Scatter Correction Method for X-Ray CT Using Primary Modulation: Theory and Preliminary Results

2006 ◽  
Vol 25 (12) ◽  
pp. 1573-1587 ◽  
Author(s):  
Lei Zhu ◽  
N. Robert Bennett ◽  
Rebecca Fahrig
Keyword(s):  
Scatter Correction ◽  
Correction Method ◽  
X Ray ◽  
Preliminary Results ◽  
Modulation Theory

scholarly journals A Deep Learning-Based Scatter Correction of Simulated X-ray Images

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 944 ◽  
Author(s):  
Heesin Lee ◽  
Joonwhoan Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Imaging System ◽  
Scatter Correction ◽  
Similarity Index ◽  
Real Data ◽  
Correction Method ◽  
Training Data ◽  
X Ray ◽  
Scatter Reduction

X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We propose a deep learning-based scatter correction method, which adopts a convolutional neural network (CNN) for restoration of degraded images. Because it is hard to obtain real data from an X-ray imaging system for training the network, Monte Carlo (MC) simulation was performed to generate the training data. For simulating X-ray images of a human chest, a cone beam CT (CBCT) was designed and modeled as an example. Then, pairs of simulated images, which correspond to scattered and scatter-free images, respectively, were obtained from the model with different doses. The scatter components, calculated by taking the differences of the pairs, were used as targets to train the weight parameters of the CNN. Compared with the MC-based iterative method, the proposed one shows better results in projected images, with as much as 58.5% reduction in root-mean-square error (RMSE), and 18.1% and 3.4% increases in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), on average, respectively.

Scatter Correction Method by Temporal Primary Modulation in X-Ray CT

2012 ◽  
Vol 59 (6) ◽  
pp. 3278-3285 ◽  
Author(s):  
K. Schorner ◽  
M. Goldammer ◽  
K. Stierstorfer ◽  
J. Stephan ◽  
P. Boni
Keyword(s):  
Scatter Correction ◽  
Correction Method ◽  
X Ray

scholarly journals Scatter correction method for x-ray CT using primary modulation: Phantom studies

2010 ◽  
Vol 37 (2) ◽  
pp. 934-946 ◽  
Author(s):  
Hewei Gao ◽  
Rebecca Fahrig ◽  
N. Robert Bennett ◽  
Mingshan Sun ◽  
Josh Star-Lack ◽  
...  
Keyword(s):  
Scatter Correction ◽  
Correction Method ◽  
X Ray ◽  
Phantom Studies

scholarly journals A software-based x-ray scatter correction method for breast tomosynthesis

2011 ◽  
Vol 38 (12) ◽  
pp. 6643-6653 ◽  
Author(s):  
Steve Si Jia Feng ◽  
Ioannis Sechopoulos
Keyword(s):  
Scatter Correction ◽  
Correction Method ◽  
Breast Tomosynthesis ◽  
X Ray

scholarly journals Quantitative Imaging of Gd Nanoparticles in Mice Using Benchtop Cone-Beam X-ray Fluorescence Computed Tomography System

2019 ◽  
Vol 20 (9) ◽  
pp. 2315 ◽  
Author(s):  
Siyuan Zhang ◽  
Liang Li ◽  
Jiayou Chen ◽  
Zhiqiang Chen ◽  
Wenli Zhang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Spatial Information ◽  
Quantitative Imaging ◽  
Scatter Correction ◽  
Photon Counting ◽  
Correction Method ◽  
Cone Beam ◽  
X Ray ◽  
Photon Counting Detector

Nanoparticles (NPs) are currently under intensive research for their application in tumor diagnosis and therapy. X-ray fluorescence computed tomography (XFCT) is considered a promising non-invasive imaging technique to obtain the bio-distribution of nanoparticles which include high-Z elements (e.g., gadolinium (Gd) or gold (Au)). In the present work, a set of experiments with quantitative imaging of GdNPs in mice were performed using our benchtop XFCT device. GdNPs solution which consists of 20 mg/mL NaGdF4 was injected into a nude mouse and two tumor-bearing mice. Each mouse was then irradiated by a cone-beam X-ray source produced by a conventional X-ray tube and a linear-array photon counting detector with a single pinhole collimator was placed on one side of the beamline to record the intensity and spatial information of the X-ray fluorescent photons. The maximum likelihood iterative algorithm with scatter correction and attenuation correction method was applied for quantitative reconstruction of the XFCT images. The results show that the distribution of GdNPs in each target slice (containing liver, kidney or tumor) was well reconstructed and the concentration of GdNPs deposited in each organ was quantitatively estimated, which indicates that this benchtop XFCT system provides convenient tools for obtaining accurate concentration distribution of NPs injected into animals and has potential for imaging of nanoparticles in vivo.

scholarly journals Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1836
Author(s):  
Bo-Hye Choi ◽  
Donghwi Hwang ◽  
Seung-Kwan Kang ◽  
Kyeong-Yun Kim ◽  
Hongyoon Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Attenuation Correction ◽  
Complex Structure ◽  
Scatter Correction ◽  
Amyloid Β ◽  
Correction Method ◽  
Percentage Error ◽  
Similarity Coefficients ◽  
Brain Pet ◽  
Positron Emission

The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET.

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