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.

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.

Estimation and comparison of the effective dose and lifetime attributable risk of thyroid cancer between males and females in routine head computed tomography scans: a multicentre study.

INTRODUCTION: A significant number of head computed tomography (CT) scans are performed annually. However, due to the close proximity of the thyroid gland to the radiation field, this procedure can expose the gland to ionising radiation. Consequently, this study aimed to estimate organ dose, effective dose (ED) and lifetime attributable risk (LAR) of thyroid cancer from head CT scans in adults. METHODS: Head CT scans of 74 patients (38 males and 36 females) were collected using three different CT scanners. Age, sex, and scanning parameters, including scan length, tube current-time product (mAs), pitch, CT dose index, and dose-length product (DLP) were collected. CT-Expo software was used to calculate thyroid dose and ED for each patient based on scan parameters. LARs were subsequently computed using the methodology presented in the Biologic Effects of Ionizing Radiation (BEIR) Phase VII report. RESULTS: Although the mean thyroid organ dose (2.66 ± 1.03 mGy) and ED (1.6 ± 0.4 mSv) were slightly higher in females, these differences were not statistically significant compared to males (mean thyroid dose, 2.52 ± 1.31 mGy; mean ED, 1.5 ± 0.4 mSv). Conversely, there was a significant difference between the mean thyroid LAR of females (0.91 ± 1.35) and males (0.20136 ± 0.29) (P = 0.001). However, the influencing parameters were virtually identical for both groups. CONCLUSIONS: The study's results indicate that females have a higher LAR than males, which can be attributed to higher radiation sensitivity of the thyroid in females. Thus, additional care in the choice of scan parameters and irradiated scan field for female patients is recommended.

Algorithm development for automatic laser alignment assessment on an ACR CT phantom and its evaluation on sixteen CT scanners.

Purpose. The aim of this study is to develop software to automatically assess the laser alignment on the ACR CT phantom and evaluate its accuracy on sixteen CT scanners.Methods. Software for an automated method of laser alignment assessment on the ACR CT phantom was developed. Laser alignment assessment was based on the positions of the ball-bearing markers at the edge of the ACR CT phantom. The automatic assessment was performed using several steps, including segmentation to acquire the coordinates of the ball-bearing markers and determination of the distances between lines connecting them with lines through the center of the image. A comparison of the results from the automatic method with those from the manual method was performed. The manual measurements were carried out using MicroDicom Viewer. A Mann-Whitney U test was performed to determine the statistical difference between both methods. The evaluation was performed on images of the ACR CT phantom scanned with 16 CT scanners from 5 different CT manufacturers.Results. The results confirmed that our software successfully segments the ball-bearing markers and determines the laser alignment assessment on the ACR CT phantom. Evaluation of the algorithm with images from the 16 CT scanners revealed that the difference between the results from automatic and manual methods were about 0.2 mm with apvalue of around 0.7 (no statistical difference). Misalignment in they-axis was larger than the misalignment in the x-axisfor the majority of the scanners tested. It was found that the phantom tended to be placed 2 mm higher than the iso-center.Conclusions. Software to automatically assess CT laser alignment with the ACR CT phantom was successfully developed and evaluated. The automatic assessment was comparable to manual assessment. In addition, the automatic method was user independent and fast.

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.

Automatic measurement of slice thickness in CT images of a Siemens phantom.

This study aims to develop a program in Python language for automatic measurement of slice thickness in computed tomography (CT) images of a Siemens phantom with different values of slice thickness, field of view (FOV), and pitch. A Siemens phantom was scanned using a Siemens 64-slice Somatom Perspective CT scanner with various slice thicknesses (i.e. 2, 4, 6, 8, and 10 mm), FOVs (i.e. 220, 260, and 300 mm), and pitch (i.e. 0.7, 0.9, and 1). Automatic measurement of slice thickness was performed by segmenting the ramp insert in the image and detecting angles of the ramp insert using the Hough transform. The resulting angles were subsequently used to rotate the image. Profiles of pixel along the ramp insert were made from the rotated images, and the slice thickness was calculated by determining the full-width at half maximum (FWHM) of the profiles. The product of the FWHM in pixels and the pixel size was corrected by the tangent of the ramp insert (i.e., 23°) to obtain the measured slice thickness. The results of the automatic measurements were compared with manual measurements carried out using a MicroDicom Viewer. The differences between the automatic and manual measurements at all slice thicknesses were less than 0.30 mm. The automatic and manual measurements had high linear correlations. For variations of the FOV and pitch, the differences between the automatic and manual measurement were less than 0.16 mm. The automatic and manual measurements were significantly different (p-value < 0.05) for slice thickness variation. In addition, the automatic and manual measurements were not significantly different (p-value > 0.05) for variations of FOV and pitch.

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.

The automated measurement of CT number linearity using an ACR accreditation phantom.

We developed a software to automatically measure the linearity between the CT numbers and densities of objects using an ACR 464 CT phantom, and investigated the CT number linearity of 16 different CT scanners. The software included a segmentation-rotation method. After segmenting five objects within the phantom image, the software computed the mean CT number of each object and plotted a graph between the CT numbers and densities of the objects. Linear regression and coefficients of regression, R2, were automatically calculated. The software was used to investigate the CT number linearity of 16 CT scanners from Toshiba, Siemens, Hitachi, and GE installed at 16 hospitals in Indonesia. The linearity of the CT number obtained on most of the scanners showed a strong linear correlation (R2> 0.99) between the CT numbers and densities of the five phantom materials. Two scanners (Siemens Emotion 16) had the strongest linear correlation withR2= 0.999, and two Hitachi Eclos scanners had the weakest linear correlation withR2< 0.99.

Impact of ROI Size on the Accuracy of Noise Measurement in CT on Computational and ACR Phantoms.

Background: The effect of region of interest (ROI) size variation on producing accurate noise levels is not yet studied. Objective: This study aimed to evaluate the influence of ROI sizes on the accuracy of noise measurement in computed tomography (CT) by using images of a computational and American College of Radiology (ACR) phantoms. Material and Methods: In this experimental study, two phantoms were used, including computational and ACR phantoms. A computational phantom was developed by using Matlab R215a software (Mathworks Inc., Natick, MA Natick, MA) with a homogeneously +100 Hounsfield Unit (HU) value and an added-Gaussian noise with various levels of 5, 10, 25, 50, 75, and 100 HU. The ACR phantom was scanned with a Philips MX-16 slice CT scanner in different slice thicknesses of 1.5, 3, 5, and 7 mm to obtain noise variation. Noise measurement was conducted at the center of the phantom images and four locations close to the edge of the phantom images using different ROI sizes from 3 × 3 to 41 × 41 pixels, with an increased size of 2 × 2 pixels. Results: The use of a minimum ROI size of 21 × 21 pixels shows noise in the range of ± 5% ground truth noise. The measured noise increases above the ± 5% range if the used ROI is smaller than 21 × 21 pixels. Conclusion: A minimum acceptable ROI size is required to maintain the accuracy of noise measurement with a size of 21 × 21 pixels.

Real-time fully automated dosimetric computation for CT images in the clinical workflow: A feasibility study.

Background: Currently, the volume computed tomography dose index (CTDIvol), the most-used quantity to express the output dose of a computed tomography (CT) patient's dose, is not related to the real size and attenuation properties of each patient. The size-specific dose estimates (SSDE), based on the water-equivalent diameter (D W) overcome those issues. The proposed methods found in the literature do not allow real-time computation of D W and SSDE. Purpose: This study aims to develop a software to compute D W and SSDE in a real-time clinical workflow. Method: In total, 430 CT studies and scans of a water-filled funnel phantom were used to compute accuracy and evaluate the times required to compute the D W and SSDE. Two one-sided tests (TOST) equivalence test, Bland-Altman analysis, and bootstrap-based confidence interval estimations were used to evaluate the differences between actual diameter and D W computed automatically and between D W computed automatically and manually. Results: The mean difference between the D W computed automatically and the actual water diameter for each slice is -0.027% with a TOST confidence interval equal to [-0.087%, 0.033%]. Bland-Altman bias is -0.009% [-0.016%, -0.001%] with lower limits of agreement (LoA) equal to -0.0010 [-0.094%, -0.068%] and upper LoA equal to 0.064% [0.051%, 0.077%]. The mean difference between D W computed automatically and manually is -0.014% with a TOST confidence interval equal to [-0.056%, 0.028%] on phantom and 0.41% with a TOST confidence interval equal to [0.358%, 0.462%] on real patients. The mean time to process a single image is 13.99 ms [13.69 ms, 14.30 ms], and the mean time to process an entire study is 11.5 s [10.62 s, 12.63 s]. Conclusion: The system shows that it is possible to have highly accurate D W and SSDE in almost real-time without affecting the clinical workflow of CT examinations.

Automated development of the contrast-detail curve based on statistical low-contrast detectability in CT images.

PURPOSE: We have developed a software to automatically find the contrast-detail (C-D) curve based on the statistical low-contrast detectability (LCD) in images of computed tomography (CT) phantoms at multiple cell sizes and to generate minimum detectable contrast (MDC) characteristics. METHODS: A simple graphical user interface was developed to set the initial parameters needed to create multiple grid region of interest of various cell sizes with a 2-pixel increment. For each cell in the grid, the average CT number was calculated to obtain the standard deviation (SD). Detectability was then calculated by multiplying the SD of the mean CT numbers by 3.29. This process was automatically repeated as many times as the cell size was set at initialization. Based on the obtained LCD, the C-D curve was obtained and the target size at an MDC of 0.6% (i.e., 6-HU difference) was determined. We subsequently investigated the consistency of the target sizes for a 0.6% MDC at four locations within the homogeneous image. We applied the software to images with six noise levels, images of two modules of the American College of Radiology CT phantom, images of four different phantoms, and images of four different CT scanners. We compared the target sizes at a 0.6% MDC based on the statistical LCD and the results from a human observer. RESULTS: The developed system was able to measure C-D curves from different phantoms and scanners. We found that the C-D curves follow a power-law fit. We found that higher noise levels resulted in a higher MDC for a target of the same size. The low-contrast module image had a slightly higher MDC than the distance module image. The minimum size of an object detected by visual observation was slightly larger than the size using statistical LCD. CONCLUSIONS: The statistical LCD measurement method can generate a C-D curve automatically, quickly, and objectively.

Visualization of dose distribution and basic study of dose estimation using plastic scintillator and digital camera.

Radiation can be visualized using a scintillator and a digital camera. If the amount of light emitted by the scintillator increases with dose, the dose estimation can be obtained from the amount of light emitted. In this study, the basic performance of the scintillator and digital camera system was evaluated by measuring computed tomography dose index (CTDI). A circular plastic scintillator plate was sandwiched between polymethyl methacrylate (PMMA) phantoms, and x-rays were irradiated to them while rotating the x-ray tube to confirm changes in light emission. In addition, CTDI was estimated from the amount of light emitted by the scintillator during the helical scan and compared with the value measured from dosimeter. The scintillator emitted light while changing its distribution according to the movement of the x-ray tube. The measured CTDIvolwas 33.20 mGy, the CTDIvolestimated from the scintillation light was approximately 46 mGy, which was 40% larger. In particular, when the scintillator was directly irradiated, the dose was overestimated compared with the value measured from the dosimeter. This overestimation can be because of the reproducibility of the position and the difference between the sensitivity of the scintillator to detect light emission and the sensitivity of the dosimeter, and the non-uniformity of position sensitivity due to the wide-angle lens.

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.

Investigation of Eye Lens Dose Estimate based on AAPM Report 293 in Head Computed Tomography.

BACKGROUND: Estimation of eye lens dose is important in head computed tomography (CT) examination since the eye lens is a sensitive organ to ionizing radiation. OBJECTIVE: The purpose of this study is to compare estimations of eye lens dose in head CT examinations using local size-specific dose estimate (SSDE) based on size-conversion factors of the American Association of Physicists in Medicine (AAPM) Report No. 293 with those based on size-conversion factors of the AAPM Report No. 220. MATERIAL AND METHODS: This experimental study is conducted on a group of patients who had undergone nasopharyngeal CT examination. Due to the longitudinal (z-axis) dose fluctuation, the average global SSDE and average local SSDE (i.e. particular slices where the eyes are located) were investigated. All estimates were compared to the measurement results using thermo-luminescent dosimeters (TLDs). The estimated and measured doses were implemented for 14 patients undergoing nasopharyngeal CT examination. RESULTS: It was found that the percentage differences of the volume CT dose index (CTDIvol), average global SSDE based on AAPM No. 220 (SSDEo,g), average local SSDE based on AAPM No. 220 (SSDEo,l), average global SSDE based on AAPM No. 293 (SSDEn,g) and average local SSDE based on AAPM No. 293 (SSDEn,l) against the measured TLD doses were 22.5, 21.7, 15.0, 9.3, and 2.1%, respectively. All comparisons between dose estimates and TLD measurements gave p-values less than 0.001, except for SSDEn,l (p-value = 0.566). CONCLUSION: SSDE based on AAPM Report No. 293 can be used to accurately estimate eye lens radiation doses by performing the calculations on a number of specific slices containing the eyes.

CORRELATION BETWEEN ANTERIOR-POSTERIOR AND LATERAL DIMENSIONS WITH THE EFFECTIVE AND WATER-EQUIVALENT DIAMETERS IN AXIAL IMAGES FROM HEAD COMPUTED TOMOGRAPHY EXAMINATIONS.

The study aims to correlate the effective diameter (Deff) and water-equivalent diameter (Dw) parameters with anterior-posterior (AP), lateral (LAT) and AP + LAT dimensions in order to estimate the patient dose in head CT examinations. Seventy-four patient datasets from head CT examinations were retrospectively collected. The patient's sizes were calculated from the middle slice using a software of IndoseCT. Dw and Deff were plotted as functions of AP, LAT and AP + LAT dimensions. The best trendline fit for LAT and AP functions was a second order polynomial, which resulted in R2 of 0.89 for Deff vs LAT, 0.88 for Dw vs LAT, 0.92 for Deff vs AP and 0.91 for Dw vs AP. A linear correlation was found for Deff vs AP + LAT, Dw vs AP + LAT and Dw vs Deff with R2 of 0.97, 0.96 and 0.98, respectively.

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.

Automated procedure for slice thickness verification of computed tomography images: Variations of slice thickness, position from iso-center, and reconstruction filter.

PURPOSE: The purpose of this study is to automate the slice thickness verification on the AAPM CT performance phantom and validate it for variations of slice thickness, position from iso-center, and reconstruction filter. METHODS: An automatic procedure for slice thickness verification on AAPM CT performance phantom was developed using MATLAB R2015b. The stair object image within the phantom was segmented, and the middle stair object was located. Its angle was determined using the Hough transformation, and the image was rotated accordingly. The profile through this object was obtained, and its full-width of half maximum (FWHM) was automatically measured. The FWHM indicated the slice thickness of the image. The automated procedure was applied with variations in three independent parameters, i.e., the slice thickness, the distance from the phantom to the iso-center, and the reconstruction filter. The automated results were compared to manual measurements made using electronic calipers. RESULTS: The differences of the automated results from the nominal slice thicknesses were within 1.0 mm. The automated results are comparable to those from manual approach (i.e., the difference of both is within 12%). The automatic procedure accurately obtained slice thickness even when the phantom was moved from the iso-center position by up to 4 cm above and 4 cm below the iso-center. The automated results were similar (to within 0.1 mm) for various reconstruction filters. CONCLUSIONS: We successfully developed an automated procedure of slice thickness verification and confirmed that the automated procedure provided accurate results. It provided an easy and effective method of determining slice thickness.

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.