A method to analyse observer disagreement in visual grading studies: example of assessed image quality in paediatric cerebral multidetector CT images

The aim of this study is to investigate the impact of CT acquisition parameter setting on organ dose and its influence on image quality metrics in pediatric phantom during CT examination. The study was performed on 64-slice multidetector CT scanner (MDCT) Siemens Definition AS (Siemens Sector Healthcare, Forchheim, Germany) using various CT CAP protocols (P1–P9). Tube potential for P1, P2, and P3 protocols were fixed at 100 kVp while P4, P5, and P6 were fixed at 80 kVp with used of various reference noise values. P7, P8, and P9 were the modification of P1 with changes on slice collimation, pitch factor, and tube current modulation (TCM), respectively. TLD-100 chips were inserted into the phantom slab number 7, 9, 10, 12, 13, and 14 to represent thyroid, lung, liver, stomach, gonads, and skin, respectively. The image quality metrics, signal to noise ratio (SNR) and contrast to noise ratio (CNR) values were obtained from the CT console. As a result, this study indicates a potential reduction in the absorbed dose up to 20% to 50% along with reducing tube voltage, tube current, and increasing the slice collimation. There is no significant difference (p > 0.05) observed between the protocols and image metrics.

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Deep Learning Enabled Deblurring of Computed Tomography Images of Porous Media

10.2118/208665-ms ◽

2021 ◽

Author(s):

Khalid Labib Alsamadony ◽

Ertugrul Umut Yildirim ◽

Guenther Glatz ◽

Umair bin Waheed ◽

Sherif M. Hanafy

Keyword(s):

Computed Tomography ◽

Image Quality ◽

Transfer Learning ◽

Temporal Resolution ◽

Exposure Time ◽

Ct Images ◽

Loss Functions ◽

High Quality ◽

X Ray ◽

Training Images

Abstract Computed tomography (CT) is an important tool to characterize rock samples allowing quantification of physical properties in 3D and 4D. The accuracy of a property delineated from CT data is strongly correlated with the CT image quality. In general, high-quality, lower noise CT Images mandate greater exposure times. With increasing exposure time, however, more wear is put on the X-Ray tube and longer cooldown periods are required, inevitably limiting the temporal resolution of the particular phenomena under investigation. In this work, we propose a deep convolutional neural network (DCNN) based approach to improve the quality of images collected during reduced exposure time scans. First, we convolve long exposure time images from medical CT scanner with a blur kernel to mimic the degradation caused because of reduced exposure time scanning. Subsequently, utilizing the high- and low-quality scan stacks, we train a DCNN. The trained network enables us to restore any low-quality scan for which high-quality reference is not available. Furthermore, we investigate several factors affecting the DCNN performance such as the number of training images, transfer learning strategies, and loss functions. The results indicate that the number of training images is an important factor since the predictive capability of the DCNN improves as the number of training images increases. We illustrate, however, that the requirement for a large training dataset can be reduced by exploiting transfer learning. In addition, training the DCNN on mean squared error (MSE) as a loss function outperforms both mean absolute error (MAE) and Peak signal-to-noise ratio (PSNR) loss functions with respect to image quality metrics. The presented approach enables the prediction of high-quality images from low exposure CT images. Consequently, this allows for continued scanning without the need for X-Ray tube to cool down, thereby maximizing the temporal resolution. This is of particular value for any core flood experiment seeking to capture the underlying dynamics.

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Neonatal digital chest radiography– should we be using additional copper filtration?

British Journal of Radiology ◽

10.1259/bjr.20211026 ◽

2021 ◽

Author(s):

Jenna Ruth Tugwell-Allsup ◽

Rhys Wyn Morris ◽

Kate Thomas ◽

Richard Hibbs ◽

Andrew England

Keyword(s):

Image Quality ◽

Radiation Dose ◽

Detrimental Effect ◽

Chest Imaging ◽

Anthropomorphic Phantom ◽

Visual Grading ◽

X Ray ◽

Visual Grading Analysis ◽

Designed Experiment ◽

Digital Chest

Objectives: Copper filtration removes lower energy X-ray photons, which do not enhance image quality but would otherwise contribute to patient dose. This study explores the use of additional copper filtration for neonatal mobile chest imaging. Methods: A controlled factorial-designed experiment was used to determine the effect of independent variables on image quality and radiation dose. These variables included: copper filtration (0Cu, 0.1Cu and 0.2Cu), exposure factors, SID and image receptor position (direct +tray). Image quality was evaluated using absolute visual grading analysis (VGA) and contrast-to-noise ratio (CNR) and entrance surface dose (ESD) was derived using an ionising chamber within the central X-ray beam. Results: VGA, CNR and ESD significantly reduced (p < 0.01) when using added copper filtration. For 0.1Cu, the percentage reduction was much greater for ESD (60%) than for VGA (14%) and CNR (20%), respectively. When compared to the optimal combinations of parameters for incubator imaging using no copper filtration, an increase in kV and mAs when using 0.1mmCu resulted in better image quality at the same radiation dose (direct) or, equal image quality at reduced dose (in-tray). The use of 0.1mmCu for neonatal chest imaging with a corresponding increase in kV and mAs is therefore recommended. Conclusions: Using additional copper filtration significantly reduces radiation dose (at increased mAs) without a detrimental effect on image quality. Advances in knowledge: This is the first study, using an anthropomorphic phantom, to explore the use of additional Cu for DR neonatal chest imaging and therefore helps inform practice to standardise and optimise this imaging examination.

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Improvement of Image Quality of Cone-beam CT Images by Three-dimensional Generative Adversarial Network

10.1109/embc46164.2021.9629952 ◽

2021 ◽

Author(s):

Takumi Hase ◽

Megumi Nakao ◽

Keiho Imanishi ◽

Mitsuhiro Nakamura ◽

Tetsuya Matsuda

Keyword(s):

Image Quality ◽

Cone Beam Ct ◽

Three Dimensional ◽

Ct Images ◽

Cone Beam ◽

Generative Adversarial Network ◽

Adversarial Network

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A comparative study on image quality of two digital intraoral sensors

Dentomaxillofacial Radiology ◽

10.1259/dmfr.20190063 ◽

2019 ◽

Vol 48 (7) ◽

pp. 20190063 ◽

Cited By ~ 2

Author(s):

Cinar Aziman ◽

Kristina Hellén-Halme ◽

Xie-Qi Shi

Keyword(s):

Image Quality ◽

Observer Agreement ◽

Visual Grading ◽

Intra Class Correlation ◽

Subjective Image Quality ◽

Significant Difference ◽

General Dentists ◽

Parallel Technique ◽

Specialist Group

Objectives The aims of this study were to evaluate the subjective image quality and reliability of two digital sensors. In addition, the image quality of the two sensors evaluated by specialists and general dentists were compared. Methods: 30 intraoral bitewings from five patients were included in the study, 15 were exposed with a Dixi sensor (CCD-based) and 15 with a ProSensor (CMOS-based) using modified parallel technique. Three radiologists and three general dentists evaluated the images in pair. A five-point scale was used to register the image quality. Visual grading characteristics (VGC) analysis was performed to compare the image quality and the observer agreement was assessed in terms of intra class correlation co-efficient. Results No statistically significant difference was found on image quality between the sensors. The average scores of the observer agreement were moderate with an average of 0.66 and an interval of 0.30 to 0.87, suggesting that there was a large variation on preference of image quality. However, there was a statistically significant difference in terms of the area under the VGC- curves between the specialist group and the general dentist group ( p = 0.043), in which the specialist group tended to favor the ProSensor. Conclusions Subjective image quality of the two intraoral sensors were comparable when evaluated by both general and oral radiologists. However, the radiologists seemed to prefer the ProSensor to the Dixi as compared to general dentists. Inter- observer conformance showed a large variation on the preference of the image quality.

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Trends in radiation dose and image quality for pediatric patients with a multidetector CT and a third-generation dual-source dual-energy CT

La radiologia medica ◽

10.1007/s11547-019-01037-5 ◽

2019 ◽

Vol 124 (8) ◽

pp. 745-752 ◽

Cited By ~ 4

Author(s):

Andrea Agostini ◽

Alberto Mari ◽

Cecilia Lanza ◽

Nicolo’ Schicchi ◽

Alessandra Borgheresi ◽

...

Keyword(s):

Image Quality ◽

Radiation Dose ◽

Pediatric Patients ◽

Multidetector Ct ◽

Dual Energy ◽

Dual Energy Ct ◽

Third Generation ◽

Dual Source

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Relationship between heart rate and optimal reconstruction phase in coronary CT angiography performed on a 256-slice multidetector CT

British Journal of Radiology ◽

10.1259/bjr.20180945 ◽

2019 ◽

Vol 92 (1101) ◽

pp. 20180945 ◽

Cited By ~ 1

Author(s):

Ching-Ching Yang ◽

Wei-Yip Law ◽

Kun-Mu Lu ◽

Tung-Hsin Wu

Keyword(s):

Heart Rate ◽

Image Quality ◽

Motion Correction ◽

Coronary Ct Angiography ◽

Multidetector Ct ◽

Correction Algorithm ◽

Coronary Ct ◽

Optimal Reconstruction ◽

The Relationship ◽

Ecg Pulsing

Objective: This study aimed to evaluate the relationship between heart rate (HR) and optimal reconstruction phase in prospectively electrocardiogram (ECG)-triggered coronary CT angiography (CCTA) performed on a newly introduced 256-slice multidetector CT (MDCT). Methods: All the cases were selected retrospectively from the patients scheduled for CCTA in our department between January and April 2017. The scanner selected the optimal exposure phase based on 10 s ECG recordings. To ensure the success of CCTA, the operator also checked patient's age, breathing control, emotional status and past medical history to decide whether the automatically selected scan phase needs manual adjustment or not. Images were reconstructed in 1% steps of the R–R interval to determine the cardiac phase with least coronary motion. If CCTA images showed moderate motion blurring or discontinuity in the course of coronary segments, a cardiac motion correction algorithm was applied to the reconstructed images. Subjective diagnostic image quality was evaluated with 4-point grading scale. Results: A total of 87 consecutive CCTA examinations were investigated in this study. Diastolic reconstruction was applied to all vessel segments in patients with HR <63 bpm, where 36.5 and 77.8% of vessel segments were reconstructed with the use of motion correction in HR ≤57 and 58–62 bpm, respectively. As for patients with HR ≥63 bpm, 89.3 and 71.7% of vessel segments were reconstructed in diastole in HR 63–67 and ≥68 bpm, respectively, while 81 and 100% of vessel segments were reconstructed with the use of motion correction in the same HR groups. Conclusion: Based on our results, a HR less than 67 bpm can be used to identify appropriate patients for diastolic reconstruction. Although the motion correction algorithm is an effective approach to reduce the impact of cardiac motion in CCTA, HR control is still important to optimize the image quality of CCTA. The relationship between HR and optimal reconstruction phase established in this study could be further used to tailor the ECG pulsing window for dose reduction in patients undergoing CCTA performed on the 256-slice MDCT. Advances in knowledge: The HR thresholds to identify patients who are the best suitable candidates for diastolic or systolic reconstruction are scanner specific. This study investigated the relationship between HR and optimal reconstruction phase in prospectively ECG-triggered CCTA for a newly introduced 256-slice MDCT. Once the relationship is established, it could be used to tailor the ECG pulsing window for radiation dose reduction.

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CAN SCATTER CORRECTION SOFTWARE REPLACE A GRID IN DR PELVIC EXAMINATIONS?

Radiation Protection Dosimetry ◽

10.1093/rpd/ncz129 ◽

2019 ◽

Vol 187 (1) ◽

pp. 8-16 ◽

Cited By ~ 1

Author(s):

Helle Precht ◽

Svea Deppe Mørup ◽

Anders Tingberg ◽

Claus Bjørn Outzen ◽

Kirsten Weber Kusk ◽

...

Keyword(s):

Image Quality ◽

Radiation Dose ◽

Scattered Radiation ◽

Scatter Correction ◽

Pelvic Examination ◽

Analysis Software ◽

Visual Grading ◽

Diagnostic Use ◽

Visual Grading Analysis ◽

Exposure Levels

Abstract The purpose was to examine if scatter correction software could replace a grid while maintaining image quality and reducing radiation dose for pelvic DR examinations. Grid images was produced with 70 kV and 16mAs. Anthropomorphic- and Contrast Detail RADiography (CDRAD) non-grid images were produced with 60 kV, 80 kV and 90 kV combined with five different mAs and scatter correction software. The anthropomorphic images were analyzed by absolute Visual Grading Analysis (VGA). The CDRAD images were analyzed using the CDRAD analysis software. The results showed a total of 54.6% non-grid images were evaluated as unsuitable for diagnostic use by the VGA. The CDRAD grid images showed that the IQF_inv values were significantly different (p = 0.0001) when compared to every group of non-grid images. Hereby, the conclusion stated that the scatter correction software did not compensate for the loss in image quality due to scattered radiation at the exposure levels included in a pelvic examination.

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Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography

British Journal of Radiology ◽

10.1259/bjr.20200677 ◽

2021 ◽

Vol 94 (1117) ◽

pp. 20200677

Author(s):

Andrea Steuwe ◽

Marie Weber ◽

Oliver Thomas Bethge ◽

Christin Rademacher ◽

Matthias Boschheidgen ◽

...

Keyword(s):

Deep Learning ◽

Image Quality ◽

Dose Reduction ◽

Low Dose ◽

Ct Images ◽

Image Noise ◽

The Novel ◽

Subjective Image Quality ◽

Image Information ◽

Noise Ratio

Objectives: Modern reconstruction and post-processing software aims at reducing image noise in CT images, potentially allowing for a reduction of the employed radiation exposure. This study aimed at assessing the influence of a novel deep-learning based software on the subjective and objective image quality compared to two traditional methods [filtered back-projection (FBP), iterative reconstruction (IR)]. Methods: In this institutional review board-approved retrospective study, abdominal low-dose CT images of 27 patients (mean age 38 ± 12 years, volumetric CT dose index 2.9 ± 1.8 mGy) were reconstructed with IR, FBP and, furthermore, post-processed using a novel software. For the three reconstructions, qualitative and quantitative image quality was evaluated by means of CT numbers, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in six different ROIs. Additionally, the reconstructions were compared using SNR, peak SNR, root mean square error and mean absolute error to assess structural differences. Results: On average, CT numbers varied within 1 Hounsfield unit (HU) for the three assessed methods in the assessed ROIs. In soft tissue, image noise was up to 42% lower compared to FBP and up to 27% lower to IR when applying the novel software. Consequently, SNR and CNR were highest with the novel software. For both IR and the novel software, subjective image quality was equal but higher than the image quality of FBP-images. Conclusion: The assessed software reduces image noise while maintaining image information, even in comparison to IR, allowing for a potential dose reduction of approximately 20% in abdominal CT imaging. Advances in knowledge: The assessed software reduces image noise by up to 27% compared to IR and 48% compared to FBP while maintaining the image information. The reduced image noise allows for a potential dose reduction of approximately 20% in abdominal imaging.

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