A Practical Method for Slice Spacing Measurement Using the American Association of Physicists in Medicine Computed Tomography Performance Phantom.

Background: The slice spacing has a crucial role in the accuracy of computed tomography (CT) images in sagittal and coronal planes. However, there is no practical method for measuring the accuracy of the slice spacing. Purpose: This study proposes a novel method to automatically measure the slice spacing using the American Association of Physicists in Medicine (AAPM) CT performance phantom. Methods: The AAPM CT performance phantom module 610-04 was used to measure slice spacing. The process of slice spacing measurement involves a pair of axial images of the module containing ramp aluminum objects located at adjacent slice positions. The middle aluminum plate of each image was automatically segmented. Next, the two segmented images were combined to produce one image with two stair objects. The centroid coordinates of two stair objects were automatically determined. Subsequently, the distance between these two centroids was measured to directly indicate the slice spacing. For comparison, the slice spacing was calculated by accessing the slice position attributes from the DICOM header of both images. The proposed method was tested on phantom images with variations in slice spacing and field of view (FOV). Results: The results showed that the automatic measurement of slice spacing was quite accurate for all variations of slice spacing and FOV, with average differences of 9.0% and 9.3%, respectively. Conclusion: A new automated method for measuring the slice spacing using the AAPM CT phantom was successfully demonstrated and tested for variations of slice spacing and FOV. Slice spacing measurement may be considered an additional parameter to be checked in addition to other established parameters.

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.

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.

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.

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.