• Journal of Magnetics
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• v.17 no.3
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• pp.219-224
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• 2012

In the present study, changes in signal-to-noise ratio (SNR) and apparent diffusion coefficient (ADC) of the diffusion-weighted images in the normal livers were investigated using changes in b values in 1.5 T MR (magnetic resonance) instruments. Respective diffusion-weighted images and ADC map images were obtained from 20 healthy individuals by increasing b values from 50 to 400 and 800 $s/mm^2$ using 1.5T MR scanner between January 2011 and November 2011. At each ADC map image obtained at each b value, ADCs in the right hepatic lobe, spleen and kidney were measured. As a result, ADCs of the right hepatic lobe, spleen and kidney have gradually decreased in the diffusion-weighted images in accordance with the reduced b value. This outcome may be used as preliminary data for applications to various abdominal diseases.

• Progress in Medical Physics
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• v.21 no.1
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• pp.52-59
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• 2010

To examine among patients with vertebral compression fracture the extent to which signal-to-noise ratio (SNR) and Apparent Diffusion Coefficient (ADC) values at the lumbar vertebral compression fracture site vary on diffusion-weighted MR images according to varying b values on the 1.5T MR device. Diffusion-weighted MR images of 30 patients with compression fracture due to chronic osteoporosis who underwent vertebral MRI from Jan. 2008 to Nov. 2009 were respectively obtained using a 1.5-T MR scanner with the b values increased from 400, 600, 800, 1,000 to $1,200\;s/mm^2$. For diffusion-weighted MR images with different b values, the signal-to-noise ratio (SNR) was assessed at three sites: the site of compression fracture of the lumbar vertebral body at L1 to L5, and both the upper and lower discs of the said fracture site, while for ADC map images with different b values, the SNR and ADC were respectively assessed at those three sites. As a quantitative analysis, diffusion-weighted MR images and ADC map images with b value of $400\;s/mm^2$ (the base b values) were respectively compared with the corresponding images with each different b value. As far as qualitative analysis is concerned, for both diffusion-weighted MR and ADC map images with b value of $400\;s/mm^2$, the extent to which signal intensity values obtained at the site of compression fracture of the lumbar vertebral body at L1 to L5 vary according to the increasing b values were examined. The quantitative analysis found that for both diffusion-weighted MR and ADC map images, as the b values increased, the SNR were relatively lowered at all the three sites, compared to the base b value. Also, it was found that as the b values increased, ADC valueswere relatively lowered at all the three sites on ADC map images. On the other hand, the qualitative analysis found that as the b values increased to more than $400\;s/mm^2$, the signal intensity gradually decreased at all the sites, while at the levels of more than $1,000\;s/mm^2$, severe image noises appeared at all of the three sites. In addition, higher signal intensity was found at the site of compression fracture of the lumbar vertebral body than at the discs. Findings showed that with the b value being increased, both the signal-to-noise ratio (SNR) and Apparent Diffusion Coefficient (ADC) values gradually decreased at all the sites of the lumbar vertebral compression fracture and both the upper and lower discs of the fracture site, suggesting that there is a possibility of a wider range of applications to assessment of various vertebral pathologies by utilizing multi b values in the diffusion-weighted MRI examination.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3407-3412
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• 2015

Background: The aim of this study was to evaluate and compare the accuracy of diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) value, and time-intensity curve (TIC) type analysis derived from dynamic contrast-enhanced MR imaging (DCE-MRI) in differentiating benign from malignant adnexal masses. Materials and Methods: 47 patients with 56 adnexal masses (27 malignant and 29 benign) underwent DWI and DCE-MRI examinations, prior to surgery. DWI signal intensity, mean ADC value, and TIC type were determined for all the masses. Results: High signal intensity on DWI and type 3 TIC were helpful in differentiating benign from malignant adnexal masses (p<0.001). The mean ADC value was significantly lower in malignant adnexal masses (p<0.001). An ADC value< $1.20{\times}10^{-3}mm^2/s$ may be the optimal cutoff for differentiating between benign and malignant tumors. The negative predictive value for low signal intensity on DWI, and type 1 TIC were 100%. The pairwise comparison among the receiver operating characteristic (ROC) curves showed that the area under the curve (AUC) of TIC was significantly larger than the AUCs of DWI and ADC (p<0.001 for comparison of TIC and DWI, p<0.02 for comparison of TIC and ADC value). Conclusions: DWI, ADC value and TIC type derived from DCE-MRI are all sensitive and relatively specific methods for differentiating benign from malignant adnexal masses. By comparing these functional MR techniques, TIC was found to be more accurate than DWI and ADC.

• Journal of Magnetics
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• v.22 no.1
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• pp.146-149
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• 2017

In this study, we compared the standardized value of each signal intensity, the apparent diffusion coefficient (ADC) that digitizes the diffusion of water molecules, and the signal to noise ratio (SNR) using b value 0 400, 1400 ($s/mm^2$). From March 2013 to December 2013, patients with suspicion of simple compound fracture and metastatic spine cancer were included in the MR readout. We used a 1.5 Tesla Achieva MRI system and a Syn-Spine Coil. Sequence is a DWI SE-EPI sagittal (diffusion weighted imaging spin echo-echo planar imaging sagittal) image with b-factor ($s/mm^2$) 0, 400, 1400 were used. Data analysis showed ROI (Region of Interest) in diseased area with high SI (signal intensity) in diffusion-weighted image b value 0 ($s/mm^2$) Using the MRIcro program, each SI was calculated with images of b-value 0, 400, and 1400 ($s/mm^2$), ADC map was obtained using Metlab Software with each image of b-value, The ADC is obtained by applying the ROI to the same position. The standardized values ($SI_{400}/SI_0$, $SI_{400}/SI_0$) of simple compression fractures were $0.47{\pm}0.04$ and $0.23{\pm}0.03$ and the standardized values ($SI_{400}/SI_0$, $SI_{400}/SI_0$) of the metastatic spine were $0.57{\pm}0.07$ and $0.32{\pm}0.08$ And the standardized values of the two diseases were statistically significant (p < 0.05). The ADC ($mm^2/s$) for b value 400 ($s/mm^2$) and 1400 ($s/mm^2$) of the simple compression fracture disease site were $1.70{\pm}0.16$ and $0.93{\pm}0.28$ and $1.24{\pm}0.21$ and $0.80{\pm}0.15$ for the metastatic spine. The ADC ($mm^2/s$) for b value 400($s/mm^2$) was statistically significant (p < 0.05) but the ADC ($mm^2/s$) for b value 1400 (p > 0.05). In conclusion, multi - b value recognition of signal changes in diffusion - weighted imaging is very important for the diagnosis of various spinal diseases.

• Investigative Magnetic Resonance Imaging
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• v.18 no.1
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• pp.25-33
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• 2014

Purpose : To investigate whether quantitative parameters derived from Diffusion-weighted magnetic resonance imaging (DW-MRI) correlate with those of Dynamic contrast-enhanced MRI (DCE-MRI). Materials and Methods: Thirteen patients with pathologically or clinically proven bony metastasis who had undergone MRI prior to treatment were included. The voxel size was $1.367{\times}1.367{\times}5mm$. A dominant tumor was selected and the apparent diffusion coefficient (ADC) value and DCE-MRI parameters were obtained by matching voxels. DCE-MRI data were analyzed yielding estimates of $K^{trans}$ (volume transfer constant) and $v_e$. (extravascular extracellular volume fraction). Statistical analysis of ADC, $K^{trans}$, and $v_e$ value was conducted using Pearson correlation analyses. Results: Fifteen lesions in pelvic bones were evaluated. Of these, 11 showed a statistically significant correlation (P<0.05) between ADC and $K^{trans}$. The ADC and $K^{trans}$ were inversely related in 7 lesions and positively related in 4 lesions. This did not depend on the primary cancer or site of metastasis. The ADC and $v_e$ of 9 lesions correlated significantly. Of these, 4 lesions were inversely related and 5 lesions were positively related. Conclusion: Unlike our theoretic hypothesis, there was no consistent correlation between ADC values and $K^{trans}$ or between ADC values and $v_e$ in metastatic bone tumors.

• Korean Journal of Digital Imaging in Medicine
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• v.14 no.1
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• pp.21-29
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• 2012

The purpose of this study is to evaluate the mutual relations by measuring SNR from T2 weighted image and ADC values on the basis of the stiffness values from liver tissues. This study was conducted that total 37 people(23 of males and 11 of females) were taken the liver MRI examination and average age was $54.5{\pm}12.7$ years old. The equipment was MAGNETOM Skyra 3.0T (SIEMENS, Erlangen, Germany) and 32 channel body-array coil. The examination were conducted with HASTE T2 weighted image by axial plane, Spin-echo EPI (echo planner image) DWI (b-value = 800) and Magnetic resonance elastography. The ROIs (region of interest: 200-300 $mm^2$) were established on the basis of the first axial stiffness image corresponded 95% confidence interval from axial stiffness image and then were measured values. After drawing the grid lines, signals were measured SNR from T2 weighted image and ADC values on the same locations that were analysed other 3 planes respectively. The results were showed correlation (0.057) that were increased to SNR from T2 weighted image by increasing stiffness value that no significant difference statistically p = 0.003. Other results were showed correlations (-0.301) that were decreased to ADC values by increasing stiffness values that no significant difference statistically p = 0.088. In the 3.0T equipment, the results may be error in much the same fashion as the 1.5T from ADC values by evaluation of fibrosis stage. However, Magnetic resonance elastography would be useful method that is used to diagnose exactly liver fibrosis stages in the 3.0T.

• Journal of Korean Neurosurgical Society
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• v.41 no.6
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• pp.371-376
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• 2007

Objective : The aim of this study was to investigate the usefulness of diffusion-weighted imaging [DWI] and apparent diffusion coefficiency [ADC] in distinguishing brain abscesses from cystic or necrotic brain tumors, which are difficult to be differentiated by conventional magnetic resonance imaging techniques. Methods : Seven patients with brain abscesses and ten patients with cystic brain tumors were studied from September 2003 to October 2005. Abscess, subdural empyema and ventriculitis were categorized to the abscess group and cystic or necrotic brain gliomas or metastatic brain tumors into the tumor group. Preoperative magnetic resonance images were performed in all patients and diffusion-weighted images and apparent diffusion coefficiency values of lesions were calculated directly from software of 1.5 tesla MRI [General Electrics, USA]. The ratio of the ADC of the lesion to contralateral regional ADC was also measured [relative ADC, rADC]. Results : The average ADC value of pyogenic abscesses group was $0.82+/-0.14{\times}10^{-3}\;[mean+/-S.D.]\;mm^2/s$ and mean rADC was 0.75. Cystic or necrotic areas had high ADC values [$2.49+/-0.79{\times}10^{-3}\;mm^2/s$, mean rADC=2.14]. ADC and rADC values of abscesses group showed about three times lower values than those of cystic or necrotic tumor group. Conclusion : This study results based on numerical comparison of signal intensities and quantitative analysis to distinguish between brain abscess and cystic or necrotic tumor, DWI and ADC mapping are thought to be very useful diagnostic tools.

• Journal of the Korean Society of Radiology
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• v.12 no.6
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• pp.755-762
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• 2018

The purpose of this study was to compare the FA(faractional anisotropy) and ADC(apparent diffusion coefficient) values, which were derived from diffusion tensor imaging in breast cancer patients. The diffusion gradient used in this study was derived from quantitative diffusion indices using 20 directions(b-value, 0 and $1,000s/mm^2$). Quantitative analysis was analyzed using Pearson's correction and qualitative analysis using for correction coefficients. As a result, $FA_{min}$, $FA_{mean}$ and $FA_{max}$ were $0.098{\pm}0.065$, $0.302{\pm}0.142$ and $0.634{\pm}0.236$, respectively(p > 0.05). The $ADC_{min}$, $ADC_{mean}$ and $ADC_{max}$ were $0.741{\pm}0.403$, $1.095{\pm}0.394$ and $1.530{\pm}0.447$, respectively(p > 0.05). The $FA_{min}$, $FA_{mean}$, and $FA_{max}$ mean values were $0.132{\pm}0.050$, $0.418{\pm}0.094$, and $0.770{\pm}0.164$ for Category 6 and Kinetic Curve Pattern III, respectively. $ADC_{min}$, $ADC_{mean}$, and $ADC_{max}$ were $0.753{\pm}0.189$, $1.120{\pm}0.236$, and $1.615{\pm}0.372$, respectively. Quantitative analysis showed negative correlation between $ADC_{mean}-FA_{mean}$ and $ADC_{max}-FA_{max}$(p = 0.001, 0.003). The qalitative analysis showed ADC 0.628(p = 0.001), FA 0.620(p = 0.001) in the internal evaluations, ADC 0.677(p = 0.001), FA 0.695(p = 0.001) in external evaluations. In conclusion, based on the morphological examination, time to signal intensity graph is the form of wash-out(pattern III) in the dynamic contrast enhance examination, As a result, the $ADC_{mean}$ $1.120{\pm}0.236$ and $FA_{mean}$ values were $0.032{\pm}0.142$ with a negative correlation (Y=1.44-1.12X). Therefore, If we understand the shape of time to signal intensity graph and the relationship between ADC and FA, It will be a criterion for distinguishing malignant diseases in breast cancer.

• Proceedings of the IEEK Conference
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• 2002.06b
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• pp.305-308
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• 2002

An 1.8V 6bit IGSPS ADC for high speed data acquisition is discussed in this paper. This ADC is based on a flash ADC architecture because the flash ADC is the only practical architecture at conversion rates of IGSPS and beyond. A straightforward 6bit full flash A/D converter consists of two resistive ladders with 63 laps, 63 comparators and digital blocks. One important source of errors in flash A/D converter is caused by the capacitive feedthrough of the high frequency input signal to the resistive reference-lauder. Consequently. the voltage at each tap of the ladder network can change its nominal DC value. This means large transistors have a large parasitic capacitance. Therefore, a dual resistive ladder with capacitor is employed to fix the DC value. Each resistive ladder generates 32 clean reference voltages which alternates with each other. And a two-stage amplifier is also used to reduce the effect of the capacitive feedthrough by minimizing the size of MOS connected to reference voltage. The proposed ADC is based on 0.18${\mu}{\textrm}{m}$ 1-poly 6-metal n-well CMOS technology, and it consumes 307㎽ at 1.8V power supply.

• Journal of the Semiconductor & Display Technology
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• v.12 no.2
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• pp.13-17
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• 2013

In lighting system where several large-area organic light-emitting diode (OLED) lighting panels are involved, panel aging may appear differently from each other, resulting in a falling-off in lighting quality. To achieve uniform light output across large-area OLED lighting panels, we have employed an optical feedback circuit. Light output from each OLED panel is monitored by the optical feedback circuit that consists of a photodiode, I-V converter, 10-bit analogdigital converter (ADC), and comparator. A photodiode generates current by detecting OLED light from one side of the glass substrate (i.e., edge emission). Namely, the target luminance from the emission area (bottom emission) of OLED panels is monitored by current generated from the photodiode mounted on a glass edge. To this end, we need to establish a mapping table between the ADC value and the luminance of bottom emission. The reference ADC value corresponds to the target luminance of OLED panels. If the ADC value is lower or higher than the reference one (i.e., when the luminance of OLED panel is lower or higher than its target luminance), a micro controller unit (MCU) adjusts the pulse width modulation (PWM) used for the control of the power supplied to OLED panels in such a way that the ADC value obtained from optical feedback is the same as the reference one. As such, the target luminance of each individual OLED panel is unchanged. With the optical feedback circuit included in the lighting system, we have observed only 2% difference in relative intensity of neighboring OLED panels.