• Journal of radiological science and technology
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• v.39 no.3
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• pp.337-344
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• 2016

The purpose of this study was to evaluate the radiolucency for the phantom output to the 3D printing technology. The 3D printing technology was applied for FDM (fused deposition modeling) method and was used the material of ABS (acrylonitrile butadiene styrene) resin. The phantom was designed in cylindrical uniformity. An image uniformity was measured by a cross-sectional images of the 3D printed phantom obtained from the CT equipment. The evaluation of radiolucency was measured exposure dose by the inserted ion-chamber from the 3D printed phantom. As a results, the average of uniformity in the cross-sectional CT image was 2.70 HU and the correlation of radiolucency between PMMA CT phantom and 3D printed ABS phantom is found to have a high correlation to 0.976. In the future, this results will be expected to be used as the basis for the phantom production of the radiation quality control by used 3D printing technology.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.85-92
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• 2023

Deep learning applications are increasingly being leveraged for disease detection tasks in medical imaging modalities such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). Most data-centric deep learning challenges necessitate the use of supervised learning methodologies to attain high accuracy and to facilitate performance evaluation through comparison with the ground truth. Supervised learning mandates a substantial amount of image and label sets, however, procuring an adequate volume of medical imaging data for training is a formidable task. Various data augmentation strategies can mitigate the underfitting issue inherent in supervised learning-based models that are trained on limited medical image and label sets. This research investigates the enhancement of a deep learning-based rib fracture segmentation model and the efficacy of data augmentation techniques such as left-right flipping, rotation, and scaling. Augmented dataset with L/R flipping and rotations(30°, 60°) increased model performance, however, dataset with rotation(90°) and ⨯0.5 rescaling decreased model performance. This indicates the usage of appropriate data augmentation methods depending on datasets and tasks.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.951-958
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• 2021

Objective: To evaluate the usefulness of virtual monochromatic images (VMIs) obtained using dual-layer dual-energy CT (DL-DECT) for evaluating brain tumors. Materials and Methods: This retrospective study included 32 patients with brain tumors who had undergone non-contrast head CT using DL-DECT. Among them, 15 had glioblastoma (GBM), 7 had malignant lymphoma, 5 had high-grade glioma other than GBM, 3 had low-grade glioma, and 2 had metastatic tumors. Conventional polychromatic images and VMIs (40-200 keV at 10 keV intervals) were generated. We compared CT attenuation, image noise, contrast, and contrast-to-noise ratio (CNR) between tumor and white matter (WM) or grey matter (GM) between VMIs showing the highest CNR (optimized VMI) and conventional CT images using the paired t test. Two radiologists subjectively assessed the contrast, margin, noise, artifact, and diagnostic confidence of optimized VMIs and conventional images on a 4-point scale. Results: The image noise of VMIs at all energy levels tested was significantly lower than that of conventional CT images (p < 0.05). The 40-keV VMIs yielded the best CNR. Furthermore, both contrast and CNR between the tumor and WM were significantly higher in the 40 keV images than in the conventional CT images (p < 0.001); however, the contrast and CNR between tumor and GM were not significantly different (p = 0.47 and p = 0.31, respectively). The subjective scores assigned to contrast, margin, and diagnostic confidence were significantly higher for 40 keV images than for conventional CT images (p < 0.01). Conclusion: In head CT for patients with brain tumors, compared with conventional CT images, 40 keV VMIs from DL-DECT yielded superior tumor contrast and diagnostic confidence, especially for brain tumors located in the WM.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.5
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• pp.311-317
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• 2015

The aim of this study is to develop a 3D registration algorithm for positron emission tomography/computed tomography (PET/CT) and magnetic resonance (MR) images acquired from independent PET/CT and MR imaging systems. Combined PET/CT images provide anatomic and functional information, and MR images have high resolution for soft tissue. With the registration technique, the strengths of each modality image can be combined to achieve higher performance in diagnosis and radiotherapy planning. The proposed method consists of two stages: normalized mutual information (NMI)-based global matching and independent component analysis (ICA)-based refinement. In global matching, the field of view of the CT and MR images are adjusted to the same size in the preprocessing step. Then, the target image is geometrically transformed, and the similarities between the two images are measured with NMI. The optimization step updates the transformation parameters to efficiently find the best matched parameter set. In the refinement stage, ICA planes from the windowed image slices are extracted and the similarity between the images is measured to determine the transformation parameters of the control points. B-spline. based freeform deformation is performed for the geometric transformation. The results show good agreement between PET/CT and MR images.

• Journal of radiological science and technology
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• v.37 no.1
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• pp.37-47
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• 2014

The purpose of this study was to analyze translational and rotational adjustments during automatic rigid image-registration by using different control parameters for a total of five groups on TomoTherapy (Accuray Inc, Sunnyvale, CA, USA). We selected a total of 50 patients and classified them in five groups (brain, head-and-neck, lung, abdomen and pelvic) and used a total of 500 megavoltage computed tomography (MVCT) image sets for the analysis. From this we calculated the overall mean value(M) for systematic and random errors after applying the different control parameters. After randomization of the patients into the five groups, we found that the overall mean value varied according to three techniques and resolutions. The deviation for the lung, abdomen and pelvic groups was approximately greater than the deviation for the brain and head-and-neck groups in all adjustments. Overall, using a "full-image" produces smaller deviations in the rotational adjustments. We found that rotational adjustment has deviations with distinctly different control parameters. We concluded that using a combination of the "full-image" technique and "standard" resolution will be helpful in assisting with patients' repositioning and in correcting for set-up errors prior to radiotherapy on TomoTherapy.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.293-299
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• 2012

Electrical impedance tomography is an imaging modality for determining the electrical properties inside a domain. Small currents are injected and the resulting voltages are measured through the electrodes. The internal electrical properties are reconstructed based on these voltage and current data. In this paper, a novel on-line Landweber algorithm was developed to fast estimate the resistivity distribution in the inverse calculation. Additionally, to enhance the reconstruction performance, a step-length was computed from the eigenvalue of the weighting matrix. The numerical experiments have been performed to evaluate the reconstruction performance of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.499-507
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• 2016

Medical imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Ultrasound (US) produce a large amount of digital medical images. Hence, compression of digital images becomes essential and is very much desired in medical applications to solve both storage and transmission problems. But at the same time, an efficient image compression scheme that reduces the size of medical images without sacrificing diagnostic information is required. This paper proposes a novel threshold-based medical image compression algorithm to reduce the size of the medical image without degradation in the diagnostic information. This algorithm discusses a novel type of thresholding to maximize Compression Ratio (CR) without sacrificing diagnostic information. The compression algorithm is designed to get image with high optimum compression efficiency and also with high fidelity, especially for Peak Signal to Noise Ratio (PSNR) greater than or equal to 36 dB. This value of PSNR is chosen because it has been suggested by previous researchers that medical images, if have PSNR from 30 dB to 50 dB, will retain diagnostic information. The compression algorithm utilizes one-level wavelet decomposition with threshold-based coefficient selection.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.131-134
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• 1995

A three dimensional fractal coding with a perceptually enhanced matching is proposed. Since most of medical images (e.g. computed tomography or magnetic resonance image) have three dimensional character, the searching region is extended to adjacent slices. For perceptually enhanced matching, a high frequency booster filter is used for prefiltering of the original image, and the least mean square error matching is applied to this edge enhanced image rather than the original image. From the simulation with the magnetic resonance images ($255{\times}255$, 8bits/pixel). the proposed algorithm provides excellent image quality with compression rations higher than 10. Compared to existing fractal coding the algorithm also provides better subjective image quality with higher compression ratio.

• 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.

• Imaging Science in Dentistry
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• v.44 no.3
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• pp.229-236
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• 2014

Purpose: To investigate the prevalence of bifid and trifid mandibular canals using cone-beam computed tomography (CBCT) images, and to measure their length, diameter, and angle. Materials and Methods: CBCT images of 500 patients, involving 755 hemi-mandibles, were used for this study. The presence and type of bifid mandibular canal was evaluated according to a modified classification of Naitoh et al. Prevalence rates were determined according to age group, gender, and type. Further, their diameter, length, and angles were measured using PACSPLUS Viewer and ImageJ 1.46r. Statistical analysis with chi-squared and analysis of variance (ANOVA) tests was performed. Results: Bifid and trifid mandibular canals were found in 22.6% of the 500 patients and 16.2% of the 755 sides. There was no significant difference between genders and among age groups. The retromolar canal type accounted for 71.3% of the identified canals; the dental canal type, 18.8%; the forward canal type, 4.1%; and the trifid canal type, 5.8%. Interestingly, seven cases of the trifid canal type, which has been rarely reported, were observed. The mean diameter of the bifid and trifid mandibular canals was 2.2 mm and that of the main mandibular canal was 4.3 mm. Their mean length was 16.9 mm; the mean superior angle was $149.2^{\circ}$, and the mean inferior angle was $37.7^{\circ}$. Conclusion: Bifid and trifid mandibular canals in the Korean population were observed at a relatively high rate through a CBCT evaluation, and the most common type was the retromolar canal. CBCT is suggested for a detailed evaluation of bifid and trifid mandibular canals before mandibular surgery.