KEYWORDS: Single photon emission computed tomography, Collimators, Data acquisition, Signal attenuation, 3D image reconstruction, Scintigraphy, Cardiology, Liver, Heart, Sensors
Recently myocardial perfusion SPECT imaging acquired using the cardiac focusing-collimator (CF) has been developed in the field of nuclear cardiology. Previously we have investigated the basic characteristics of CF using physical phantoms. This study was aimed at determining the acquisition time for CF that enables to acquire the SPECT images equivalent to those acquired by the conventional method in 201TlCl myocardial perfusion SPECT. In this study, Siemens Symbia T6 was used by setting the torso phantom equipped with the cardiac, pulmonary, and hepatic components. 201TlCl solution were filled in the left ventricular (LV) myocardium and liver. Each of CF, the low energy high resolution collimator (LEHR), and the low medium energy general purpose collimator (LMEGP) was set on the SPECT equipment. Data acquisitions were made by regarding the center of the phantom as the center of the heart in CF at various acquisition times. Acquired data were reconstructed, and the polar maps were created from the reconstructed images. Coefficient of variation (CV) was calculated as the mean counts determined on the polar maps with their standard deviations. When CF was used, CV was lower at longer acquisition times. CV calculated from the polar maps acquired using CF at 2.83 min of acquisition time was equivalent to CV calculated from those acquired using LEHR in a 180°acquisition range at 20 min of acquisition time.
KEYWORDS: Modulation transfer functions, Digital imaging, Optical filters, Chromium, Image filtering, Digital filtering, Computing systems, Sensors, Digital mammography, Radiography
To reduce the patients’ exposure, several low-dose images are necessary to obtain an image that can be used for
diagnosis. However, it is clinically undesirable to expose a patient to multiple exposures in order to obtain an optimal
image. The purpose of this study was to simulate a low-dose image from the image generated by a routine-dose. Images
of acrylic steps were obtained using multiple doses in digital mammography to generate additional noise. The additional
noise was calculated as three different noise sources. This study used the digital mammography system with different
detectors. It is computed radiography (CR) system and flat panel detector (FPD) system. This noise was added to take
into account the resolution of the X-ray detector using the following filters. The filters were designed based on the
presampled modulation transfer function (MTF) and digital MTF containing aliasing. The image simulated using the
presampled MTF filter was less similar to an actual low-dose image using the CR system. The image simulated using the
digital MTF filter was closer to an actual low-dose image compared to the image simulated using the presampled MTF
filter using the CR system. The image simulated using the digital MTF filter of the FPD system was similar to an actual
low-dose image. By using the proposed method, we were able to obtain a simulated low-dose image from an image
generated by a routine-dose.
KEYWORDS: Breast, Signal to noise ratio, Image quality, Modulation transfer functions, Digital mammography, X-rays, Spatial frequencies, Breast cancer, Mammography, Polymethylmethacrylate
Breast density has a close relationship with breast cancer risk. The exposure parameters must be appropriately chosen for
each breast. However, the optimal exposure conditions for digital mammography are uncertain in clinical. The exposure
parameters in digital mammography must be optimized with maximization of image quality and minimization of
radiation dose. We evaluated image quality under different exposure conditions to investigate the most advantageous
tube voltage. For different compressed breast phantom thicknesses and compositions, we measured the Wiener spectrum
(WS), noise-equivalent number of quanta (NEQ), and detective quantum efficiency (DQE). In this study, the
signal-to-noise ratios were derived from a perceived statistical decision theory model with the internal noise of eye-brain
system (SNRi), contrived and studied by Loo et al.1 and Ishida et al.2 These were calculated under a fixed average
glandular dose. The WS values were obtained with a fixed image contrast. For 4-cm-thick and 50% glandular breast
phantoms, the NEQ showed that high voltages gave a superior noise property of images, especially for thick breasts, but
the improvement in the NEQ by tube voltage was not so remarkable. On the other hand, the SNRi value with a Mo filter
was larger than that with a Rh filter. The SNRi increased when the tube voltage decreased. The result differed from those
of WS and NEQ. In this study, the SNRi depended on the contrast of signal. Accuracy should be high with an intense,
low-contrast object.
In dentistry, computed tomography (CT) is essential for diagnosis. Recently, cone-beam CT has come into use. We
used an "Alphard 3030" cone-beam CT equipped with an FPD system. This system can obtain fluoroscopic and CT
images. Moreover, the Alphard has 4 exposure modes for CT, and each mode has a different field of view (FOV) and
voxel size. We examined the image quality of kinetic and CT images obtained using the cone-beam CT system. To
evaluate kinetic image quality, we calculated the Wiener spectrum (WS) and modulation transfer function (MTF). We
then analyzed the lag images and exposed a phantom. To evaluate CT image quality, we calculated WS and MTF at
various places in the FOV and examined the influence of extension of the cone beam X-ray on voxel size. Furthermore,
we compared the WS and MTF values of cone-beam CT to those of another CT system. Evaluation of the kinetic images
showed that cone-beam CT is sufficient for clinical diagnosis and provides better image quality than the other system
tested. However, during exposure of a CT image, the distance from the center influences image quality (especially MTF).
Further, differences in voxel size affect image quality. It is therefore necessary to carefully position the region of interest
and select an appropriate mode.
With recent developments, digital mammograms can be obtained with a small pixel size, i.e., high resolution; however,
the matrix size increases. Therefore, when the image is thinned out, image information is lost when the image is
displayed on a liquid crystal display (LCD). To resolve this issue, we have developed a super high resolution liquid
crystal display (SHR-LCD) by using a novel resolution enhancement technology for independent subpixel driving (ISD)
with three subpixels in each pixel element. However, the lack of image information caused by thinning of the image
cannot be ignored because the matrix size of a phase contrast mammogram (PCM) is very large as compared to that of a
conventional mammogram. We obtained noise and edge images by using the geometrical layouts of the PCM (7080 x
9480). We measured the Wiener spectrum (WS), modulation transfer function (MTF), and noise-equivalent number of
quanta (NEQ) of the images reduced by the nearest-neighbor, bilinear, and bicubic (sharpness and smooth) interpolations.
The reduction rate was approximately 0.14. We measured the WS and MTF when the PCM image was displayed on a
5-megapixel (MP) and 15-MP LCD. The bilinear interpolation technique gave the best image quality. The image quality
was further improved by using a 15-MP SHR-LCD.
KEYWORDS: LCDs, Signal detection, Medical imaging, Diagnostics, Image quality, Sensors, Mammography, Modulation transfer functions, Image compression, Health sciences
In the soft copy diagnosis, each pixel of the detector is displayed to the correspondent pixel of liquid crystal display
(LCD). But when the image is displayed at the first time, the entire image may be reduced. We examined the influence
that the difference of image reduction rate on LCD exerts on detection performance by using observer performance
experiment. Moreover, to find the best interpolation method, we investigated the several interpolation methods. We
made a simulation image which is similar to Burger phantom. This image consists of 288 signals, each of a different size
and contrast. The matrix size is the same as Phase Contrast Mammography (PCM). We gradated the simulation image by
using an MTF of a geometric blur, and the image was added to the noise image which is uniformly exposed with PCM.
Then the image was reduced by using the nearest-neighbor, the bilinear, and the bicubic methods. The reduction rates
were calculated as the ratios of the number of pixels of LCDs to those of PCM. We displayed the reduced images on
LCD and examined the detection performance. Results of physical evaluation examined before showed that sharpness
and granularity have worsened both in proportion to the reduction rate. The detection performance deteriorated as the
reduction rate becomes high. In the comparison of the interpolation methods, the detection performance of the nearestneighbor
method was worse than those of other interpolation methods. The bilinear method is the most suitable for the
reduction of the image.
KEYWORDS: Signal to noise ratio, Signal detection, Visualization, Image quality, Modulation transfer functions, Interference (communication), Spatial frequencies, Eye models, Visual process modeling, Image visualization
The effects of imaging parameters on detectability have not yet been clarified. Therefore, we investigated the
usefulness of signal-to-noise ratios (SNRs) considered as human visual characteristics, such as the visual spatial
frequency response and the internal noise in the eye-brain system.
We examined the amplitude model (SNRa), matched filter model (SNRm), and internal noise model (SNRi) to study
the relationship between these SNRs and the visual image quality for signal detection. The test images were simulated by
the superimposition of low-contrast signals on a uniform noisy background. The SNRs were obtained for 15 imaging
cases with various signal sizes, signal contrasts, exposure levels, and number of acrylic plates used as breast phantoms.
The SNRs were calculated by measuring the spatial frequency characteristics of the signal, modulation transfer
function (MTF) of the system, display MTF, and overall Wiener spectrum (WS).
In the perceptual evaluation, we applied the 16-alternative forced choice (16-AFC) method. The signal detectability
was defined as the number of detected signals divided by the total number of signals. We studied the relationship
between SNR and signal detectability using Spearman's rank correlation coefficient.
The correlation coefficient of SNRi was 0.93, making it the highest among the three SNR types. That of SNRm was
0.91; it correlated at the same level as SNRi although it is not considered human visual characteristics. That of SNRa
was 0.45. SNRi, which incorporated the visual characteristics, explained the visual image quality well.
Soft-copy diagnosis of medical images is currently widespread. Because the pixel size of a digital mammogram is very
small, the matrix size is extremely large. Especially the matrix size of phase contrast mammography (PCM) is very large
compared with a conventional mammography. When such an image is displayed on a liquid crystal display (LCD), it is
displayed as a reduction image. Therefore, it is necessary to use an appropriate reduction rate and an interpolation
method such that the reduction processing does not influence the diagnosis. We obtained a uniform image exposure and
measured the noise power spectrum (NPS) of the image reduced by using the nearest neighbor, bilinear, and bicubic
methods with several reduction rates. Our results showed that the best interpolation method was the bilinear method.
Moreover, the NPS value increased by a factor of the square of the inverse of the reduction rate.
KEYWORDS: Signal to noise ratio, Curium, Modulation transfer functions, X-rays, Photons, Mammography, Spatial frequencies, Digital mammography, Computing systems, Sensors
Recently, with developments in medicine, digital systems such as computed radiography (CR) and flat-panel
detector (FPD) systems are being employed for mammography instead of analog systems such as the screen-film system.
Phase-contrast mammography (PCM) is a commercially available digital system that uses images with a magnification
of 1.75x. To study the effect of the air gap in PCM, we measured the scatter fraction ratio (SFR) and calculated the
signal-to-noise ratio (SNR) in PCM, and compared it to that in conventional mammography (CM). Then, to extend the
SNR to the spatial frequency domain, we calculated the noise equivalent quanta (NEQ) and detective quantum efficiency
(DQE) used by the modulation transfer function (MTF), noise power spectrum of the pixel value (NPSΔPV), gradient of
the digital characteristic curve, and number of X-ray photons. The obtained results indicated that the SFR of the PCM
was as low as that of the CM with a grid. When the exposure dose was constant, the SNR of the PCM was the highest in
all systems. Moreover, the NEQ and DQE for the PCM were higher than those for the CM (G-) in the spatial frequency
domain over 2.5 cycles/mm. These results showed that the number of scattered X-rays was reduced sufficiently by the air
gap in the PCM and the NEQ and DQE for PCM were influenced by the presampled MTF in the high-spatial-frequency
domain.
KEYWORDS: Signal to noise ratio, X-rays, Modulation transfer functions, Digital mammography, Interference (communication), Mammography, Image quality, Breast, Spatial frequencies, Visualization
The use of digital mammography systems has become widespread recently. However, the optimal exposure parameters
are uncertain in clinical practice. We need to optimize the exposure parameter in digital mammography while
maximizing image quality and minimizing patient dose. The purpose of this study was to evaluate the most beneficial
exposure variable-tube voltage for each compressed breast
thickness-with these indices: noise power spectrum, noise
equivalent quanta, detective quantum efficiency, and signal-to-noise ratios (SNR). In this study, the SNRs were derived
from the perceived statistical decision theory model with the internal noise of eye-brain system (SNRi), contrived and
studied by Loo LN1), Ishida M et al. 2) These image quality indices were obtained under a fixed average glandular dose
(AGD) and a fixed image contrast. Our results indicated that when the image contrast and AGD was constant, for
phantom thinner than 5 cm, an increase of the tube voltage did not improve the noise property of images very much. The
results also showed that image property with the target/filter Mo/Rh was better than that with Mo/Mo for phantom
thicker than 4 cm. In general, it is said that high tube voltage delivers improved noise property. Our result indicates that
this common theory is not realized with the x-ray energy level for mammography.
Screen-film systems are used in mammography even now. Therefore, it is important to measure their physical
properties such as modulation transfer function (MTF) or noise power spectrum (NPS). The MTF and NPS of
screen-film systems are mostly measured by using a microdensitometer. However, since microdensitometers are not
commonly used in general hospitals, it is difficult to carry out these measurements regularly. In the past, Ichikawa et al.
have measured and evaluated the physical properties of medical liquid crystal displays by using a high-performance
digital camera. By this method, the physical properties of screen-film systems can be measured easily without using a
microdensitometer. Therefore, we have proposed a simple method for measuring the MTF and NPS of screen-film
systems by using a high-performance digital camera. The proposed method is based on the edge method (for evaluating
MTF) and the one-dimensional fast Fourier transform (FFT) method (for evaluating NPS), respectively. As a result, the
MTF and NPS evaluated by using the high-performance digital camera approximately corresponded with those evaluated
by using a microdensitometer. It is possible to substitute the calculation of MTF and NPS by using a high-performance
digital camera for that by using a microdensitometer. Further, this method also simplifies the evaluation of the physical
properties of screen-film systems.
Mammography techniques have recently advanced from those using analog systems (the screen-film system) to those using digital systems; for example, computed radiography (CR) and flat-panel detectors (FPDs) are nowadays used in mammography. Further, phase contrast mammography (PCM)-a digital technique by which images with a magnification of 1.75× can be obtained-is now available in the market. We studied the effect of the air gap in PCM and evaluated the effectiveness of an antiscatter x-ray grid in conventional mammography (CM) by measuring the scatter fraction ratio (SFR) and relative signal-to-noise ratio (rSNR) and comparing them between PCM and the digital CM. The results indicated that the SFRs for the CM images obtained with a grid were the lowest and that these ratios were almost the same as those for the PCM images. In contrast, the rSNRs for the PCM images were the highest, which means that the scattering of x-rays was sufficiently reduced by the air gap without the loss of primary x-rays.
Generally, the modulation transfer function (MTF) of a computed tomography (CT) scanner is calculated based on the
CT value. However, it is impossible to measure the MTF directly because the CT value is defined as a nonlinear function
of the X-ray intensity. Due to this characteristic, the MTF varies with the subject's contrast. Therefore, we measured the
MTFs of a CT scanner using high- and low-contrast wire phantoms. We selected thick copper wire in water as the
high-contrast subject and thin copper wire in water as the low-contrast subject. The MTF measured with the
low-contrast subject was decreased relative to that measured with the high-contrast subject because the CT value was
nonlinear. Thus, to evaluate the spatial resolution in a low-contrast subject such as the human body, we should measure
the MTF with a low-contrast wire phantom. In addition, by using low-contrast subjects, we can approximately determine
the CT value using a linear function.
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