ◽  
Reconstruction Algorithm ◽  
Computational Time ◽  
Spectral Information ◽  
Ct Reconstruction ◽  
Spectral Ct ◽  
Clinical Images ◽  
X Ray ◽  
Spiral Scanning ◽  
Spectral Reconstruction

Spectral CT utilizes spectral information of X-ray sources to reconstruct energy-resolved X-ray images and has wide medical applications. Compared with conventional energy-integrated CT scanners, however, spectral CT faces serious technical difficulties in hardware, and hence its clinical use has been expensive and limited. The goal of this paper is to present a software solution and an implementation of a framelet-based spectral reconstruction algorithm for multi-slice spiral scanning based on a conventional energy-integrated CT hardware platform. In the present work, we implement the framelet-based spectral reconstruction algorithm using compute unified device architecture (CUDA) with bowtie filtration. The platform CUDA enables fast execution of the program, while the bowtie filter reduces radiation exposure. We also adopt an order-subset technique to accelerate the convergence. The multi-slice spiral scanning geometry with these additional features will make the framelet-based spectral reconstruction algorithm more powerful for clinical applications. The method provides spectral information from just one scan with a standard energy-integrating detector and produces color CT images, spectral curves of the attenuation coefficient at every point inside the object, and photoelectric images, which are all valuable imaging tools in cancerous diagnosis. The proposed algorithm is tested with a Catphan phantom and real patient data sets for its performance. In experiments with the Catphan 504 phantom, the synthesized color image reveals changes in the level of colors and details and the yellow color in Teflon indicates a special spectral property which is invisible in regular CT reconstruction. In experiments with clinical images, the synthesized color images provide some extra details which are helpful for clinical diagnosis, for example, details about the renal pelvis and lumbar join. The numerical studies indicate that the proposed method provides spectral image information which can reveal fine structures in clinical images and that the algorithm is efficient regarding to the computational time. Thus, the proposed algorithm has a great potential in practical application.

scholarly journals Near-Infrared Characterization of Breast Tumors In Vivo using Spectrally-Constrained Reconstruction

2005 ◽  
Vol 4 (5) ◽  
pp. 513-526 ◽  
Author(s):  
Subhadra Srinivasan ◽  
Brian W. Pogue ◽  
Ben Brooksby ◽  
Shudong Jiang ◽  
Hamid Dehghani ◽  
...  
Keyword(s):  
Oxygen Saturation ◽  
Near Infrared ◽  
Reconstruction Algorithm ◽  
Breast Tumors ◽  
Ultrasound Images ◽  
Constrained Reconstruction ◽  
Benign Condition ◽  
Spectral Reconstruction ◽  
Multi Wavelength

Multi-wavelength Near-Infrared (NIR) Tomography was utilized in this study to non-invasively quantify physiological parameters of breast tumors using direct spectral reconstruction. Frequency domain NIR measurements were incorporated with a new spectrally constrained direct chromophore and scattering image reconstruction algorithm, which was validated in simulations and experimental phantoms. Images of total hemoglobin, oxygen saturation, water, and scatter parameters were obtained with higher accuracy than previously reported. Using this spectral approach, in vivo NIR images are presented and interpreted through a series of case studies (n=6 subjects) having differing abnormalities. The corresponding mammograms and ultrasound images are also evaluated. Three of six cases were malignant (infiltrating ductal carcinomas) and showed higher hemoglobin (34–86% increase), a reduction in oxygen saturation, an increase in water content as well as scatter changes relative to surrounding normal tissue. Three of six cases were benign, two of which were diagnosed with fibrocystic disease and showed a dominant contrast in water, consistent with fluid filled cysts. Scatter amplitude was the main source of contrast in the volunteer with the benign condition fibrosis, which typically contains denser collagen tissue. The changes monitored correspond to physiological changes associated with angiogenesis, hypoxia and cell proliferation anticipated in cancers. These changes represent potential diagnostic indicators, which can be assessed to characterize breast tumors.

Nondestructive Inspection of Thin, Low-Z Samples using Multiplexed Compton Scatter Tomography

1997 ◽  
Vol 503 ◽  
Author(s):  
B. L. Evans ◽  
J. B. Martin ◽  
L. W. Burggraf
Keyword(s):  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Line Broadening ◽  
Compton Scatter ◽  
X Ray ◽  
Tomography System ◽  
Transmission Computed Tomography ◽  
High Purity Germanium ◽  
Scattered Energy

ABSTRACTThe viability of a Compton scattering tomography system for nondestructively inspecting thin, low Z samples for corrosion is examined. This technique differs from conventional x-ray backscatter NDI because it does not rely on narrow collimation of source and detectors to examine small volumes in the sample. Instead, photons of a single energy are backscattered from the sample and their scattered energy spectra are measured at multiple detector locations, and these spectra are then used to reconstruct an image of the object. This multiplexed Compton scatter tomography technique interrogates multiple volume elements simultaneously. Thin samples less than 1 cm thick and made of low Z materials are best imaged with gamma rays at or below 100 keV energy. At this energy, Compton line broadening becomes an important resolution limitation. An analytical model has been developed to simulate the signals collected in a demonstration system consisting of an array of planar high-purity germanium detectors. A technique for deconvolving the effects of Compton broadening and detector energy resolution from signals with additive noise is also presented. A filtered backprojection image reconstruction algorithm with similarities to that used in conventional transmission computed tomography is developed. A simulation of a 360–degree inspection gives distortion-free results. In a simulation of a single-sided inspection, a 5 mm × 5 mm corrosion flaw with 50% density is readily identified in 1-cm thick aluminum phantom when the signal to noise ratio in the data exceeds 28.

MICROWAVE IMAGING OF EMBEDDED OBJECT USING AN ENHANCED IMAGE RECONSTRUCTION ALGORITHM

2015 ◽  
Vol 74 (20) ◽  
pp. 1793-1801
Author(s):  
Sidi Mohammed Chouiti ◽  
Lotfi Merad ◽  
Sidi Mohammed Meriah ◽  
Xavier Raimundo
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Microwave Imaging ◽  
Image Reconstruction Algorithm
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