attenuation correction
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2022 ◽  
Author(s):  
Maike E. Lindemann ◽  
Marcel Gratz ◽  
Jan Ole Blumhagen ◽  
Bjoern Jakoby ◽  
Harald H. Quick

Author(s):  
Nguyen Chi Thanh

This article evaluates the effectiveness of using a deep learning network model to generate reliable attenuation corrected the single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). The authors collected myocardial perfusion imaging data of 88 patients from a SPECT/CT machine, with an average age of 62.47 years. Then, two datasets are created from the original data: set A includes the deep learning-based attenuation corrected images (Generated Attenuation Correction - GenAC), and the non-attenuation corrected images; set B contains only non-attenuation corrected images. These datasets were diagnosed by two doctors (in which, one has 7 years of experience and the other has 10 years of experience in reading SPECT MPI). The doctors diagnose based on the image data without knowing which dataset it belongs to. The sensitivity, specificity, diagnostic accuracy, and lesion rate were evaluated between the two data sets. Results: The average specificity, sensitivity, and accuracy of the set with the deep learning-based attenuation corrected images were 0.87, 0.86, 0.86, while the results with the non-attenuation corrected images are 0.69, 0.83, and 0.78.


2021 ◽  
pp. 17-25
Author(s):  
Mario Serrano-Sosa ◽  
Ana M. Franceschi ◽  
Chuan Huang

2021 ◽  
Vol 893 (1) ◽  
pp. 012054
Author(s):  
M F Handoyo ◽  
M P Hadi ◽  
S Suprayogi

Abstract A weather radar is an active system remote sensing tool that observes precipitation indirectly. Weather radar has an advantage in estimating precipitation because it has a high spatial resolution (up to 0.5 km). Reflectivity generated by weather radar still has signal interference caused by attenuation factors. Attenuation causes the Quantitative Precipitation Estimation (QPE) by the C-band weather radar to underestimate. Therefore attenuation correction on C-band weather radar is needed to eliminate precipitation estimation errors. This study aims to apply attenuation correction to determine Quantitative Precipitation Estimation (QPE) on the c-band weather radar in Bengkulu in December 2018. Gate-by-gate method attenuation correction with Kraemer approach has applied to c-band weather radar data from the Indonesian Agency for Meteorology and Geophysics (BMKG) weather radar network Bengkulu. This method uses reflectivity as the only input. Quantitative Precipitation Estimation (QPE) has obtained by comparing weather radar-based rain estimates to 10 observation rain gauges over a month with the Z-R relation equation. Root Mean Square Error (RMSE) is used to calculate the estimation error. Weather radar data are processed using Python-based libraries Wradlib and ArcGIS 10.5. As a result, the calculation between the weather radar estimate precipitation and the observed rainfall obtained equation Z=2,65R1,3. The attenuation correction process with Kreamer's approach on the c-band weather radar has reduced error in the Qualitative Precipitation Estimation (QPE). Corrected precipitation has a smaller error value (r = 0.88; RMSE = 8.38) than the uncorrected precipitation (r = 0.83; RMSE = 11.70).


2021 ◽  
Author(s):  
Maike E. Lindemann ◽  
Mark Oehmigen ◽  
Titus Lanz ◽  
Hong Grafe ◽  
Nils Martin Bruckmann ◽  
...  

2021 ◽  
Vol 8 ◽  
Author(s):  
Jei-Yie Huang ◽  
Chun-Kai Huang ◽  
Ruoh-Fang Yen ◽  
Kuo-Liong Chien ◽  
Yen-Wen Wu

Background: The aim of this study was to determine whether, and if so how, attenuation correction (AC) improves the diagnostic performance of myocardial perfusion imaging (MPI) in different coronary artery-supplied territories, using coronary angiography as the reference standard.Methods: PubMed and EMBASE were searched until December 2020 for studies evaluating AC MPI for the diagnosis of coronary artery disease (CAD) with vessel-based data. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. For each study, the sensitivity, specificity, diagnostic odds ratios and areas under summary receiver operating characteristic curves (AUC) with 95% confidence intervals were calculated to determine the diagnostic accuracy of AC compared to non-AC MPI. A bivariate mixed-effects model was used to pool the data. Subgroup analyses considering the type of radiotracer and type of AC were performed.Results: A total of 264 articles were screened, of which 22 studies (2,608 patients) were enrolled. Significant improvements in specificity [0.78 vs. 0.58 in overall CAD, 0.87 vs. 0.61 in right coronary artery (RCA)] and diagnostic odds ratios (16 vs. 8 in overall CAD, 18 vs. 7 in RCA) after AC were shown in overall CAD at a patient level and RCA stenosis. Improvements in AUC were also noted. MPI had a similar diagnostic performance for detecting left anterior descending and left circumflex coronary artery stenosis with or without AC. There were trends of decreased sensitivity after AC, but none were significant. Diagnostic odds ratio showed significant improvement after AC only in the technetium-99m subgroup.Conclusion: The results of this study suggest that AC should be applied to MPI to improve the diagnosis of CAD regardless of which type of radiotracer, and that AC MPI can improve the specificity of detecting RCA stenosis.


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