• Journal of Biomedical Engineering Research
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• v.25 no.1
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• pp.33-41
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• 2004

Accuracy of registration between images acquired from various medical image modalities is one of the critical issues in radiation treatment planing. In this study, a method of accuracy evaluation of image registration using a homemade brain phantom was investigated. Chamfer matching of CT-MR and CT-SPECT imaging was applied for the multimodal image registration. The accuracy of image correlation was evaluated by comparing the center points of the inserted targets of the phantom. The three dimensional root-mean-square translation deviations of the CT-MR and CT-SPECT registration were 2.1${\pm}$0.8 mm and 2.8${\pm}$1.4 mm, respectively. The rotational errors were ＜ 2$^{\circ}$ for the three orthogonal axes. These errors were within a reasonable margin compared with the previous phantom studies. A visual inspection of the superimposed CT-MR and CT- SPECT images also showed good matching results.

• Korean Journal of Radiology
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• v.24 no.10
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• pp.1006-1016
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• 2023

Objective: Computed tomography (CT) is an established method for the diagnosis, staging, and treatment of multiple myeloma. Here, we investigated the potential of photon-counting detector computed tomography (PCD-CT) in terms of image quality, diagnostic confidence, and radiation dose compared with energy-integrating detector CT (EID-CT). Materials and Methods: In this prospective study, patients with known multiple myeloma underwent clinically indicated whole-body PCD-CT. The image quality of PCD-CT was assessed qualitatively by three independent radiologists for overall image quality, edge sharpness, image noise, lesion conspicuity, and diagnostic confidence using a 5-point Likert scale (5 = excellent), and quantitatively for signal homogeneity using the coefficient of variation (CV) of Hounsfield Units (HU) values and modulation transfer function (MTF) via the full width at half maximum (FWHM) in the frequency space. The results were compared with those of the current clinical standard EID-CT protocols as controls. Additionally, the radiation dose (CTDIvol) was determined. Results: We enrolled 35 patients with multiple myeloma (mean age 69.8 ± 9.1 years; 18 [51%] males). Qualitative image analysis revealed superior scores (median [interquartile range]) for PCD-CT regarding overall image quality (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), edge sharpness (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), image noise (4.0 [4.0-4.0] vs. 3.0 [3.0-4.0]), lesion conspicuity (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), and diagnostic confidence (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]) compared with EID-CT (P ≤ 0.004). In quantitative image analyses, PCD-CT compared with EID-CT revealed a substantially lower FWHM (2.89 vs. 25.68 cy/pixel) and a significantly more homogeneous signal (mean CV ± standard deviation [SD], 0.99 ± 0.65 vs. 1.66 ± 0.5; P < 0.001) at a significantly lower radiation dose (mean CTDIvol ± SD, 3.33 ± 0.82 vs. 7.19 ± 3.57 mGy; P < 0.001). Conclusion: Whole-body PCD-CT provides significantly higher subjective and objective image quality at significantly reduced radiation doses than the current clinical standard EID-CT protocols, along with readily available multi-spectral data, facilitating the potential for further advanced post-processing.

• Journal of the Korean Society of Radiology
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• v.13 no.3
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• pp.473-479
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• 2019

PET-CT improves performance and reduces the time by combining PET and CT of spatial resolution, and uses CT scan for attenuation correction. This study analyzed PET image evaluation. The condition of the tube voltage and current of CT will be changed using. Uniformity phantom and resolution phantom were injected with 37 MBq $^{18}F$ (fluorine ; 511 keV, half life - 109.7 min), respectively. PET-CT (Biograph, siemens, US) was used to perform emission scan (30 min) and penetration scan. And then the collected image data were reconstructed in OSEM-3D. The same ROI was set on the image data with a analyzer (Vinci 2.54, Germany) and profile was used to analyze and compare spatial resolution and image quality through FWHM and SI. Analyzing profile with pre-defined ROI in each phantom, PET image was not influenced by the change of tube voltage or exposure dose. However, CT image was influenced by tube voltage, but not by exposure dose. When tube voltage was fixed and exposure dose changed, exposure dose changed too, increasing dose value. When exposure dose was fixed at 150 mA and tube voltage was varied, the result was 10.56, 24.6 and 35.61 mGy in each variables (in resolution phantom). In this study, attenuation image showed no significant difference when exposure dose was changed. However, when exposure dose increased, the amount of dose that patient absorbed increased too, which indicates that CT exposure dose should be decreased to minimum to lower the exposure dose that patient absorbs. Therefore future study needs to discuss the conditions that could minimize exposure dose that gets absorbed by patient during PET-CT scan.

• Journal of the Korean Society of Radiology
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• v.14 no.3
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• pp.221-233
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• 2020

CT(computed tomography, CT) examinations is one of the most useful diagnostic equipment for identifying information in the human body in diagnostic radiology. Recently, the number of CT scans is increasing every year due to the high reliability of CT scans. Increasing the number of tests will accelerate the aging of CT devices, which is why the importance of quality management for CT devices is on the rise. Particularly in CT, quality management refers to a behavior of figuring out and correcting all sorts of hindrance factors that can cause all the problems related to the equipment associated with the diminishment of diagnosed area due to the reduction of image quality in clinical imaging in advance and maintaining a consistent level of image quality and obtaining a proper image. Here, these researchers aim to summarize and report the general contents of quality management in CT.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.717-723
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• 2023

Liver disease is highly associated with death, and other abdominal diseases are also important causes affecting a person's lifespan, and a CT scan is essential when treating abdominal diseases. High radiation exposure is essential to create images that are good for reading, but managing the patient's radiation exposure is also essential. In this study, a post-processing wavelet algorithm was proposed to improve the image quality of abdominal CT images. Wavelets have the disadvantage of having to set a threshold value depending on the type of input image. Therefore, we experimentally proposed the threshold value of the wavelet and evaluated whether the image quality was effective. As a result of the experiment, the optimal threshold value for abdominal CT images was calculated to be 50. In the case of image 1, noise was improved by 49% and in the case of image 2, by 29%, and the contrast also increased. if the results of this study are applied for post-processing after abdominal CT, image quality can be improved and it will be helpful in disease diagnosis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.271-274
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• 2016

CT quality assurance imaging evaluation and enforcement as quantitative assessment by phantom image evaluation, assessment items include There are also contrasting the water attenuation coefficient, uniformity, noise, resolution, spatial resolution, 10mm slice thickness evaluation, contrast resolution, space for the resolution, the slice thickness evaluation, it is possible to estimate the error due to the evaluation by the subjective judgment of the tester, using a subjective error image processing program to be computed to minimize the objective evaluation. Basic recording conditions of the CT image quality control assessment is the same as special medical equipment quality control checks, the images were evaluated quantitatively using IMAGE J. For a CT attenuation coefficient, the uniformity, noise evaluation, were evaluated as CT quality control image the standard deviation of the measured value of the digital processing of image smaller and less noise uniform images than the, contrast and resolution assessment is the size of the diameter of a circle having a large the 1 inch, 0.75 inch, 0.5 inch quality if the diameter of the circle, was evaluated in the small circle in the near circle ellipse. Spatial resolution is evaluated by using a self-extracting features of an image processing program, all of the groups of members comprising the acceptance criteria to automatically extract, was evaluated to be very useful for the quantitative assessment. When CT image quality control assessment on the basis of the results such as the above, if using an image processing program to minimize the subjective judgment of the error evaluator and is determined more efficient than would be made quantitative evaluation.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1597-1608
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• 2021

Cardiovascular computed tomography (CT) is among the most active fields with ongoing technical innovation related to image acquisition and analysis. Artificial intelligence can be incorporated into various clinical applications of cardiovascular CT, including imaging of the heart valves and coronary arteries, as well as imaging to evaluate myocardial function and congenital heart disease. This review summarizes the latest research on the application of deep learning to cardiovascular CT. The areas covered range from image quality improvement to automatic analysis of CT images, including methods such as calcium scoring, image segmentation, and coronary artery evaluation.

• ETRI Journal
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• v.37 no.6
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• pp.1251-1258
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• 2015

We investigate an image recovery method for sparse-view computed tomography (CT) using an iterative shrinkage algorithm based on a second-order approach. The two-step iterative shrinkage-thresholding (TwIST) algorithm including a total variation regularization technique is elucidated to be more robust than other first-order methods; it enables a perfect restoration of an original image even if given only a few projection views of a parallel-beam geometry. We find that the incoherency of a projection system matrix in CT geometry sufficiently satisfies the exact reconstruction principle even when the matrix itself has a large condition number. Image reconstruction from fan-beam CT can be well carried out, but the retrieval performance is very low when compared to a parallel-beam geometry. This is considered to be due to the matrix complexity of the projection geometry. We also evaluate the image retrieval performance of the TwIST algorithm -sing measured projection data.

• Journal of the Korean Society of Radiology
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• v.12 no.7
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• pp.821-826
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• 2018

This study was performed randomly from all the patients who visited the University Hospital in Gyeonggi-do from January 1, 2018 to June 30, 2018 for the abdominal CT scan. We divided the patients into three groups and evaluated the extent of effective dose and image quality according to the area of the abdominal CT image. As a result, the effective dose was 7.34 mSv in the average area group, 8.39 mSv in the average area and 5.89 mSv in the average area. For the analysis of image quality, ROI was plotted in the same three regions according to the abdominal area. As a result, CT values were significantly different in the abdominal area classified into 3 groups (p <0.05). The results of this study can be used as a basic data for the development of a protocol that can be applied in actual clinical practice. It is thought that it can help to reduce the image quality and the radiation dose.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.37-44
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• 2024

Even though MR can reveal excellent soft-tissue contrast and functional information, CT is also required for electron density information for accurate dose calculation in Radiotherapy. For the fusion of MRI and CT images in RT treatment planning workflow, patients are normally scanned on both MRI and CT imaging modalities. Recently deep-learning-based generations of CT images from MR images became possible owing to machine learning technology. This eliminated CT scanning work. This study implemented a CycleGan deep-learning-based CT image generation from MR images. Three CT generators whose learning is based on T1- , T2- , or T1-&T2-weighted MR images were created, respectively. We found that the T1-weighted MR image-based generator can generate better than other CT generators when T1-weighted MR images are input. In contrast, a T2-weighted MR image-based generator can generate better than other CT generators do when T2-weighted MR images are input. The results say that the CT generator from MR images is just outside the practical clinics and the specific weight MR image-based machine-learning generator can generate better CT images than other sequence MR image-based generators do.