The purpose of this study is to accurately and effectively automate the calculation of the water‐equivalent diameter () from 3D CT images for estimating the size‐specific dose. is the metric that characterizes the patient size and attenuation. In this study, was calculated for standard CTDI phantoms and patient images. Two types of phantom were used, one representing the head with a diameter of 16 cm and the other representing the body with a diameter of 32 cm. Images of 63 patients were also taken, 32 who had undergone a CT head examination and 31 who had undergone a CT thorax examination. There are three main parts to our algorithm for automated calculation. The first part is to read 3D images and convert the CT data into Hounsfield units (HU). The second part is to find the contour of the phantoms or patients automatically. And the third part is to automate the calculation of based on the automated contouring for every slice (). The results of this study show that the automated calculation of and the manual calculation are in good agreement for phantoms and patients. The differences between the automated calculation of and the manual calculation are less than 0.5%. The results of this study also show that the estimating of using (central slice along longitudinal axis) produces percentage differences of and , and estimating using produces percentage differences of and , for thorax and head examinations, respectively. From this study, the percentage differences between normalized size‐specific dose estimate for every slice () and are and for thorax and head examinations, respectively; between and are and for thorax and head examinations, respectively.PACS number(s): 87.57.Q‐, 87.57.uq‐

Purpose: The purpose of this study was to introduce a new algorithm for automated measurement of the modulation transfer function (MTF) using an edge of a readily available phantom and to evaluate the effect of reconstruction filter and field of view (FOV) on the spatial resolution in the CT images.Methods: Our automated MTF measurement consisted of several steps. The center of the image was established and an appropriate region of interest (ROI) designated. The edge spread function (ESF) was determined, and a suitably interpolated ESF curve was differentiated to obtain the line spread function (LSF). The LSF was Fourier transformed to obtain the MTF. All these steps were accomplished automatically without user intervention. The results of the automated MTF from the edge phantom were validated by comparing them with a point image, and the results of the automated calculation were validated by the standard fitting method. The automated MTF calculation was then applied to the images of two polymethyl methacrylate (PMMA) phantoms and a wire phantom which had been scanned by a Toshiba Alexion 4‐slice CT scanner and reconstructed with various filter types and FOVs.Results: The difference in the 50% MTF values obtained from the edge and point phantoms were within ±4%. The values from the automated and fitted methods agreed to within ±2%, indicating that the automated MTF calculation was accurate. The automated MTF calculation was able to differentiate MTF curves for various filters. The spatial resolution values were 0.37 ± 0.00, 0.71 ± 0.01, and 0.78 ± 0.01 cycles/mm for FC13, FC30 and FC52 filters, respectively. The spatial resolution of the images decrease linearly (R² > 0.98) with increasing FOVs.Conclusion: An automated MTF method was successfully developed using an edge phantom, the PMMA phantom. The method is easy to implement in a clinical environment and is not influenced by user experience.

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.

The purpose of this study is to investigate truncated axial computed tomography (CT) images in the clinical environment and to produce correction factors for abdomen, thoracic and head regions based on clinical data, in order to accurately predict the water-equivalent diameter (D_W) and size-specific dose estimate (SSDE). We investigated axial images of 75 patients who underwent CT examinations. Truncated axial images were characterized by the truncation percentage (TP). Correction factors were calculated by using the value of D_W for a certain TP (truncated image) divided by the value of D_W for TP = 0% (the non-truncated image). Most of the thorax images acquired for this study were truncated images (86.2%), in the abdomen region about half of the images were truncated (48.1%), and in the head region only a small portion were truncated (9.1%). In the thorax region the value of TP for the truncated images varied up to 50%, in the abdomen region it varied up to 35%, and in the head region it was smaller than 10%. We have shown how to accurately estimate D_W and SSDE by applying a correction factor to the truncated images. The correction factors increase exponentially with increasing TP. The corrected D_W and SSDE for the truncated images were significant in the thoracic region, but were not significant in the abdomen and head regions.

Background: Noise reduction is a method for reducing CT dose; however, it can reduce image quality. Objective: This study aims to propose a selective mean filter (SMF) and evaluate its effectiveness for noise suppression in CT images.Material and Methods: This experimental study proposed and implemented the new noise reduction algorithm. The proposed algorithm is based on a mean filter (MF), but the calculation of the mean pixel value using the neighboring pixels in a kernel selectively applied a threshold value based on the noise of the image. The SMF method was evaluated using images of phantoms. The dose reduction was estimated by comparing the image noise acquired with a lower dose after implementing the SMF method and the noise in the original image acquired with a higher dose. For comparison, the images were also filtered with an adaptive mean filter (AMF) and a bilateral filter (BF). Results: The spatial resolution of the image filtered with the SMF was similar to the original images and the images filtered with the BF. While using the AMF, spatial resolution was significantly corrupted. The noise reduction achieved using the SMF was up to 75%, while it was up to 50% using the BF. Conclusion: SMF significantly reduces the noise and preserves the spatial resolution of the image. The noise reduction was more pronounced with BF, and less pronounced with AMF.

A fully automated calculation of size-specific dose estimates (SSDE) in thoracic and head CT examinations View the table of contents for this issue, or go to the journal homepage for more 2016 J. Phys.: Conf. Ser. 694 012030