• 한국의학물리학회지:의학물리
• /
• 제18권2호
• /
• pp.55-64
• /
• 2007

CT에서 커널에 따른 노이즈, 공간분해능 및 MTF를 측정하고 임상에 적용하여 영상의 질을 평가하였다. 커널은 B30 (body medium smooth), H30 (head medium smooth), S80 (special), U95 (ultra sharp)를 이용하여 측정하였다. 커널에 따른 결과에서 노이즈는 B30 (7.6 HU), U95 (38.2 HU)이었고, 공간분해능은 H30, B30 커널이 0.8 mm, U95 커널은 0.6 mm로 33.3% 영상의 질이 향상되었다. MTF (50%, 10%, 2%)에서 H30 커널은 3.25 5.68, 7.45 Ip/cm, B30 커널은 3.84, 6.25, 7.72 Ip/cm, S80 커널에서는 4.69, 9.49, 12.34 Ip/cm, U95 커널은 14.19, 20.31, 24.67 Ip/cm이었다. 커널 종류별로 영상의 임상 적용에서 두부는 H30 커널, 복부는 B30 커널, 측두골 및 폐에서는 U95 커널이 임상적 평가가 높았다. 임상에서 진단에 적용하기 위해서는 검사 부위에 맞는 커널을 선택하여 검사하여야 한다.

• 대한방사선기술학회지:방사선기술과학
• /
• 제31권1호
• /
• pp.71-81
• /
• 2008

CT 영상은 커널의 종류와 재구성 방법에 따라 다양하게 나타나며, 관심 영역의 CT감약계수 및 노이즈는 정밀도에 영향을 준다. 커널의 종류에 따른 노이즈, 공간분해능 및 MTF를 측정하여 영상을 평가하였다. 다중채널CT 스캐너를 이용하여 팬텀 및 복부를 스캔 하였고, 커널은 B10(very smooth), B20(smooth), B30(medium smooth), B40(medium), B50(medium sharp), B60(sharp), B70(very sharp), B80(ultra sharp)으로 재구성하여 물, 공기, 간의 실질 조직, 근육, 지방 부위를 ROI 기능을 이용하여 평균의 CT감약계수와 표준편차인 노이즈를 정량적으로 측정하여 영상을 비교하였다. 그 결과CT 감약계수는 물($1.1{\sim}1.8\;HU$), 공기($-998{\sim}-1,000\;HU$)이고, 물에서의 노이즈($5.4{\sim}44.8\;HU$), 공기($3.6{\sim}31.4\;HU$)이다. 인체에서 간 실질 조직과 지방, 근육의 CT 감약계수와 노이즈를 커널에 따라 측정하였다. 지방의 CT 감약계수($-2.2{\sim}0.8\;HU$), 간의 실질 조직에서 CT감약계수($60.4{\sim}62.2\;HU$), 노이즈($7.6{\sim}63.8\;HU$), 근육의 CT감약계수($53.3{\sim}54.3\;HU$), 노이즈($10.4{\sim}70.7\;HU$) 사이에서 분포하였고, 커널이 높아질수록 노이즈도 증가하였다. 영상의 질을 높이기 위해서는 검사부위에 따라 노이즈를 감소하기 위해 적절한 커널을 선택하여 CT 검사를 하여야 한다.

• 한국작물학회지
• /
• 제47권5호
• /
• pp.384-389
• /
• 2002

No-tillage (NT) practice for corn production has advantages of reduction of soil erosion and energy conservation. Research on effects of NT for sweet corn or super sweet corn is very limited. Hybrids of sugary (su) and shrunken-2 (sh2) were tested under NT and conventional tillage (CT) practices to investigate plant characters, ear characters, fresh yield, and grain yield. Sugary hybrids were Golden Cross Bantam 70 (GCB70), Sprint, Geumdanok, and Danok3. Shrunken-2 hybrids were BSS9472, Cambella90, GSS9299, Jubilee, KS-Y-65, and Chodangok1. Emergence rates under NT were lower than those under CT for su, while there was no difference between tillage systems for sh2. There were no differences between CT and NT for days to tasseling and silking, plant height, and ear height for both su and sh2. Ear characters such as ear length, number of kernel rows, number of kernels per row, and t100-kernel weight under NT were not significantly different from those under CT. There were no differences between two tillage practice for fresh and grain yield, rather they showed trend of increases under NT practices. Results from this trial indicate that NT practice for both su and sh2 cultivation may be possible to recommend to farmers.

• Journal of Magnetics
• /
• 제22권2호
• /
• pp.227-233
• /
• 2017

The purpose of this study was to evaluate the effects of X-ray photon energy, tissue density, and the kernel essential for image reconstruction on the image quality by measuring HU and noise. Images were obtained by scanning the RMI density phantom within the CT device, and HU and noise were measured as follows: images were obtained by varying the tube voltages, the tube currents and eight different kernels. The greater the voltage-dependent change in the HU value but the noise was decreased. At all densities, changes in the tube current did not exert any significant influence on the HU value, whereas the noise value gradually decreased as the tube current increased. At all densities, changes in the kernel did not exert any significant influence on the HU value. The noise value gradually increased in the lower kernel range, but rapidly increased in the higher kernel range. HU is influenced by voltage and density, and noise is influenced by voltage, current, kernel, and density. This affects contrast resolution and spatial resolution.

• 한국임상수의학회지
• /
• 제30권4호
• /
• pp.268-272
• /
• 2013

The purpose of this study was to assess the effects of reconstruction kernel, and slice thickness on the accuracy of spiral CT-based volume assessment over a range of object sizes typical of synthetic simulated tumor. Spiral CT scanning was performed at various reconstruction kernels (soft tissue, standard, bone), and slice thickness (1, 2, 3 mm) using a phantom made of gelatin and 10 synthetic simulated tumors of different sizes (diameter 3.0-12.0 mm). Three-dimensional volume assessments were obtained using an automated software tool. Results were compared with the reference volume by calculating the percentage error. Statistical analysis was performed using ANOVA and setting statistical significance at P < 0.05. In general, smaller slice thickness and larger sphere diameters produced more accurate volume assessment than larger slice thickness and smaller sphere diameter. The measured volumes were larger than the actual volumes by a common factor depending on slice thickness; in 100HU simulated tumors that had statistically significant, 1 mm slice thickness produced on average 27.41%, 2 mm slice thickness produced 45.61%, 3 mm slice thickness produced 93.36% overestimates of volume. However, there was no statistically significant difference in volume error for spiral CT scans taken with techniques where only reconstruction kernel was changed. These results supported that synthetic simulated tumor size, slice thickness were significant parameters in determining volume measurement errors. For an accurate volumetric measurement of an object, it is critical to select an appropriate slice thickness and to consider the size of an object.

• 한국의학물리학회:학술대회논문집
• /
• 한국의학물리학회 1999년도 Japanese Journal of Medical Physics
• /
• pp.268-270
• /
• 1999

• 한국멀티미디어학회논문지
• /
• 제23권9호
• /
• pp.1129-1138
• /
• 2020

The performance of convolutional deep learning networks is generally determined according to parameters of target dataset, structure of network, convolution kernel, activation function, and optimization algorithm. In this paper, a genetic algorithm is used to select the appropriate deep learning model and parameters for Alzheimer's classification and to compare the learning results with preliminary experiment. We compare and analyze the Alzheimer's disease classification performance of VGG-16, GoogLeNet, and ResNet to select an effective network for detecting AD and MCI. The simulation results show that the network structure is ResNet, the activation function is ReLU, the optimization algorithm is Adam, and the convolution kernel has a 3-dilated convolution filter for the accuracy of dementia medical images.

• Korean Journal of Radiology
• /
• 제21권7호
• /
• pp.891-899
• /
• 2020

Objective: To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT. Materials and Methods: We retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455). Results: All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level [per CT scan: rib fracture(s): yes/no]. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement. Conclusion: We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.

• Imaging Science in Dentistry
• /
• 제49권4호
• /
• pp.273-279
• /
• 2019

Purpose: This study was performed to investigate the effects of energy level, reconstruction kernel, and tube rotation time on Hounsfield unit (HU) values of hydroxyapatite (HA) in virtual monochromatic images (VMIs) obtained with dual-energy computed tomography (DECT)(Siemens Healthineers, Erlangen, Germany). Materials and Methods: A bone density calibration phantom with 3 HA inserts of different densities(CTWATER^®; 0, 100, and 200 mg of HA/㎤) was scanned using a twin-beam DECT scanner at 120 kVp with tube rotation times of 0.5 and 1.0 seconds. The VMIs were reconstructed by changing the energy level (with options of 40 keV, 70 keV, and 140 keV). In order to investigate the impact of the reconstruction kernel, virtual monochromatic images were reconstructed after changing the kernel from body regular 40 (Br40) to head regular 40 (Hr40) in the reconstruction phase. The mean HU value was measured by placing a circular region of interests (ROIs) in the middle of each insert obtained from the VMIs. The HU values were compared with regard to energy level, reconstruction kernel, and tube rotation time. Results: Hydroxyapatite density was strongly correlated with HU values(correlation coefficient=0.678, P<0.05). For the HA 100 and 200 inserts, HU decreased significantly at increased energy levels(correlation coefficient= -0.538, P<0.05) but increased by 70 HU when using Hr40 rather than Br40 (correlation coefficient=0.158, P<0.05). The tube rotation time did not significantly affect the HU(P>0.05). Conclusion: The HU values of hydroxyapatite were strongly correlated with hydroxyapatite density and energy level in VMIs obtained with DECT.

• 한국통신학회논문지
• /
• 제23권9A호
• /
• pp.2365-2371
• /
• 1998

이산 여현 변환(Discrete Cosine Transform: DCT)은 음성 및 영상 신호의 압축에 광범위하게 응용되고 있다. 본 논문에서는 $2^{m}$-포인트의 일반적인 경우로 확장이 가능한 새로운 고속 DCT 알고리즘과 구조를 제안한다. 제안한 알고리즘은 커널의 대칭성을 이용하여 N-포인트의 DCT를 N/2-포인트의 DCT로 나누어 처리하며 이를 재귀적으로 적용해 나간다. 제안한 알고리즘은 적은 덧셈 및 곱셈 연산을 통해 변환을 수행하며, 변환을 위해 통과해야 하는 곱셈 연산단의 수가 적고 대부분의 곱셈 연산이 흐름도상의 후반부에서 일괄적으로 수행되므로 고정 소수점 연산시에 발생할 수 있는 오차의 전파를 줄일 수 있다.