Evaluation of an attenuation correction method for PET/MR imaging of the head based on substitute CT images

2012 ◽  
Vol 26 (1) ◽  
pp. 127-136 ◽  
Author(s):  
Anne Larsson ◽  
Adam Johansson ◽  
Jan Axelsson ◽  
Tufve Nyholm ◽  
Thomas Asklund ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Attenuation Correction ◽  
Correction Method ◽  
Ct Images

scholarly journals Whole-Body PET/MR Imaging: Quantitative Evaluation of a Novel Model-Based MR Attenuation Correction Method Including Bone

2015 ◽  
Vol 56 (7) ◽  
pp. 1061-1066 ◽  
Author(s):  
D. H. Paulus ◽  
H. H. Quick ◽  
C. Geppert ◽  
M. Fenchel ◽  
Y. Zhan ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Attenuation Correction ◽  
Quantitative Evaluation ◽  
Correction Method ◽  
Whole Body ◽  
Model Based ◽  
Novel Model

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.

Method for transforming CT images for attenuation correction in PET/CT imaging

2006 ◽  
Vol 33 (4) ◽  
pp. 976-983 ◽  
Author(s):  
Jonathan P. J. Carney ◽  
David W. Townsend ◽  
Vitaliy Rappoport ◽  
Bernard Bendriem
Keyword(s):  
Attenuation Correction ◽  
Ct Images ◽  
Ct Imaging ◽  
Pet Ct

Integrated PET/MR imaging: Automatic attenuation correction of flexible RF coils

2013 ◽  
Vol 40 (8) ◽  
pp. 082301 ◽  
Author(s):  
René Kartmann ◽  
Daniel H. Paulus ◽  
Harald Braun ◽  
Bassim Aklan ◽  
Susanne Ziegler ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Attenuation Correction ◽  
Rf Coils

scholarly journals Attenuation correction for a high-resolution polarimetric X-band weather radar

2010 ◽  
Vol 8 ◽  
pp. 279-284
Author(s):  
T. Otto ◽  
H. W. J. Russchenberg
Keyword(s):  
Attenuation Correction ◽  
Hydrological Cycle ◽  
Correction Method ◽  
Phase Constraint ◽  
Spatial And Temporal Variability ◽  
Range Resolution ◽  
Raindrop Size Distribution ◽  
University Of Technology ◽  
X Band ◽  
Raindrop Size

Abstract. In 2007, IRCTR (Delft University of Technology) installed a new polarimetric X-band LFMCW radar (IDRA) at the meteorological observation site of Cabauw, The Netherlands. It provides plan position indicators (PPI) at a fixed elevation with a high range resolution of either 3 m or 30 m at a maximum observation range of 1.5 km and 15 km, respectively. IDRA aims to monitor precipitation events for the long-term analysis of the hydrological cycle. Due to the specifications of IDRA, the spatial and temporal variability of a large range of rainfall intensities (from drizzle to heavy convective rain) can be studied. Even though the usual observation range of IDRA is limited to 15 km, attenuation due to precipitation can be large enough to seriously affect the measurements. In this contribution we evaluate the application of a combined method to correct for the specific and the differential attenuation, and in the same vein estimate the parameters of the raindrop-size distribution. The estimated attenuations are compared to a phase constraint attenuation correction method.

scholarly journals Developing an efficient phase-matched attenuation correction method for quiescent period PET in abdominal PET/MRI

2018 ◽  
Vol 63 (18) ◽  
pp. 185002 ◽  
Author(s):  
Jaewon Yang ◽  
Jing Liu ◽  
Florian Wiesinger ◽  
Anne Menini ◽  
Xucheng Zhu ◽  
...  
Keyword(s):  
Attenuation Correction ◽  
Correction Method ◽  
Quiescent Period

scholarly journals Feasibility of simultaneous whole-brain imaging on an integrated PET-MRI system using an enhanced 2-point Dixon attenuation correction method

2015 ◽  
Vol 8 ◽  
Author(s):  
Udunna C. Anazodo ◽  
Jonathan D. Thiessen ◽  
Tracy Ssali ◽  
Jonathan Mandel ◽  
Matthias Günther ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Attenuation Correction ◽  
Correction Method ◽  
Whole Brain

scholarly journals Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Zhenghao Shi ◽  
Jiejue Ma ◽  
Minghua Zhao ◽  
Yonghong Liu ◽  
Yaning Feng ◽  
...  
Keyword(s):  
Region Growing ◽  
Disease Diagnosis ◽  
Correction Method ◽  
Ct Images ◽  
Segmentation Method ◽  
Lung Segmentation ◽  
Multiple Strategies ◽  
Simple Strategy ◽  
Growing Strategy ◽  
Ct Slices

Accurate lung segmentation is an essential step in developing a computer-aided lung disease diagnosis system. However, because of the high variability of computerized tomography (CT) images, it remains a difficult task to accurately segment lung tissue in CT slices using a simple strategy. Motived by the aforementioned, a novel CT lung segmentation method based on the integration of multiple strategies was proposed in this paper. Firstly, in order to avoid noise, the input CT slice was smoothed using the guided filter. Then, the smoothed slice was transformed into a binary image using an optimized threshold. Next, a region growing strategy was employed to extract thorax regions. Then, lung regions were segmented from the thorax regions using a seed-based random walk algorithm. The segmented lung contour was then smoothed and corrected with a curvature-based correction method on each axis slice. Finally, with the lung masks, the lung region was automatically segmented from a CT slice. The proposed method was validated on a CT database consisting of 23 scans, including a number of 883 2D slices (the number of slices per scan is 38 slices), by comparing it to the commonly used lung segmentation method. Experimental results show that the proposed method accurately segmented lung regions in CT slices.

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