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

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.

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.

Automation of slice thickness measurements in computed tomography images of AAPM CT performance phantom using a non-rotational method

Purpose: The current study proposes a method for automatically measuring slice thickness using a non-rotational method on the middle stair object of the AAPM CT performance phantom image.

Method: The AAPM CT performance phantom was scanned by a GE Healthcare 128-slice CT scanner with nominal slice thicknesses of 0.625, 1.25, 2.5, 3.75, 5, 7.5 and 10 mm. The automated slice thickness was measured as the full width at half maximum (FWHM) of the profile of the middle stair object using a non-rotational method. The non-rotational method avoided rotating the image of the phantom. Instead, the lines to make the profiles were automatically rotated to confirm the stair’s location and rotation. The results of this non-rotational method were compared with those from a previous rotational method.

Results: The slice thicknesses from the non-rotational method were 1.55, 1.86, 3.27, 4.86, 6.58, 7.57, and 9.66 mm for nominal slice thicknesses of 0.625, 1.25, 2.4, 3.75, 5, 7.5, and 10 mm, respectively. By comparison, the slice thicknesses from the rotational method were 1.53, 1.87, 3.32, 4.98, 6.77, 7.75, and 9.80 mm, respectively. The results of the nonrotational method were slightly lower (i.e. 0.25%) than the results of the rotational method for each nominal slice thickness, except for the smallest slice thickness.

Conclusions: An alternative algorithm using a non-rotational method to measure the slice thickness of the middle stair object in the AAPM CT performance phantom was successfully implemented. The slice thicknesses from the nonrotational method results were slightly lower than the rotational method results for each nominal slice thickness, except at the smallest nominal slice thickness (0.625 mm).

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.

Root canal length estimated by cone-beam computed tomography at different slice thicknesses, dedicated endodontic software, or measured by an electronic apex locator

The aim of the present study was to evaluate the agreements between the root canal length estimations using cone-beam computed tomography (CBCT) at different slice thicknesses, dedicated software, or an electronic apex locator (EAL) and the actual lengths (AL). In total, 111 extracted human molars with 302 root canals were chosen. Teeth were scanned using a CBCT device at a voxel size of 0.075 mm. Root canal lengths were estimated using CBCT software at different slice thicknesses (0.6, 1.2, and 2.4 mm) and dedicated software for proposed or operator lengths. The endodontic access cavities were created, and root canal lengths were estimated with an EAL for electronic length (EL) and a ruler for AL. Data were tested using paired t-tests and Bland–Altman plots to detect the differences between the methods in length estimation at a significance of 0.05. The accuracy in the range of ± 0.5 mm was 100% for the EAL. There was an agreement between the EL and CBCT at a slice thickness of 1.2 mm (p = 0.349). CBCT at the smallest slice thickness estimation was not the best modality in agreement with the AL. The EAL was an accurate and reliable method for root canal length measurement.