scholarly journals A dóziscsökkentés lehetőségei CT-képalkotás során az iteratív képrekonstrukció alkalmazásával

2019 ◽  
Vol 160 (35) ◽  
pp. 1387-1394
Author(s):  
Gábor Bajzik ◽  
Anett Tóth ◽  
Tamás Donkó ◽  
Péter Kovács ◽  
Dávid Sipos ◽  
...  
Keyword(s):  
Image Quality ◽  
Effective Dose ◽  
Dose Reduction ◽  
Iterative Reconstruction ◽  
Chest Ct ◽  
Ct Scans ◽  
Rank Test ◽  
Dose Length Product ◽  
Contrast Enhanced ◽  
Significant Difference

Abstract: Introduction and aim: In case of imaging modalities using ionizing radiation, radiation exposure of the patients is a vital issue. It is important to survey the various dose-reducing techniques to achieve optimal radiation protection while keeping image quality on an optimal level. Method: We reprocessed 105 patients’ data prospectively between February and April 2017. The determination of the radiation dose was based on the effective dose, calculated by multiplying the dose-length product (DLP) and dose-conversation coefficient. In case of image quality we used signal-to-noise ratio (SNR) based on manual segmentation of region of interest (ROI). For statistical analysis, one sample t-test and Wilcoxon signed rank test were used. Results: Using iterative reconstruction, the effective dose was significantly lower (p<0.001) in both native and contrast-enhanced abdominal, contrast-enhanced chest CT scans and in the case of the total effective dose. At native and contrast-enhanced abdominal CT scans, the noise content of the images showed significantly lower (p<0.001) values for iterative reconstruction images. At contrast-enhanced chest CT scans there was no significant difference between the noise content of the images (p>0.05). Conclusion: Using iterative reconstruction, it was possible to achieve significant dose reduction. Since the noise content of the images was not significantly higher using the iterative reconstruction compared to the filtered back projection, further dose reduction can be achievable while preserving the optimal quality of the images. Orv Hetil. 2019; 160(35): 1387–1394.

scholarly journals Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

2021 ◽  
Author(s):  
Haesung Yoon ◽  
Jisoo Kim ◽  
Hyun Ji Lim ◽  
Mi-Jung Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Noise Reduction ◽  
Iterative Reconstruction ◽  
Ct Images ◽  
Chest Ct ◽  
Abdomen Ct ◽  
Contrast Enhanced ◽  
Pediatric Chest ◽  
Body Ct

Abstract Background Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images.Methods This retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1–18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). Quantitative and qualitative parameters were compared using repeated measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests.Results DLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8% and 53.1% respectively. The subjective assessment of overall image quality and noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods.Conclusion Compared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.

scholarly journals Iterative reconstruction improves image quality and reduces radiation dose in trauma protocols; A human cadaver study

2021 ◽  
Vol 10 (10) ◽  
pp. 205846012110553
Author(s):  
Johannes Clemens Godt ◽  
Cathrine K Johansen ◽  
Anne Catrine T Martinsen ◽  
Anselm Schulz ◽  
Helga M Brøgger ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Dose Reduction ◽  
Iterative Reconstruction ◽  
Low Dose ◽  
Human Cadaver ◽  
Full Dose ◽  
Significant Difference ◽  
Noise Ratio ◽  
Dose Levels

Background Radiation-related cancer risk is an object of concern in CT of trauma patients, as these represent a young population. Different radiation reducing methods, including iterative reconstruction (IR), and spilt bolus techniques have been introduced in the recent years in different large scale trauma centers. Purpose To compare image quality in human cadaver exposed to thoracoabdominal computed tomography using IR and standard filtered back-projection (FBP) at different dose levels. Material and methods Ten cadavers were scanned at full dose and a dose reduction in CTDIvol of 5 mGy (low dose 1) and 7.5 mGy (low dose 2) on a Siemens Definition Flash 128-slice computed tomography scanner. Low dose images were reconstructed with FBP and Sinogram affirmed iterative reconstruction (SAFIRE) level 2 and 4. Quantitative image quality was analyzed by comparison of contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). Qualitative image quality was evaluated by use of visual grading regression (VGR) by four radiologists. Results Readers preferred SAFIRE reconstructed images over FBP at a dose reduction of 40% (low dose 1) and 56% (low dose 2), with significant difference in overall impression of image quality. CNR and SNR showed significant improvement for images reconstructed with SAFIRE 2 and 4 compared to FBP at both low dose levels. Conclusions Iterative image reconstruction, SAFIRE 2 and 4, resulted in equal or improved image quality at a dose reduction of up to 56% compared to full dose FBP and may be used a strong radiation reduction tool in the young trauma population.

scholarly journals Pengaruh Tegangan Tabung terhadap Kualitas Citra pada Pemeriksaan Computed Tomography Kepala menggunakan Iterative Reconstruction

2021 ◽  
Vol 9 (1) ◽  
pp. 103-110
Author(s):  
Muhammad Irsal ◽  
◽  
Nurbaiti Nurbaiti ◽  
Aulia Narendra Mukhtar ◽  
Shinta Gunawati ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Effective Dose ◽  
Iterative Reconstruction ◽  
Signal To Noise Ratio ◽  
Hounsfield Unit ◽  
P Value ◽  
Dose Length Product ◽  
Reconstruction Image

Iterative reconstruction can optimize radiation dose and improve image quality on CT scan. This research method is quantitative analytic with the analysis of the results of the head CT examination parameters associated with image quality to changes in variations of 80 kV, 100 kV, 120 kV with the use of iterative reconstruction. Image quality measurements are the Hounsfield Unit (HU) value, standard deviation, and Signal to Noise Ratio (SNR) using Radiant Viewers. Effective dose measurement using the Dose Length Product (DLP). Then perform the Kruskal Wallis test to find out whether there is an effect of tube voltage and Iterative Reconstruction on the SNR value using IBM SPSS version 24. The results image quality of the HU value increase due to changes in the kV value, but the value does not change significantly when the iDose changes, for the standard The deviation has decreased due to changes in kV, but the value of the value does not experience a significant change at the time of change in iDose, while SNR increases due to changes in kV value and changes in iDose. The percentage ratio of the effective dose in the use of standard kV with 80 kV decreased radiation dose by 62%, while at 100 kV there was a decrease of 25%, and the use of 120 kV experienced an increase of 25%. The results of the Kruskal Wallis test p-value <0.001, therefore it can be concluded that there is a difference in the SNR image quality at each change in iDose and kV parameters.

scholarly journals Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Haesung Yoon ◽  
Jisoo Kim ◽  
Hyun Ji Lim ◽  
Mi-Jung Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Noise Reduction ◽  
Iterative Reconstruction ◽  
Ct Images ◽  
Chest Ct ◽  
Abdomen Ct ◽  
Contrast Enhanced ◽  
Pediatric Chest ◽  
Body Ct

Abstract Background Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare the objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images. Methods This retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1–18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). A phantom scan was performed including the dose range of the clinical images used in our study, and the noise power spectrum (NPS) was calculated. Quantitative and qualitative parameters were compared using repeated-measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests. Results DLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8%, and 53.1% respectively. The subjective assessment of overall image quality and the noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods. From NPS analysis, DLR methods showed a pattern of reducing the magnitude of noise while maintaining the texture. Conclusion Compared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.

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