scholarly journals Manual kidney stone size measurements in computed tomography are most accurate using multiplanar image reformatations and bone window settings

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Robert Peter Reimer ◽  
Konstantin Klein ◽  
Miriam Rinneburger ◽  
David Zopfs ◽  
Simon Lennartz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Treatment Decision ◽  
Reconstruction Algorithm ◽  
Slice Thickness ◽  
Reconstruction Algorithms ◽  
Stone Size ◽  
Bone Window ◽  
3D Printed ◽  
Measurement Strategies ◽  
Digital Caliper

AbstractComputed tomography in suspected urolithiasis provides information about the presence, location and size of stones. Particularly stone size is a key parameter in treatment decision; however, data on impact of reformatation and measurement strategies is sparse. This study aimed to investigate the influence of different image reformatations, slice thicknesses and window settings on stone size measurements. Reference stone sizes of 47 kidney stones representative for clinically encountered compositions were measured manually using a digital caliper (Man-M). Afterwards stones were placed in a 3D-printed, semi-anthropomorphic phantom, and scanned using a low dose protocol (CTDIvol 2 mGy). Images were reconstructed using hybrid-iterative and model-based iterative reconstruction algorithms (HIR, MBIR) with different slice thicknesses. Two independent readers measured largest stone diameter on axial (2 mm and 5 mm) and multiplanar reformatations (based upon 0.67 mm reconstructions) using different window settings (soft-tissue and bone). Statistics were conducted using ANOVA ± correction for multiple comparisons. Overall stone size in CT was underestimated compared to Man-M (8.8 ± 2.9 vs. 7.7 ± 2.7 mm, p < 0.05), yet closely correlated (r = 0.70). Reconstruction algorithm and slice thickness did not significantly impact measurements (p > 0.05), while image reformatations and window settings did (p < 0.05). CT measurements using multiplanar reformatation with a bone window setting showed closest agreement with Man-M (8.7 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05, r = 0.83). Manual CT-based stone size measurements are most accurate using multiplanar image reformatation with a bone window setting, while measurements on axial planes with different slice thicknesses underestimate true stone size. Therefore, this procedure is recommended when impacting treatment decision.

scholarly journals Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Hsuan-Ming Huang ◽  
Ing-Tsung Hsiao
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Low Dose ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Acceleration Techniques ◽  
Reconstruction Methods ◽  
Speed Up ◽  
Phantom Studies

Background and Objective. Over the past decade, image quality in low-dose computed tomography has been greatly improved by various compressive sensing- (CS-) based reconstruction methods. However, these methods have some disadvantages including high computational cost and slow convergence rate. Many different speed-up techniques for CS-based reconstruction algorithms have been developed. The purpose of this paper is to propose a fast reconstruction framework that combines a CS-based reconstruction algorithm with several speed-up techniques.Methods. First, total difference minimization (TDM) was implemented using the soft-threshold filtering (STF). Second, we combined TDM-STF with the ordered subsets transmission (OSTR) algorithm for accelerating the convergence. To further speed up the convergence of the proposed method, we applied the power factor and the fast iterative shrinkage thresholding algorithm to OSTR and TDM-STF, respectively.Results. Results obtained from simulation and phantom studies showed that many speed-up techniques could be combined to greatly improve the convergence speed of a CS-based reconstruction algorithm. More importantly, the increased computation time (≤10%) was minor as compared to the acceleration provided by the proposed method.Conclusions. In this paper, we have presented a CS-based reconstruction framework that combines several acceleration techniques. Both simulation and phantom studies provide evidence that the proposed method has the potential to satisfy the requirement of fast image reconstruction in practical CT.

Peripheral bronchial luminal conspicuity on dynamic-ventilation computed tomography: association with radiation doses and temporal resolution by using an ex vivo porcine lung phantom

2020 ◽  
Vol 61 (12) ◽  
pp. 1608-1617
Author(s):  
Yukihiro Nagatani ◽  
Makoto Yoshigoe ◽  
Shinsuke Tsukagoshi ◽  
Noritoshi Ushio ◽  
Kohei Ohashi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Temporal Resolution ◽  
Ex Vivo ◽  
Reconstruction Algorithm ◽  
Radiation Doses ◽  
Reconstruction Algorithms ◽  
Movement Velocity ◽  
Full Reconstruction ◽  
Luminal Area ◽  
Lung Phantom

Background It is still unclear which image reconstruction algorithm is appropriate for peripheral bronchial luminal conspicuity (PBLC) on dynamic-ventilation computed tomography (DVCT). Purpose To assess the influence of radiation doses and temporal resolution (TR) on the association between movement velocity (MV) and PBLC on DVCT. Material and Methods An ex vivo porcine lung phantom with simulated respiratory movement was scanned by 320-row CT at 240 mA and 10 mA. Peak and dip CT density and luminal area adjusted by values at end-inspiration (CTDpeak and CTDdip, luminal area ratio [LAR]) for PBLC and MVs were measured and visual scores (VS) were obtained at 12 measurement points on 13 frame images obtained at half and full reconstructions (TR 340 and 190 ms) during expiration. Size-specific dose estimate (SSDE) was applied to presume radiation dose. VS, CTDpeak, CTDdip, LAR, and their cross-correlation coefficients with MV (CCC) were compared among four methods with combinations of two reconstruction algorithms and two doses. Results The dose at 10 mA was presumed as 26 mA by SSDE for standard proportion adults. VS, CTDdip, CTDpeak, and LAR with half reconstruction at 10 mA (2.52 ± 0.59, 1.016 ± 0.221, 0.948 ± 0.103, and 0.990 ± 0.527) were similar to those at 240 mA except for VS, and different from those with full reconstruction at both doses (2.24 ± 0.85, 0.830 ± 0.209, 0.986 ± 0.065, and 1.012 ± 0.438 at 240 mA) ( P < 0.05). CCC for CTDdip with half reconstruction (–0.024 ± 0.552) at 10 mA was higher compared with full reconstruction (–0.503 ± 0.291) ( P < 0.05). Conclusion PBLC with half reconstruction at 10 mA was comparable to that at 240 mA and better than those with full reconstruction on DVCT.

Detection of mild emphysema by computed tomography density measurements

2005 ◽  
Vol 46 (3) ◽  
pp. 237-245 ◽  
Author(s):  
J. Vikgren ◽  
O. Friman ◽  
M. Borga ◽  
M. Boijsen ◽  
S. Gustavsson ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Reconstruction Algorithm ◽  
Threshold Level ◽  
Slice Thickness ◽  
Healthy Male ◽  
High Resolution Computed Tomography ◽  
Diagnostic Ability ◽  
Density Measurements ◽  
Factors Influencing ◽  
Never Smokers

Purpose: To assess the ability of a conventional density mask method to detect mild emphysema by high‐resolution computed tomography (HRCT); to analyze factors influencing quantification of mild emphysema; and to validate a new algorithm for detection of mild emphysema. Material and Methods: Fifty‐five healthy male smokers and 34 never‐smokers, 61–62 years of age, were examined. Emphysema was evaluated visually, by the conventional density mask method, and by a new algorithm compensating for the effects of gravity and artifacts due to motion and the reconstruction algorithm. Effects of the reconstruction algorithm, slice thickness, and various threshold levels on the outcome of the density mask area were evaluated. Results: Forty‐nine percent of the smokers had mild emphysema. The density mask area was higher the thinner the slice irrespective of the reconstruction algorithm and threshold level. The sharp algorithm resulted in increased density mask area. The new reconstruction algorithm could discriminate between smokers with and those without mild emphysema, whereas the density mask method could not. The diagnostic ability of the new algorithm was dependent on lung level. At about 90% specificity, sensitivity was 65–100% in the apical levels, but low in the rest of the lung. Conclusion: The conventional density mask method is inadequate for detecting mild emphysema, while the new algorithm improves the diagnostic ability but is nevertheless still imperfect.

scholarly journals New algorithms for computed tomography image reconstruction to eliminate artifacts

2021 ◽  
Author(s):  
Eli Lechtman
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Tomography Image ◽  
Ct Simulation ◽  
Novel Algorithms ◽  
Computed Tomography Image

Computed tomography (CT) relies on computational algorithms to reconstruct images from CT projections. Current filtered backprojection reconstruction methods have inherent limitations in situations with sharp density gradients and limited beam views. In this thesis two novel reconstruction algorithms were introduced: the Algebraic Image Reconstruction (AIR) algorithm, and the Geometric Image Reconstruction Algorithm (GIRA). A CT simulation was developed to test these novel algorithms and compare their images to filtered backprojection images. AIR and GIRA each demonstrated their proof of principle in these preliminary tests. AIR and its extension, the Parsed AIR algorithm (PAIR), were able to reconstruct optimal images compared to filtered backprojection after empirically determining parameters relevant to the algorithms. While GIRA reconstructed optimal images in preliminary tests, reconstruction was complicated by error propagation for larger imaging domains. The initial success of these novel approaches justifies continued research and development to determine their feasibility for practical CT image reconstruction.

Computed tomography of the urinary tract: optimalization of low-dose stone protocol in a clinical setting

2005 ◽  
Vol 46 (7) ◽  
pp. 764-768 ◽  
Author(s):  
A. S. F. Larsen ◽  
R. Pedersen ◽  
G. Sandbaek
Keyword(s):  
Computed Tomography ◽  
Low Dose ◽  
Reconstruction Algorithm ◽  
Abdominal Fat ◽  
Urinary Stones ◽  
Slice Thickness ◽  
Intravenous Contrast ◽  
Related Factors ◽  
Low Dose Computed Tomography ◽  
Intravenous Contrast Media

Purpose: To establish whether information would be lost if slice reconstruction thickness was increased from 3 to 5 mm, and whether this altered how difficult it was to interpret the examinations. Material and Methods: Twenty-three consecutive patients referred with suspected or known urinary stones were included. All examinations were performed without intravenous contrast media. The original series, with effective mAs 50, were reconstructed with slice thickness 3 and 5 mm, respectively. All demographic and examination data were removed and the series reviewed in PACS by two independent radiologists. Objective findings, i.e. number and size of stones, signs of obstruction, and evaluation of interpretation difficulty, were registered. Results: Identical findings were registered in 18 of the series of 3 mm ( n = 23) and 19 of the series of 5 mm ( n = 23). In two series reconstructed with 3 mm slice thickness and in one series with 5 mm slice thickness, the observers disagreed on the presence of urinary stones. Main reasons for interpretation difficulties were given as “lack of intra-abdominal fat” and “many phleboliths in the pelvic region”, but never “disturbing noise”. Conclusion: To determine the presence and size of urinary stones at low-dose computed tomography, 5 mm reconstruction algorithm seems equal to 3 mm. Patient-related factors influence the interpretation more than image quality.

scholarly journals The Effect of CT Scan Parameters on the Measurement of CT Radiomic Features: A Lung Nodule Phantom Study

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Young Jae Kim ◽  
Hyun-Ju Lee ◽  
Kwang Gi Kim ◽  
Seung Hyun Lee
Keyword(s):  
Radiation Dose ◽  
Ct Scan ◽  
Lung Nodule ◽  
Reconstruction Algorithm ◽  
Image Features ◽  
Slice Thickness ◽  
Reconstruction Algorithms ◽  
Significant Difference ◽  
The Difference ◽  
Ct Density

The purpose of this study was to explore the effects of CT slice thickness, reconstruction algorithm, and radiation dose on quantification of CT features to characterize lung nodules using a chest phantom. Spherical lung nodule phantoms of known densities (−630 and + 100 HU) were inserted into an anthropomorphic thorax phantom. CT scan was performed ten times with relocations. CT data were reconstructed using 12 different imaging settings; three different slice thicknesses of 1.25, 2.5, and 5.0 mm, two reconstruction kernels of sharp and standard, and two radiation dose of 30 mAs and 12 mAs. Lesions were segmented using a semiautomated method. Twenty representative CT quantitative features representing CT density and texture were compared using multiple regression analysis. In 100 HU nodule phantoms, 18 and 19 among 20 computer features showed significant difference between different mAs and reconstruction algorithms, respectively (p≤0.05). 20, 19, and 19 computer features showed difference between slice thickness of 5.0 vs 1.25, 5.0 vs 2.5, and 2.5 vs 1.25 mm, respectively (p≤0.05). In −630 HU nodule phantoms, 18 and 19 showed significant difference between different mAs and reconstruction algorithms, respectively (p≤0.05). 18, 11, and 17 computer features showed difference between slice thickness of 5.0 vs 1.25, 5.0 vs 2.5, and 2.5 vs 1.25 mm, respectively (p≤0.05). When comparing the absolute value of regression coefficient, the effect of slice thickness in 100 HU nodule and reconstruction algorithm in −630 HU nodule was greater than the effect of remaining scan parameters. The slice thickness, mAs, and reconstruction algorithm had a significant impact on the quantitative image features. In clinical studies involving deep learning or radiomics, it should be noted that differences in values can occur when using computer features obtained from different CT scan parameters in combination. Therefore, when interpreting the statistical analysis results, it is necessary to reflect the difference in the computer features depending on the scan parameters.

scholarly journals A Computationally Efficient Reconstruction Algorithm for Circular Cone-Beam Computed Tomography Using Shallow Neural Networks

2020 ◽  
Vol 6 (12) ◽  
pp. 135
Author(s):  
Marinus J. Lagerwerf ◽  
Daniël M. Pelt ◽  
Willem Jan Palenstijn ◽  
Kees Joost Batenburg
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Reconstruction Algorithm ◽  
Circular Cone ◽  
Alternative Methods ◽  
Cone Beam ◽  
Reconstruction Algorithms ◽  
Reconstruction Accuracy ◽  
Fdk Algorithm

Circular cone-beam (CCB) Computed Tomography (CT) has become an integral part of industrial quality control, materials science and medical imaging. The need to acquire and process each scan in a short time naturally leads to trade-offs between speed and reconstruction quality, creating a need for fast reconstruction algorithms capable of creating accurate reconstructions from limited data. In this paper, we introduce the Neural Network Feldkamp–Davis–Kress (NN-FDK) algorithm. This algorithm adds a machine learning component to the FDK algorithm to improve its reconstruction accuracy while maintaining its computational efficiency. Moreover, the NN-FDK algorithm is designed such that it has low training data requirements and is fast to train. This ensures that the proposed algorithm can be used to improve image quality in high-throughput CT scanning settings, where FDK is currently used to keep pace with the acquisition speed using readily available computational resources. We compare the NN-FDK algorithm to two standard CT reconstruction algorithms and to two popular deep neural networks trained to remove reconstruction artifacts from the 2D slices of an FDK reconstruction. We show that the NN-FDK reconstruction algorithm is substantially faster in computing a reconstruction than all the tested alternative methods except for the standard FDK algorithm and we show it can compute accurate CCB CT reconstructions in cases of high noise, a low number of projection angles or large cone angles. Moreover, we show that the training time of an NN-FDK network is orders of magnitude lower than the considered deep neural networks, with only a slight reduction in reconstruction accuracy.

High-Resolution Computed Tomography Examinations for Chronic Suppurative Lung Disease in Early Childhood: Radiation Exposure and Image Quality Evaluations with Iterative Reconstruction Algorithm Use

2016 ◽  
Vol 67 (3) ◽  
pp. 218-224 ◽  
Author(s):  
Magdalini Smarda ◽  
Efstathios Efstathopoulos ◽  
Argyro Mazioti ◽  
Sofia Kordolaimi ◽  
Agapi Ploussi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Image Quality ◽  
Radiation Exposure ◽  
Iterative Reconstruction ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Reconstruction Algorithms ◽  
High Resolution Computed Tomography ◽  
Iterative Reconstruction Algorithm

Purpose High radiosensitivity of children undergoing repetitive computed tomography examinations necessitates the use of iterative reconstruction algorithms in order to achieve a significant radiation dose reduction. The goal of this study is to compare the iDose iterative reconstruction algorithm with filtered backprojection in terms of radiation exposure and image quality in 33 chest high-resolution computed tomography examinations performed in young children with chronic bronchitis. Methods Fourteen patients were scanned using the filtered backprojection protocol while 19 patients using the iDose protocol and reduced milliampere-seconds, both on a 64-detector row computed tomography scanner. The iDose group images were reconstructed with different iDose levels (2, 4, and 6). Radiation exposure quantities were estimated, while subjective and objective image qualities were evaluated. Unpaired t tests were used for data statistical analysis. Results The iDose application allowed significant effective dose reduction (about 80%). Subjective image quality evaluation showed satisfactory results even with iDose level 2, whereas it approached excellent image with iDose level 6. Subjective image noise was comparable between the 2 groups with the use of iDose level 4, while objective noise was comparable between filtered backprojection and iterative reconstruction level 6 images. Conclusions The iDose algorithm use in pediatric chest high-resolution computed tomography reduces radiation exposure without compromising image quality. Further evaluation with iterative reconstruction algorithms is needed in order to establish high-resolution computed tomography as the gold standard low-dose method for children suffering from chronic lung diseases.

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