Choirul Anam

Automated Calculation of Water-equivalent Diameter (DW) Based on AAPM Task Group 220

The purpose of this study is to accurately and effectively automate the calculation of the water‐equivalent diameter (DW) from 3D CT images for estimating the size‐specific dose. DW is the metric that characterizes the patient size and attenuation. In this study, DW 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 DW 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 DW based on the automated contouring for every slice (DW,all). The results of this study show that the automated calculation of DW and the manual calculation are in good agreement for phantoms and patients. The differences between the automated calculation of DW and the manual calculation are less than 0.5%. The results of this study also show that the estimating of DW,all using DW,n=1 (central slice along longitudinal axis) produces percentage differences of −0.92%±3.37% and 6.75%±1.92%, and estimating DW,all using DW,n=9 produces percentage differences of 0.23%±0.16% and 0.87%±0.36%, for thorax and head examinations, respectively. From this study, the percentage differences between normalized size‐specific dose estimate for every slice (nSSDEall) and nSSDEn=1 are 0.74%±2.82% and −4.35%±1.18% for thorax and head examinations, respectively; between nSSDEall and nSSDEn=9 are 0.00%±0.46% and −0.60%±0.24% for thorax and head examinations, respectively. PACS number(s): 87.57.Q‐, 87.57.uq‐The purpose of this study is to accurately and effectively automate the calculation of the water-equivalent diameter (DW) from 3D CT images for estimating the size-specific dose. DW is the metric that characterizes the patient size and attenuation. In this study, DW 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 DW 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 DW based on the automated contouring for every slice (DW,all). The results of this study show that the automated calculation of DW and the manual calculation are in good agreement for phantoms and patients. The differences between the automated calculation of DW and the manual calculation are less than 0.5%. The results of this study also show that the estimating of DW,all using DW,n=1 (central slice along longitudinal axis) produces percentage differences of -0.92%±3.37% and 6.75%±1.92%, and estimating DW,all using DW,n=9 produces percentage differences of 0.23%±0.16% and 0.87%±0.36%, for thorax and head examinations, respectively. From this study, the percentage differences between normalized size-specific dose estimate for every slice (nSSDEall) and nSSDEn=1 are 0.74%±2.82% and -4.35%±1.18% for thorax and head examinations, respectively; between nSSDEall and nSSDEn=9 are 0.00%±0.46% and -0.60%±0.24% for thorax and head examinations, respectively. PACS number(s): 87.57.Q-, 87.57.uq.

A fully automated calculation of size-specific dose estimates (SSDE) in thoracic and head CT examinations

The purpose of this study is to automatically calculate and then investigate the size- specific dose estimate (SSDE) in thoracic and head CT examinations undertaken using standard imaging protocols. The effective diameter (Deff ), the water equivalent diameter (Dw ), and the SSDE were calculated automatically from patient images. We investigated sixteen adult patients who underwent a CT head examination and thirty adult patients who underwent a CT thorax examination. Our results showed that the Dw value in the thoracic region was 4.5% lower than the value of Deff , while the Dw value in the head region was 8.6% higher than the value of Deff . The relationships between diameter (Deff and Dw ) and CTDIvol were distinctive. In the head region, decreasing the patient diameter resulted in a constant CTDIvol due to the tube current modulation (TCM) being off, while in the thoracic region decreasing the patient diameter resulted in a decrease in value of CTDIvol due to TCM being on. In the head region, decreasing the patient diameter resulted in an increase in the value of SSDE, while in the thoracic region decreasing the patient diameter resulted in a decrease in the value of SSDE.

Profile of CT scan output dose in axial and helical modes using convolution

The profile of the CT scan output dose is crucial for establishing the patient dose profile. The purpose of this study is to investigate the profile of the CT scan output dose in both axial and helical modes using convolution. A single scan output dose profile (SSDP) in the center of a head phantom was measured using a solid-state detector. The multiple scan output dose profile (MSDP) in the axial mode was calculated using convolution between SSDP and delta function, whereas for the helical mode MSDP was calculated using convolution between SSDP and the rectangular function. MSDPs were calculated for a number of scans (5, 10, 15, 20 and 25). The multiple scan average dose (MSAD) for differing numbers of scans was compared to the value of CT dose index (CTDI). Finally, the edge values of MSDP for every scan number were compared to the corresponding MSAD values. MSDPs were successfully generated by using convolution between a SSDP and the appropriate function. We found that CTDI only accurately estimates MSAD when the number of scans was more than 10. We also found that the edge values of the profiles were 42% to 93% lower than that the corresponding MSADs.

A SIMPLE METHOD FOR CALIBRATING PIXEL VALUES OF THE CT LOCALIZER RADIOGRAPH FOR CALCULATING WATER-EQUIVALENT DIAMETER AND SIZE-SPECIFIC DOSE ESTIMATE

The purpose of this study is to establish the relationship between the pixel value (I) of the CT localizer radiograph and water-equivalent thickness (tw) in a straightforward procedure. We used a body CTDI phantom, which was scanned in the AP and LAT projections. After transformation from the pixel values of the images to tw, water-equivalent diameter (Dw) and size-specific dose estimate were calculated on an anthropomorphic phantom and 30 patients retrospectively. We found a linear correlation between I and tw, with R2 ≥ 0.980. The Dw values based on the CT localizer radiograph were comparable to those calculated using axial images. The Dw difference for the anthropomorphic phantom between AP projection and axial images was 5.4 ± 4.2%, and between LAT projection and axial images was 6.7 ± 5.3%. The Dw differences for the patients between CT localizer radiograph and axial images was 2.3 ± 3.2%.

An algorithm for automated modulation transfer function measurement using an edge of a PMMA phantom: Impact of field of view on spatial resolution of CT images

Abstract 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 (R 2 > 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.

New noise reduction method for reducing CT scan dose: Combining Wiener filtering and edge detection algorithm

New noise reduction method for reducing dose of CT scans has been proposed. The new method is expected to address the major problems in the noise reduction algorithm, i.e. the decreasing in the spatial resolution of the image. The proposed method was developed by combining adaptive Wiener filtering and edge detection algorithms. The first step, the image was filtered with a Wiener filter. Separately, edge detection operation performed on the original image using the Prewitt method. The next step, a new image was generated based on the edge detection operation. At the edge area, the image was taken from the original image, while at the non-edge area, the image was taken from the image that had been filtered with a Wiener filter. The new method was tested on a CT image of the spatial resolution phantom, which was scanned by different current-time multiplication, namely 80, 130 and 200 mAs, while other exposure factors were kept in constant conditions. The spatial resolution phantom consists of six sets of bar pattern made of plexi-glass and separated at some distance by water. The new image quality assessed from the amount of noise and the magnitude of spatial resolution. Noise was calculated by determining the standard deviation of the homogeneous regions, while the spatial resolution was assessed by observation of the area sets of the bar pattern. In addition, to evaluate the performance of this new method has also been tested on patient CT images. From the measurements, the new method can reduce the noise to an average 64.85%, with a spatial resolution does not decrease significantly. Visually, the third set bar on the image phantom (the distance between the bar 1.0 mm) can still be distinguished, as well as on the original image. Meanwhile, if the image is only processed using Wiener filter, the second set bar (the distance between the bar 1.3 mm) are distinguishable. Testing this new method to patient image, its results in relatively the same. Thus, using this new method, it provides the potential for dose reduction more than 60%.New noise reduction method for reducing dose of CT scans has been proposed. The new method is expected to address the major problems in the noise reduction algorithm, i.e. the decreasing in the spatial resolution of the image. The proposed method was developed by combining adaptive Wiener filtering and edge detection algorithms. The first step, the image was filtered with a Wiener filter. Separately, edge detection operation performed on the original image using the Prewitt method. The next step, a new image was generated based on the edge detection operation. At the edge area, the image was taken from the original image, while at the non-edge area, the image was taken from the image that had been filtered with a Wiener filter. The new method was tested on a CT image of the spatial resolution phantom, which was scanned by different current-time multiplication, namely 80, 130 and 200 mAs, while other exposure factors were kept in constant conditions. The spatial resolution phantom consists of six sets of b...