• Title/Summary/Keyword: modified U-net

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Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction

  • Jung Hee Hong;Eun-Ah Park;Whal Lee;Chulkyun Ahn;Jong-Hyo Kim
    • Korean Journal of Radiology
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    • v.21 no.10
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    • pp.1165-1177
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    • 2020
  • Objective: To assess the feasibility of applying a deep learning-based denoising technique to coronary CT angiography (CCTA) along with iterative reconstruction for additional noise reduction. Materials and Methods: We retrospectively enrolled 82 consecutive patients (male:female = 60:22; mean age, 67.0 ± 10.8 years) who had undergone both CCTA and invasive coronary artery angiography from March 2017 to June 2018. All included patients underwent CCTA with iterative reconstruction (ADMIRE level 3, Siemens Healthineers). We developed a deep learning based denoising technique (ClariCT.AI, ClariPI), which was based on a modified U-net type convolutional neural net model designed to predict the possible occurrence of low-dose noise in the originals. Denoised images were obtained by subtracting the predicted noise from the originals. Image noise, CT attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were objectively calculated. The edge rise distance (ERD) was measured as an indicator of image sharpness. Two blinded readers subjectively graded the image quality using a 5-point scale. Diagnostic performance of the CCTA was evaluated based on the presence or absence of significant stenosis (≥ 50% lumen reduction). Results: Objective image qualities (original vs. denoised: image noise, 67.22 ± 25.74 vs. 52.64 ± 27.40; SNR [left main], 21.91 ± 6.38 vs. 30.35 ± 10.46; CNR [left main], 23.24 ± 6.52 vs. 31.93 ± 10.72; all p < 0.001) and subjective image quality (2.45 ± 0.62 vs. 3.65 ± 0.60, p < 0.001) improved significantly in the denoised images. The average ERDs of the denoised images were significantly smaller than those of originals (0.98 ± 0.08 vs. 0.09 ± 0.08, p < 0.001). With regard to diagnostic accuracy, no significant differences were observed among paired comparisons. Conclusion: Application of the deep learning technique along with iterative reconstruction can enhance the noise reduction performance with a significant improvement in objective and subjective image qualities of CCTA images.

Quantitative Structure Activity Relationship (QSAR) Analyses on the Farnesyl Protein Transferase Inhibition Activity of Hetero Ring Substituted Chalcone Derivatives by the Hansch and Free-Wilson Method (Hansch와 Free-Wilson 방법에 의한 헤테로 고리 치환 chalcone 유도체들의 farnesyl protein transferase 저해활성에 대한 정량적 구조 활성 관계(QSAR) 의 분석)

  • Yu, Seong-Jae;Myung, Pyung-Keun;Kwon, Byung-Mok;Sung, Nack-Do
    • Applied Biological Chemistry
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    • v.43 no.2
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    • pp.95-99
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    • 2000
  • A series of hetero ring (X) substitued chalcone derivatives with farnesyl protein transferase (FPTase) inhibition activities $(pI_{50})$ values determined in vitro is analyzed by modified Free-Wilson (F-W) and Hansch method for quantitative structure activity relationship (QSARs). On the basis of F-W analysis on the FPTase inhibitory activity of a training set of the compounds, none of the (X)-substituents were not contribute the activity. But the net charge of ${\alpha}$ carbon atom is contribute the activity than that of ${\beta}$ carbon atom. And the relative orders of the (Y)-substituents on the activity are ortho>meta>para-substituents. According to Hansch approach, the activities would depend largely on the optimal, $(R_{opt.}=-0.35)$ resonance effect with ortho substituted $(I_o>0)$ electron donating group (R<0) and STERIMOL parameter, $B_1$ constant. The inhibition activity between hetro ring substituents have been a proportioned with each others and none substituent(H), 45 showed the highest FPTase inhibition $(pI_{50}=4.30)$ activity.

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