A novel method for developing contrast-detail curves from clinical patient images based on statistical low-contrast detectability.

Purpose. To develop a method to extract statistical low-contrast detectability (LCD) and contrast-detail (C-D) curves from clinical patient images.Method. We used the region of air surrounding the patient as an alternative for a homogeneous region within a patient. A simple graphical user interface (GUI) was created to set the initial configuration for region of interest (ROI), ROI size, and minimum detectable contrast (MDC). The process was started by segmenting the air surrounding the patient with a threshold between -980 HU (Hounsfield units) and -1024 HU to get an air mask. The mask was trimmed using the patient center coordinates to avoid distortion from the patient table. It was used to automatically place square ROIs of a predetermined size. The mean pixel values in HU within each ROI were calculated, and the standard deviation (SD) from all the means was obtained. The MDC for a particular target size was generated by multiplying the SD by 3.29. A C-D curve was obtained by iterating this process for the other ROI sizes. This method was applied to the homogeneous area from the uniformity module of an ACR CT phantom to find the correlation between the parameters inside and outside the phantom, for 30 thoracic, 26 abdominal, and 23 head images.Results. The phantom images showed a significant linear correlation between the LCDs obtained from outside and inside the phantom, with R2values of 0.67 and 0.99 for variations in tube currents and tube voltages. This indicated that the air region outside the phantom can act as a surrogate for the homogenous region inside the phantom to obtain the LCD and C-D curves.Conclusion. The C-D curves obtained from outside the ACR CT phantom show a strong linear correlation with those from inside the phantom. The proposed method can also be used to extract the LCD from patient images by using the region of air outside as a surrogate for a region inside the patient.

Acute Kidney Injury Prediction Model Using Cystatin-C, Beta-2 Microglobulin, and Neutrophil Gelatinase-Associated Lipocalin Biomarker in Sepsis Patients.

Introduction: AKI is a frequent complication in sepsis patients and is estimated to occur in almost half of patients with severe sepsis. However, there is currently no effective therapy for AKI in sepsis. Therefore, the therapeutic approach is focused on prevention. Based on this, there is an opportunity to examine a panel of biomarker models for predicting AKI. Material and Methods: This prospective cohort study analysed the differences in Cystatin C, Beta-2 Microglobulin, and NGAL levels in sepsis patients with AKI and sepsis patients without AKI. The biomarker modelling of AKI prediction was done using machine learning, namely Orange Data Mining. In this study, 130 samples were analysed by machine learning. The parameters used to obtain the biomarker panel were 23 laboratory examination parameters. Results: This study used SVM and the Naïve Bayes model of machine learning. The SVM model's sensitivity, specificity, NPV, and PPV were 50%, 94.4%, 71.4%, and 87.5%, respectively. For the Naïve Bayes model, the sensitivity, specificity, NPV, and PPV were 83.3%, 77.8%, 87.5%, and 71.4%, respectively. Discussion: This study's SVM machine learning model has higher AUC and specificity but lower sensitivity. The Naïve Bayes model had better sensitivity; it can be used to predict AKI in sepsis patients. Conclusion: The Naïve Bayes machine learning model in this study is useful for predicting AKI in sepsis patients.

A challenge and solution for automatic thin slice thickness measurements on images of the Catphan phantom.

Purpose. The use of the Hough transform for angle detection is quite accurate for relatively wide slice thickness. However, the Hough transform fails to accurately detect the angle for thin slice thickness. This study proposes a method for automatically measuring the thickness of thin slices on images of a Catphan phantom.Methods. In the proposed method, the angle of the phantom's orientation was determined based on the relative coordinates of the four hole objects in the phantom. After the angles of the wires were determined, the profiles of pixel values across the wire objects were constructed. Finally, their full widths at half maximum (FWHMs) were determined and multiplied bytan23° to obtain the slice thicknesses of the images. The results of the proposed method were compared to a previous method, which used the Hough transform to obtain the phantom's orientation. We used slice thicknesses ranging from 0.8 mm to 5.0 mm, and phantom angles from 0° to 10°.Results. Our proposed method detected the angle of the phantom accurately for thin slices, whereas a previous method did not accurately detect the angle. The results of the slice thickness using this current method were slightly higher (within 7.9%) compared to the previous method. However, the results of the two methods did not differ significantly (p-value > 0.05). Using different angles, the current method detected all the angles more accurately. Again, the slice thicknesses were not significantly different from the previous method (p-value > 0.05).Conclusion. The proposed method for measuring the thickness of thin slices in an image of a Catphan phantom, based on the relative coordinates of the four hole objects in the phantom, outperformed a previous method based on the Hough transform.

Impact of Noise Level on the Accuracy of Automated Measurement of CT Number Linearity on ACR CT and Computational Phantoms.

Background: Methods for segmentation, i.e., Full-segmentation (FS) and Segmentation-rotation (SR), are proposed for maintaining Computed Tomography (CT) number linearity. However, their effectiveness has not yet been tested against noise. Objective: This study aimed to evaluate the influence of noise on the accuracy of CT number linearity of the FS and SR methods on American College of Radiology (ACR) CT and computational phantoms. Material and Methods: This experimental study utilized two phantoms, ACR CT and computational phantoms. An ACR CT phantom was scanned by a 128-slice CT scanner with various tube currents from 80 to 200 mA to acquire various noises, with other constant parameters. The computational phantom was added by different Gaussian noises between 20 and 120 Hounsfield Units (HU). The CT number linearity was measured by the FS and SR methods, and the accuracy of CT number linearity was computed on two phantoms. Results: The two methods successfully segmented both phantoms at low noise, i.e., less than 60 HU. However, segmentation and measurement of CT number linearity are not accurate on a computational phantom using the FS method for more than 60-HU noise. The SR method is still accurate up to 120 HU of noise. Conclusion: The SR method outperformed the FS method to measure the CT number linearity due to its endurance in extreme noise.

Automatic slice thickness measurement on three types of Catphan CT phantoms.

Objective. To develop an algorithm to measure slice thickness running on three types of Catphan phantoms with the ability to adapt to any misalignment and rotation of the phantoms.Method. Images of Catphan 500, 504, and 604 phantoms were examined. In addition, images with various slice thicknesses ranging from 1.5 to 10.0 mm, distance to the iso-center and phantom rotations were also examined. The automatic slice thickness algorithm was carried out by processing only objects within a circle having a diameter of half the diameter of the phantom. A segmentation was performed within an inner circle with dynamic thresholds to produce binary images with wire and bead objects within it. Region properties were used to distinguish wire ramps and bead objects. At each identified wire ramp, the angle was detected using the Hough transform. Profile lines were then placed on each ramp based on the centroid coordinates and detected angles, and the full-width at half maximum (FWHM) was determined for the average profile. The slice thickness was obtained by multiplying the FWHM by the tangent of the ramp angle (23°).Results. Automatic measurements work well and have only a small difference (<0.5 mm) from manual measurements. For slice thickness variation, automatic measurement successfully performs segmentation and correctly locates the profile line on all wire ramps. The results show measured slice thicknesses that are close (<3 mm) to the nominal thickness at thin slices, but slightly deviated for thicker slices. There is a strong correlation (R2= 0.873) between automatic and manual measurements. Testing the algorithm at various distances from the iso-center and phantom rotation angle also produced accurate results.Conclusion. An automated algorithm for measuring slice thickness on three types of Catphan CT phantom images has been developed. The algorithm works well on various thicknesses, distances from the iso-center, and phantom rotations.

Evaluation of silicone rubber-lead shield's effectiveness in protecting the breast during thoracic CT.

Radiation of thoracic computed tomography (CT) involves the breast although it is not considered an organ of interest. According to the International Commission on Radiological Protection (ICRP) No. 103, the breast is an organ with a high level of sensitivity when interacting with x-rays, increasing the potential risk of breast cancer. Therefore, the radiation dose must be optimized while maintaining image quality. The dose optimization can be accomplished using a radiation shield. This study aims to determine the effect of silicone rubber (SR)-lead (Pb) in various thicknesses as an alternative protective material limiting dose and preserving the image quality of the breast in thoracic CT. SR-Pb was made from SR and Pb by a simple method. The SR-Pb had thicknesses of 3, 6, 9, and 12 mm. The breast dose was measured using a CT dose profiler on the surface of the breast phantom. The CT number and the noise level of the resulting image were determined quantitatively. The dose without the radiation shield was 5.4 mGy. The doses measured using shielding with thicknesses of 3, 6, 9, and 12 mm were 5.2, 4.5, 4.3, and 3.3 mGy, respectively. Radiation shielding with a thickness of 12 mm reduced breast surface dose by up to 38%. The CT numbers and noise levels for the left and right breast phantom images were almost the same as those ​​without radiation shields indicating there were only slight artifacts in the image. Therefore, SR-Pb is considered a good shielding material which can be pplied in a clinical setting by placing it directly on the breast surface for dose optimization.

Identification of the computed tomography dose index for tube voltage variations in a polyester-resin phantom.

The aim of this study is to measure the volumetric computed tomography dose index (CTDIvol) for different tube voltages for a polyester-resin (PESR) phantom, and to compare it to values for a standard polymethyl methacrylate (PMMA) phantom. Both phantoms are head phantoms with a diameter of 16 cm. The phantoms were scanned by a CT scanner (GE Revolution EVO 64/128 slice) with tube voltages of 80, 100, 120, and 140 kV. The other scan parameters were constant (i.e. tube current of 100 mA, rotation time of 1 s, and collimation width of 10 mm). The CTDI100,c and CTDI100,p were obtained by measuring the dose with an ionization chamber inserted into five holes within the phantoms. The CTDIvol was calculated based on the CTDI100,c and CTDI100,p values. The measurements were repeated three times for each hole. It was found that the CTDIvol values for the PESR phantom were dependent on tube voltage value, and were similar to the dependency in a PMMA phantom. The maximum CTDIvol difference between the PESR and PMMA phantoms was 7.5%. We conclude that the dose measured in the PESR phantom is similar to that in the PMMA phantom and that the PESR phantom can be used as an alternative if the PMMA phantom is not available.

Predictive Value of Sequential Organ Failure Assessment, Quick Sequential Organ Failure Assessment, Acute Physiology and Chronic Health Evaluation II, and New Early Warning Signs Scores Estimate Mortality of COVID-19 Patients Requiring Intensive Care Unit.

Introduction: Various mortality predictive score models for coronavirus disease-2019 (COVID-19) have been deliberated. We studied how sequential organ failure assessment (SOFA), quick sequential organ failure assessment (qSOFA), acute physiology and chronic health evaluation II (APACHE II), and new early warning signs (NEWS-2) scores estimate mortality in COVID-19 patients. Materials and methods: We conducted a prospective cohort study of 53 patients with moderate-to-severe COVID-19. We calculated qSOFA, SOFA, APACHE II, and NEWS-2 on initial admission and re-evaluated on day 5. We performed logistic regression analysis to differentiate the predictors of qSOFA, SOFA, APACHE II, and NEWS-2 scores on mortality. Result: qSOFA, SOFA, APACHE II, and NEWS-2 scores on day 5 exhibited a difference between survivors and nonsurvivors (p <0.05), also between ICU and non-ICU admission (p <0.05). The initial NEWS-2 revealed a higher AUC value than the qSOFA, APACHE II, and SOFA score in estimating mortality (0.867; 0.83; 0.822; 0.794). In ICU, APACHE II score revealed a higher AUC value than the SOFA, NEWS-2, and qSOFA score (0.853; 0.832; 0.813; 0.809). Concurrently, evaluation on day 5 showed that qSOFA AUC had higher scores than the NEWS-2, APACHE II, and SOFA (0.979; 0.965; 0.939; 0.933) in predicting mortality, while SOFA and APACHE II AUC were higher in ICU admission than NEWS-2 and qSOFA (0.968; 0.964; 0.939; 0.934). According to the cutoff score, APACHE II on day 5 revealed the highest sensitivity and specificity in predicting the mortality (sensitivity 95.7%, specificity 86.7%). Conclusion: All scores signify good predictive values on COVID-19 patients mortality following the evaluation on the day 5. Nonetheless, APACHE-II appears to be the best at predicting mortality and ICU admission rate. How to cite this article: Asmarawati TP, Suryantoro SD, Rosyid AN, Marfiani E, Windradi C, Mahdi BA, et al. Predictive Value of Sequential Organ Failure Assessment, Quick Sequential Organ Failure Assessment, Acute Physiology and Chronic Health Evaluation II, and New Early Warning Signs Scores Estimate Mortality of COVID-19 Patients Requiring Intensive Care Unit. Indian J Crit Care Med 2022;26(4):464-471.

Effect of variation of silicone rubber RTV 52 and bluesil catalyst 60 R composition on bolus material for electron beam radiotherapy application.

A bolus is a material equivalent to soft tissue and is directly placed on the skin surface during radiotherapy. It is commonly used to increase the dose on the skin surface in electron beam radiation. A typical material for a bolus is silicone rubber (SR). We made a bolus with dimensions of 17 × 17 × 1 cm3by varying silicone rubber (SR) RTV 52 and hardening material (bluesil catalyst 60 R) using a simple molded method. We characterized it using a CT scan to find the relative electron density (RED) and examined it using the electron beam of a linear accelerator (LINAC) at energies of 5 and 7 MeV to investigate the percentage of surface dose (PSD). The PSD value is relative to the dose at maximum doses (dmax). The RED value of the bolus was from 1.168 ± 0.021 to 1.176 ± 0.019, higher than the soft tissue (muscle) value of 1.043. The percentage of surface dose (PSD) test at 5 and 7 MeV LINAC energy showed that the highest PSD without using a bolus were 84.79±0.06% and 86.03±0.07%, respectively. With a bolus, the PSD values were 112.52±0.16% and 111.14±0.03%, respectively. The results indicate that bolus fabrication using SR RTV 52 and bluesil 60R is very effective for radiotherapy in the treatment of skin cancer due to an increase in surface dose.

Green Synthesized Silver Nanoparticles Immobilized on Activated Carbon Nanoparticles: Antibacterial Activity Enhancement Study and Its Application on Textiles Fabrics.

This research aimed to enhance the antibacterial activity of silver nanoparticles (AgNPs) synthesized from silver nitrate (AgNO3) using aloe vera extract. It was performed by means of incorporating AgNPs on an activated carbon nanoparticle (ACNPs) under ultrasonic agitation (40 kHz, 2 × 50 watt) for 30 min in an aqueous colloidal medium. The successful AgNPs synthesis was clarified with both Ultraviolet-Visible (UV-Vis) and Fourier Transform Infrared (FTIR) spectrophotometers. The successful AgNPs-ACNPs incorporation and its particle size analysis was performed using Transmission Electron Microscope (TEM). The brown color suspension generation and UV-Vis's spectra maximum wavelength at around 480 nm confirmed the existence of AgNPs. The particle sizes of the produced AgNPs were about 5 to 10 nm in the majority number, which collectively surrounded the aloe vera extract secondary metabolites formed core-shell like nanostructure of 8.20 ± 2.05 nm in average size, while ACNPs themselves were about 20.10 ± 1.52 nm in average size formed particles cluster, and 48.00 ± 8.37 nm in average size as stacking of other particles. The antibacterial activity of the synthesized AgNPs and AgNPs-immobilized ACNPs was 57.58% and 63.64%, respectively (for E. coli); 61.25%, and 93.49%, respectively (for S. aureus). In addition, when the AgNPs-immobilized ACNPs material was coated on the cotton and polyester fabrics, the antibacterial activity of the materials changed, becoming 19.23% (cotton; E. coli), 31.73% (polyester; E. coli), 13.36% (cotton; S. aureus), 21.15% (polyester; S. aureus).

An improved method for automated calculation of the water-equivalent diameter for estimating size-specific dose in CT.

PURPOSE: The aim of this study is to propose an algorithm for the automated calculation of water-equivalent diameter (Dw ) and size-specific dose estimation (SSDE) from clinical computed tomography (CT) images containing one or more substantial body part. METHODS: All CT datasets were retrospectively acquired by the Toshiba Aquilion 128 CT scanner. The proposed algorithm consisted of a contouring stage for the Dw calculation, carried out by taking the six largest objects in the cross-sectional image of the patient's body, followed by the removal of the CT table depending on the center position (y-axis) of each object. Validation of the proposed algorithm used images of patients who had undergone chest examination with both arms raised up, one arm placed down and both arms placed down, images of the pelvic region consisting of one substantial object, and images of the lower extremities consisting of two separated areas. RESULTS: The proposed algorithm gave the same results for Dw and SSDE as the previous algorithm when images consisted of one substantial body part. However, when images consisted of more than one substantial body part, the new algorithm was able to detect all parts of the patient within the image. The Dw values from the proposed algorithm were 9.5%, 15.4%, and 39.6% greater than the previous algorithm for the chest region with one arm placed down, both arms placed down, and images with two legs, respectively. The SSDE values from the proposed algorithm were 8.2%, 11.2%, and 20.6% lower than the previous algorithm for the same images, respectively. CONCLUSIONS: We have presented an improved algorithm for automated calculation of Dw and SSDE. The proposed algorithm is more general and gives accurate results for both Dw and SSDE whether the CT images contain one or more than one substantial body part.

An Improved Method of Automated Noise Measurement System in CT Images.

BACKGROUND: It is necessary to have an automated noise measurement system working accurately to optimize dose in computerized tomography (CT) examinations. OBJECTIVE: This study aims to develop an algorithm to automate noise measurement that can be implemented in CT images of all body regions. MATERIALS AND METHODS: In this retrospective study, our automated noise measurement method consists of three steps as follows: the first is segmenting the image of the patient. The second is developing a standard deviation (SD) map by calculating the SD value for each pixel with a sliding window operation. The third step is estimating the noise as the smallest SD from the SD map. The proposed method was applied to the images of a homogenous phantom and a full body adult anthropomorphic phantom, and retrospectively applied to 27 abdominal images of patients. RESULTS: For a homogeneous phantom, the noises calculated using our proposed and previous algorithms have a linear correlation with R2 = 0.997. It is found that the noise magnitude closely follows the magnitude of the water equivalent diameter (Dw) in all body regions. The proposed algorithm is able to distinguish the noise magnitude due to variations in tube currents and different noise suppression techniques such as strong, standard, mild, and weak ones in a reconstructed image using the AIDR 3D algorithm. CONCLUSION: An automated noise calculation has been proposed and successfully implemented in all body regions. It is not only accurate and easy to implement but also not influenced by the subjectivity of user.

Comparison of central, peripheral, and weighted size-specific dose in CT.

The objective of this study is to determine X-ray dose distribution and the correlation between central, peripheral and weighted-centre peripheral doses for various phantom sizes and tube voltages in computed tomography (CT). We used phantoms developed in-house, with various water-equivalent diameters (Dw) from 8.5 up to 42.1 cm. The phantoms have one hole in the centre and four holes at the periphery. By using these five holes, it is possible to measure the size-specific central dose (Ds,c), peripheral dose (Ds,p), and weighted dose (Ds,w).The phantoms are scanned using a CT scanner (Siemens Somatom Definition AS), with the tube voltage varied from 80 up to 140 kVps. The doses are measured using a pencil ionization chamber (Ray safe X2 CT Sensor) in every hole for all phantoms. The relationships between Ds,c, Ds,p, and Ds,w, and the water-equivalent diameter are established. The size-conversion factors are calculated. Comparisons between Ds,c, Ds,p, and Ds,ware also established. We observe that the dose is relatively homogeneous over the phantom for water-equivalent diameters of 12-14 cm. For water-equivalent diameters less than 12 cm, the dose in the centre is higher than at the periphery, whereas for water-equivalent diameters greater than 14 cm, the dose at the centre is lower than that at the periphery. We also find that the distribution of the doses is influenced by the tube voltage. These dose distributions may be useful for calculating organ doses for specific patients using their CT images in future clinical practice.

Development of a head CT dose index (CTDI) phantom based on polyester resin and methyl ethyl ketone peroxide (MEKP): a preliminary study.

This paper aims to develop phantoms for measurement of computed tomography dose index (CTDI) based on a polyester resin mixed with methyl ethyl ketone peroxide (MEKP) as catalyst. CT number and CTDI values of the polyester resin phantoms were compared with a standard polymethyl methacrylate (PMMA) phantom as reference. The percentage of MEKP was varied from 0.3 to 0.6 wt%. The polyester resin phantoms had diameter of 160 mm, length of 150 mm and five cylindrical holes with diameter of 13.5 mm. One hole was positioned at the centre of the phantom and the other four near its periphery, 10 mm from the edge. The results show that the CT number of the polyester resin phantom was about 1%-9% higher than that of the standard PMMA phantom. Among the polyester resin phantoms, the one with 0.3 wt% MEKP is closest to the standard PMMA phantom in terms of CT number. In addition, the difference in weighted CTDI value between the 0.3 wt% polyester resin phantom and the PMMA is less than 5%. Thus, the 0.3 wt% polyester resin is potentially used as an alternative to the standard PMMA, with the advantage of a lower cost.

An artifact-free thyroid shield in CT examination: a phantom study.

This study was to evaluate dose reduction and resulting image quality of a new synthetic thyroid shield based on silicon rubber (SR)-lead (Pb) composites and compare to tungsten paper (WP) and a Radibabarrier thyroid shields in CT examination of the neck. The synthetic SR-Pb thyroid shield had a Pb percentage from 0 to 5 wt% and a thickness of 0.6 cm. Scanning on the neck of an anthropomorphic phantom was performed with and without the SR-Pb, WP, and Radibarrier thyroid shields. The thyroid shields were placed directly on the neck surface. The thyroid dose was measured using radiophoto-luminescence (RPL) detectors. Image quality was characterized by consistency of the Hounsfield unit (HU) on the areas of anterior, posterior and lateral of the neck phantom. Detailed evaluation of the image quality was employed by image subtraction. It was found that the thyroid dose at the surface decreased with an increase of Pb percentage in the SR-Pb shield. The thyroid dose reduction was 34% for a Pb percentage of 5 wt%. The reduction of the dose using WP and Radibarrier were 36% and 67%, respectively. The dose reduction when using the WP and Radibarrier was higher than when using the SR-Pb 5 wt% thyroid shield. However the existence of artifact in the WP and the Radibarrier reduced the image quality, indicated by a significant change of HU, i.e. the increases of HU in the posterior area were 77% for the WP and 553% for the Radibarrier. The SR-Pb shield produced only a very small artifact, resulting in an increase of HU in the posterior area of only 9%. The SR-Pb shield is suitable in the daily clinical setting for thyroid dose reduction in CT examinations while maintaining image quality.

Development of a computational phantom for validation of automated noise measurement in CT images.

The purpose of this study was to develop a computational phantom for validation of automatic noise calculations applied to all parts of the body, to investigate kernel size in determining noise, and to validate the accuracy of automatic noise calculation for several noise levels. The phantom consisted of objects with a very wide range of HU values, from -1000 to +950. The incremental value for each object was 10 HU. Each object had a size of 15 × 15 pixels separated by a distance of 5 pixels. There was no dominant homogeneous part in the phantom. The image of the phantom was then degraded to mimic the real image quality of CT by convolving it with a point spread function (PSF) and by addition of Gaussian noise. The magnitude of the Gaussian noises was varied (5, 10, 25, 50, 75 and 100 HUs), and they were considered as the ground truth noise (NG). We also used a computational phantom with added actual noise from a CT scanner. The phantom was used to validate the automated noise measurement based on the average of the ten smallest standard deviations (SD) from the standard deviation map (SDM). Kernel sizes from 3 × 3 up to 27 × 27 pixels were examined in this study. A computational phantom for automated noise calculations validation has been successfully developed. It was found that the measured noise (NM) was influenced by the kernel size. For kernels of 15 × 15 pixels or smaller, the NMvalue was much smaller than the NG. For kernel sizes from 17 × 17 to 21 × 21 pixels, the NMvalue was about 90% of NG. And for kernel sizes of 23 × 23 pixels and above, NMis greater than NG. It was also found that even with small kernel sizes the relationship between NMand NGis linear with R2more than 0.995. Thus accurate noise levels can be automatically obtained even with small kernel sizes without any concern regarding the inhomogeneity of the object.

Development of a novel artifact-free eye shield based on silicon rubber-lead composition in the CT examination of the head.

The aim of this work was to develop a novel artifact-free eye shield and evaluate its effect on the dose received by the eye lens and the resulting image quality in the CT examination of the head. A new material for an eye shield was synthesised from silicon rubber (SR) and lead (Pb) using a simple method. The percentage of Pb was varied from 0 to 5% wt. An anthropomorphic head phantom was scanned with and without the SR-Pb eye shield, and compared with a tungsten paper (WP) eye shield. The distance from the eye shield and head was varied from 0 to 5 cm. The dose to the eye lens was measured using photo-luminescence detectors (PLDs). The presence of artifacts was determined by measuring CT numbers at different eye lens locations and by subtracting images with and without the eye shield. The dose reduction increases with increasing Pb content in the SR-Pb eye shield. A 5% wt SR-Pb eye shield reduced the eye lens dose by up to 50%, whereas the WP eye shield reduced the dose by up to 86%. The CT numbers in images with the SR-Pb eye shield in the regions of both eyes and the center of the head phantom is similar to those without the eye shield, indicating that there is no artifact in the resulting image. Using the WP eye shield, there is considerable artifact with the CT number increasing by up to 700% in the regions of both eyes and the center of the head. It is found that the distance between the SR-Pb eye shield and the head does not affect either the dose or the resulting images. A SR-Pb-based eye shield can be applied in clinical environments and should be placed directly above the eye surface for dose optimisation.

Assessment of patient dose and noise level of clinical CT images: automated measurements.

We investigated comparisons between patient dose and noise in pelvic, abdominal, thoracic and head CT images using an automatic method. 113 patient images (37 pelvis, 34 abdominal, 25 thoracic, and 17 head examinations) were retrospectively and automatically examined in this study. Water-equivalent diameter (Dw), size-specific dose estimates (SSDE) and noise were automatically calculated from the center slice for every patient image. The Dw was calculated based on auto-contouring of the patients' edges, and the SSDE was calculated as the product of the volume CT dose index (CTDIvol) extracted from the Digital Imaging and Communications in Medicine (DICOM) header and the size conversion factor based on the Dw obtained from AAPM 204. The noise was automatically measured as a minimum standard deviation in the map of standard deviations. A square region of interest of about 1 cm2 was used in the automated noise measurement. The SSDE values for the pelvis, abdomen, thorax, and head were 21.8 ± 7.3 mGy, 22.0 ± 4.5 mGy, 21.5 ± 4.7 mGy, and 65.1 ± 1.7 mGy, respectively. The SSDEs for the pelvis, abdomen, and thorax increased linearly with increasing Dw, and for the head with constant tube current, the SSDE decreased with increasing Dw. The noise in the pelvis, abdomen, thorax, and head were 5.9 ± 1.5 HU, 5.2 ± 1.4 HU, 4.9 ± 0.8 HU and 3.9 ± 0.2 HU, respectively. The noise levels for the pelvis, abdomen, and thorax of the patients were relatively constant with Dw because of tube current modulation. The noise in the head image was also relatively constant because Dw variations in the head are very small. The automated approach provides a convenient and objective tool for dose optimizations.